From bbc825d43e6f290e664f7ee04bd6a8118c181d09 Mon Sep 17 00:00:00 2001 From: DanielaMPinzon <160845437+DanielaMPinzon@users.noreply.github.com> Date: Wed, 27 Aug 2025 18:09:08 +0200 Subject: [PATCH 1/6] Add files via upload --- scripts/feature_selection_sfs.py | 22 +++++++++++ scripts/reg_Lasso.py | 63 ++++++++++++++++++++++++++++++++ 2 files changed, 85 insertions(+) create mode 100644 scripts/feature_selection_sfs.py create mode 100644 scripts/reg_Lasso.py diff --git a/scripts/feature_selection_sfs.py b/scripts/feature_selection_sfs.py new file mode 100644 index 0000000..d4de759 --- /dev/null +++ b/scripts/feature_selection_sfs.py @@ -0,0 +1,22 @@ + +from sklearn.feature_selection import SequentialFeatureSelector +from sklearn.neighbors import KNeighborsRegressor + +def select_features(X, y, n_features=10): + knn = KNeighborsRegressor(n_neighbors=3) + sfs = SequentialFeatureSelector(knn, n_features_to_select=n_features, direction='forward') + sfs.fit(X, y) + sfs.get_support() + # Transform the data to select the features + X_selected = sfs.transform(X) + X_selected.shape + + # keep the selected feature names + selected_features = X.columns[sfs.get_support()] + print("Selected features:", selected_features) + + return sfs, X_selected, selected_features + +if __name__ == "__main__": + # Example usage + sfs, X_selected, selected_features = select_features(X, y, n_features=10) \ No newline at end of file diff --git a/scripts/reg_Lasso.py b/scripts/reg_Lasso.py new file mode 100644 index 0000000..1d1ee66 --- /dev/null +++ b/scripts/reg_Lasso.py @@ -0,0 +1,63 @@ +from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve +from sklearn import linear_model +from sklearn.metrics import mean_squared_error, r2_score +import matplotlib.pyplot as plt +import numpy as np + + +def linear_reg_lasso(X, y): + # train-test split + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + # applying Lasso regression + reg_lasso = linear_model.Lasso() + reg_lasso.fit(X_train, y_train) + # make predictions + y_pred = reg_lasso.predict(X_test) + + print("Lasso Regression") + print("paramètres Lasso : ", reg_lasso.get_params()) + + # halving grid search pour optimiser les hyperparamètres + param_grid = { + 'alpha': [0.01, 0.1, 1.0, 10.0], + 'max_iter': [1000, 5000], + 'tol': [1e-4, 1e-3, 1e-2] + } + + grid_search = GridSearchCV(reg_lasso, param_grid, cv=5, scoring='neg_mean_squared_error') + grid_search.fit(X_train, y_train) + y_Grid_pred = grid_search.predict(X_test) + best_regLasso = grid_search.best_params_ + + # Meilleurs paramètres + print("Meilleurs paramètres trouvés : ", best_regLasso) + + return reg_lasso, X_train, X_test, y_train, y_test, y_pred, y_Grid_pred, best_regLasso + + + +# Courbe d'apprentissage / Learning curve (Lr): +def plot_learning_curve(model, X, y, cv=5): + train_sizes, train_scores, test_scores = learning_curve( + model, X, y, cv=cv, n_jobs=-1, train_sizes=np.linspace(0.1, 1.0, 10) + ) + train_scores_mean = train_scores.mean(axis=1) + test_scores_mean = test_scores.mean(axis=1) + + # Plot learning curve + plt.figure() + plt.plot(train_sizes, train_scores_mean, label="Training score") + plt.plot(train_sizes, test_scores_mean, label="Cross-validation score") + plt.xlabel("Training examples") + plt.ylabel("Score") + plt.title("Learning Curve") + plt.grid() + plt.legend() + plt.show() + + return train_sizes, train_scores_mean, test_scores_mean + +if __name__ == "__main__": + # Example usage + reg_lasso, X_train, X_test, y_train, y_test, y_pred, y_Grid_pred, best_regLasso = linear_reg_lasso(X, y) + train_sizes, train_scores_mean, test_scores_mean = plot_learning_curve(reg_lasso, X_train, y_train) \ No newline at end of file From 093b6d425822574e651298e7376e30bed4d0922b Mon Sep 17 00:00:00 2001 From: DanielaMPinzon <160845437+DanielaMPinzon@users.noreply.github.com> Date: Wed, 27 Aug 2025 18:09:49 +0200 Subject: [PATCH 2/6] Add files via upload --- notebooks/project_starter.ipynb | 13028 ++++++------------------------ 1 file changed, 2488 insertions(+), 10540 deletions(-) diff --git a/notebooks/project_starter.ipynb b/notebooks/project_starter.ipynb index 3cc1cc5..90ee0b4 100644 --- a/notebooks/project_starter.ipynb +++ b/notebooks/project_starter.ipynb @@ -11,47 +11,66 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Chargement des données " + "#### 1. Chargement des données " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### A partir du format csv" + "##### A partir du format csv" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "import pandas as pd" + "import os\n", + "import sys\n", + "\n", + "path = '/home/daniela/Documents/PROJECTS/DESU_Data_Science/N_Rocher/OFF_Project/OFFProject/scripts'\n", + "os.chdir(path)\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '..'))) \n", + "sys.path.append(\"../../\")\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from scripts import Data_filter_Jess as dfj\n", + "from scripts import encoding_func \n", + "from scripts import Imputing\n", + "from scripts import Scaling\n", + "from encoding_func import *\n", + "from Scaling import *\n", + "from Imputing import *\n" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_2284\\3952692224.py:2: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n" + "/tmp/ipykernel_49241/1388454853.py:2: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n" ] } ], "source": [ "path = \"https://static.openfoodfacts.org/data/en.openfoodfacts.org.products.csv.gz\"\n", - "df = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)" + "df = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -116,7 +135,7 @@ { "name": "product_name", "rawType": "object", - "type": "unknown" + "type": "string" }, { "name": "abbreviated_product_name", @@ -261,17 +280,17 @@ { "name": "countries", "rawType": "object", - "type": "unknown" + "type": "string" }, { "name": "countries_tags", "rawType": "object", - "type": "unknown" + "type": "string" }, { "name": "countries_en", "rawType": "object", - "type": "unknown" + "type": "string" }, { "name": "ingredients_text", @@ -356,7 +375,7 @@ { "name": "nutriscore_grade", "rawType": "object", - "type": "unknown" + "type": "string" }, { "name": "nova_group", @@ -366,12 +385,12 @@ { "name": "pnns_groups_1", "rawType": "object", - "type": "unknown" + "type": "string" }, { "name": "pnns_groups_2", "rawType": "object", - "type": "unknown" + "type": "string" }, { "name": "food_groups", @@ -416,7 +435,7 @@ { "name": "environmental_score_grade", "rawType": "object", - "type": "unknown" + "type": "string" }, { "name": "nutrient_levels_tags", @@ -461,7 +480,7 @@ { "name": "last_image_datetime", "rawType": "object", - "type": "unknown" + "type": "string" }, { "name": "main_category", @@ -476,12 +495,12 @@ { "name": "image_url", "rawType": "object", - "type": "unknown" + "type": "string" }, { "name": "image_small_url", "rawType": "object", - "type": "unknown" + "type": "string" }, { "name": "image_ingredients_url", @@ -758,6 +777,11 @@ "rawType": "float64", "type": "float" }, + { + "name": "psicose_100g", + "rawType": "float64", + "type": "float" + }, { "name": "starch_100g", "rawType": "float64", @@ -773,6 +797,21 @@ "rawType": "float64", "type": "float" }, + { + "name": "isomalt_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "maltitol_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "sorbitol_100g", + "rawType": "float64", + "type": "float" + }, { "name": "fiber_100g", "rawType": "float64", @@ -1114,7 +1153,7 @@ "type": "float" } ], - "ref": "3dbea738-64fe-4b08-9844-5363de35a490", + "ref": "8b1eca09-f6a2-4943-bfc7-a52d26fc5277", "rows": [ [ "0", @@ -1327,6 +1366,10 @@ null, null, null, + null, + null, + null, + null, null ], [ @@ -1476,6 +1519,10 @@ null, null, null, + null, + null, + null, + null, "17.0", null, null, @@ -1689,6 +1736,10 @@ null, null, null, + null, + null, + null, + null, "7.1", null, null, @@ -1948,6 +1999,10 @@ null, null, null, + null, + null, + null, + null, "0.0113351004464306", null, null, @@ -2112,6 +2167,10 @@ null, null, null, + null, + null, + null, + null, "2.2", null, null, @@ -2180,9596 +2239,1183 @@ null, null, null - ], - [ - "5", - "3", - "http://world-en.openfoodfacts.org/product/00000003/soja-sauce-tai-shan", - "prepperapp", - "1716818343", - "2024-05-27T13:59:03Z", - "1750615537", - "2025-06-22T18:05:37Z", - "waistline-app", - "1750615537", - "2025-06-22T18:05:37Z", - "Soja-Sauce", - null, - null, - "700ml", - null, - null, - null, - null, - "Tai Shan", - "xx:tai-shan", - "tai-shan", - "Hazelnut spread", - "en:breakfasts,en:spreads,en:sweet-spreads,fr:pates-a-tartiner,en:hazelnut-spreads", - "Breakfasts,Spreads,Sweet spreads,fr:Pâtes à tartiner,Hazelnut spreads", - null, - null, - null, - "Canada", - "canada", - "No alcohol, en:vegan", - "en:vegetarian,en:vegan,en:no-alcohol", - "Vegetarian,Vegan,No alcohol", - "DE MV-006 EC", - "de-mv-006-ec", - null, - null, - null, - null, - null, - "Griechenland, Germany", - "en:germany,en:greece", - "Germany,Greece", - "Soursop leaves", - "en:soursop-leaves", - "en:palm-oil-content-unknown,en:vegan,en:vegetarian", - null, - null, - null, - null, - null, - "100 ml", - "100.0", - null, - "0.0", - null, - null, - null, - "20.0", - "e", - null, - "Sugary snacks", - "Sweets", - "en:sweets", - "en:sugary-snacks,en:sweets", - "Sugary snacks,Sweets", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-validated, en:packaging-photo-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-validated,en:packaging-photo-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos validated,Packaging photo selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - "31.0", - "d", - "en:fat-in-low-quantity,en:saturated-fat-in-low-quantity,en:sugars-in-low-quantity,en:salt-in-high-quantity", - "700.0", - null, - "en:nutrition-saturated-fat-greater-than-fat", - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-es-scans-2024,top-100000-es-scans-2024,top-country-es-scans-2024", - "0.8", - "1750615523.0", - "2025-06-22T18:05:23Z", - "en:hazelnut-spreads", - "Hazelnut spreads", - "https://images.openfoodfacts.org/images/products/invalid/front_en.141.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.141.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.134.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.134.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.136.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.136.200.jpg", - null, - "61.0", - "255.0", - null, - "0.0", - "0.012", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "6.1", - "1.5", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "2.2", - null, - null, - "9.0", - null, - null, - null, - "19.0", - null, - "7.6", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - "20.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "6", - "4", - "http://world-en.openfoodfacts.org/product/00000004/entrecoesteack-highland-beef-pg-tips", - "elcoco", - "1560176426", - "2019-06-10T14:20:26Z", - "1748094869", - "2025-05-24T13:54:29Z", - "smoothie-app", - "1748094869", - "2025-05-24T13:54:29Z", - "Entrecôesteack - Highland Beef", - null, - null, - "1000g", - "Glas", - "en:glass", - "Glass", - null, - "PG Tips, green organic", - "xx:pg-tips,xx:green-organic", - "pg-tips,green-organic", - "Nutrition drink mix", - "en:nutrition-drink-mix", - "Nutrition-drink-mix", - "Nizozemsko,Peru", - "en:netherlands,en:peru", - "Netherlands,Peru", - null, - null, - "Fair trade, Organic, EU Organic, Certified B Corporation, CH-BIO-006, FSC, FSC Mix, Soil Association Organic, en:no-preservatives", - "en:fair-trade,en:organic,en:eu-organic,en:no-preservatives,en:certified-b-corporation,en:ch-bio-006,en:fsc,en:fsc-mix,en:soil-association-organic", - "Fair trade,Organic,EU Organic,No preservatives,Certified B Corporation,CH-BIO-006,FSC,FSC Mix,Soil Association Organic", - null, - null, - null, - null, - null, - "United Kingdom", - "Amazon", - "Brasilien, Germany", - "en:brazil,en:germany", - "Brazil,Germany", - "Organic Ashwagandha KSM-66, Vegetable Capsule Shell (HydroxyPropylMethyl Cellulose), Organic Black Pepper.", - "es:organic-ashwagandha-ksm-66,es:vegetable-capsule-shell,es:organic-black-pepper,es:hydroxypropylmethyl-cellulose", - "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", - "en:nuts,en:soybeans", - null, - null, - null, - null, - "0g", - "0.0", - null, - "0.0", - null, - null, - null, - "15.0", - "d", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-completed, en:origins-completed, en:categories-completed, en:brands-completed, en:packaging-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-completed,en:origins-completed,en:categories-completed,en:brands-completed,en:packaging-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics completed,Origins completed,Categories completed,Brands completed,Packaging completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - "en:fat-in-moderate-quantity,en:saturated-fat-in-high-quantity,en:sugars-in-moderate-quantity,en:salt-in-moderate-quantity", - "1000.0", - null, - "en:energy-value-in-kcal-does-not-match-value-in-kj,en:energy-value-in-kj-does-not-match-value-computed-from-other-nutrients", - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-ch-scans-2024,top-100000-ch-scans-2024,top-country-ch-scans-2024", - "0.8875", - "1748094869.0", - "2025-05-24T13:54:29Z", - "en:nutrition-drink-mix", - "Nutrition-drink-mix", - "https://images.openfoodfacts.org/images/products/invalid/front_en.135.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.135.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.121.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.121.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.118.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.118.200.jpg", - "2401.0", - "324.0", - "2401.0", - 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"kiliweb", - "1714206330", - "2024-04-27T08:25:30Z", - "1714207074", - "2024-04-27T08:37:54Z", - "roboto-app", - "1740362538", - "2025-02-24T02:02:18Z", - "Confiture extra citron de Menton", - null, - null, - "230 g", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "en:fr", - "en:france", - "France", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "unknown", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-to-be-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-to-be-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands to be completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - "230.0", - null, - null, - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-country-fr-scans-2024", - "0.375", - "1714206331.0", - "2024-04-27T08:25:31Z", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/front_fr.3.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_fr.3.200.jpg", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.5.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.5.200.jpg", - null, - "222.0", - "929.0", - null, - "0.0", - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "50.8", - "50.6", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.8", - null, - null, - null, - "0.0", - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "8", - "5", - "http://world-en.openfoodfacts.org/product/00000005/five-grain-granola-roger-s", - "touchette", - "1605337720", - "2020-11-14T07:08:40Z", - "1749334226", - "2025-06-07T22:10:26Z", - "smoothie-app", - "1749334226", - "2025-06-07T22:10:26Z", - "five grain granola", - null, - null, - "1000ml", - "Verre, barquette", - "en:glass,en:tray", - "Glass,Tray", - null, - "Roger’s", - "xx:roger-s", - "roger-s", - "Plant-based foods and beverages, Plant-based foods, Breakfasts, Cereals and potatoes, Cereals and their products, Breakfast cereals, Flakes, Cereal flakes, Condiment", - "en:plant-based-foods-and-beverages,en:plant-based-foods,en:breakfasts,en:cereals-and-potatoes,en:condiments,en:cereals-and-their-products,en:breakfast-cereals,en:flakes,en:cereal-flakes", - "Plant-based foods and beverages,Plant-based foods,Breakfasts,Cereals and potatoes,Condiments,Cereals and their products,Breakfast cereals,Flakes,Cereal flakes", - null, - null, - null, - "bénivay-ollon", - "benivay-ollon", - "No lactose", - "en:no-lactose", - "No lactose", - "13089c", - "13089c", - null, - null, - null, - "France", - null, - "Frankreich, Germany", - "en:france,en:germany", - "France,Germany", - "Jus et purée d'abricots (50%), eau, sucre.", - "en:apricot-juice-and-puree,en:fruit,en:prunus-species-fruit,en:apricot,en:apricot-juice,en:apricot-puree,en:water,en:sugar,en:added-sugar,en:disaccharide", - "en:palm-oil-free,en:vegan,en:vegetarian", - null, - null, - null, - null, - null, - "0g", - "0.0", - null, - "0.0", - null, - null, - null, - "11.0", - "d", - "3.0", - "Cereals and potatoes", - "Breakfast cereals", - "en:breakfast-cereals", - "en:cereals-and-potatoes,en:breakfast-cereals", - "Cereals and potatoes,Breakfast cereals", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-to-be-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-to-be-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo to be selected,Photos uploaded", - null, - "38.0", - "d", - "en:fat-in-low-quantity,en:saturated-fat-in-low-quantity,en:sugars-in-low-quantity,en:salt-in-high-quantity", - "1000.0", - null, - null, - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-ma-scans-2024,top-100000-ma-scans-2024,top-country-ma-scans-2024", - "0.85", - "1746375622.0", - "2025-05-04T16:20:22Z", - "en:cereal-flakes", - "Cereal flakes", - "https://images.openfoodfacts.org/images/products/invalid/front_en.56.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.56.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.53.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.53.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_de.17.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_de.17.200.jpg", - "1620.0", - "376.0", - "1620.0", - null, - "1.6", - "0.2", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "6.7", - "1.3", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "2.9", - null, - null, - "82.0", - null, - null, - null, - "1.7", - null, - "0.68", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "50.0", - null, - null, - null, - null, - null, - "11.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "9", - "6", - "http://world-en.openfoodfacts.org/product/00000006/triple-cheese-puff", - "maldan", - "1732037972", - "2024-11-19T17:39:32Z", - "1749357659", - "2025-06-08T04:40:59Z", - "smoothie-app", - "1749357659", - "2025-06-08T04:40:59Z", - "Triple cheese puff", - null, - null, - "500g", - null, - null, - null, - null, - null, - null, - null, - "Boissons et préparations de boissons, Boissons, en:cinnamon roll", - "en:beverages-and-beverages-preparations,en:beverages,en:cinnamon-roll", - "Beverages and beverages preparations,Beverages,Cinnamon-roll", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "Germany, United States, en:france", - "en:france,en:germany,en:united-states", - "France,Germany,United States", - null, - null, - null, - null, - null, - null, - null, - null, - "100 g", - "100.0", - null, - null, - null, - null, - null, - "4.0", - "c", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-to-be-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-to-be-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands to be completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - "en:fat-in-high-quantity,en:saturated-fat-in-moderate-quantity,en:sugars-in-low-quantity,en:salt-in-high-quantity", - "500.0", - null, - null, - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-ma-scans-2024,top-100000-ma-scans-2024,top-country-ma-scans-2024", - "0.4875", - "1749357657.0", - "2025-06-08T04:40:57Z", - "en:cinnamon-roll", - "Cinnamon-roll", - "https://images.openfoodfacts.org/images/products/invalid/front_en.149.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.149.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.151.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.151.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.116.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.116.200.jpg", - null, - "363.0", - "1520.0", - null, - "11.0", - "2.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - "0.01", - "25.0", - "0.98", - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "9.0", - null, - null, - "22.0", - null, - null, - null, - "0.95", - null, - "0.38", - "0.0", - null, - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "4.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "10", - "6666", - "http://world-en.openfoodfacts.org/product/00000006666/gemuse-gurke", - "prepperapp", - "1709219541", - "2024-02-29T15:12:21Z", - "1738680456", - "2025-02-04T14:47:36Z", - "scanbot", - "1740134585", - "2025-02-21T10:43:05Z", - "Gemüse - Gurke", - null, - null, - "1pcs", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "Germany, en:france", - "en:france,en:germany", - "France,Germany", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "unknown", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-to-be-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-to-be-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts to be completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands to be completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - "0.0", - null, - null, - "6.0", - "at-least-5-scans-2024,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-fr-scans-2024,top-100000-fr-scans-2024,top-country-fr-scans-2024,at-least-5-fr-scans-2024", - "0.2625", - "1709219542.0", - "2024-02-29T15:12:22Z", - null, - null, - "https://images.openfoodfacts.org/images/products/000/000/000/6666/front_de.3.400.jpg", - "https://images.openfoodfacts.org/images/products/000/000/000/6666/front_de.3.200.jpg", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "11", - "7", - "http://world-en.openfoodfacts.org/product/00000007/granola-bio-le-chocolate-mg-ricarica", - "smoothie-app", - "1678803019", - "2023-03-14T14:10:19Z", - "1748262529", - "2025-05-26T12:28:49Z", - "smoothie-app", - "1748262529", - "2025-05-26T12:28:49Z", - "granola Bio le Chocolaté", - null, - null, - "450g", - null, - null, - null, - null, - "Mg ricarica", - "xx:mg-ricarica", - "mg-ricarica", - "Aliments et boissons à base de végétaux, Aliments d'origine végétale, Aliments à base de fruits et de légumes, Fruits et produits dérivés, Produits déshydratés, Aliments à base de plantes séchées, Fruits secs", - "en:plant-based-foods-and-beverages,en:plant-based-foods,en:fruits-and-vegetables-based-foods,en:dried-products,en:fruits-based-foods,en:dried-plant-based-foods,en:dried-fruits", - "Plant-based foods and beverages,Plant-based foods,Fruits and vegetables based foods,Dried products,Fruits based foods,Dried plant-based foods,Dried fruits", - null, - null, - null, - null, - null, - "en:made-in-france", - "en:made-in-france", - "Made in France", - null, - null, - null, - null, - null, - null, - null, - "Spanien, Germany", - "en:germany,en:spain", - "Germany,Spain", - "HONIG stillende Frauen nicht geeignet. D bestrahlung vermeiden und be trocken lagern. X000V0XH07", - "es:honig-stillende-frauen-nicht-geeignet,es:d-bestrahlung-vermeiden-und-be-trocken-lagern,es:x000v0xh07", - "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", - null, - null, - null, - null, - null, - "25 gram", - "25.0", - null, - "0.0", - null, - null, - null, - "4.0", - "c", - null, - "Fruits and vegetables", - "Dried fruits", - "en:dried-fruits", - "en:fruits-and-vegetables,en:dried-fruits", - "Fruits and vegetables,Dried fruits", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - "en:fat-in-low-quantity,en:saturated-fat-in-low-quantity,en:sugars-in-low-quantity,en:salt-in-moderate-quantity", - "450.0", - null, - "en:energy-value-in-kcal-does-not-match-value-computed-from-other-nutrients", - "2.0", - "top-75-percent-scans-2023,top-80-percent-scans-2023,top-85-percent-scans-2023,top-90-percent-scans-2023,top-50000-de-scans-2023,top-100000-de-scans-2023,top-country-de-scans-2023,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-country-fr-scans-2024,top-100000-us-scans-2024", - "0.7625", - "1748262414.0", - "2025-05-26T12:26:54Z", - "en:dried-fruits", - "Dried fruits", - "https://images.openfoodfacts.org/images/products/invalid/front_en.35.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.35.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.5.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.5.200.jpg", - null, - null, - null, - "1.0", - "4.0", - null, - "1.0", - "1.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "1.0", - "1.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "1.0", - null, - null, - "1.0", - null, - null, - null, - "1.0", - null, - "0.4", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - "4.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "12", - "8", - "http://world-en.openfoodfacts.org/product/00000008/zuegg", - "halal-app-chakib", - "1609862762", - "2021-01-05T16:06:02Z", - "1746874519", - "2025-05-10T10:55:19Z", - "roboto-app", - "1746874519", - "2025-05-10T10:55:19Z", - null, - null, - null, - "700ml", - null, - null, - null, - null, - "Zuegg", - "xx:zuegg", - "zuegg", - "Cornish clotted cream shortbread", - "en:cornish-clotted-cream-shortbread", - "Cornish-clotted-cream-shortbread", - null, - null, - null, - null, - null, - "Halal, No added sugar, No lactose, en:no-gluten", - "en:no-gluten,en:halal,en:no-added-sugar,en:no-lactose", - "No gluten,Halal,No added sugar,No lactose", - null, - null, - null, - null, - null, - null, - null, - "Vereinigte Staaten von Amerika, Germany", - "en:germany,en:united-states", - "Germany,United States", - "Sojaproteinisolat, Weizen - protein, Kaffee-Extrakt (6 %), Reisprotein, Erbsenprotein - isolat, Kakaopulver stark ent - ölt, Sonnenblumenprotein, Mandelprotein, Aroma, Sü - Bungsmittel (Sucralose).", - "en:soy-protein-isolate,en:protein,en:plant-protein,en:soy-protein,en:wheat-protein,en:coffee,en:rice-protein,en:pea-protein,de:isolat,de:kakaopulver-stark-ent,de:ölt,en:sunflower-protein,en:almond-protein,en:flavouring,de:sü,de:bungsmittel,en:e955", - "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", - null, - null, - null, - null, - null, - "30 g", - "30.0", - null, - "1.0", - null, - "en:e955", - "E955 - Sucralose", - "6.0", - "c", - "4.0", - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-to-be-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-to-be-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name to be completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - "en:fat-in-low-quantity,en:saturated-fat-in-low-quantity,en:sugars-in-low-quantity,en:salt-in-moderate-quantity", - "700.0", - null, - null, - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-ma-scans-2024,top-100000-ma-scans-2024,top-country-ma-scans-2024", - "0.6875", - "1746873012.0", - "2025-05-10T10:30:12Z", - "en:cornish-clotted-cream-shortbread", - "Cornish-clotted-cream-shortbread", - "https://images.openfoodfacts.org/images/products/invalid/front_fr.60.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_fr.60.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.62.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.62.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.44.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.44.200.jpg", - "1510.0", - "358.0", - "1510.0", - null, - "2.0", - "0.5", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "6.7", - "1.7", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "10.714286", - null, - null, - "76.0", - null, - null, - null, - "1.5", - null, - "0.6", - null, - "0.00023214286", - null, - null, - "0.00071428576", - null, - "0.07142857", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.78571427", - null, - "0.17857143", - "0.25", - "0.008928571", - "0.11428572", - "0.021428572", - "0.0017857144", - "0.0017857144", - null, - null, - null, - null, - null, - "0.15", - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - "6.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "13", - "9", - "http://world-en.openfoodfacts.org/product/00000009/protein-plant-powered-wrap-mission", - "prepperapp", - "1677761447", - "2023-03-02T12:50:47Z", - "1727982393", - "2024-10-03T19:06:33Z", - "maldan", - "1727982393", - "2024-10-03T19:06:33Z", - "Protein Plant Powered Wrap", - null, - null, - "1pcs", - null, - null, - null, - null, - "Mission", - "mission", - "Mission", - "Sandwiches, Wraps, Protein", - "en:sandwiches,en:wraps,en:protein", - "Sandwiches,Wraps,Protein", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "en:germany, World", - "en:germany,en:world", - "Germany,World", - null, - null, - null, - null, - null, - null, - null, - null, - "0g", - "0.0", - null, - null, - null, - null, - null, - null, - "unknown", - null, - "Composite foods", - "Sandwiches", - "en:sandwiches", - "en:composite-foods,en:sandwiches", - "Composite foods,Sandwiches", - "en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-uploaded", - "en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-uploaded", - "To be completed,Nutrition facts to be completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be uploaded", - null, - null, - null, - null, - "0.0", - "org-intermarche", - null, - null, - null, - "0.4", - null, - null, - "en:protein", - "Protein", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "14", - "9", - "http://world-en.openfoodfacts.org/product/00000009/xytitol-pastilles-xylimgxyling", - "openfoodfacts-contributors", - "1527242583", - "2018-05-25T10:03:03Z", - "1749674566", - "2025-06-11T20:42:46Z", - "smoothie-app", - "1749674566", - "2025-06-11T20:42:46Z", - "xytitol pastilles", - null, - null, - "700ml", - null, - null, - null, - null, - "xylimgxyling", - "xx:xylimgxyling", - "xylimgxyling", - "it:Gestione sovrappeso, it:obesità, Wrap", - "en:sandwiches,en:wraps,it:gestione-sovrappeso,it:obesita", - "Sandwiches,Wraps,it:gestione-sovrappeso,it:obesita", - null, - null, - null, - null, - null, - "Organic, Vegetarian, EU Organic, Vegan, DE-ÖKO-006, en:made-in-france", - "en:organic,en:vegetarian,en:eu-organic,en:vegan,en:de-oko-006,en:made-in-france", - "Organic,Vegetarian,EU Organic,Vegan,DE-ÖKO-006,Made in France", - null, - null, - null, - null, - null, - null, - null, - "Germany,Spain", - "en:germany,en:spain", - "Germany,Spain", - null, - null, - "en:vegan,en:vegetarian", - null, - null, - null, - null, - null, - "0g", - "0.0", - null, - null, - null, - null, - null, - "-11.0", - "a", - null, - "Composite foods", - "Sandwiches", - "en:sandwiches", - "en:composite-foods,en:sandwiches", - "Composite foods,Sandwiches", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - "en:fat-in-low-quantity,en:saturated-fat-in-low-quantity,en:sugars-in-low-quantity,en:salt-in-low-quantity", - "700.0", - null, - "en:energy-value-in-kcal-does-not-match-value-computed-from-other-nutrients", - "1.0", - "bottom-25-percent-scans-2019,top-80-percent-scans-2019,top-85-percent-scans-2019,top-90-percent-scans-2019,top-100000-fr-scans-2019,top-country-fr-scans-2019,bottom-25-percent-scans-2020,top-80-percent-scans-2020,top-85-percent-scans-2020,top-90-percent-scans-2020,top-country-fr-scans-2020,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-us-scans-2024,top-100000-us-scans-2024,top-country-us-scans-2024", - "0.575", - "1746606796.0", - "2025-05-07T08:33:16Z", - "it:obesita", - "it:obesita", - "https://images.openfoodfacts.org/images/products/invalid/front_en.66.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.66.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.83.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.83.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.69.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.69.200.jpg", - null, - "70.0", - "293.0", - null, - "0.5", - "0.06", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "2.0", - "0.24", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "88.0", - null, - null, - "18.0", - null, - null, - null, - "0.275", - null, - "0.11", - null, - null, - null, - null, - null, - null, - "0.09", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "-11.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "15", - "10", - "http://world-en.openfoodfacts.org/product/00000010/xxx", - "jeanbono", - "1476947941", - "2016-10-20T07:19:01Z", - "1750695017", - "2025-06-23T16:10:17Z", - "smoothie-app", - "1750695017", - "2025-06-23T16:10:17Z", - "xxx", - null, - null, - "700ml", - "Plastic,Cardboard,fr:Boîte en carton,fr:Film en plastique", - "en:plastic,en:cardboard,fr:boite-en-carton,fr:film-en-plastique", - "Plastic,Cardboard,fr:boite-en-carton,fr:film-en-plastique", - null, - "xxx", - "xx:xxx", - "xxx", - "Beverages and beverages preparations, Beverages, Snacks, Desserts, Sweet snacks, Biscuits and cakes, Biscuits and crackers, Biscuits, Cakes, Pound Cake, Madeleines, Plain madeleines", - "en:beverages-and-beverages-preparations,en:beverages,en:snacks,en:desserts,en:sweet-snacks,en:biscuits-and-cakes,en:biscuits-and-crackers,en:biscuits,en:cakes,en:pound-cake,en:madeleines,en:plain-madeleines", - "Beverages and beverages preparations,Beverages,Snacks,Desserts,Sweet snacks,Biscuits and cakes,Biscuits and crackers,Biscuits,Cakes,Pound Cake,Madeleines,Plain madeleines", - null, - null, - null, - "France", - "france", - "xxx", - "it:xxx", - "it:xxx", - null, - null, - null, - null, - null, - "Lyon,France,Limoges", - "M2I,Bijou", - "DE", - "en:germany", - "Germany", - "Farine de blé 33%, sucre, huile de colza, œufs de poules élevées en plein air 18%, sirop de glucose-fructose, Un stabilisant : glycérol, poudres à lever : carbonates d'ammonium-carbonates de sodium - citrates de sodium (blé), sel, gluten de blé, lait écrémé en poudre, fibres végétales, arôme naturel.", - "en:wheat-flour,en:cereal,en:flour,en:wheat,en:cereal-flour,en:sugar,en:added-sugar,en:disaccharide,en:colza-oil,en:oil-and-fat,en:vegetable-oil-and-fat,en:rapeseed-oil,en:free-range-chicken-eggs,en:egg,en:chicken-egg,en:free-range-eggs,en:glucose-fructose-syrup,en:monosaccharide,en:fructose,en:glucose,en:stabiliser,en:raising-agent,en:e331,en:salt,en:wheat-gluten,en:gluten,en:skimmed-milk-powder,en:dairy,en:milk-powder,en:vegetable-fiber,en:fiber,en:natural-flavouring,en:flavouring,en:e422,fr:carbonates-d-ammonium-carbonates-de-sodium", - "en:palm-oil-free,en:non-vegan,en:vegetarian-status-unknown", - "en:eggs,en:gluten,en:milk", - null, - "en:nuts,en:soybeans", - "en:nuts,en:soybeans", - "Nuts,Soybeans", - "17,6g", - "17.6", - null, - "3.0", - null, - "en:e331,en:e422,en:e503", - "E331 - Sodium citrates,E422 - Glycerol,E503 - Ammonium carbonates", - null, - "unknown", - "4.0", - "Beverages", - "Sweetened beverages", - "en:sweetened-beverages", - "en:beverages,en:sweetened-beverages", - "Beverages,Sweetened beverages", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-to-be-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-to-be-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo to be selected,Photos uploaded", - null, - "7.0", - "f", - "en:fat-in-low-quantity", - "700.0", - null, - "en:energy-value-in-kcal-does-not-match-value-in-kj", - "3.0", - "top-50000-scans-2019,top-100000-scans-2019,at-least-5-scans-2019,at-least-10-scans-2019,top-75-percent-scans-2019,top-80-percent-scans-2019,top-85-percent-scans-2019,top-90-percent-scans-2019,top-50000-fr-scans-2019,top-100000-fr-scans-2019,top-country-fr-scans-2019,at-least-5-fr-scans-2019,at-least-10-fr-scans-2019,top-50000-it-scans-2019,top-100000-it-scans-2019,top-50000-scans-2020,top-100000-scans-2020,at-least-5-scans-2020,at-least-10-scans-2020,top-75-percent-scans-2020,top-80-percent-scans-2020,top-85-percent-scans-2020,top-90-percent-scans-2020,top-50000-fr-scans-2020,top-100000-fr-scans-2020,top-country-fr-scans-2020,at-least-5-fr-scans-2020,at-least-10-fr-scans-2020,top-50000-scans-2021,top-100000-scans-2021,at-least-5-scans-2021,at-least-10-scans-2021,top-75-percent-scans-2021,top-80-percent-scans-2021,top-85-percent-scans-2021,top-90-percent-scans-2021,top-50000-fr-scans-2021,top-100000-fr-scans-2021,top-country-fr-scans-2021,at-least-5-fr-scans-2021,at-least-10-fr-scans-2021,top-50000-scans-2022,top-100000-scans-2022,at-least-5-scans-2022,at-least-10-scans-2022,top-75-percent-scans-2022,top-80-percent-scans-2022,top-85-percent-scans-2022,top-90-percent-scans-2022,top-50000-fr-scans-2022,top-100000-fr-scans-2022,top-country-fr-scans-2022,at-least-5-fr-scans-2022,at-least-10-fr-scans-2022,top-50000-scans-2023,top-100000-scans-2023,at-least-5-scans-2023,at-least-10-scans-2023,top-75-percent-scans-2023,top-80-percent-scans-2023,top-85-percent-scans-2023,top-90-percent-scans-2023,top-50000-fr-scans-2023,top-100000-fr-scans-2023,top-country-fr-scans-2023,at-least-5-fr-scans-2023,at-least-10-fr-scans-2023,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-100000-fr-scans-2024,top-country-fr-scans-2024", - "0.85", - "1742216010.0", - "2025-03-17T12:53:30Z", - "en:plain-madeleines", - "Plain madeleines", - "https://images.openfoodfacts.org/images/products/invalid/front_en.48.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.48.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.24.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.24.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.27.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.27.200.jpg", - "1852.0", - "360.0", - "1852.0", - null, - "1.4", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "4.5", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "83.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0010001", - null, - null, - null, - "0.03", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "22.6666666666667", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "16", - "11", - "http://world-en.openfoodfacts.org/product/00000011/kugler", - "prepperapp", - "1718649410", - "2024-06-17T18:36:50Z", - "1744750362", - "2025-04-15T20:52:42Z", - "foodless", - "1744750362", - "2025-04-15T20:52:42Z", - null, - null, - null, - "1pcs", - null, - null, - null, - null, - "Kugler, Pyrat", - "xx:kugler,xx:pyrat", - "kugler,pyrat", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "en:germany", - "en:germany", - "Germany", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "unknown", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-to-be-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-to-be-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts to be completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name to be completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - "0.0", - null, - null, - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-1000-id-scans-2024,top-5000-id-scans-2024,top-10000-id-scans-2024,top-50000-id-scans-2024,top-100000-id-scans-2024,top-country-id-scans-2024", - "0.2625", - "1744750125.0", - "2025-04-15T20:48:45Z", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/front_en.21.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.21.200.jpg", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "17", - "12", - "http://world-en.openfoodfacts.org/product/00000012/besan-chilla-with-stuffed-chciken-toneop", - "elcoco", - "1578683912", - "2020-01-10T19:18:32Z", - "1749658622", - "2025-06-11T16:17:02Z", - "smoothie-app", - "1749658622", - "2025-06-11T16:17:02Z", - "Besan chilla with stuffed chciken", - null, - null, - "830 g", - null, - null, - null, - null, - "Toneop", - "xx:toneop", - "toneop", - "Dietary supplements,Bodybuilding supplements,Groceries,Supplement", - "en:dietary-supplements,en:bodybuilding-supplements,en:groceries,en:supplement", - "Dietary supplements,Bodybuilding supplements,Groceries,Supplement", - "it:mondo", - "en:world", - "World", - null, - null, - null, - null, - null, - "CE", - "ce", - null, - null, - null, - null, - "https://supplements-online.ecwid.com/Instructions-for-discounts-on-herbalife-products-p210989798", - "Germany", - "en:germany", - "Germany", - null, - null, - null, - "en:milk", - null, - "en:milk", - "en:milk", - "Milk", - "1 cup", - null, - null, - null, - null, - null, - null, - null, - "not-applicable", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-to-be-completed, en:expiration-date-completed, en:packaging-code-completed, en:characteristics-to-be-completed, en:origins-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-completed,en:packaging-code-completed,en:characteristics-to-be-completed,en:origins-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date completed,Packaging code completed,Characteristics to be completed,Origins completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - "830.0", - null, - null, - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-100000-us-scans-2024,top-country-us-scans-2024", - "0.875", - "1747887935.0", - "2025-05-22T04:25:35Z", - "en:supplement", - "Supplement", - "https://images.openfoodfacts.org/images/products/invalid/front_en.22.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.22.200.jpg", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.24.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.24.200.jpg", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "18", - "13", - "http://world-en.openfoodfacts.org/product/00000013/powdered-peanut-butter-pbfit", - "jeff-krab", - "1553970319", - "2019-03-30T18:25:19Z", - "1744243884", - "2025-04-10T00:11:24Z", - "maldan", - "1744243884", - "2025-04-10T00:11:24Z", - "Powdered peanut butter", - null, - null, - "300 g", - "pot plastique", - "pot-plastique", - "Pot-plastique", - null, - "Pbfit", - "xx:pbfit", - "pbfit", - "Snacks, Meals, Rice dishes, Risottos, Powder peanut butter", - "en:snacks,en:meals,en:rice-dishes,en:risottos,en:powder-peanut-butter", - "Snacks,Meals,Rice dishes,Risottos,Powder-peanut-butter", - null, - null, - null, - null, - null, - null, - null, - null, - "EMB 41106C,FR 41.108.001 EC", - "emb-41106c,fr-41-108-001-ec", - "47.6,2.016667", - null, - "lamotte-beuvron-loir-et-cher-france,lancome-loir-et-cher-france", - "France", - "TGV", - "en:Switzerland", - "en:switzerland", - "Switzerland", - "Water, Leptospermum Scoparium Mel (Manuka Honey), Sodium C14-C16 Olefin Sulfonate, Cocamide DIPA , Cocamidopropyl Betaine (Coconut Oil), Cocamidopropyl Betaine (Coconut Oil), Glycereth-2 Cocoate, Melaleuca Alternifolia (Tea Tree) Leaf Oil, Malus Domestica Fruit Cell Culture Extract, Phenoxyethanol (and) Ethylhexylglycerin", - "en:water,en:leptospermum-scoparium-mel,en:sodium-c14-c16-olefin-sulfonate,en:cocamide-dipa,en:cocamidopropyl-betaine,en:glycereth-2-cocoate,en:melaleuca-alternifolia,en:oil,en:oil-and-fat,en:malus-domestica-fruit-cell-culture-extract,en:phenoxyethanol,en:ethylhexylglycerin,en:manuka-honey,en:added-sugar,en:honey,en:coconut-oil,en:vegetable-oil-and-fat,en:vegetable-oil,en:tea-tree", - "en:may-contain-palm-oil,en:non-vegan,en:vegetarian-status-unknown", - null, - null, - "en:crustaceans,en:fish,en:gluten,en:molluscs,en:mustard,en:nuts,en:peanuts,en:sesame-seeds,fr:Peut contenir tenir des traces d'œuf", - "en:crustaceans,en:fish,en:gluten,en:molluscs,en:mustard,en:nuts,en:peanuts,en:sesame-seeds,fr:peut-contenir-tenir-des-traces-d-oeuf", - "Crustaceans,Fish,Gluten,Molluscs,Mustard,Nuts,Peanuts,Sesame seeds,fr:peut-contenir-tenir-des-traces-d-oeuf", - "0g", - "0.0", - null, - "0.0", - null, - null, - null, - "3.0", - "c", - null, - "Composite foods", - "One-dish meals", - "en:one-dish-meals", - "en:composite-foods,en:one-dish-meals", - "Composite foods,One-dish meals", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date completed,Packaging code completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - "en:fat-in-moderate-quantity,en:saturated-fat-in-high-quantity,en:sugars-in-low-quantity,en:salt-in-low-quantity", - "300.0", - null, - "en:energy-value-in-kcal-does-not-match-value-computed-from-other-nutrients", - "2.0", - "top-90-percent-scans-2019,top-95-percent-scans-2019,top-50000-be-scans-2019,top-100000-be-scans-2019,top-country-be-scans-2019,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-country-fr-scans-2024,top-50000-us-scans-2024,top-100000-us-scans-2024", - "0.975", - "1740497724.0", - "2025-02-25T15:35:24Z", - "en:powder-peanut-butter", - "Powder-peanut-butter", - "https://images.openfoodfacts.org/images/products/invalid/front_en.40.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.40.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.31.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.31.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.48.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.48.200.jpg", - null, - "45.0", - "188.0", - null, - "13.0", - "6.7", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "15.0", - "3.6", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "11.0", - null, - null, - null, - "0.0625", - null, - "0.025", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - "3.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "19", - "15", - "http://world-en.openfoodfacts.org/product/00000015/madeleines-chocolait-apple-bandit", - "openfoodfacts-contributors", - "1523810594", - "2018-04-15T16:43:14Z", - "1746561377", - "2025-05-06T19:56:17Z", - "detrumpezvous", - "1746561377", - "2025-05-06T19:56:17Z", - "Madeleines ChocoLait", - null, - null, - "1000 g", - "Plastique, Carton", - "en:plastic,en:cardboard", - "Plastic,Cardboard", - null, - "Apple bandit", - "xx:apple-bandit", - "apple-bandit", - "Snacks, Snacks sucrés, Biscuits et gâteaux, Gâteaux, Gâteaux au chocolat, Madeleines, Madeleines au chocolat", - "en:snacks,en:sweet-snacks,en:biscuits-and-cakes,en:cakes,en:chocolate-cakes,en:madeleines,en:chocolate-madeleines", - "Snacks,Sweet snacks,Biscuits and cakes,Cakes,Chocolate cakes,Madeleines,Chocolate madeleines", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "France", - "en:france", - "France", - "Farine de blé 27%, chocolat au lait 18% (sucre, beurre de cacao, lait entier en poudre, pâte de cacao, émulsifiant : lécithines (soja), arôme naturel de vanille), sucre, huile de colza, œufs de poules élevées en plein air 14.5%, sirop de glucose-fructose, stabilisant : glycérol, poudres à lever : carbonates d'ammonium - carbonates de sodium - citrates de sodium (blé), sel, gluten de blé, lait écrémé en poudre, fibres végétales, arôme naturel. Peut contenir des traces de fruits à coque.", - "en:wheat-flour,en:cereal,en:flour,en:wheat,en:cereal-flour,en:milk-chocolate,en:chocolate,en:sugar,en:added-sugar,en:disaccharide,en:colza-oil,en:oil-and-fat,en:vegetable-oil-and-fat,en:rapeseed-oil,en:free-range-chicken-eggs,en:egg,en:chicken-egg,en:free-range-eggs,en:glucose-fructose-syrup,en:monosaccharide,en:fructose,en:glucose,en:stabiliser,en:raising-agent,en:e500,en:e331,en:salt,en:wheat-gluten,en:gluten,en:skimmed-milk-powder,en:dairy,en:milk-powder,en:vegetable-fiber,en:fiber,en:natural-flavouring,en:flavouring,en:cocoa-butter,en:plant,en:cocoa,en:whole-milk-powder,en:cocoa-paste,en:emulsifier,en:natural-vanilla-flavouring,en:vanilla-flavouring,en:e422,en:e503,en:e322,en:soya-lecithin,en:e322i", - "en:palm-oil-free,en:non-vegan,en:maybe-vegetarian", - null, - null, - "en:nuts", - "en:nuts", - "Nuts", - "21,6g", - "21.6", - null, - "6.0", - null, - "en:e322,en:e322i,en:e331,en:e422,en:e500,en:e503", - "E322 - Lecithins,E322i - Lecithin,E331 - Sodium citrates,E422 - Glycerol,E500 - Sodium carbonates,E503 - Ammonium carbonates", - "20.0", - "e", - "4.0", - "Sugary snacks", - "Biscuits and cakes", - "en:biscuits-and-cakes", - "en:sugary-snacks,en:biscuits-and-cakes", - "Sugary snacks,Biscuits and cakes", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - "32.0", - "d", - "en:fat-in-high-quantity,en:saturated-fat-in-high-quantity,en:sugars-in-high-quantity,en:salt-in-moderate-quantity", - "1000.0", - null, - null, - "1.0", - "top-50000-scans-2019,top-100000-scans-2019,at-least-5-scans-2019,at-least-10-scans-2019,top-75-percent-scans-2019,top-80-percent-scans-2019,top-85-percent-scans-2019,top-90-percent-scans-2019,top-50000-fr-scans-2019,top-100000-fr-scans-2019,top-country-fr-scans-2019,at-least-5-fr-scans-2019,at-least-10-fr-scans-2019,top-5000-us-scans-2019,top-10000-us-scans-2019,top-50000-us-scans-2019,top-100000-us-scans-2019,top-50000-scans-2020,top-100000-scans-2020,at-least-5-scans-2020,at-least-10-scans-2020,top-75-percent-scans-2020,top-80-percent-scans-2020,top-85-percent-scans-2020,top-90-percent-scans-2020,top-50000-fr-scans-2020,top-100000-fr-scans-2020,top-country-fr-scans-2020,at-least-5-fr-scans-2020,at-least-10-fr-scans-2020,top-50000-scans-2021,top-100000-scans-2021,at-least-5-scans-2021,at-least-10-scans-2021,top-75-percent-scans-2021,top-80-percent-scans-2021,top-85-percent-scans-2021,top-90-percent-scans-2021,top-50000-fr-scans-2021,top-100000-fr-scans-2021,top-country-fr-scans-2021,at-least-5-fr-scans-2021,at-least-10-fr-scans-2021,top-50000-es-scans-2021,top-100000-es-scans-2021,top-100000-de-scans-2021,top-50000-scans-2022,top-100000-scans-2022,at-least-5-scans-2022,top-75-percent-scans-2022,top-80-percent-scans-2022,top-85-percent-scans-2022,top-90-percent-scans-2022,top-50000-fr-scans-2022,top-100000-fr-scans-2022,top-country-fr-scans-2022,at-least-5-fr-scans-2022,top-50000-scans-2023,top-100000-scans-2023,at-least-5-scans-2023,at-least-10-scans-2023,top-75-percent-scans-2023,top-80-percent-scans-2023,top-85-percent-scans-2023,top-90-percent-scans-2023,top-50000-fr-scans-2023,top-100000-fr-scans-2023,top-country-fr-scans-2023,at-least-5-fr-scans-2023,at-least-10-fr-scans-2023,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-country-fr-scans-2024", - "0.7875", - "1746561376.0", - "2025-05-06T19:56:16Z", - "en:chocolate-madeleines", - "Chocolate madeleines", - "https://images.openfoodfacts.org/images/products/invalid/front_fr.40.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_fr.40.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.42.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.42.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.24.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.24.200.jpg", - "1926.0", - "460.0", - "1926.0", - null, - "24.0", - "6.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "54.0", - "31.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "1.4", - null, - null, - "6.4", - null, - null, - null, - "0.48", - null, - "0.192", - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "16.25", - null, - null, - null, - null, - null, - "20.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "20", - "16", - "http://world-en.openfoodfacts.org/product/00000016/velvety-vanilla-cake-mix-betty-crocker", - "maldan", - "1703127173", - "2023-12-21T02:52:53Z", - "1746300432", - "2025-05-03T19:27:12Z", - "detrumpezvous", - "1746300432", - "2025-05-03T19:27:12Z", - "velvety vanilla cake mix", - null, - null, - null, - null, - null, - null, - null, - "betty crocker", - "xx:betty-crocker", - "betty-crocker", - "Dietary supplements, Vitamins", - "en:dietary-supplements,en:vitamins", - "Dietary supplements,Vitamins", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "Beef liver", - "en:beef-liver,en:animal,en:beef", - "en:palm-oil-free,en:non-vegan,en:non-vegetarian", - null, - null, - null, - null, - null, - "0g", - "0.0", - null, - "0.0", - null, - null, - null, - null, - "not-applicable", - "1.0", - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-to-be-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts to be completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity to be completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - null, - null, - null, - null, - null, - "0.475", - "1746300432.0", - "2025-05-03T19:27:12Z", - "en:vitamins", - "Vitamins", - "https://images.openfoodfacts.org/images/products/invalid/front_en.3.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.3.200.jpg", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.5.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.5.200.jpg", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "21", - "17", - "http://world-en.openfoodfacts.org/product/00000017/collagen-for-her-bodylab", - "foodvisor", - "1728736811", - "2024-10-12T12:40:11Z", - "1749711483", - "2025-06-12T06:58:03Z", - "macrofactor", - "1749711483", - "2025-06-12T06:58:03Z", - "Collagen For Her", - null, - null, - "1.0 kg", - null, - null, - null, - null, - "Bodylab", - "xx:bodylab", - "Bodylab", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "Ireland, en:france", - "en:france,en:ireland", - "France,Ireland", - "whey protein concentrate, milk protein concentrate, flavoring, thickener (xanthan gum), sweetener (sucralose)", - "en:whey-protein,en:protein,en:animal-protein,en:milk-proteins,en:milk-protein-concentrate,en:flavouring,en:thickener,en:sweetener,en:e415,en:e955", - "en:palm-oil-free,en:non-vegan,en:maybe-vegetarian", - null, - null, - null, - null, - null, - "2 tortillas (56.699 g)", - "56.699", - null, - "2.0", - null, - "en:e415,en:e955", - "E415 - Xanthan gum,E955 - Sucralose", - null, - "unknown", - "4.0", - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - "1000.0", - null, - null, - "6.0", - "top-100000-scans-2024,at-least-5-scans-2024,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-fr-scans-2024,top-100000-fr-scans-2024,top-country-fr-scans-2024,top-50000-ro-scans-2024,top-100000-ro-scans-2024,top-50000-ma-scans-2024,top-100000-ma-scans-2024", - "0.5625", - "1745047139.0", - "2025-04-19T07:18:59Z", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/front_en.31.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.31.200.jpg", - null, - null, - null, - null, - null, - "123.0", - "517.0", - null, - "1.76", - "0.882", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - "24.7", - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "1.76", - null, - null, - "1.76", - null, - null, - null, - "0.0882", - null, - "0.0353", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "22", - "18", - "http://world-en.openfoodfacts.org/product/00000018/chocolate-peanut-butter-protein-pays-gourmand", - "foodvisor", - "1729882037", - "2024-10-25T18:47:17Z", - "1745346792", - "2025-04-22T18:33:12Z", - "roboto-app", - "1745346792", - "2025-04-22T18:33:12Z", - "Chocolate peanut butter protein", - null, - null, - null, - null, - null, - null, - null, - "Pays Gourmand", - "xx:pays-gourmand", - "pays-gourmand", - "Fasern,Pflanzliche Lebensmittel", - "en:plant-based-foods-and-beverages,en:plant-based-foods,de:fasern", - "Plant-based foods and beverages,Plant-based foods,de:fasern", - null, - null, - null, - null, - null, - "en:nutriscore", - "en:nutriscore", - "Nutriscore", - null, - null, - null, - null, - null, - null, - null, - "Irland, Germany", - "en:germany,en:ireland", - "Germany,Ireland", - null, - null, - null, - null, - null, - null, - null, - null, - "28.0g", - "28.0", - null, - null, - null, - null, - null, - null, - "unknown", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-to-be-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity to be completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - "en:fat-in-moderate-quantity,en:salt-in-moderate-quantity", - null, - null, - null, - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-100000-gb-scans-2024,top-country-gb-scans-2024", - "0.4875", - "1745345747.0", - "2025-04-22T18:15:47Z", - "de:fasern", - "de:fasern", - "https://images.openfoodfacts.org/images/products/invalid/front_de.15.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_de.15.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.13.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.13.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_de.11.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_de.11.200.jpg", - null, - "357.14", - "1494.0", - null, - "5.36", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "14.29", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "7.14", - null, - null, - "71.43", - null, - null, - null, - "0.45", - null, - "0.18", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "23", - "19", - "http://world-en.openfoodfacts.org/product/00000019/erdbeeren-beerenbruder", - "prepperapp", - "1718717714", - "2024-06-18T13:35:14Z", - "1729700894", - "2024-10-23T16:28:14Z", - "foodvisor", - "1743605654", - "2025-04-02T14:54:14Z", - "Erdbeeren", - null, - null, - "250g", - null, - null, - null, - null, - "BeerenBrüder", - "xx:beerenbruder", - "beerenbruder", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "en:germany", - "en:germany", - "Germany", - null, - null, - null, - null, - null, - null, - null, - null, - "261.0g", - "261.0", - null, - null, - null, - null, - null, - null, - "unknown", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - "250.0", - null, - null, - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-country-fr-scans-2024", - "0.4625", - "1718717715.0", - "2024-06-18T13:35:15Z", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/front_de.3.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_de.3.200.jpg", - null, - null, - null, - null, - null, - "176.6", - "739.0", - null, - "6.9", - "3.1", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "20.69", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "2.07", - null, - null, - "7.66", - null, - null, - null, - "1.083325", - null, - "0.43333", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "24", - "20", - "http://world-en.openfoodfacts.org/product/00000020/nesquik-moins-de-sucre-nestle", - "openfoodfacts-contributors", - "1536930846", - "2018-09-14T13:14:06Z", - "1750084921", - "2025-06-16T14:42:01Z", - "yaron", - "1750084921", - "2025-06-16T14:42:01Z", - "Nesquik moins de sucre", - null, - null, - "1080 g / 50 madeleines", - "1 boîte en carton à recycler 50 sachets individuels à recycler", - "fr:1-boite-en-carton-a-recycler-50-sachets-individuels-a-recycler", - "fr:1-boite-en-carton-a-recycler-50-sachets-individuels-a-recycler", - null, - "Nestlé", - "xx:nestle", - "Nestlé", - "Snacks, Snacks sucrés, Biscuits et gâteaux, Gâteaux, Quatre-quarts, Gâteaux au chocolat, Madeleines, Madeleines au chocolat", - "en:snacks,en:sweet-snacks,en:biscuits-and-cakes,en:cakes,en:pound-cake,en:chocolate-cakes,en:madeleines,en:chocolate-madeleines", - "Snacks,Sweet snacks,Biscuits and cakes,Cakes,Pound Cake,Chocolate cakes,Madeleines,Chocolate madeleines", - "fr:Blé origine France,fr:Œufs origine France", - "fr:ble-origine-france,fr:oeufs-origine-france", - "fr:ble-origine-france,fr:oeufs-origine-france", - "Saint-Yrieix,France", - "saint-yrieix,france", - "No gluten, Organic, Free range, Kosher, No preservatives, USDA Organic, Free range eggs, Green Dot, Made in France, No colorings, No palm oil, Nutriscore, Pure cocoa butter, fr:Blé français, Triman, fr:Fabriqué en Nouvelle-Aquitaine, en:nutriscore-grade-a", - "en:no-gluten,en:organic,en:free-range,en:kosher,en:no-preservatives,en:usda-organic,en:free-range-eggs,en:green-dot,en:made-in-france,en:no-colorings,en:no-palm-oil,en:nutriscore,en:nutriscore-grade-a,en:pure-cocoa-butter,fr:ble-francais,fr:triman,fr:fabrique-en-nouvelle-aquitaine", - "No gluten,Organic,Free range,Kosher,No preservatives,USDA Organic,Free range eggs,Green Dot,Made in France,No colorings,No palm oil,Nutriscore,Nutriscore Grade A,Pure cocoa butter,fr:Blé français,Triman,fr:fabrique-en-nouvelle-aquitaine", - null, - null, - null, - null, - null, - "France", - "magasin d'usine,magasin Bijou bordeaux,magasin Bijou Brive", - "Frankreich, Germany", - "en:france,en:germany", - "France,Germany", - "Nuts (Peanuts, Almonds), Prebiotic Blend (Tapioca Fiber, Vegetable Fiber), Protein Blend (Pea Protein Crisps [Pea Protein, Tapioca Starch], Pea Protein), Natural Flavors, Peanut Flour, Coconut Oil, Unsweetened Chocolate, Cocoa, Cocoa Butter, Sea Salt, Lion's Mane, Stevia Plant Extract, Vitamin E", - "en:nut,en:prebiotic-blend,en:protein-blend,en:natural-flavouring,en:flavouring,en:peanut-flour,en:peanut,en:coconut-oil,en:oil-and-fat,en:vegetable-oil-and-fat,en:vegetable-oil,en:chocolate,en:cocoa,en:plant,en:cocoa-butter,en:sea-salt,en:salt,en:lion-s-mane,en:stevia-plant-extract,en:vitamin-e,en:vitamins,en:almond,en:tree-nut,en:tapioca-fiber,en:vegetable-fiber,en:fiber,en:pea-protein-crisps,en:pea-protein,en:protein,en:plant-protein,en:tapioca,en:starch", - "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", - "en:eggs,en:gluten,en:milk,en:soybeans", - null, - "en:nuts", - "en:nuts", - "Nuts", - "2 scoops (35 g)", - "35.0", - null, - "0.0", - null, - null, - null, - null, - "unknown", - "4.0", - "Sugary snacks", - "Biscuits and cakes", - "en:biscuits-and-cakes", - "en:sugary-snacks,en:biscuits-and-cakes", - "Sugary snacks,Biscuits and cakes", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-completed, en:origins-completed, en:categories-completed, en:brands-completed, en:packaging-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-completed,en:origins-completed,en:categories-completed,en:brands-completed,en:packaging-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics completed,Origins completed,Categories completed,Brands completed,Packaging completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - "32.0", - "d", - "en:fat-in-moderate-quantity,en:sugars-in-moderate-quantity,en:salt-in-high-quantity", - "1080.0", - null, - null, - "1.0", - "top-50000-scans-2019,top-100000-scans-2019,at-least-5-scans-2019,at-least-10-scans-2019,top-75-percent-scans-2019,top-80-percent-scans-2019,top-85-percent-scans-2019,top-90-percent-scans-2019,top-10000-fr-scans-2019,top-50000-fr-scans-2019,top-100000-fr-scans-2019,top-country-fr-scans-2019,at-least-5-fr-scans-2019,at-least-10-fr-scans-2019,top-50000-it-scans-2019,top-100000-it-scans-2019,top-50000-scans-2020,top-100000-scans-2020,at-least-5-scans-2020,at-least-10-scans-2020,top-75-percent-scans-2020,top-80-percent-scans-2020,top-85-percent-scans-2020,top-90-percent-scans-2020,top-50000-fr-scans-2020,top-100000-fr-scans-2020,top-country-fr-scans-2020,at-least-5-fr-scans-2020,at-least-10-fr-scans-2020,top-50000-scans-2021,top-100000-scans-2021,at-least-5-scans-2021,at-least-10-scans-2021,top-75-percent-scans-2021,top-80-percent-scans-2021,top-85-percent-scans-2021,top-90-percent-scans-2021,top-50000-fr-scans-2021,top-100000-fr-scans-2021,top-country-fr-scans-2021,at-least-5-fr-scans-2021,at-least-10-fr-scans-2021,top-50000-scans-2022,top-100000-scans-2022,at-least-5-scans-2022,top-75-percent-scans-2022,top-80-percent-scans-2022,top-85-percent-scans-2022,top-90-percent-scans-2022,top-50000-fr-scans-2022,top-100000-fr-scans-2022,top-country-fr-scans-2022,at-least-5-fr-scans-2022,top-50000-scans-2023,top-100000-scans-2023,at-least-5-scans-2023,at-least-10-scans-2023,top-75-percent-scans-2023,top-80-percent-scans-2023,top-85-percent-scans-2023,top-90-percent-scans-2023,top-10000-fr-scans-2023,top-50000-fr-scans-2023,top-100000-fr-scans-2023,top-country-fr-scans-2023,at-least-5-fr-scans-2023,at-least-10-fr-scans-2023,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-5000-tn-scans-2024,top-10000-tn-scans-2024,top-50000-tn-scans-2024,top-100000-tn-scans-2024,top-country-tn-scans-2024", - "0.875", - "1746552938.0", - "2025-05-06T17:35:38Z", - "en:chocolate-madeleines", - "Chocolate madeleines", - "https://images.openfoodfacts.org/images/products/invalid/front_en.77.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.77.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.91.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.91.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.43.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.43.200.jpg", - null, - "343.0", - "1430.0", - null, - "5.71", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "31.4", - "8.57", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "14.3", - null, - null, - "57.1", - null, - null, - null, - "1.79", - null, - "0.714", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.357", - null, - "0.0914", - null, - "0.0171", - "0.143", - "0.00857", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "56.3461538461538", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "25", - "21", - "http://world-en.openfoodfacts.org/product/00000021/inulin-prebiotic-fiber-it-s-just", - "openfoodfacts-contributors", - "1560170250", - "2019-06-10T12:37:30Z", - "1750851740", - "2025-06-25T11:42:20Z", - "smoothie-app", - "1750851740", - "2025-06-25T11:42:20Z", - "Inulin Prebiotic Fiber", - null, - null, - null, - null, - null, - null, - null, - "It's Just!", - "xx:it-s-just", - "it-s-just", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "en:France", - "en:france", - "France", - "Inulin Prebiotic Fiber (Chicory Root Powder)", - "en:inulin-prebiotic-fiber,en:chicory-root,en:vegetable,en:shoot-vegetable,en:chicory", - "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", - null, - null, - null, - null, - null, - "3g", - "3.0", - null, - "0.0", - null, - null, - null, - null, - "unknown", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-to-be-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity to be completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - null, - null, - "en:energy-value-in-kcal-does-not-match-value-computed-from-other-nutrients", - "1.0", - "bottom-25-percent-scans-2019,top-80-percent-scans-2019,top-85-percent-scans-2019,top-90-percent-scans-2019,top-100000-fr-scans-2019,top-country-fr-scans-2019,top-75-percent-scans-2020,top-80-percent-scans-2020,top-85-percent-scans-2020,top-90-percent-scans-2020,top-100000-fr-scans-2020,top-country-fr-scans-2020,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-5000-th-scans-2024,top-10000-th-scans-2024,top-50000-th-scans-2024,top-100000-th-scans-2024,top-country-th-scans-2024", - "0.5625", - "1743546541.0", - "2025-04-01T22:29:01Z", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/front_en.22.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.22.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.7.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.7.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.10.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.10.200.jpg", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "26", - "22", - "http://world-en.openfoodfacts.org/product/00000022/farandole-de-madeleine-coca-cola", - "openfoodfacts-contributors", - "1614525537", - "2021-02-28T15:18:57Z", - "1746529342", - "2025-05-06T11:02:22Z", - "detrumpezvous", - "1746529342", - "2025-05-06T11:02:22Z", - "Farandole de madeleine", - null, - null, - "590 g", - "Boîte en carton, Film en plastique", - "fr:boite-en-carton,fr:film-en-plastique", - "fr:boite-en-carton,fr:film-en-plastique", - null, - "Coca-Cola", - "xx:coca-cola", - "coca-cola", - "Snacks, Snacks sucrés, Biscuits et gâteaux, Gâteaux, Gâteaux au chocolat, Madeleines, Madeleines au chocolat, Madeleines longues", - "en:snacks,en:sweet-snacks,en:biscuits-and-cakes,en:cakes,en:chocolate-cakes,en:madeleines,en:chocolate-madeleines,en:long-madeleines", - "Snacks,Sweet snacks,Biscuits and cakes,Cakes,Chocolate cakes,Madeleines,Chocolate madeleines,Long madeleines", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "Brasil", - "en:brazil", - "Brazil", - "Madeleines ChocoNoir - Madeleines nappées de chocolat noir : farine de blé 27%, chocolat noir 18% (pâte de cacao, sucre, beurre de cacao, matière grasse laitière anhydre, émulsifiant : lécithines (soja), arôme naturel de vanille), sucre, huile de colza, œufs de poules élevées en plein air 14.5%, sirop de glucose-fructose, stabilisant : glycérol, poudres à lever : carbonates d'ammonium - carbonates de sodium - citrates de sodium (blé), sel, gluten de blé, lait écrémé en poudre, fibres végétales, arôme naturel. Peut contenir des traces de fruits à coque. Toutes vos remarques et suggestions sont les bienvenues : contactez notre Service Qualité en indiquant le nom et le numéro du lot du produit concerné figurant sur le dessus de la boîte. Pour conserver toutes les qualités de ce produit, le maintenir dans un milieu tempéré à l'abri de la chaleur, de l'humidité et du froid.", - "fr:madeleines-choconoir,fr:madeleines-nappees-de-chocolat-noir,en:dark-chocolate,en:chocolate,en:sugar,en:added-sugar,en:disaccharide,en:colza-oil,en:oil-and-fat,en:vegetable-oil-and-fat,en:rapeseed-oil,en:free-range-chicken-eggs,en:egg,en:chicken-egg,en:free-range-eggs,en:glucose-fructose-syrup,en:monosaccharide,en:fructose,en:glucose,en:stabiliser,en:raising-agent,en:e500,en:e331,en:salt,en:wheat-gluten,en:gluten,en:skimmed-milk-powder,en:dairy,en:milk-powder,en:vegetable-fiber,en:fiber,en:natural-flavouring,en:flavouring,fr:toutes-vos-remarques-et-suggestions-sont-les-bienvenues,fr:le-maintenir-dans-un-milieu-tempere-a-l-abri-de-la-chaleur,fr:de-l-humidite-et-du-froid,en:wheat-flour,en:cereal,en:flour,en:wheat,en:cereal-flour,en:cocoa-paste,en:plant,en:cocoa,en:cocoa-butter,fr:matiere-grasse-de-lait-anhydre,en:emulsifier,en:natural-vanilla-flavouring,en:vanilla-flavouring,en:e422,en:e503,fr:contactez-notre-service-qualite-en-indiquant-le-nom-et-le-numero-du-lot-du-produit-concerne-figurant-sur-le-dessus-de-la-boite,fr:pour-conserver-toutes-les-qualites-de-ce-produit,en:e322,en:soya-lecithin,en:e322i", - "en:palm-oil-content-unknown,en:non-vegan,en:vegetarian-status-unknown", - null, - null, - null, - "en:nuts", - "Nuts", - "54g", - "54.0", - "off", - "6.0", - null, - "en:e322,en:e322i,en:e331,en:e422,en:e500,en:e503", - "E322 - Lecithins,E322i - Lecithin,E331 - Sodium citrates,E422 - Glycerol,E500 - Sodium carbonates,E503 - Ammonium carbonates", - "9.0", - "c", - "4.0", - "Sugary snacks", - "Biscuits and cakes", - "en:biscuits-and-cakes", - "en:sugary-snacks,en:biscuits-and-cakes", - "Sugary snacks,Biscuits and cakes", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-completed, en:quantity-completed, en:product-name-completed, en:photos-validated, en:packaging-photo-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-completed,en:quantity-completed,en:product-name-completed,en:photos-validated,en:packaging-photo-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging completed,Quantity completed,Product name completed,Photos validated,Packaging photo selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - "41.0", - "d", - "en:fat-in-moderate-quantity,en:saturated-fat-in-high-quantity,en:sugars-in-low-quantity,en:salt-in-moderate-quantity", - 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"en:snacks,en:sweet-snacks,en:biscuits-and-cakes,en:cakes,en:chocolate-cakes,en:madeleines,en:chocolate-madeleines", - "Snacks,Sweet snacks,Biscuits and cakes,Cakes,Chocolate cakes,Madeleines,Chocolate madeleines", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "Bijou", - "Germany", - "en:germany", - "Germany", - "69 While MTR Waffel mit feiner Milchhaselnusscreme Tour www Schokolade Zuidien: Wele Schokolade 28% WEIZENBICH EMAGERMILCHPULVER ( Sonnenblumenöl MILCHEIWEISS Emula War mit feiner Milchhaselnusscreme-Füllung (53%), NAT 75204263 3x390 Schokolade Zudien. Weiße Schokolade 28 % (Kakaobutter, Zucker, MAGERMILCHPULVER, BUTTERREINFETT, EMUI mol, WEIZENMEHL, MAGERMILUTI MAGERMILCHPULVER (85 %). VOLLMILCHPULVER (5,5 %), HASELNUSSE (5 %), SÜSSMO akao, Sonnenblumenöl, MILCHEIWEISS, Emulgator Lecithine (SOJA), Backtriebmittel: Ammomumiyrencal DVERTHINKET MED HVID CHOKOLADE. SPRÖD WAFER FYLLD MED CREME AV MJÖLK OCH HASSELNÖTTER (53%), OVERDRAGEN FYLT MED KREM AV MELK OG HASSELNØTTER (53%) DEKKET MED HVIT SJOKOLADE. Ingredienser: Hvit sjokola UNMETMELKSPULVER/SKUMMJÖLKSPULVER, konsentrert SMØR/SMÖRKONCENTRAT, emulgator: lecitiner (SOJA); vanil ER (5%), VALLEPULVER/VASSLEPULVER/MYSEPULVER, STIVELSE/VETESTÄRKELSE, fettredusert esamtmilchbestandteile im Produkt 21,5 %. DK SE NOR LÆKKER SPRØD VAFFEL FYLDT MED CREME AF MÆL VETEMJOL, SKUMMETMÆLKSPULVER/SKUMMJÖLKSPULVER (8,5%), HELMELKPULVER/SØDMELKSPULV ER/MJÖLKPROTEINER, emulgator: lecitiner (SOJA), hevemidler / bakpulver (ammoniumhydrogenkarbona Erbonat/natriumvätekarbonat), aromaer, salt. Mælkeandel/Mjölkbeståndsdelar/Melkekomponenter totalt: 21 intiaine: lesitiinit (SOIJA); vanillini), sokeri, palmuöljy, asvaton MAITOJAUHE (8,5%), TÄYSMAITOJAUHE (5,5%), (5%). HERAJAUHE, VEHNÄTÄRKKELYS, vähärasvainen ukkaöljy, MAITOPROTEIINIA, emulgointiaine: lesitiinit (SOLJA), Cammoniumvetykarbonaatti, natriumvetykarbonaatti), aromit, va-ainella yhteensä: 21.5% 43.4", - "en:69-while-mtr-waffel-mit-feiner-milchhaselnusscreme-tour-www-schokolade-zuidien,en:nat-75204263-3x390-schokolade-zudien,en:weisse-schokolade,en:sokeri,en:palmuoljy,en:asvaton-maitojauhe,en:taysmaitojauhe,en:herajauhe,en:vehnatarkkelys,en:vaharasvainen-ukkaoljy,en:maitoproteiinia,en:emulgointiaine,en:cammoniumvetykarbonaatti,en:natriumvetykarbonaatti,en:aromit,en:va-ainella-yhteensa,en:wele-schokolade-28-weizenbich-emagermilchpulver,en:sonnenblumenol-milcheiweiss-emula-war-mit-feiner-milchhaselnusscreme-fullung,en:kakaobutter,en:zucker,en:magermilchpulver,en:butterreinfett,en:emui-mol,en:weizenmehl,en:magermiluti-magermilchpulver,en:vollmilchpulver,en:haselnusse,en:sussmo-akao,en:sonnenblumenol,en:milcheiweiss,en:emulgator-lecithine,en:backtriebmittel,en:overdragen-fylt-med-krem-av-melk-og-hasselnøtter,en:dekket-med-hvit-sjokolade,en:ingredienser,en:konsentrert-smør,en:smorkoncentrat,en:emulgator,en:vanil-er,en:vallepulver,en:vasslepulver,en:mysepulver,en:stivelse,en:vetestarkelse,en:fettredusert-esamtmilchbestandteile-im-produkt,en:dk-se-nor-laekker-sprød-vaffel-fyldt-med-creme-af-mael-vetemjol,en:skummetmaelkspulver,en:skummjolkspulver,en:helmelkpulver,en:sødmelkspulv-er,en:mjolkproteiner,en:hevemidler,en:bakpulver,en:aromaer,en:salt,en:maelkeandel,en:mjolkbestandsdelar,en:melkekomponenter-totalt,en:lesitiinit,en:vanillini,en:43-4,en:ammomumiyrencal-dverthinket-med-hvid-chokolade,en:sprod-wafer-fylld-med-creme-av-mjolk-och-hasselnotter,en:hvit-sjokola-unmetmelkspulver,en:lecitiner,en:ammoniumhydrogenkarbona-erbonat,en:natriumvatekarbonat,en:21-intiaine,en:soija,en:solja", - "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", - "en:soybeans", - null, - null, - null, - null, - "100 ml", - "100.0", - null, - "0.0", - null, - null, - null, - "7.0", - "c", - null, - "Sugary snacks", - "Biscuits and cakes", - "en:biscuits-and-cakes", - "en:sugary-snacks,en:biscuits-and-cakes", - "Sugary snacks,Biscuits and cakes", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - "28.0", - "e", - "en:fat-in-low-quantity,en:saturated-fat-in-low-quantity,en:sugars-in-moderate-quantity,en:salt-in-low-quantity", - "260.0", - null, - "en:energy-value-in-kcal-does-not-match-value-in-kj,en:energy-value-in-kj-does-not-match-value-computed-from-other-nutrients", - "1.0", - "top-75-percent-scans-2023,top-80-percent-scans-2023,top-85-percent-scans-2023,top-90-percent-scans-2023,top-country-fr-scans-2023,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-country-fr-scans-2024", - "0.7625", - "1749160723.0", - "2025-06-05T21:58:43Z", - "en:chocolate-madeleines", - "Chocolate madeleines", - "https://images.openfoodfacts.org/images/products/invalid/front_en.35.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.35.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.31.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.31.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_de.23.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_de.23.200.jpg", - "1917.0", - "32.0", - "1917.0", - null, - "0.0", - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "7.7", - "7.6", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - "0.0", - null, - null, - null, - "0.02", - null, - "0.008", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - "7.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "28", - "24", - "http://world-en.openfoodfacts.org/product/00000024/croccante-con-pistacchi-e-miele-nutella", - "kiliweb", - "1579212608", - "2020-01-16T22:10:08Z", - "1749285284", - "2025-06-07T08:34:44Z", - "roboto-app", - "1749285284", - "2025-06-07T08:34:44Z", - "Croccante con pistacchi e miele", - null, - null, - null, - "Packung(en)", - "de:packung-en", - "de:packung-en", - null, - "NUTELLA", - "xx:nutella", - "nutella", - "it:bieta da costa", - "it:bieta-da-costa", - "it:bieta-da-costa", - null, - null, - null, - null, - null, - "Organic, EU Organic, Non-EU Agriculture, EU Agriculture, EU/non-EU Agriculture, NL-BIO-01", - "en:organic,en:eu-organic,en:non-eu-agriculture,en:eu-agriculture,en:eu-non-eu-agriculture,en:nl-bio-01", - "Organic,EU Organic,Non-EU Agriculture,EU Agriculture,EU/non-EU Agriculture,NL-BIO-01", - null, - null, - null, - null, - null, - null, - null, - "Belgien, Germany", - "en:belgium,en:germany", - "Belgium,Germany", - "Pommes séchées - Dried Apple* Origine: Autriche - Origin: Austria Ce produit provient d'une fabrique où les noix, cacahuètes, sésame, blé et gluten sont utilisés. This producti 024", - "en:dried-apple,en:fruit,en:malaceous-fruit,en:apple,fr:dried-apple,en:peanut,en:nut,en:sesame,en:seed,en:wheat,en:cereal,fr:gluten-sont-utilises,fr:this-producti-024", - "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", - null, - null, - null, - null, - null, - "33.3g", - "33.3", - null, - "0.0", - null, - null, - null, - null, - "unknown", - "1.0", - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-completed, en:quantity-to-be-completed, en:product-name-completed, en:photos-validated, en:packaging-photo-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-validated,en:packaging-photo-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging completed,Quantity to be completed,Product name completed,Photos validated,Packaging photo selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - null, - null, - null, - "1.0", - "bottom-25-percent-scans-2020,top-80-percent-scans-2020,top-85-percent-scans-2020,top-90-percent-scans-2020,top-50000-be-scans-2020,top-100000-be-scans-2020,top-country-be-scans-2020,bottom-25-percent-scans-2021,top-80-percent-scans-2021,top-85-percent-scans-2021,top-90-percent-scans-2021,top-country-fr-scans-2021,top-75-percent-scans-2023,top-80-percent-scans-2023,top-85-percent-scans-2023,top-90-percent-scans-2023,top-50000-ch-scans-2023,top-100000-ch-scans-2023,top-country-ch-scans-2023,top-50000-us-scans-2023,top-100000-us-scans-2023,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-country-fr-scans-2024", - "0.7", - "1749284545.0", - "2025-06-07T08:22:25Z", - "it:bieta-da-costa", - "it:bieta-da-costa", - "https://images.openfoodfacts.org/images/products/invalid/front_en.35.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.35.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.48.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.48.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.49.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.49.200.jpg", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - 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"frog-fuel", - "Protein", - "en:protein", - "Protein", - null, - null, - null, - null, - null, - "fr:sans-conservateurs, fr:sans-colorants, triman", - "en:no-preservatives,en:no-colorings,fr:triman", - "No preservatives,No colorings,Triman", - null, - null, - null, - null, - null, - null, - null, - "en:France", - "en:france", - "France", - "glucose syrup, sugar, water, pectin, black carrot concentrate and chlorophyll (color), citric acid, trisodium citrate, natural flavor", - "en:glucose-syrup,en:added-sugar,en:monosaccharide,en:glucose,en:sugar,en:disaccharide,en:water,en:e440a,en:concentrated-black-carrot,en:vegetable,en:root-vegetable,en:taproot-vegetable,en:carrot,en:e163,en:black-carrot,en:e140i,en:e140,en:e330,en:e331iii,en:e331,en:natural-flavouring,en:flavouring,en:colour", - "en:palm-oil-free,en:maybe-vegan,en:maybe-vegetarian", - null, - null, - null, - null, - null, - "2g", - "2.0", - null, - "4.0", - null, - "en:e140,en:e140i,en:e330,en:e331,en:e331iii,en:e440", - "E140 - Chlorophylls and Chlorophyllins,E140i - 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"To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo to be selected,Photos uploaded", - null, - null, - "unknown", - null, - "610.0", - null, - null, - "2.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-ma-scans-2024,top-100000-ma-scans-2024,top-country-ma-scans-2024,top-100000-gb-scans-2024", - "0.65", - "1673331990.0", - "2023-01-10T06:26:30Z", - "en:protein", - "Protein", - "https://images.openfoodfacts.org/images/products/invalid/front_fr.3.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_fr.3.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.7.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.7.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.5.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.5.200.jpg", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "2.77777777777778", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "30", - "26", - "http://world-en.openfoodfacts.org/product/00000026/the-smartest-cookie-people-s-choice-beef-jerky", - "elcoco", - "1572188426", - "2019-10-27T15:00:26Z", - "1750594545", - "2025-06-22T12:15:45Z", - "foodless", - "1750594545", - "2025-06-22T12:15:45Z", - "The Smartest Cookie", - null, - null, - null, - null, - null, - null, - null, - "People's Choice Beef Jerky", - "xx:people-s-choice-beef-jerky", - "people-s-choice-beef-jerky", - null, - null, - null, - null, - null, - null, - null, - null, - "Sin gluten, Vegetariano, Vegano", - "en:no-gluten,en:vegetarian,en:vegan", - "No gluten,Vegetarian,Vegan", - null, - null, - null, - null, - null, - null, - null, - "España", - "en:spain", - "Spain", - "beef, seasoning (sugar, garlic, onion, salt, spices), soy sauce (water, wheat, soybeans, salt), water, liquid smoke, sodium nitrite", - "en:beef,en:animal,en:coating,en:soy-sauce,en:sauce,en:water,en:liquid-smoke,en:smoke,en:e250,en:sugar-coating,en:added-sugar,en:disaccharide,en:sugar,en:garlic,en:vegetable,en:root-vegetable,en:onion-family-vegetable,en:onion,en:salt,en:spice,en:condiment,en:wheat,en:cereal,en:soya-bean,en:legume,en:pulse,en:soya", - "en:palm-oil-free,en:maybe-vegan,en:vegetarian", - null, - null, - null, - null, - null, - "28g", - "28.0", - null, - "1.0", - null, - "en:e250", - "E250 - Sodium nitrite", - null, - "unknown", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-to-be-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity to be completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - null, - null, - "en:energy-value-in-kcal-does-not-match-value-in-kj,en:energy-value-in-kj-does-not-match-value-computed-from-other-nutrients,en:vegan-label-but-non-vegan-ingredient,en:vegetarian-label-but-non-vegetarian-ingredient", - null, - null, - "0.475", - "1745657806.0", - "2025-04-26T08:56:46Z", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/front_es.27.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_es.27.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_it.33.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_it.33.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_es.6.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_es.6.200.jpg", - "1940.0", - "286.0", - "1940.0", - null, - "5.36", - "1.79", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - "0.0893", - "28.6", - "25.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - "35.7", - null, - null, - null, - "2.68", - null, - "1.07", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "9.11458333333333", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "31", - "27", - "http://world-en.openfoodfacts.org/product/00000027/volle-yoghurt-zuivelmeester", - "openfoodfacts-contributors", - "1559030989", - "2019-05-28T08:09:49Z", - "1747132672", - "2025-05-13T10:37:52Z", - "bazcalou", - "1747132672", - "2025-05-13T10:37:52Z", - "Volle yoghurt", - null, - null, - "700 ml", - null, - null, - null, - null, - "Zuivelmeester", - "xx:zuivelmeester", - "zuivelmeester", - "Beverages and beverages preparations, Beverages", - "en:beverages-and-beverages-preparations,en:beverages", - "Beverages and beverages preparations,Beverages", - null, - null, - null, - null, - null, - "Nutriscore, en:nutriscore-grade-b", - "en:nutriscore,en:nutriscore-grade-b", - "Nutriscore,Nutriscore Grade B", - null, - null, - null, - null, - null, - null, - null, - "Frankreich, Germany", - "en:france,en:germany", - "France,Germany", - "NIK ODŻYWCZY WSPIERA PRAWIDŁOWE FUNKCJONOWANIE ORGANIZMU ZESKANUJ WIĘCEJA SPOTKAJMY SIN SPRÓBLU JAK PYSZNA IWARTOŚCIOWA MOŻE BYĆ TWOJA PRZEKĄSKA mieste dal twistenue p amach ne zachowania", - "en:nik-odżywczy-wspiera-prawidłowe-funkcjonowanie-organizmu-zeskanuj-więceja-spotkajmy-sin-sproblu-jak-pyszna-iwartościowa-może-być-twoja-przekąska-mieste-dal-twistenue-p-amach-ne-zachowania", - "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", - null, - null, - null, - null, - null, - "700 ml", - "700.0", - null, - "0.0", - null, - null, - null, - null, - "unknown", - null, - "Beverages", - "Unsweetened beverages", - "en:unsweetened-beverages", - "en:beverages,en:unsweetened-beverages", - "Beverages,Unsweetened beverages", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - "en:fat-in-moderate-quantity", - "700.0", - null, - null, - "2.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-100000-fr-scans-2024,top-country-fr-scans-2024", - "0.6625", - "1745154835.0", - "2025-04-20T13:13:55Z", - "en:beverages", - "Beverages", - "https://images.openfoodfacts.org/images/products/invalid/front_en.20.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.20.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.16.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.16.200.jpg", - null, - null, - null, - "63.0", - "264.0", - null, - "3.1", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "4.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "3.9", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "32", - "28", - "http://world-en.openfoodfacts.org/product/00000028/hershey-s-syrup", - "openfoodfacts-contributors", - "1626699977", - "2021-07-19T13:06:17Z", - "1746240646", - "2025-05-03T02:50:46Z", - "detrumpezvous", - "1746240646", - "2025-05-03T02:50:46Z", - "Hershey’s Syrup", - null, - null, - "700ml", - "Plastic", - "en:plastic", - "Plastic", - null, - "Hershey’s", - "xx:hershey-s", - "hershey-s", - "Beverages and beverages preparations, Beverages, Supplement", - "en:beverages-and-beverages-preparations,en:beverages,en:supplement", - "Beverages and beverages preparations,Beverages,Supplement", - "organic defeated hemp seed powder,organic coconut sugar,organic natural flavor", - "en:organic-coconut-sugar,en:organic-defeated-hemp-seed-powder,en:organic-natural-flavor", - "Organic-coconut-sugar,Organic-defeated-hemp-seed-powder,Organic-natural-flavor", - "Canada", - "canada", - "Organic hemp protein", - "en:organic-hemp-protein", - "Organic-hemp-protein", - null, - null, - null, - null, - null, - null, - null, - "Germany, United States, en:france", - "en:france,en:germany,en:united-states", - "France,Germany,United States", - "Organic Defatted Hemp Seed Powder. Certified Organic by Quality Assurance International (QA).", - "en:hemp-seed,en:seed,en:by-quality-assurance-international", - "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", - null, - null, - "en:na", - "en:na", - "Na", - "30g", - "30.0", - null, - "0.0", - null, - null, - null, - null, - "unknown", - "1.0", - "Beverages", - "Unsweetened beverages", - "en:unsweetened-beverages", - "en:beverages,en:unsweetened-beverages", - "Beverages,Unsweetened beverages", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-completed, en:origins-completed, en:categories-completed, en:brands-completed, en:packaging-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-completed,en:origins-completed,en:categories-completed,en:brands-completed,en:packaging-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics completed,Origins completed,Categories completed,Brands completed,Packaging completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - "en:fat-in-moderate-quantity,en:sugars-in-moderate-quantity,en:salt-in-low-quantity", - "700.0", - null, - null, - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-country-fr-scans-2024", - "0.8875", - "1746240645.0", - "2025-05-03T02:50:45Z", - "en:supplement", - "Supplement", - "https://images.openfoodfacts.org/images/products/invalid/front_en.20.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.20.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.9.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.9.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.12.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.12.200.jpg", - null, - null, - null, - null, - "2.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "15.0", - "6.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "10.0", - null, - null, - "9.0", - null, - null, - null, - "0.0", - null, - "0.0", - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "33", - "29", - "http://world-en.openfoodfacts.org/product/00000029/anthony-s-organic-cocoa-butter-chunks", - "foodvisor", - "1648551873", - "2022-03-29T11:04:33Z", - "1734028143", - "2024-12-12T18:29:03Z", - "navig491", - "1738846634", - "2025-02-06T12:57:14Z", - "Anthony's Organic Cocoa Butter Chunks", - null, - null, - null, - null, - null, - null, - null, - "Anthony's", - "anthony-s", - "Anthony-s", - "Sandwiches, Wraps", - "en:sandwiches,en:wraps", - "Sandwiches,Wraps", - null, - null, - null, - null, - null, - "No gluten, Vegetarian, Vegan, en:usda-organic", - "en:no-gluten,en:organic,en:vegetarian,en:usda-organic,en:vegan", - "No gluten,Organic,Vegetarian,USDA Organic,Vegan", - null, - null, - null, - null, - null, - null, - null, - "United States", - "en:united-states", - "United States", - "Allulose", - "en:allulose", - "en:palm-oil-content-unknown,en:vegan,en:vegetarian", - null, - null, - null, - null, - null, - "10 g", - "10.0", - null, - "0.0", - null, - null, - null, - "20.0", - "e", - null, - "Composite foods", - "Sandwiches", - "en:sandwiches", - "en:composite-foods,en:sandwiches", - "Composite foods,Sandwiches", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-to-be-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity to be completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - null, - "en:fat-in-high-quantity,en:saturated-fat-in-high-quantity,en:sugars-in-low-quantity,en:salt-in-low-quantity", - null, - null, - null, - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-500-pk-scans-2024,top-1000-pk-scans-2024,top-5000-pk-scans-2024,top-10000-pk-scans-2024,top-50000-pk-scans-2024,top-100000-pk-scans-2024,top-country-pk-scans-2024", - "0.575", - "1733578445.0", - "2024-12-07T13:34:05Z", - "en:wraps", - "Wraps", - "https://images.openfoodfacts.org/images/products/invalid/front_en.23.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.23.200.jpg", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.10.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.10.200.jpg", - null, - "900.0", - "3770.0", - null, - "100.0", - "60.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - "0.0", - "0.0", - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - "0.0", - null, - null, - null, - "0.075", - null, - "0.03", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.25", - null, - "0.13", - null, - "0.01", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - "20.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "34", - "30", - "http://world-en.openfoodfacts.org/product/00000030/valeriana-system-vital-proteins", - "beniben", - "1481840144", - "2016-12-15T22:15:44Z", - "1747557615", - "2025-05-18T08:40:15Z", - "gioia8", - "1747557615", - "2025-05-18T08:40:15Z", - "Valeriana System®", - null, - "Pâtisseries aux raisins secs.", - "900 g", - "crta", - "it:crta", - "it:crta", - null, - "Vital Proteins", - "xx:vital-proteins", - "vital-proteins", - "integratore alimentare di vitamina B6", - "it:integratore-alimentare-di-vitamina-b6", - "it:integratore-alimentare-di-vitamina-b6", - null, - null, - null, - "France", - "france", - "Point Vert, Fabriqué en France", - "en:green-dot,en:made-in-france", - "Green Dot,Made in France", - null, - null, - null, - null, - null, - "France,Nantes", - "Correspondance", - "FR", - "en:france", - "France", - "Collagen peptides", - "en:collagen-peptides", - "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", - "en:eggs,en:gluten,en:milk", - null, - "en:nuts,en:soybeans,it:vitamina B6", - "en:nuts,en:soybeans,it:vitamina-b6", - "Nuts,Soybeans,it:vitamina-b6", - "20g", - "20.0", - "on", - "0.0", - null, - null, - null, - null, - "unknown", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-completed, en:quantity-completed, en:product-name-completed, en:photos-validated, en:packaging-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-completed,en:quantity-completed,en:product-name-completed,en:photos-validated,en:packaging-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging completed,Quantity completed,Product name completed,Photos validated,Packaging photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - "900.0", - null, - null, - "1.0", - "bottom-25-percent-scans-2019,top-80-percent-scans-2019,top-85-percent-scans-2019,top-90-percent-scans-2019,top-100000-fr-scans-2019,top-country-fr-scans-2019,bottom-25-percent-scans-2020,top-80-percent-scans-2020,top-85-percent-scans-2020,top-90-percent-scans-2020,top-100000-fr-scans-2020,top-country-fr-scans-2020,top-75-percent-scans-2021,top-80-percent-scans-2021,top-85-percent-scans-2021,top-90-percent-scans-2021,top-country-fr-scans-2021,top-75-percent-scans-2022,top-80-percent-scans-2022,top-85-percent-scans-2022,top-90-percent-scans-2022,top-100000-fr-scans-2022,top-country-fr-scans-2022,top-75-percent-scans-2023,top-80-percent-scans-2023,top-85-percent-scans-2023,top-90-percent-scans-2023,top-country-fr-scans-2023", - "0.9", - "1747557614.0", - "2025-05-18T08:40:14Z", - "it:integratore-alimentare-di-vitamina-b6", - "it:integratore-alimentare-di-vitamina-b6", - "https://images.openfoodfacts.org/images/products/invalid/front_it.50.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_it.50.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_it.52.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_it.52.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.21.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.21.200.jpg", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "35", - "31", - "http://world-en.openfoodfacts.org/product/00000031/lindt-vollmilch-schokolade", - "kiliweb", - "1673637219", - "2023-01-13T19:13:39Z", - "1746290311", - "2025-05-03T16:38:31Z", - "detrumpezvous", - "1746290311", - "2025-05-03T16:38:31Z", - "Lindt Vollmilch Schokolade", - null, - null, - "100g", - null, - null, - null, - null, - "Lindt", - "xx:lindt", - "lindt", - "Dried products, Dried products to be rehydrated, Broths, Dehydrated broths, Bouillon cubes", - "en:dried-products,en:dried-products-to-be-rehydrated,en:broths,en:dehydrated-broths,en:bouillon-cubes", - "Dried products,Dried products to be rehydrated,Broths,Dehydrated broths,Bouillon cubes", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "Tesco", - "Frankreich, Germany", - "en:france,en:germany", - "France,Germany", - "salt, potato starch, palm oil, yeast extract, sugar, chicken fat, flavouring, chicken powder, onion powder, turmeric powder, celery seed, parsley, rice flour, black pepper, lemon juice powder", - "en:salt,en:potato-starch,en:starch,en:palm-oil,en:oil-and-fat,en:vegetable-oil-and-fat,en:palm-oil-and-fat,en:yeast-extract,en:yeast,en:sugar,en:added-sugar,en:disaccharide,en:chicken-fat,en:fat,en:animal-fat,en:poultry-fat,en:flavouring,en:chicken,en:poultry,en:onion,en:vegetable,en:root-vegetable,en:onion-family-vegetable,en:turmeric-powder,en:condiment,en:spice,en:turmeric,en:celery-seed,en:seed,en:stalk-vegetable,en:celery,en:parsley,en:herb,en:leaf-vegetable,en:rice-flour,en:flour,en:rice,en:black-pepper,en:pepper,en:lemon-juice,en:fruit,en:juice,en:fruit-juice", - "en:palm-oil,en:non-vegan,en:non-vegetarian", - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - "unknown", - "4.0", - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-validated, en:packaging-photo-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-validated,en:packaging-photo-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos validated,Packaging photo selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - "100.0", - null, - null, - "2.0", - "bottom-25-percent-scans-2022,bottom-20-percent-scans-2022,top-85-percent-scans-2022,top-90-percent-scans-2022,top-5000-dz-scans-2022,top-10000-dz-scans-2022,top-50000-dz-scans-2022,top-100000-dz-scans-2022,top-country-dz-scans-2022,top-75-percent-scans-2023,top-80-percent-scans-2023,top-85-percent-scans-2023,top-90-percent-scans-2023,top-country-fr-scans-2023,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-5000-gr-scans-2024,top-10000-gr-scans-2024,top-50000-gr-scans-2024,top-100000-gr-scans-2024,top-country-gr-scans-2024,top-50000-ro-scans-2024,top-100000-ro-scans-2024", - "0.7", - "1746290311.0", - "2025-05-03T16:38:31Z", - "en:bouillon-cubes", - "Bouillon cubes", - "https://images.openfoodfacts.org/images/products/invalid/front_en.31.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.31.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.33.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.33.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.35.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.35.200.jpg", - "26.0", - "6.0", - "26.0", - null, - "0.3", - "0.2", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.5", - "0.2", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.1", - null, - null, - "0.3", - null, - null, - null, - "0.8", - null, - "0.32", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.2", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "36", - "32", - "http://world-en.openfoodfacts.org/product/00000032/hydrolyzed-bone-broth-protein-double-chocolate-zammex", - "prepperapp", - "1719401058", - "2024-06-26T11:24:18Z", - "1748312051", - "2025-05-27T02:14:11Z", - "roboto-app", - "1748312051", - "2025-05-27T02:14:11Z", - "Hydrolyzed Bone Broth Protein Double Chocolate", - null, - null, - "0.5l", - null, - null, - null, - null, - "Zammex", - "xx:zammex", - "zammex", - null, - null, - null, - null, - null, - null, - null, - null, - "en:no-gluten", - "en:no-gluten", - "No gluten", - null, - null, - null, - null, - null, - null, - null, - "en:germany", - "en:germany", - "Germany", - "Cocoa Powder, Natural Flavor, Monk Fruit Extract", - "en:cocoa-powder,en:plant,en:cocoa,en:natural-flavouring,en:flavouring,en:monk-fruit-extract,en:sweetener", - "en:palm-oil-free,en:vegan-status-unknown,en:vegetarian-status-unknown", - null, - null, - null, - null, - null, - "11g", - "11.0", - null, - "0.0", - null, - null, - null, - null, - "unknown", - "4.0", - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - "500.0", - null, - null, - null, - null, - "0.575", - "1744630202.0", - "2025-04-14T11:30:02Z", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/front_en.21.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.21.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.13.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.13.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.10.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.10.200.jpg", - null, - "318.0", - "1330.0", - null, - "0.0", - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - "0.0", - "0.0", - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - "84.5", - null, - null, - null, - "1.14", - null, - "0.455", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "37", - "33", - "http://world-en.openfoodfacts.org/product/00000033/vegan-protein-3k-chocolate-flavor-nu", - "openfoodfacts-contributors", - "1622534821", - "2021-06-01T08:07:01Z", - "1744478998", - "2025-04-12T17:29:58Z", - "insectproductadd", - "1744478998", - "2025-04-12T17:29:58Z", - "Vegan Protein 3k Chocolate Flavor", - null, - null, - null, - null, - null, - null, - null, - "nu³", - "xx:nu", - "nu", - null, - null, - null, - null, - null, - null, - null, - null, - "Vegetarisch, Vegan", - "en:vegetarian,en:vegan", - "Vegetarian,Vegan", - null, - null, - null, - null, - null, - null, - null, - "European Union", - "en:european-union", - "European Union", - "MAT TRACCIATO PERONI 10 INDISCUTIBILME 46", - "en:mat-tracciato-peroni-10-indiscutibilme-46", - "en:palm-oil-content-unknown,en:vegan,en:vegetarian", - null, - null, - null, - null, - null, - "200mg", - "0.2", - null, - "0.0", - null, - null, - null, - null, - "unknown", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-to-be-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity to be completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - null, - null, - "en:energy-value-in-kcal-does-not-match-value-in-kj,en:energy-value-in-kj-does-not-match-value-computed-from-other-nutrients", - null, - null, - "0.475", - "1744478991.0", - "2025-04-12T17:29:51Z", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/front_en.7.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.7.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.18.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.18.200.jpg", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "38", - "34", - "http://world-en.openfoodfacts.org/product/00000034/graines-de-chia-nestle-carnation", - "openfoodfacts-contributors", - "1536945788", - "2018-09-14T17:23:08Z", - "1748394913", - "2025-05-28T01:15:13Z", - "nazzarenos", - "1748394913", - "2025-05-28T01:15:13Z", - "Graines de Chia", - null, - null, - "1kg", - "Plastique", - "en:plastic", - "Plastic", - null, - "Nestle Carnation", - "xx:nestle-carnation", - "nestle-carnation", - "Aliments et boissons à base de végétaux, Aliments d'origine végétale, Céréales et pommes de terre, Graines, Céréales et dérivés, Céréales en grains, Chia", - "en:plant-based-foods-and-beverages,en:plant-based-foods,en:cereals-and-potatoes,en:seeds,en:cereals-and-their-products,en:cereal-grains,en:chia", - "Plant-based foods and beverages,Plant-based foods,Cereals and potatoes,Seeds,Cereals and their products,Cereal grains,Chia", - null, - null, - null, - null, - null, - "Sans gluten, Bio, Végétarien, Bio européen, Agriculture non UE, Végétalien, Agriculture UE, Agriculture UE/Non UE, FR-BIO-01, en:Soil Association Organic", - "en:no-gluten,en:organic,en:vegetarian,en:eu-organic,en:non-eu-agriculture,en:vegan,en:eu-agriculture,en:eu-non-eu-agriculture,en:fr-bio-01,en:soil-association-organic", - "No gluten,Organic,Vegetarian,EU Organic,Non-EU Agriculture,Vegan,EU Agriculture,EU/non-EU Agriculture,FR-BIO-01,Soil Association Organic", - "EMB 40168A", - "emb-40168a", - "43.783333,-1.216667", - null, - "magescq-landes-france", - null, - null, - "France", - "en:france", - "France", - "Graines de chia", - "en:chia-seed,en:seed,en:chia", - "en:palm-oil-free,en:vegan,en:vegetarian", - null, - null, - null, - null, - null, - "1g", - "1.0", - null, - "0.0", - null, - null, - null, - null, - "unknown", - "1.0", - "Cereals and potatoes", - "Cereals", - "en:cereals", - "en:cereals-and-potatoes,en:cereals", - "Cereals and potatoes,Cereals", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - "40.0", - "d", - null, - "1000.0", - null, - "en:energy-value-in-kcal-does-not-match-value-computed-from-other-nutrients", - "1.0", - "bottom-25-percent-scans-2020,bottom-20-percent-scans-2020,top-85-percent-scans-2020,top-90-percent-scans-2020,top-5000-nl-scans-2020,top-10000-nl-scans-2020,top-50000-nl-scans-2020,top-100000-nl-scans-2020,top-country-nl-scans-2020,bottom-25-percent-scans-2021,bottom-20-percent-scans-2021,top-85-percent-scans-2021,top-90-percent-scans-2021,top-50000-es-scans-2021,top-100000-es-scans-2021,top-country-es-scans-2021,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-1000-mu-scans-2024,top-5000-mu-scans-2024,top-10000-mu-scans-2024,top-50000-mu-scans-2024,top-100000-mu-scans-2024,top-country-mu-scans-2024", - "0.8875", - "1748394905.0", - "2025-05-28T01:15:05Z", - "en:chia", - "Chia", - "https://images.openfoodfacts.org/images/products/invalid/front_en.25.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.25.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.38.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.38.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.15.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.15.200.jpg", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "39", - "35", - "http://world-en.openfoodfacts.org/product/00000035/cardiofitmd-1md-nutrition", - "x2", - "1545469597", - "2018-12-22T09:06:37Z", - "1742883997", - "2025-03-25T06:26:37Z", - "smoothie-app", - "1743343806", - "2025-03-30T14:10:06Z", - "Cardiofitmd", - null, - null, - "600 g", - "Plastique", - "en:plastic", - "Plastic", - null, - "1MD Nutrition", - "xx:1md-nutrition", - "1md-nutrition", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "en:FR", - "en:france", - "France", - "Oat Fiber, Tahl Gum (Acacia seyal), Apple Fiber, Guar Gum, Rice Bran Fiber, Beet Root Powder, Beet Juice Powder, Rhodiola Root Powder, Bacillus Coagulans, Natural Vanilla and Lemonade Flavors, Other Natural Flavors, Malic Acid, Stevia Leaf Extract, Fumaric Acid, Ginger Root Powder, Tartaric Acid", - "en:oat-fibre,en:cereal,en:fiber,en:oat,en:vegetable-fiber,en:tahl-gum,en:apple-fiber,en:e412,en:rice-bran-fiber,en:beetroot-powder,en:vegetable,en:root-vegetable,en:taproot-vegetable,en:beetroot,en:beetroot-juice,en:rhodiola-root-powder,en:bacillus-coagulans,en:ferment,en:microbial-culture,en:lactic-ferments,en:natural-vanilla,en:plant,en:vanilla,en:lemonade-flavors,en:with-other-natural-flavouring,en:flavouring,en:natural-flavouring,en:e296,en:e960,en:e297,en:ginger-powder,en:condiment,en:spice,en:ginger,en:e334,en:acacia-seyal", - "en:palm-oil-free,en:vegan-status-unknown,en:vegetarian-status-unknown", - null, - null, - "en:nuts", - "en:nuts", - "Nuts", - "16g", - "16.0", - null, - "5.0", - null, - "en:e296,en:e297,en:e334,en:e412,en:e960", - "E296 - Malic acid,E297 - Fumaric acid,E334 - L(+)-tartaric acid,E412 - Guar gum,E960 - Steviol glycosides", - null, - "unknown", - "4.0", - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - "600.0", - null, - null, - "2.0", - "bottom-25-percent-scans-2019,top-80-percent-scans-2019,top-85-percent-scans-2019,top-90-percent-scans-2019,top-100000-fr-scans-2019,top-country-fr-scans-2019,top-75-percent-scans-2020,top-80-percent-scans-2020,top-85-percent-scans-2020,top-90-percent-scans-2020,top-100000-fr-scans-2020,top-country-fr-scans-2020,bottom-25-percent-scans-2021,bottom-20-percent-scans-2021,top-85-percent-scans-2021,top-90-percent-scans-2021,top-country-fr-scans-2021,bottom-25-percent-scans-2022,top-80-percent-scans-2022,top-85-percent-scans-2022,top-90-percent-scans-2022,top-100000-fr-scans-2022,top-country-fr-scans-2022,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-gb-scans-2024,top-100000-gb-scans-2024,top-country-gb-scans-2024", - "0.6625", - "1742883995.0", - "2025-03-25T06:26:35Z", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/front_en.38.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.38.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.10.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.10.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.12.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.12.200.jpg", - null, - "188.0", - "788.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "68.8", - "12.5", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "50.0", - null, - null, - null, - null, - null, - null, - "0.547", - null, - "0.219", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "1.47058823529412", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "40", - "36", - "http://world-en.openfoodfacts.org/product/00000036/multi-patents-collagen-peptides-vev", - "openfoodfacts-contributors", - "1537366963", - "2018-09-19T14:22:43Z", - "1749514977", - "2025-06-10T00:22:57Z", - "roboto-app", - "1749514977", - "2025-06-10T00:22:57Z", - "Multi Patents Collagen Peptides", - null, - null, - "490 g", - null, - null, - null, - null, - "VEV", - "xx:vev", - "vev", - "Snacks, Sweet snacks, Biscuits and cakes, Cakes, Madeleines, Long madeleines", - "en:snacks,en:sweet-snacks,en:biscuits-and-cakes,en:cakes,en:madeleines,en:long-madeleines", - "Snacks,Sweet snacks,Biscuits and cakes,Cakes,Madeleines,Long madeleines", - null, - null, - null, - null, - null, - "Vegetarian, Vegan, en:no-additives", - "en:vegetarian,en:vegan,en:no-additives", - "Vegetarian,Vegan,No additives", - null, - null, - null, - null, - null, - null, - null, - "France", - "en:france", - "France", - "Multi Collagen Complex, Hydrolyzed Bovine Collagen Peptides, Hydrolyzed Fish Collagen Peptides, Chicken Bone Broth Protein Concentrate, Eggshell Membrane Collagen, Hyaluronic Acid, Superfoods Blend (Millet, Amaranth, Apple Pulp, Kale, Quinoa, Buckwheat, Cinnamon, Chia Seed, Flax Seed, Barley Sprout, Millet Sprout, Garbanzo Bean Sprout, Alfalfa Sprout, Pumpkin Seed Sprout, Flax Oil, Sunflower Seed, Lentil Sprout, Adzuki Sprout, Broccoli Sprout, Sunflower Seed Sprout, Wheat Grass, Oat Grass, Spinach, Green Bell Pepper, Turmeric, Banana, Cranberry, Mango, Beet, Broccoli, Carrot, Spinach, Onion, Blueberry, Parsley, Raspberry, Strawberry, Asparagus, Celery, Cucumber, Chlorella, Spirulina, Graviola Leaf, Mangosteen, Noni Fruit, Acai, Pomegranate, Grape)", - "en:multi-collagen-complex,en:hydrolyzed-bovine-collagen-peptides,en:hydrolyzed-fish-collagen-peptides,en:chicken-bone-broth-protein-concentrate,en:eggshell-membrane-collagen,en:hyaluronic-acid,en:superfoods-blend,en:millet,en:cereal,en:e123,en:apple-pulp,en:fruit,en:malaceous-fruit,en:apple,en:kale,en:vegetable,en:brassica,en:cabbage,en:quinoa,en:plant,en:buckwheat,en:cinnamon,en:condiment,en:spice,en:chia-seed,en:seed,en:chia,en:flax-seed,en:flax,en:barley-sprout,en:millet-sprout,en:garbanzo-bean-sprout,en:alfalfa-sprout,en:pumpkin-seed-sprout,en:flax-oil,en:sunflower-seed,en:sunflower,en:lentil-sprout,en:adzuki-sprout,en:broccoli-sprout,en:sunflower-seed-sprout,en:wheat-grass,en:oat-grass,en:spinach,en:leaf-vegetable,en:green-bell-pepper,en:fruit-vegetable,en:bell-pepper,en:turmeric,en:banana,en:cranberry,en:mango,en:beetroot,en:root-vegetable,en:taproot-vegetable,en:broccoli,en:carrot,en:onion,en:onion-family-vegetable,en:blueberry,en:berries,en:parsley,en:herb,en:raspberry,en:strawberry,en:asparagus,en:shoot-vegetable,en:celery,en:stalk-vegetable,en:cucumber,en:chlorella,en:algae,en:spirulina,en:graviola-leaf,en:mangosteen,en:noni-fruit,en:acai-berry,en:pomegranate,en:grape", - "en:palm-oil-content-unknown,en:vegan,en:vegetarian", - null, - null, - "en:nuts,en:soybeans", - "en:nuts,en:soybeans", - "Nuts,Soybeans", - "11g", - "11.0", - null, - "0.0", - null, - null, - null, - "-1.0", - "a", - "3.0", - "Sugary snacks", - "Biscuits and cakes", - "en:biscuits-and-cakes", - "en:sugary-snacks,en:biscuits-and-cakes", - "Sugary snacks,Biscuits and cakes", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - "60.0", - "b", - "en:fat-in-low-quantity,en:saturated-fat-in-low-quantity,en:sugars-in-moderate-quantity,en:salt-in-low-quantity", - "490.0", - null, - "en:nutrition-sugars-plus-starch-greater-than-carbohydrates", - "1.0", - "top-100000-scans-2019,at-least-5-scans-2019,top-75-percent-scans-2019,top-80-percent-scans-2019,top-85-percent-scans-2019,top-90-percent-scans-2019,top-100000-fr-scans-2019,top-country-fr-scans-2019,at-least-5-fr-scans-2019,top-75-percent-scans-2020,top-80-percent-scans-2020,top-85-percent-scans-2020,top-90-percent-scans-2020,top-100000-fr-scans-2020,top-country-fr-scans-2020,bottom-25-percent-scans-2021,top-80-percent-scans-2021,top-85-percent-scans-2021,top-90-percent-scans-2021,top-100000-fr-scans-2021,top-country-fr-scans-2021,top-75-percent-scans-2022,top-80-percent-scans-2022,top-85-percent-scans-2022,top-90-percent-scans-2022,top-country-fr-scans-2022,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-100000-gb-scans-2024,top-country-gb-scans-2024", - "0.6625", - "1749514363.0", - "2025-06-10T00:12:43Z", - "en:long-madeleines", - "Long madeleines", - "https://images.openfoodfacts.org/images/products/invalid/front_en.31.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.31.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.12.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.12.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.14.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.14.200.jpg", - null, - "350.0", - "1460.0", - null, - "0.0", - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - "0.0", - "0.0", - "9.09", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - "90.9", - null, - null, - null, - "0.0", - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.25111635082508", - null, - null, - null, - null, - null, - "-1.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "41", - "37", - "http://world-en.openfoodfacts.org/product/00000037/sea-moss-gummies-just-nutrients", - "openfoodfacts-contributors", - "1551029683", - "2019-02-24T17:34:43Z", - "1745461663", - "2025-04-24T02:27:43Z", - "roboto-app", - "1745461663", - "2025-04-24T02:27:43Z", - "Sea Moss Gummies", - null, - null, - "1.5 kg", - null, - null, - null, - null, - "Just Nutrients", - "xx:just-nutrients", - "just-nutrients", - null, - null, - null, - null, - null, - null, - null, - null, - "en:no-gluten", - "en:no-gluten", - "No gluten", - null, - null, - null, - null, - null, - null, - null, - "selestosina", - "en:selestosina", - "Selestosina", - "Glucose Syrup, Sugar, Water, Citric Acid, Pectin, Natural Flavors, Sodium Citrate, Coconut Oil, Carnauba Wax", - "en:glucose-syrup,en:added-sugar,en:monosaccharide,en:glucose,en:sugar,en:disaccharide,en:water,en:e330,en:e440a,en:natural-flavouring,en:flavouring,en:sodium-citrate,en:minerals,en:sodium,en:coconut-oil,en:oil-and-fat,en:vegetable-oil-and-fat,en:vegetable-oil,en:e903", - "en:palm-oil-free,en:vegan-status-unknown,en:vegetarian-status-unknown", - null, - null, - null, - null, - null, - "2g", - "2.0", - null, - "4.0", - null, - "en:e330,en:e331,en:e440,en:e903", - "E330 - Citric acid,E331 - Sodium citrates,E440 - Pectins,E903 - Carnauba wax", - null, - "unknown", - "4.0", - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - "1500.0", - null, - "en:energy-value-in-kcal-does-not-match-value-computed-from-other-nutrients", - null, - null, - "0.5625", - "1745460960.0", - "2025-04-24T02:16:00Z", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/front_en.34.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.34.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.7.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.7.200.jpg", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "42", - "38", - "http://world-en.openfoodfacts.org/product/00000038/madeleines-bijou", - "date-limite-app", - "1535603981", - "2018-08-30T04:39:41Z", - "1728034740", - "2024-10-04T09:39:00Z", - "fix-code-bot", - "1743361088", - "2025-03-30T18:58:08Z", - "Madeleines", - null, - null, - null, - null, - null, - null, - null, - "Bijou", - "xx:bijou", - "bijou", - "Snacks, Snacks sucrés, Biscuits et gâteaux, Gâteaux, Madeleines", - "en:snacks,en:sweet-snacks,en:biscuits-and-cakes,en:cakes,en:madeleines", - "Snacks,Sweet snacks,Biscuits and cakes,Cakes,Madeleines", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "France", - "en:france", - "France", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "unknown", - null, - "Sugary snacks", - "Biscuits and cakes", - "en:biscuits-and-cakes", - "en:sugary-snacks,en:biscuits-and-cakes", - "Sugary snacks,Biscuits and cakes", - "en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-to-be-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts to be completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity to be completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - "60.0", - "b", - null, - null, - null, - null, - "1.0", - "top-100000-scans-2019,at-least-5-scans-2019,at-least-10-scans-2019,top-75-percent-scans-2019,top-80-percent-scans-2019,top-85-percent-scans-2019,top-90-percent-scans-2019,top-50000-fr-scans-2019,top-100000-fr-scans-2019,top-country-fr-scans-2019,at-least-5-fr-scans-2019,at-least-10-fr-scans-2019,at-least-5-scans-2020,top-75-percent-scans-2020,top-80-percent-scans-2020,top-85-percent-scans-2020,top-90-percent-scans-2020,top-100000-fr-scans-2020,top-country-fr-scans-2020,at-least-5-fr-scans-2020,top-100000-scans-2021,top-75-percent-scans-2021,top-80-percent-scans-2021,top-85-percent-scans-2021,top-90-percent-scans-2021,top-100000-fr-scans-2021,top-country-fr-scans-2021,top-100000-scans-2022,top-75-percent-scans-2022,top-80-percent-scans-2022,top-85-percent-scans-2022,top-90-percent-scans-2022,top-50000-fr-scans-2022,top-100000-fr-scans-2022,top-country-fr-scans-2022,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50-om-scans-2024,top-100-om-scans-2024,top-500-om-scans-2024,top-1000-om-scans-2024,top-5000-om-scans-2024,top-10000-om-scans-2024,top-50000-om-scans-2024,top-100000-om-scans-2024,top-country-om-scans-2024", - "0.375", - "1559933237.0", - "2019-06-07T18:47:17Z", - "en:madeleines", - "Madeleines", - "https://images.openfoodfacts.org/images/products/invalid/front_fr.3.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_fr.3.200.jpg", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.17.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.17.200.jpg", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "43", - "39", - "http://world-en.openfoodfacts.org/product/00000039/yerba-mate-microingredients", - "openfoodfacts-contributors", - "1537945936", - "2018-09-26T07:12:16Z", - "1749584468", - "2025-06-10T19:41:08Z", - "foodiq", - "1749584468", - "2025-06-10T19:41:08Z", - "Yerba Mate", - null, - null, - "1 kg", - null, - null, - null, - null, - "microingredients", - "xx:microingredients", - "microingredients", - null, - null, - null, - "Argentine", - "en:argentina", - "Argentina", - "Argentine", - "argentine", - null, - null, - null, - null, - null, - null, - null, - null, - "France", - "internet", - "France", - "en:france", - "France", - "Nutrition Facts 20 servings per container Serving size (1.7oz.) in 16.9 fl.oz.water Amount per serving Calories 21 Total Carbohydrate 4.7mg Protein 0.6g Total Fat Og % Daily Value* 2% 1% 0% Saturated Fat Og Trans Fat Og Dietary Fiber Og 0% 0% 0% Sodium 6mg Percent Daily Values are based on a 2,000 calories diet. Nutrition Facts Valeur nutritive Per 250 ml brewed mate (from 60 g dry leaves) pour 250 ml de maté infusé (à partir de 60 g de feuilles sèches) Amount Teneur % Daily Value Calories/Calories Fat/Lipides Sodium/Sodium Carbohydrate/Glucides 6 g % valeur quotídien ne 0% 2% 25 Og 10 mg 0% 1g Sugars/Sucres Protein/Protéines. Source négligeable de lipides saturés, lipides trans, cholestérol, fibres, vitamine A, vitamine C, calcium et fer. gg 210", - "es:nutrition-facts-20-servings-per-container-serving-size,es:in-16-9-fl-oz-water-amount-per-serving-calories-21-total-carbohydrate-4-7mg-protein-0-6g-total-fat-og-daily-value-2-1-0-saturated-fat-og-trans-fat-og-dietary-fiber-og-0-0-0-sodium-6mg-percent-daily-values-are-based-on-a-2-000-calories-diet,es:nutrition-facts-valeur-nutritive-per-250-ml-brewed-mate,es:pour-250-ml-de-mate-infuse,es:amount-teneur-daily-value-calories,es:calories-fat,es:lipides-sodium,es:sodium-carbohydrate,es:glucides-6-g-valeur-quotidien-ne-0-2-25-og-10-mg-0-1g-sugars,es:sucres-protein,es:proteines,es:source-negligeable-de-lipides-satures,es:lipides-trans,es:cholesterol,es:fibres,es:vitamine-a,es:vitamine-c,es:calcium-et-fer,es:gg-210,es:1-7oz,es:from-60-g-dry-leaves,es:a-partir-de-60-g-de-feuilles-seches", - "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", - null, - null, - null, - null, - null, - "33g", - "33.0", - null, - "0.0", - null, - null, - null, - null, - "unknown", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - "1000.0", - null, - null, - null, - null, - "0.675", - "1749584429.0", - "2025-06-10T19:40:29Z", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/front_es.28.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_es.28.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_es.8.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_es.8.200.jpg", - null, - null, - null, - "576.0", - "2410.0", - null, - "42.4", - "7.58", - 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"foodvisor", - "1727434748", - "2024-09-27T10:59:08Z", - "1745075931", - "2025-04-19T15:18:51Z", - "foodless", - "1745075931", - "2025-04-19T15:18:51Z", - "scourban Plus 100 ml", - null, - null, - null, - null, - null, - null, - null, - "Amazing grass", - "xx:amazing-grass", - "amazing-grass", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "en:Ireland", - "en:ireland", - "Ireland", - null, - null, - null, - null, - null, - null, - null, - null, - "8.0g", - "8.0", - null, - null, - null, - null, - null, - null, - "unknown", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-to-be-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity to be completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - null, - null, - null, - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-5000-in-scans-2024,top-10000-in-scans-2024,top-50000-in-scans-2024,top-100000-in-scans-2024,top-country-in-scans-2024", - "0.3625", - "1739612373.0", - "2025-02-15T09:39:33Z", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/front_en.4.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.4.200.jpg", - null, - null, - null, - null, - null, - "30.0", - "126.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "5.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "3.0", - null, - 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codeurlcreatorcreated_tcreated_datetimelast_modified_tlast_modified_datetimelast_modified_bylast_updated_tlast_updated_datetime...water-hardness_100gcholine_100gphylloquinone_100gbeta-glucan_100ginositol_100gcarnitine_100gsulphate_100gnitrate_100gacidity_100gcarbohydrates-total_100g
054http://world-en.openfoodfacts.org/product/0000...kiliweb15825690312020-02-24T18:30:31Z17330852042024-12-01T20:33:24ZNaN17402054222025-02-22T06:23:42Z...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
163http://world-en.openfoodfacts.org/product/0000...kiliweb16736203072023-01-13T14:31:47Z17500613862025-06-16T08:09:46Zbodysupport17500613862025-06-16T08:09:46Z...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2114http://world-en.openfoodfacts.org/product/0000...kiliweb15800664822020-01-26T19:21:22Z17510356582025-06-27T14:47:38Zteolemon17510356582025-06-27T14:47:38Z...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3105http://world-en.openfoodfacts.org/product/0000...kiliweb15721177432019-10-26T19:22:23Z17380735702025-01-28T14:12:50ZNaN17436534962025-04-03T04:11:36Z...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " code url creator \\\n", + "0 54 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", + "1 63 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", + "2 114 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", + "3 105 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", + "4 2 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", + "\n", + " created_t created_datetime last_modified_t last_modified_datetime \\\n", + "0 1582569031 2020-02-24T18:30:31Z 1733085204 2024-12-01T20:33:24Z \n", + "1 1673620307 2023-01-13T14:31:47Z 1750061386 2025-06-16T08:09:46Z \n", + "2 1580066482 2020-01-26T19:21:22Z 1751035658 2025-06-27T14:47:38Z \n", + "3 1572117743 2019-10-26T19:22:23Z 1738073570 2025-01-28T14:12:50Z \n", + "4 1722606455 2024-08-02T13:47:35Z 1749171851 2025-06-06T01:04:11Z \n", + "\n", + " last_modified_by last_updated_t last_updated_datetime ... \\\n", + "0 NaN 1740205422 2025-02-22T06:23:42Z ... \n", + "1 bodysupport 1750061386 2025-06-16T08:09:46Z ... \n", + "2 teolemon 1751035658 2025-06-27T14:47:38Z ... \n", + "3 NaN 1743653496 2025-04-03T04:11:36Z ... \n", + "4 altroconsumo 1749171851 2025-06-06T01:04:11Z ... \n", + "\n", + " water-hardness_100g choline_100g phylloquinone_100g beta-glucan_100g \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + " inositol_100g carnitine_100g sulphate_100g nitrate_100g acidity_100g \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " carbohydrates-total_100g \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + "[5 rows x 214 columns]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Pre-processing " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.1 Data curation " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/daniela/Documents/PROJECTS/DESU_Data_Science/N_Rocher/OFF_Project/OFFProject/scripts/Data_filter_Jess.py:58: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " num_keep.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n" + ] + } + ], + "source": [ + "filtered_df = dfj.filter_nutriscore_data(df)\n", + "cat_df = dfj.categorical_filter(filtered_df, cat_keep= True)\n", + "num_df = dfj.numerical_filter(filtered_df, num_drop= True)\n", + "final_df = dfj.final_df(cat_df, num_df)\n", + "final_df.head()\n", + "target = df['nutriscore_score']\n", + "final_df = final_df.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.2 Enconding " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['unknown', 'Beverages', nan, 'Fruits and vegetables',\n", + " 'Composite foods', 'Sugary snacks', 'Cereals and potatoes',\n", + " 'Salty snacks', 'Fat and sauces', 'Milk and dairy products',\n", + " 'Fish Meat Eggs', 'Alcoholic beverages'], dtype=object)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['pnns_groups_1'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " pnns_groups_1 PNNS_pro\n", + "6 unknown NA\n", + "9 unknown NA\n", + "11 Fruits and vegetables Plant_based\n", + "12 unknown NA\n", + "14 Composite foods Processed\n", + "... ... ...\n", + "9946 Composite foods Processed\n", + "9955 Milk and dairy products Animal_based\n", + "9961 Sugary snacks Snacks\n", + "9970 Sugary snacks Snacks\n", + "9990 Fat and sauces Processed\n", + "\n", + "[1208 rows x 2 columns]\n" + ] + } + ], + "source": [ + "pnns_mapping = {\"unknown\" : 'NA', \n", + " 'Beverages' : 'Drinks',\n", + " 'nan' : 'NA',\n", + " 'Fruits and vegetables' : 'Plant_based',\n", + " 'Composite foods' : 'Processed',\n", + " 'Sugary snacks' : 'Snacks',\n", + " 'Salty snacks' : 'Snacks',\n", + " 'Cereals and potatoes' : 'Plant_based',\n", + " 'Fat and sauces' : 'Processed', \n", + " 'Milk and dairy products' : 'Animal_based',\n", + " 'Fish Meat Eggs' : 'Animal_based',\n", + " 'Alcoholic beverages' : 'Drinks'\n", + "}\n", + "\n", + "final_df['PNNS_pro'] = final_df['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n", + "print(final_df[['pnns_groups_1', 'PNNS_pro']])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filtered_df = one_hot_encode_column(final_df, 'PNNS_pro')\n", + "filtered_df.head()\n", + "filtered_df = filtered_df.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### maybe we should reduce pnns_groups by : plant_based; animal; processed" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "code", + "rawType": "float64", + "type": "float" + }, + { + "name": "additives_n", + "rawType": "float64", + "type": "float" + }, + { + "name": "nutriscore_score", + "rawType": "float64", + "type": "float" + }, + { + "name": "energy_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "saturated-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "trans-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "cholesterol_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "carbohydrates_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "sugars_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fiber_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "proteins_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "salt_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "sodium_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-a_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-c_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "calcium_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "iron_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Animal_based", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Drinks", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_NA", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Plant_based", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Processed", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Snacks", + "rawType": "float64", + "type": "float" + } + ], + "ref": "a267ec65-d71b-41a1-a29f-6be3b4dd45d5", + "rows": [ [ - "46", - "42", - "http://world-en.openfoodfacts.org/product/00000042/multivitamins-minerals-seara", - "voltrum", - "1635498273", - "2021-10-29T09:04:33Z", - "1742159126", - "2025-03-16T21:05:26Z", - "prepperapp", - "1743584910", - "2025-04-02T09:08:30Z", - "Multivitamins & Minerals", - null, - null, - "180 comprimés", - "Plastique", - "en:plastic", - "Plastic", - null, - "Seara", - "xx:seara", - "seara", - "Compléments alimentaires, Vitamines", - "en:dietary-supplements,en:vitamins", - "Dietary supplements,Vitamins", - null, - null, - null, - "uk", - "uk", - "Végétarien, Sans OGM, Végétalien", - "en:vegetarian,en:no-gmos,en:vegan", - "Vegetarian,No GMOs,Vegan", - null, - null, - null, - null, - null, - "France", - "Amazon", - "Belgien, Germany", - "en:belgium,en:germany", - "Belgium,Germany", - "La cellulose Microcristalline, Maltodextrine, Calcium (Carbonate de Calcium), Vitamine C (Acide ascorbique), Magnésium (0xyde de Magnésium), Fécule de maïis, Vitamine B3 (Nicotinamide), Fer (Fumarate Ferreux), Vitamine E (D-a Acétate de tocophéryle), Palmitate d'ascorbyle, Zinc (0xyde de Zinc), Vitamine B5 (Pantothénate de D-Calcium), Vitamine B2 (Riboflavine), Vitamine B6 (Le Chlorhydrate de Pyridoxine), Manganèse (Sulfate de manganèse), Vitamine B1 (Chlorhydrate de Thiamine). Cuivre (Gluconate cuivrique), Vitamine A (Acétate de Rétinyle), Acide folique, lode (lodure de potassium), Vitamine K2 (Menaquinone-7), Sélénium (Sélénite de sodium), Vitamine B12 (Méthyl cobalamine), Biotine, Molybdène (Molybdate de Sodium), Chrome (Picolinate de chrome), Vitamine D3 (Cholécalciférol). Stabilisateurs: Polyvinylpyrrolidone K 30, Hydroxypropyl méthylcellulose", - "en:e460i,en:e460,en:maltodextrin,en:calcium,en:minerals,en:e300,en:magnesium,fr:fecule-de-maiis,en:e375,en:iron,en:vitamin-e,en:e304i,en:e304,en:zinc,en:pantothenic-acid,en:vitamins,en:e101,en:vitamin-b6,en:manganese,en:thiamin,en:copper,en:vitamin-a,en:folic-acid,en:folate,fr:lode,en:menaquinone,en:vitamin-k,en:selenium,en:vitamin-b12,en:biotin,en:molybdenum,en:chromium,en:cholecalciferol,en:vitamin-d,en:stabiliser,en:e464,en:e170i,en:e170,fr:0xyde-de-magnesium,en:iron-fumarate,fr:d-a-acetate-de-tocopheryle,fr:0xyde-de-zinc,en:d-pantothenate-calcium,en:pyridoxine-hydrochloride,en:manganese-sulfate,en:thiamin-hydrochloride,fr:gluconate-cuivrique,fr:acetate-de-retinyle,fr:lodure-de-potassium,fr:menaquinone-7,en:sodium-selenite,fr:methyl-cobalamine,en:sodium-molybdate,en:chromium-picolinate,fr:polyvinylpyrrolidone-k-30", - "en:may-contain-palm-oil,en:vegan,en:vegetarian", - null, - null, - null, - null, - null, - "100.0g", - "100.0", - null, - "4.0", - null, - "en:e1201,en:e304,en:e304i,en:e460,en:e460i,en:e464", - "E1201 - Polyvinylpyrrolidone,E304 - Fatty acid esters of ascorbic acid,E304i - Ascorbyl palmitate,E460 - Cellulose,E460i - Microcrystalline cellulose,E464 - Hydroxypropyl methyl cellulose", - null, - "not-applicable", + "6", "4.0", - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - "en:fat-in-moderate-quantity,en:saturated-fat-in-moderate-quantity,en:sugars-in-low-quantity,en:salt-in-low-quantity", "0.0", - null, - "en:energy-value-in-kcal-does-not-match-value-computed-from-other-nutrients", - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-ma-scans-2024,top-100000-ma-scans-2024,top-country-ma-scans-2024", - "0.8875", - "1635499189.0", - "2021-10-29T09:19:49Z", - "en:vitamins", - "Vitamins", - "https://images.openfoodfacts.org/images/products/invalid/front_fr.29.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_fr.29.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.22.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_fr.22.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.25.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.25.200.jpg", - null, - "169.0", - "707.0", - null, - "9.0", - "3.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "19.45", - "4.05", - null, - null, - null, - null, - null, - null, - null, - null, + "15.0", + "2401.0", + "12.0", + "10.5", + "0.0", + "0.0", + "13.0", + "9.0", + "36.0", + "23.0", + "0.3", + "0.12", null, null, null, - "1.95", null, + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "1.0" + ], + [ + "9", + "6.0", null, + "4.0", + "1520.0", + "11.0", + "2.0", + "0.0", + "0.01", + "25.0", + "0.98", + "9.0", "22.0", - null, - null, - null, - "0.125", - null, - "0.05", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, + "0.95", + "0.38", null, null, null, null, null, "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null + "0.0", + "0.0", + "0.0", + "1.0", + "0.0" ], [ - "47", - "43", - "http://world-en.openfoodfacts.org/product/00000043/butter-burst-herbalife", - "elcoco", - "1577550049", - "2019-12-28T16:20:49Z", - "1746725582", - "2025-05-08T17:33:02Z", - "thaialagata", - "1746725582", - "2025-05-08T17:33:02Z", - "Butter Burst", - null, - null, - "30 compresse", - "Boîte", - "en:box", - "Box", - null, - "Herbalife", - "xx:herbalife", - "herbalife", - "pt:pão com queijo", - "pt:pao-com-queijo", - "pt:pao-com-queijo", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "Francia,España", - "en:france,en:spain", - "France,Spain", - null, - null, - null, - null, - null, - null, - null, - null, - "14 g", - "14.0", - null, - null, - null, - null, - null, - "45.0", - "e", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-to-be-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - "en:fat-in-high-quantity,en:saturated-fat-in-high-quantity,en:sugars-in-high-quantity,en:salt-in-high-quantity", + "11", + "7.0", "0.0", - null, - "en:energy-value-in-kcal-does-not-match-value-in-kj,en:nutrition-value-over-105-carbohydrates,en:nutrition-value-total-over-105,en:nutrition-value-over-3800-energy,en:energy-value-in-kcal-does-not-match-value-computed-from-other-nutrients", - "3.0", - "bottom-25-percent-scans-2020,bottom-20-percent-scans-2020,top-85-percent-scans-2020,top-90-percent-scans-2020,top-country-fr-scans-2020,bottom-25-percent-scans-2022,top-80-percent-scans-2022,top-85-percent-scans-2022,top-90-percent-scans-2022,top-5000-ro-scans-2022,top-10000-ro-scans-2022,top-50000-ro-scans-2022,top-100000-ro-scans-2022,top-country-ro-scans-2022,top-75-percent-scans-2023,top-80-percent-scans-2023,top-85-percent-scans-2023,top-90-percent-scans-2023,top-10000-ro-scans-2023,top-50000-ro-scans-2023,top-100000-ro-scans-2023,top-country-ro-scans-2023,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-100000-gb-scans-2024,top-country-gb-scans-2024,top-50000-ch-scans-2024,top-100000-ch-scans-2024,top-10000-bg-scans-2024,top-50000-bg-scans-2024,top-100000-bg-scans-2024", - 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\n", + "" + ], + "text/plain": [ + " code additives_n nutriscore_score energy_100g fat_100g \\\n", + "6 4.0 0.0 15.0 2401.0 12.0 \n", + "9 6.0 1.8 4.0 1520.0 11.0 \n", + "11 7.0 0.0 4.0 4.0 1.0 \n", + "12 8.0 1.0 6.0 1510.0 2.0 \n", + "14 9.0 0.6 -11.0 293.0 0.5 \n", + "\n", + " saturated-fat_100g trans-fat_100g cholesterol_100g carbohydrates_100g \\\n", + "6 10.50 0.000000 0.000000 13.0 \n", + "9 2.00 0.000000 0.010000 25.0 \n", + "11 1.00 0.129619 0.011300 1.0 \n", + "12 0.50 0.112994 0.016745 6.7 \n", + "14 0.06 0.129619 0.009760 2.0 \n", + "\n", + " sugars_100g ... vitamin-c_100g calcium_100g iron_100g \\\n", + "6 9.00 ... 0.058186 0.176374 0.010165 \n", + "9 0.98 ... 0.058186 0.171798 0.008665 \n", + "11 1.00 ... 0.058186 0.196138 0.015799 \n", + "12 1.70 ... 0.071429 0.178571 0.008929 \n", + "14 0.24 ... 0.090000 0.171798 0.008665 \n", + "\n", + " fruits-vegetables-nuts-estimate-from-ingredients_100g \\\n", + "6 0.000000 \n", + "9 20.400223 \n", + "11 0.000000 \n", + "12 0.000000 \n", + "14 20.000291 \n", + "\n", + " PNNS_pro_Animal_based PNNS_pro_Drinks PNNS_pro_NA PNNS_pro_Plant_based \\\n", + "6 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "11 0.0 0.0 0.0 1.0 \n", + "12 0.0 0.0 1.0 0.0 \n", + "14 0.0 0.0 1.0 0.0 \n", + "\n", + " PNNS_pro_Processed PNNS_pro_Snacks \n", + "6 0.0 1.0 \n", + "9 1.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "14 0.0 0.0 \n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "imputed_df = knn_impute_numeric(filtered_df, n_neighbors=5)\n", + "imputed_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.4 Scaling" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ": shape of df with only numeric features=(2298, 24)\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'pd' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[63]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m#work_df = scaler_numeric(imputed_df, 'nutriscore_score')\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m work_df = Scaling.scaler_numeric(imputed_df, \u001b[33m'\u001b[39m\u001b[33mnutriscore_score\u001b[39m\u001b[33m'\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/PROJECTS/DESU_Data_Science/N_Rocher/OFF_Project/OFFProject/scripts/Scaling.py:16\u001b[39m, in \u001b[36mscaler_numeric\u001b[39m\u001b[34m(df, target_col)\u001b[39m\n\u001b[32m 13\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m: shape of df with only numeric features=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mX_numeric.shape\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 15\u001b[39m \u001b[38;5;66;03m#scale the numerical values and put the scaled numerical data into a dataframe\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m16\u001b[39m scaler = RobustScaler()\n\u001b[32m 17\u001b[39m X_scaled = scaler.fit_transform(X_numeric)\n\u001b[32m 18\u001b[39m X_scaled_df = pd.DataFrame(X_scaled, columns=X_numeric.columns, index=X_numeric.index)\n", + "\u001b[31mNameError\u001b[39m: name 'pd' is not defined" + ] + } + ], + "source": [ + "#work_df = scaler_numeric(imputed_df, 'nutriscore_score')\n", + "work_df = Scaling.scaler_numeric(imputed_df, 'nutriscore_score')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def scaler_numeric(df, target_col=''):\n", + "\n", + " #separate the nutriscore and the rest of the values to do the scaling\n", + " X = df.drop([target_col], axis = 1)\n", + " y = df[target_col]\n", + "\n", + " #select only the numerical variables to do the scaling\n", + " X_numeric = X.select_dtypes(include=['float','int'])\n", + " print(f\": shape of df with only numeric features={X_numeric.shape}\")\n", + "\n", + " #scale the numerical values and put the scaled numerical data into a dataframe\n", + " scaler = RobustScaler()\n", + " X_scaled = scaler.fit_transform(X_numeric)\n", + " X_scaled_df = pd.DataFrame(X_scaled, columns=X_numeric.columns, index=X_numeric.index)\n", + "\n", + " #combine the scaled df with the nutriscore\n", + " X_non_numeric = X.select_dtypes(exclude=['float','int'])\n", + " X_processed = pd.concat([X_scaled_df, X_non_numeric], axis=1)\n", + " scaled_df = pd.concat([X_processed, y], axis=1)\n", + "\n", + " # Ensure the column order is the same as the original dataframe\n", + " scaled_df = scaled_df[df.columns]\n", + "\n", + " return scaled_df\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ": shape of df with only numeric features=(2298, 24)\n" + ] + }, + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "code", + "rawType": "float64", + "type": "float" + }, + { + "name": "additives_n", + "rawType": "float64", + "type": "float" + }, + { + "name": "nutriscore_score", + "rawType": "float64", + "type": "float" + }, + { + "name": "energy_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "saturated-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "trans-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "cholesterol_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": 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4996602013125http://world-en.openfoodfacts.org/product/0000...kiliweb15382254712018-09-29T12:51:11Z15665925492019-08-23T20:35:49Zroboto-app17433544992025-03-30T17:08:19Z9-0.1353370.8754.0-0.418765-0.419933-0.7092200.000000.977335-0.093210-0.642886...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-0.100514-0.011996-0.189521-0.3496930.00.0-0.50.01.5-1.0
49976021http://world-en.openfoodfacts.org/product/0000...kiliweb16734780172023-01-11T23:00:17Z17286543092024-10-11T13:45:09Zfix-code-bot17388476382025-02-06T13:13:58Z11-0.135337-0.2504.0-3.235300-0.854196-0.8865257.796611.144866-0.833951-0.641927...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN-0.1005140.1732430.711839-0.7136680.00.0-0.55.0-1.0-1.0
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49996023http://world-en.openfoodfacts.org/product/0000...foodless17303256612024-10-30T22:01:01Z17303256712024-10-30T22:01:11Zfoodless17436698332025-04-03T08:43:53Z14-0.1353370.125-11.0-2.698374-0.875909-1.0531917.796610.946442-0.803086-0.678389...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.379107-0.011996-0.189521-0.3568290.00.02.00.0-1.0-1.0
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5000 rows × 210 columns

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" ], "text/plain": [ - " code url creator \\\n", - "0 54 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", - "1 63 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", - "2 114 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", - "3 105 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", - "4 2 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", - "... ... ... ... \n", - "4995 6015 http://world-en.openfoodfacts.org/product/0000... smudidu \n", - "4996 602013125 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", - "4997 6021 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", - "4998 602220004 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", - "4999 6023 http://world-en.openfoodfacts.org/product/0000... foodless \n", + " code additives_n nutriscore_score energy_100g fat_100g \\\n", + "6 -0.135337 -0.250 15.0 1.218021 -0.376506 \n", + "9 -0.135337 0.875 4.0 -0.418765 -0.419933 \n", + "11 -0.135337 -0.250 4.0 -3.235300 -0.854196 \n", + "12 -0.135337 0.375 6.0 -0.437343 -0.810770 \n", + "14 -0.135337 0.125 -11.0 -2.698374 -0.875909 \n", "\n", - " created_t created_datetime last_modified_t \\\n", - "0 1582569031 2020-02-24T18:30:31Z 1733085204 \n", - "1 1673620307 2023-01-13T14:31:47Z 1750061386 \n", - "2 1580066482 2020-01-26T19:21:22Z 1751035658 \n", - "3 1572117743 2019-10-26T19:22:23Z 1738073570 \n", - "4 1722606455 2024-08-02T13:47:35Z 1749171851 \n", - "... ... ... ... \n", - "4995 1640194085 2021-12-22T17:28:05Z 1728034851 \n", - "4996 1538225471 2018-09-29T12:51:11Z 1566592549 \n", - "4997 1673478017 2023-01-11T23:00:17Z 1728654309 \n", - "4998 1661176281 2022-08-22T13:51:21Z 1728038823 \n", - "4999 1730325661 2024-10-30T22:01:01Z 1730325671 \n", + " saturated-fat_100g trans-fat_100g cholesterol_100g carbohydrates_100g \\\n", + "6 0.797872 0.00000 -0.311282 -0.463580 \n", + "9 -0.709220 0.00000 0.977335 -0.093210 \n", + "11 -0.886525 7.79661 1.144866 -0.833951 \n", + "12 -0.975177 6.79661 1.846459 -0.658025 \n", + "14 -1.053191 7.79661 0.946442 -0.803086 \n", "\n", - " last_modified_datetime last_modified_by last_updated_t \\\n", - "0 2024-12-01T20:33:24Z NaN 1740205422 \n", - "1 2025-06-16T08:09:46Z bodysupport 1750061386 \n", - "2 2025-06-27T14:47:38Z teolemon 1751035658 \n", - "3 2025-01-28T14:12:50Z NaN 1743653496 \n", - "4 2025-06-06T01:04:11Z altroconsumo 1749171851 \n", - "... ... ... ... \n", - "4995 2024-10-04T09:40:51Z fix-code-bot 1738846635 \n", - "4996 2019-08-23T20:35:49Z roboto-app 1743354499 \n", - "4997 2024-10-11T13:45:09Z fix-code-bot 1738847638 \n", - "4998 2024-10-04T10:47:03Z fix-code-bot 1728038823 \n", - "4999 2024-10-30T22:01:11Z foodless 1743669833 \n", + " sugars_100g ... vitamin-c_100g calcium_100g iron_100g \\\n", + "6 -0.258114 ... -0.100514 0.022831 0.000000 \n", + "9 -0.642886 ... -0.100514 -0.011996 -0.189521 \n", + "11 -0.641927 ... -0.100514 0.173243 0.711839 \n", + "12 -0.608343 ... 0.099131 0.039553 -0.156182 \n", + "14 -0.678389 ... 0.379107 -0.011996 -0.189521 \n", "\n", - " last_updated_datetime ... water-hardness_100g choline_100g \\\n", - "0 2025-02-22T06:23:42Z ... NaN NaN \n", - "1 2025-06-16T08:09:46Z ... NaN NaN \n", - "2 2025-06-27T14:47:38Z ... NaN NaN \n", - "3 2025-04-03T04:11:36Z ... NaN NaN \n", - "4 2025-06-06T01:04:11Z ... NaN NaN \n", - "... ... ... ... ... \n", - "4995 2025-02-06T12:57:15Z ... NaN NaN \n", - "4996 2025-03-30T17:08:19Z ... NaN NaN \n", - "4997 2025-02-06T13:13:58Z ... NaN NaN \n", - "4998 2024-10-04T10:47:03Z ... NaN NaN \n", - "4999 2025-04-03T08:43:53Z ... NaN NaN \n", + " fruits-vegetables-nuts-estimate-from-ingredients_100g \\\n", + "6 -0.713668 \n", + "9 -0.349693 \n", + "11 -0.713668 \n", + "12 -0.713668 \n", + "14 -0.356829 \n", "\n", - " phylloquinone_100g beta-glucan_100g inositol_100g carnitine_100g \\\n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - "... ... ... ... ... \n", - "4995 NaN NaN NaN NaN \n", - "4996 NaN NaN NaN NaN \n", - "4997 NaN NaN NaN NaN \n", - "4998 NaN NaN NaN NaN \n", - "4999 NaN NaN NaN NaN \n", + " PNNS_pro_Animal_based PNNS_pro_Drinks PNNS_pro_NA PNNS_pro_Plant_based \\\n", + "6 0.0 0.0 -0.5 0.0 \n", + "9 0.0 0.0 -0.5 0.0 \n", + "11 0.0 0.0 -0.5 5.0 \n", + "12 0.0 0.0 2.0 0.0 \n", + "14 0.0 0.0 2.0 0.0 \n", "\n", - " sulphate_100g nitrate_100g acidity_100g carbohydrates-total_100g \n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - "... ... ... ... ... \n", - "4995 NaN NaN NaN NaN \n", - "4996 NaN NaN NaN NaN \n", - "4997 NaN NaN NaN NaN \n", - "4998 NaN NaN NaN NaN \n", - "4999 NaN NaN NaN NaN \n", + " PNNS_pro_Processed PNNS_pro_Snacks \n", + "6 -1.0 4.0 \n", + "9 1.5 -1.0 \n", + "11 -1.0 -1.0 \n", + "12 -1.0 -1.0 \n", + "14 -1.0 -1.0 \n", "\n", - "[5000 rows x 210 columns]" + "[5 rows x 25 columns]" ] }, - "execution_count": 18, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df" + "work_df = scaler_numeric(imputed_df, target_col='nutriscore_score') \n", + "work_df.head()" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 97, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selected features: Index(['saturated-fat_100g', 'carbohydrates_100g', 'sugars_100g', 'salt_100g',\n", + " 'sodium_100g', 'vitamin-a_100g',\n", + " 'fruits-vegetables-nuts-estimate-from-ingredients_100g',\n", + " 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA'],\n", + " dtype='object')\n" + ] + }, { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "index", - 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"name": "countries", - "rawType": "object", - "type": "string" + "name": "saturated-fat_100g", + "rawType": "float64", + "type": "float" }, { - "name": "countries_tags", - "rawType": "object", - "type": "string" + "name": "carbohydrates_100g", + "rawType": "float64", + "type": "float" }, { - "name": "countries_en", - "rawType": "object", - "type": "string" + "name": "sugars_100g", + "rawType": "float64", + "type": "float" }, { - "name": "nutriscore_grade", - "rawType": "object", - "type": "string" + "name": "salt_100g", + "rawType": "float64", + "type": "float" }, { - "name": "pnns_groups_1", - "rawType": "object", - "type": "string" + "name": "sodium_100g", + "rawType": "float64", + "type": "float" }, { - "name": "pnns_groups_2", - "rawType": "object", - "type": "string" + "name": "vitamin-a_100g", + "rawType": "float64", + "type": "float" }, { - "name": "states", - "rawType": "object", - "type": "string" + "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g", + "rawType": "float64", + "type": "float" }, { - "name": "states_tags", - "rawType": "object", - "type": "string" + "name": "PNNS_pro_Animal_based", + "rawType": "float64", + "type": "float" }, { - "name": "states_en", - "rawType": "object", - "type": "string" + "name": "PNNS_pro_Drinks", + "rawType": "float64", + "type": "float" }, { - "name": "completeness", + "name": "PNNS_pro_NA", "rawType": "float64", "type": "float" } ], - "ref": "561620cc-4713-4ea9-b047-613bbd4d312d", + "ref": "4c77ba6e-3858-4fdc-8d46-d80569dde80c", "rows": [ [ - "5", - "20.0", - "3", - "http://world-en.openfoodfacts.org/product/00000003/soja-sauce-tai-shan", - "prepperapp", - "1716818343", - "2024-05-27T13:59:03Z", - "1750615537", - "2025-06-22T18:05:37Z", - "waistline-app", - "1750615537", - "2025-06-22T18:05:37Z", - "Soja-Sauce", - "Griechenland, Germany", - "en:germany,en:greece", - "Germany,Greece", - "e", - "Sugary snacks", - "Sweets", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-validated, en:packaging-photo-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-validated,en:packaging-photo-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos validated,Packaging photo selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - "0.8" + "0", + "0.7978723539528367", + "-0.46358024632489264", + "-0.2581140394332294", + "-0.7375875193926286", + "-0.7375875238701466", + "0.0", + "-0.713668045138673", + "0.0", + "0.0", + "-0.5" ], [ - "6", - "15.0", - "4", - "http://world-en.openfoodfacts.org/product/00000004/entrecoesteack-highland-beef-pg-tips", - "elcoco", - "1560176426", - "2019-06-10T14:20:26Z", - "1748094869", - "2025-05-24T13:54:29Z", - "smoothie-app", - "1748094869", - "2025-05-24T13:54:29Z", - "Entrecôesteack - Highland Beef", - "Brasilien, Germany", - "en:brazil,en:germany", - "Brazil,Germany", - "d", - "unknown", - "unknown", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-completed, en:origins-completed, en:categories-completed, en:brands-completed, en:packaging-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-completed,en:origins-completed,en:categories-completed,en:brands-completed,en:packaging-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics completed,Origins completed,Categories completed,Brands completed,Packaging completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - "0.8875" + "1", + "-0.7092198446287235", + "-0.09320987595452225", + "-0.6428862705844275", + "-0.5307129184842322", + "-0.53071292170592", + "0.0", + "-0.3496933585983946", + "0.0", + "0.0", + "-0.5" ], [ - "8", - "11.0", - "5", - "http://world-en.openfoodfacts.org/product/00000005/five-grain-granola-roger-s", - "touchette", - "1605337720", - "2020-11-14T07:08:40Z", - "1749334226", - "2025-06-07T22:10:26Z", - "smoothie-app", - "1749334226", - "2025-06-07T22:10:26Z", - "five grain granola", - "Frankreich, Germany", - "en:france,en:germany", - "France,Germany", - "d", - "Cereals and potatoes", - "Breakfast cereals", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-to-be-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-to-be-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo to be selected,Photos uploaded", - "0.85" + "2", + "-0.8865248091677305", + "-0.833950616695263", + "-0.6419267388359208", + "-0.5147994876451247", + "-0.5147994907702101", + "0.0", + "-0.713668045138673", + "0.0", + "0.0", + "-0.5" ], [ - "9", - "4.0", - "6", - "http://world-en.openfoodfacts.org/product/00000006/triple-cheese-puff", - "maldan", - "1732037972", - "2024-11-19T17:39:32Z", - "1749357659", - "2025-06-08T04:40:59Z", - "smoothie-app", - "1749357659", - "2025-06-08T04:40:59Z", - "Triple cheese puff", - "Germany, United States, en:france", - "en:france,en:germany,en:united-states", - "France,Germany,United States", - "c", - "unknown", - "unknown", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-to-be-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-to-be-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands to be completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - "0.4875" + "3", + "-0.975177291437234", + "-0.6580246907693371", + "-0.6083431276381853", + "-0.3556651792540506", + "-0.3556651814131128", + "-0.9864357615942851", + "-0.713668045138673", + "0.0", + "0.0", + "2.0" ], [ - "11", - "4.0", - "7", - "http://world-en.openfoodfacts.org/product/00000007/granola-bio-le-chocolate-mg-ricarica", - "smoothie-app", - "1678803019", - "2023-03-14T14:10:19Z", - "1748262529", - "2025-05-26T12:28:49Z", - "smoothie-app", - "1748262529", - "2025-05-26T12:28:49Z", - "granola Bio le Chocolaté", - "Spanien, Germany", - "en:germany,en:spain", - "Germany,Spain", - "c", - "Fruits and vegetables", - "Dried fruits", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - "0.7625" + "4", + "-1.0531914758343972", + "-0.8030864191643988", + "-0.6783889452791765", + "-0.7455442348121822", + "-0.7455442393380014", + "0.0", + "-0.35682882520766857", + "0.0", + "0.0", + "2.0" ] ], "shape": { - "columns": 22, + "columns": 10, "rows": 5 } }, @@ -12452,525 +4399,526 @@ " \n", " \n", " \n", - " nutriscore_score\n", - " code\n", - " url\n", - " creator\n", - " created_t\n", - " created_datetime\n", - " last_modified_t\n", - " last_modified_datetime\n", - " last_modified_by\n", - " last_updated_t\n", - " ...\n", - " countries\n", - " countries_tags\n", - " countries_en\n", - " nutriscore_grade\n", - " pnns_groups_1\n", - " pnns_groups_2\n", - " states\n", - " states_tags\n", - " states_en\n", - " completeness\n", + " saturated-fat_100g\n", + " carbohydrates_100g\n", + " sugars_100g\n", + " salt_100g\n", + " sodium_100g\n", + " vitamin-a_100g\n", + " fruits-vegetables-nuts-estimate-from-ingredients_100g\n", + " PNNS_pro_Animal_based\n", + " PNNS_pro_Drinks\n", + " PNNS_pro_NA\n", " \n", " \n", " \n", " \n", - " 5\n", - " 20.0\n", - " 3\n", - " http://world-en.openfoodfacts.org/product/0000...\n", - " prepperapp\n", - " 1716818343\n", - " 2024-05-27T13:59:03Z\n", - " 1750615537\n", - " 2025-06-22T18:05:37Z\n", - " waistline-app\n", - " 1750615537\n", - " ...\n", - " Griechenland, Germany\n", - " en:germany,en:greece\n", - " Germany,Greece\n", - " e\n", - " Sugary snacks\n", - " Sweets\n", - " en:to-be-completed, en:nutrition-facts-complet...\n", - " en:to-be-completed,en:nutrition-facts-complete...\n", - " To be completed,Nutrition facts completed,Ingr...\n", - " 0.8000\n", + " 0\n", + " 0.797872\n", + " -0.463580\n", + " -0.258114\n", + " -0.737588\n", + " -0.737588\n", + " 0.000000\n", + " -0.713668\n", + " 0.0\n", + " 0.0\n", + " -0.5\n", " \n", " \n", - " 6\n", - " 15.0\n", - " 4\n", - " http://world-en.openfoodfacts.org/product/0000...\n", - " elcoco\n", - " 1560176426\n", - " 2019-06-10T14:20:26Z\n", - " 1748094869\n", - " 2025-05-24T13:54:29Z\n", - " smoothie-app\n", - " 1748094869\n", - " ...\n", - " Brasilien, Germany\n", - " en:brazil,en:germany\n", - " Brazil,Germany\n", - " d\n", - " unknown\n", - " unknown\n", - " en:to-be-completed, en:nutrition-facts-complet...\n", - " en:to-be-completed,en:nutrition-facts-complete...\n", - " To be completed,Nutrition facts completed,Ingr...\n", - " 0.8875\n", + " 1\n", + " -0.709220\n", + " -0.093210\n", + " -0.642886\n", + " -0.530713\n", + " -0.530713\n", + " 0.000000\n", + " -0.349693\n", + " 0.0\n", + " 0.0\n", + " -0.5\n", " \n", " \n", - " 8\n", - " 11.0\n", - " 5\n", - " http://world-en.openfoodfacts.org/product/0000...\n", - " touchette\n", - " 1605337720\n", - " 2020-11-14T07:08:40Z\n", - " 1749334226\n", - " 2025-06-07T22:10:26Z\n", - " smoothie-app\n", - " 1749334226\n", - " ...\n", - " Frankreich, Germany\n", - " en:france,en:germany\n", - " France,Germany\n", - " d\n", - " Cereals and potatoes\n", - " Breakfast cereals\n", - " en:to-be-completed, en:nutrition-facts-complet...\n", - " en:to-be-completed,en:nutrition-facts-complete...\n", - " To be completed,Nutrition facts completed,Ingr...\n", - " 0.8500\n", + " 2\n", + " -0.886525\n", + " -0.833951\n", + " -0.641927\n", + " -0.514799\n", + " -0.514799\n", + " 0.000000\n", + " -0.713668\n", + " 0.0\n", + " 0.0\n", + " -0.5\n", " \n", " \n", - " 9\n", - " 4.0\n", - " 6\n", - " http://world-en.openfoodfacts.org/product/0000...\n", - " maldan\n", - " 1732037972\n", - " 2024-11-19T17:39:32Z\n", - " 1749357659\n", - " 2025-06-08T04:40:59Z\n", - " smoothie-app\n", - " 1749357659\n", - " ...\n", - " Germany, United States, en:france\n", - " en:france,en:germany,en:united-states\n", - " France,Germany,United States\n", - " c\n", - " unknown\n", - " unknown\n", - " en:to-be-completed, en:nutrition-facts-complet...\n", - " en:to-be-completed,en:nutrition-facts-complete...\n", - " To be completed,Nutrition facts completed,Ingr...\n", - " 0.4875\n", + " 3\n", + " -0.975177\n", + " -0.658025\n", + " -0.608343\n", + " -0.355665\n", + " -0.355665\n", + " -0.986436\n", + " -0.713668\n", + " 0.0\n", + " 0.0\n", + " 2.0\n", " \n", " \n", - " 11\n", - " 4.0\n", - " 7\n", - " http://world-en.openfoodfacts.org/product/0000...\n", - " smoothie-app\n", - " 1678803019\n", - " 2023-03-14T14:10:19Z\n", - " 1748262529\n", - " 2025-05-26T12:28:49Z\n", - " smoothie-app\n", - " 1748262529\n", - " ...\n", - " Spanien, Germany\n", - " en:germany,en:spain\n", - " Germany,Spain\n", - " c\n", - " Fruits and vegetables\n", - " Dried fruits\n", - " en:to-be-completed, en:nutrition-facts-complet...\n", - " en:to-be-completed,en:nutrition-facts-complete...\n", - " To be completed,Nutrition facts completed,Ingr...\n", - " 0.7625\n", + " 4\n", + " -1.053191\n", + " -0.803086\n", + " -0.678389\n", + " -0.745544\n", + " -0.745544\n", + " 0.000000\n", + " -0.356829\n", + " 0.0\n", + " 0.0\n", + " 2.0\n", " \n", " \n", "\n", - "

5 rows × 22 columns

\n", "" ], "text/plain": [ - " nutriscore_score code url \\\n", - "5 20.0 3 http://world-en.openfoodfacts.org/product/0000... \n", - "6 15.0 4 http://world-en.openfoodfacts.org/product/0000... \n", - "8 11.0 5 http://world-en.openfoodfacts.org/product/0000... \n", - "9 4.0 6 http://world-en.openfoodfacts.org/product/0000... \n", - "11 4.0 7 http://world-en.openfoodfacts.org/product/0000... \n", - "\n", - " creator created_t created_datetime last_modified_t \\\n", - "5 prepperapp 1716818343 2024-05-27T13:59:03Z 1750615537 \n", - "6 elcoco 1560176426 2019-06-10T14:20:26Z 1748094869 \n", - "8 touchette 1605337720 2020-11-14T07:08:40Z 1749334226 \n", - "9 maldan 1732037972 2024-11-19T17:39:32Z 1749357659 \n", - "11 smoothie-app 1678803019 2023-03-14T14:10:19Z 1748262529 \n", + " saturated-fat_100g carbohydrates_100g sugars_100g salt_100g \\\n", + "0 0.797872 -0.463580 -0.258114 -0.737588 \n", + "1 -0.709220 -0.093210 -0.642886 -0.530713 \n", + "2 -0.886525 -0.833951 -0.641927 -0.514799 \n", + "3 -0.975177 -0.658025 -0.608343 -0.355665 \n", + "4 -1.053191 -0.803086 -0.678389 -0.745544 \n", "\n", - " last_modified_datetime last_modified_by last_updated_t ... \\\n", - "5 2025-06-22T18:05:37Z waistline-app 1750615537 ... \n", - "6 2025-05-24T13:54:29Z smoothie-app 1748094869 ... \n", - "8 2025-06-07T22:10:26Z smoothie-app 1749334226 ... \n", - "9 2025-06-08T04:40:59Z smoothie-app 1749357659 ... \n", - "11 2025-05-26T12:28:49Z smoothie-app 1748262529 ... \n", + " sodium_100g vitamin-a_100g \\\n", + "0 -0.737588 0.000000 \n", + "1 -0.530713 0.000000 \n", + "2 -0.514799 0.000000 \n", + "3 -0.355665 -0.986436 \n", + "4 -0.745544 0.000000 \n", "\n", - " countries countries_tags \\\n", - "5 Griechenland, Germany en:germany,en:greece \n", - "6 Brasilien, Germany en:brazil,en:germany \n", - "8 Frankreich, Germany en:france,en:germany \n", - "9 Germany, United States, en:france en:france,en:germany,en:united-states \n", - "11 Spanien, Germany en:germany,en:spain \n", + " fruits-vegetables-nuts-estimate-from-ingredients_100g \\\n", + "0 -0.713668 \n", + "1 -0.349693 \n", + "2 -0.713668 \n", + "3 -0.713668 \n", + "4 -0.356829 \n", "\n", - " countries_en nutriscore_grade pnns_groups_1 \\\n", - "5 Germany,Greece e Sugary snacks \n", - "6 Brazil,Germany d unknown \n", - "8 France,Germany d Cereals and potatoes \n", - "9 France,Germany,United States c unknown \n", - "11 Germany,Spain c Fruits and vegetables \n", - "\n", - " pnns_groups_2 states \\\n", - "5 Sweets en:to-be-completed, en:nutrition-facts-complet... \n", - "6 unknown en:to-be-completed, en:nutrition-facts-complet... \n", - "8 Breakfast cereals en:to-be-completed, en:nutrition-facts-complet... \n", - "9 unknown en:to-be-completed, en:nutrition-facts-complet... \n", - "11 Dried fruits en:to-be-completed, en:nutrition-facts-complet... \n", - "\n", - " states_tags \\\n", - "5 en:to-be-completed,en:nutrition-facts-complete... \n", - "6 en:to-be-completed,en:nutrition-facts-complete... \n", - "8 en:to-be-completed,en:nutrition-facts-complete... \n", - "9 en:to-be-completed,en:nutrition-facts-complete... \n", - "11 en:to-be-completed,en:nutrition-facts-complete... \n", - "\n", - " states_en completeness \n", - "5 To be completed,Nutrition facts completed,Ingr... 0.8000 \n", - "6 To be completed,Nutrition facts completed,Ingr... 0.8875 \n", - "8 To be completed,Nutrition facts completed,Ingr... 0.8500 \n", - "9 To be completed,Nutrition facts completed,Ingr... 0.4875 \n", - "11 To be completed,Nutrition facts completed,Ingr... 0.7625 \n", - "\n", - "[5 rows x 22 columns]" + " PNNS_pro_Animal_based PNNS_pro_Drinks PNNS_pro_NA \n", + "0 0.0 0.0 -0.5 \n", + "1 0.0 0.0 -0.5 \n", + "2 0.0 0.0 -0.5 \n", + "3 0.0 0.0 2.0 \n", + "4 0.0 0.0 2.0 " ] }, - "execution_count": 19, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "#### eliminate columns with more than 70% missing values\n", - "threshold = 0.7 * len(df)\n", + "# feature selection - selecting top 10 features using Sequential Feature Selector (SFS)\n", + "X = work_df.drop(\"nutriscore_score\", axis=1) \n", + "y = work_df[\"nutriscore_score\"]\n", "\n", - "# Drop columns with less than 70% non-NA values, but keep 'nutriscore_score' no matter what\n", - "cols_to_keep = ['nutriscore_score'] + [\n", - " col for col in df.columns if col != 'nutriscore_score' and df[col].notna().sum() >= threshold\n", - "]\n", - "df = df[cols_to_keep]\n", + "from scripts import feature_selection_sfs\n", + "# use the function to select features\n", + "sfs, X_selected, selected_features = feature_selection_sfs.select_features(X, y, n_features=10)\n", "\n", - "# Eliminate rows for which the nutriscore_score is not available\n", - "filtered_df = df[df['nutriscore_score'].notna()]\n", - "\n", - "filtered_df.head()" + "# merge selected features with their names\n", + "X_selected_df = pd.DataFrame(X_selected, columns=selected_features)\n", + "X_selected_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Predicting the nutriscore" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 3.1 : Regression Model - Lasso" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((2298, 24), (2298,))" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# using the selected features for modeling\n", + "X = X_selected_df.drop(\"nutriscore_score\", axis=1) \n", + "y = work_df[\"nutriscore_score\"]\n", + "X.shape, y.shape" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 88, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "RangeIndex: 5000 entries, 0 to 4999\n", - "Data columns (total 210 columns):\n", - " # Column Dtype \n", - "--- ------ ----- \n", - " 0 code int64 \n", - " 1 url object \n", - " 2 creator object \n", - " 3 created_t int64 \n", - " 4 created_datetime object \n", - " 5 last_modified_t int64 \n", - " 6 last_modified_datetime object \n", - " 7 last_modified_by object \n", - " 8 last_updated_t int64 \n", - " 9 last_updated_datetime object \n", - " 10 product_name object \n", - " 11 abbreviated_product_name object \n", - " 12 generic_name object \n", - " 13 quantity object \n", - " 14 packaging object \n", - " 15 packaging_tags object \n", - " 16 packaging_en object \n", - " 17 packaging_text object \n", - " 18 brands object \n", - " 19 brands_tags object \n", - " 20 brands_en object \n", - " 21 categories object \n", - " 22 categories_tags object \n", - " 23 categories_en object \n", - " 24 origins object \n", - " 25 origins_tags object \n", - " 26 origins_en object \n", - " 27 manufacturing_places object \n", - " 28 manufacturing_places_tags object \n", - " 29 labels object \n", - " 30 labels_tags object \n", - " 31 labels_en object \n", - " 32 emb_codes object \n", - " 33 emb_codes_tags object \n", - " 34 first_packaging_code_geo object \n", - " 35 cities float64\n", - " 36 cities_tags object \n", - " 37 purchase_places object \n", - " 38 stores object \n", - " 39 countries object \n", - " 40 countries_tags object \n", - " 41 countries_en object \n", - " 42 ingredients_text object \n", - " 43 ingredients_tags object \n", - " 44 ingredients_analysis_tags object \n", - " 45 allergens object \n", - " 46 allergens_en float64\n", - " 47 traces object \n", - " 48 traces_tags object \n", - " 49 traces_en object \n", - " 50 serving_size object \n", - " 51 serving_quantity float64\n", - " 52 no_nutrition_data object \n", - " 53 additives_n float64\n", - " 54 additives float64\n", - " 55 additives_tags object \n", - " 56 additives_en object \n", - " 57 nutriscore_score float64\n", - " 58 nutriscore_grade object \n", - " 59 nova_group float64\n", - " 60 pnns_groups_1 object \n", - " 61 pnns_groups_2 object \n", - " 62 food_groups object \n", - " 63 food_groups_tags object \n", - " 64 food_groups_en object \n", - " 65 states object \n", - " 66 states_tags object \n", - " 67 states_en object \n", - " 68 brand_owner object \n", - " 69 environmental_score_score float64\n", - " 70 environmental_score_grade object \n", - " 71 nutrient_levels_tags object \n", - " 72 product_quantity float64\n", - " 73 owner object \n", - " 74 data_quality_errors_tags object \n", - " 75 unique_scans_n float64\n", - " 76 popularity_tags object \n", - " 77 completeness float64\n", - " 78 last_image_t float64\n", - " 79 last_image_datetime object \n", - " 80 main_category object \n", - " 81 main_category_en object \n", - " 82 image_url object \n", - " 83 image_small_url object \n", - " 84 image_ingredients_url object \n", - " 85 image_ingredients_small_url object \n", - " 86 image_nutrition_url object \n", - " 87 image_nutrition_small_url object \n", - " 88 energy-kj_100g float64\n", - " 89 energy-kcal_100g float64\n", - " 90 energy_100g float64\n", - " 91 energy-from-fat_100g float64\n", - " 92 fat_100g float64\n", - " 93 saturated-fat_100g float64\n", - " 94 butyric-acid_100g float64\n", - " 95 caproic-acid_100g float64\n", - " 96 caprylic-acid_100g float64\n", - " 97 capric-acid_100g float64\n", - " 98 lauric-acid_100g float64\n", - " 99 myristic-acid_100g float64\n", - " 100 palmitic-acid_100g float64\n", - " 101 stearic-acid_100g float64\n", - " 102 arachidic-acid_100g float64\n", - " 103 behenic-acid_100g float64\n", - " 104 lignoceric-acid_100g float64\n", - " 105 cerotic-acid_100g float64\n", - " 106 montanic-acid_100g float64\n", - " 107 melissic-acid_100g float64\n", - " 108 unsaturated-fat_100g float64\n", - " 109 monounsaturated-fat_100g float64\n", - " 110 omega-9-fat_100g float64\n", - " 111 polyunsaturated-fat_100g float64\n", - " 112 omega-3-fat_100g float64\n", - " 113 omega-6-fat_100g float64\n", - " 114 alpha-linolenic-acid_100g float64\n", - " 115 eicosapentaenoic-acid_100g float64\n", - " 116 docosahexaenoic-acid_100g float64\n", - " 117 linoleic-acid_100g float64\n", - " 118 arachidonic-acid_100g float64\n", - " 119 gamma-linolenic-acid_100g float64\n", - " 120 dihomo-gamma-linolenic-acid_100g float64\n", - " 121 oleic-acid_100g float64\n", - " 122 elaidic-acid_100g float64\n", - " 123 gondoic-acid_100g float64\n", - " 124 mead-acid_100g float64\n", - " 125 erucic-acid_100g float64\n", - " 126 nervonic-acid_100g float64\n", - " 127 trans-fat_100g float64\n", - " 128 cholesterol_100g float64\n", - " 129 carbohydrates_100g float64\n", - " 130 sugars_100g float64\n", - " 131 added-sugars_100g float64\n", - " 132 sucrose_100g float64\n", - " 133 glucose_100g float64\n", - " 134 fructose_100g float64\n", - " 135 galactose_100g float64\n", - " 136 lactose_100g float64\n", - " 137 maltose_100g float64\n", - " 138 maltodextrins_100g float64\n", - " 139 starch_100g float64\n", - " 140 polyols_100g float64\n", - " 141 erythritol_100g float64\n", - " 142 fiber_100g float64\n", - " 143 soluble-fiber_100g float64\n", - " 144 insoluble-fiber_100g float64\n", - " 145 proteins_100g float64\n", - " 146 casein_100g float64\n", - " 147 serum-proteins_100g float64\n", - " 148 nucleotides_100g float64\n", - " 149 salt_100g float64\n", - " 150 added-salt_100g float64\n", - " 151 sodium_100g float64\n", - " 152 alcohol_100g float64\n", - " 153 vitamin-a_100g float64\n", - " 154 beta-carotene_100g float64\n", - " 155 vitamin-d_100g float64\n", - " 156 vitamin-e_100g float64\n", - " 157 vitamin-k_100g float64\n", - " 158 vitamin-c_100g float64\n", - " 159 vitamin-b1_100g float64\n", - " 160 vitamin-b2_100g float64\n", - " 161 vitamin-pp_100g float64\n", - " 162 vitamin-b6_100g float64\n", - " 163 vitamin-b9_100g float64\n", - " 164 folates_100g float64\n", - " 165 vitamin-b12_100g float64\n", - " 166 biotin_100g float64\n", - " 167 pantothenic-acid_100g float64\n", - " 168 silica_100g float64\n", - " 169 bicarbonate_100g float64\n", - " 170 potassium_100g float64\n", - " 171 chloride_100g float64\n", - " 172 calcium_100g float64\n", - " 173 phosphorus_100g float64\n", - " 174 iron_100g float64\n", - " 175 magnesium_100g float64\n", - " 176 zinc_100g float64\n", - " 177 copper_100g float64\n", - " 178 manganese_100g float64\n", - " 179 fluoride_100g float64\n", - " 180 selenium_100g float64\n", - " 181 chromium_100g float64\n", - " 182 molybdenum_100g float64\n", - " 183 iodine_100g float64\n", - " 184 caffeine_100g float64\n", - " 185 taurine_100g float64\n", - " 186 methylsulfonylmethane_100g float64\n", - " 187 ph_100g float64\n", - " 188 fruits-vegetables-nuts_100g float64\n", - " 189 fruits-vegetables-nuts-dried_100g float64\n", - " 190 fruits-vegetables-nuts-estimate_100g float64\n", - " 191 fruits-vegetables-nuts-estimate-from-ingredients_100g float64\n", - " 192 collagen-meat-protein-ratio_100g float64\n", - " 193 cocoa_100g float64\n", - " 194 chlorophyl_100g float64\n", - " 195 carbon-footprint_100g float64\n", - " 196 carbon-footprint-from-meat-or-fish_100g float64\n", - " 197 nutrition-score-fr_100g float64\n", - " 198 nutrition-score-uk_100g float64\n", - " 199 glycemic-index_100g float64\n", - " 200 water-hardness_100g float64\n", - " 201 choline_100g float64\n", - " 202 phylloquinone_100g float64\n", - " 203 beta-glucan_100g float64\n", - " 204 inositol_100g float64\n", - " 205 carnitine_100g float64\n", - " 206 sulphate_100g float64\n", - " 207 nitrate_100g float64\n", - " 208 acidity_100g float64\n", - " 209 carbohydrates-total_100g float64\n", - "dtypes: float64(134), int64(4), object(72)\n", - "memory usage: 8.0+ MB\n" + "Lasso Regression\n", + "paramètres Lasso : {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': 1000, 'positive': False, 'precompute': False, 'random_state': None, 'selection': 'cyclic', 'tol': 0.0001, 'warm_start': False}\n", + "Meilleurs paramètres trouvés : {'alpha': 1.0, 'max_iter': 1000, 'tol': 0.0001}\n", + "Mean Squared Error: 53.21311486260403\n", + "R^2 Score: 0.4793291186543984\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "df.info(verbose=True)" + "from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n", + "from sklearn import linear_model\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# train-test split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# applying Lasso regression \n", + "reg_lasso = linear_model.Lasso()\n", + "reg_lasso.fit(X_train, y_train)\n", + "# make predictions\n", + "y_pred = reg_lasso.predict(X_test)\n", + "\n", + "\n", + "print(\"Lasso Regression\")\n", + "print(\"paramètres Lasso : \", reg_lasso.get_params())\n", + "\n", + "# halving grid search pour optimiser les hyperparamètres\n", + "param_grid = {\n", + " 'alpha': [0.01, 0.1, 1.0, 10.0],\n", + " 'max_iter': [1000, 5000],\n", + " 'tol': [1e-4, 1e-3, 1e-2]\n", + "}\n", + "\n", + "grid_search = GridSearchCV(reg_lasso, param_grid, cv=5, scoring='neg_mean_squared_error')\n", + "grid_search.fit(X_train, y_train)\n", + "y_Grid_pred = grid_search.predict(X_test)\n", + "best_regLasso = grid_search.best_params_\n", + "\n", + "# Meilleurs paramètres\n", + "print(\"Meilleurs paramètres trouvés : \", best_regLasso)\n", + "\n", + "# Courbe d'apprentissage / Learning curve (Lr):\n", + "def plot_learning_curve(model, X, y, cv=5):\n", + " train_sizes, train_scores, test_scores = learning_curve(\n", + " model, X, y, cv=cv, n_jobs=-1, train_sizes=np.linspace(0.1, 1.0, 10)\n", + " )\n", + " train_scores_mean = train_scores.mean(axis=1)\n", + " test_scores_mean = test_scores.mean(axis=1)\n", + "\n", + " return train_sizes, train_scores_mean, test_scores_mean\n", + "\n", + "train_sizes, train_scores_mean, test_scores_mean = plot_learning_curve(reg_lasso, X, y)\n", + "\n", + "\n", + "# Estimate the loss function\n", + "mse = mean_squared_error(y_test, y_Grid_pred)\n", + "print(f\"Mean Squared Error: {mse}\")\n", + "r2 = r2_score(y_test, y_Grid_pred)\n", + "print(f\"R^2 Score: {r2}\") \n", + "\n", + "\n", + "# Plot learning curve\n", + "plt.figure() \n", + "plt.plot(train_sizes, train_scores_mean, label=\"Training score\")\n", + "plt.plot(train_sizes, test_scores_mean, label=\"Cross-validation score\")\n", + "plt.xlabel(\"Training examples\")\n", + "plt.ylabel(\"Score\")\n", + "plt.title(\"Learning Curve\")\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.show()\n" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "### A partir du format parquet " + "\n", + "# Loss function plot\n", + "plt.figure()\n", + "plt.scatter(y_test, y_Grid_pred, color='blue', alpha=0.5)\n", + "plt.plot([y.min(), y.max()], [y.min(), y.max()], 'k---', lw=2)\n", + "plt.xlabel('True Values')\n", + "plt.ylabel('Predictions')\n", + "plt.title('Lasso Regression: True vs Predicted Values')\n", + "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "parquet est un format optimisé pour la maniupulation de gros data set" + "##### 3.2 : Regression Model - SVM" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "on peut charger le data set sous ce format, à partir de [son emplacement sur HuggingFace](https://huggingface.co/datasets/openfoodfacts/product-database) (attention il faudra installer les librairies suivantes pour cela) " + "##### 3.3 : Decision tree" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "### code for that" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Si ca n'est pas déja fait, télécharger les librairies nécessaires : \n", - "```\n", - "pip install huggingface-hub\n", - "pip fastparquet\n", - "\n", - "```" + "##### 3.4 Random forest" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:528: FitFailedWarning: \n", + "540 fits failed out of a total of 1620.\n", + "The score on these train-test partitions for these parameters will be set to nan.\n", + "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", + "\n", + "Below are more details about the failures:\n", + "--------------------------------------------------------------------------------\n", + "268 fits failed with the following error:\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n", + " estimator.fit(X_train, y_train, **fit_params)\n", + " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 1382, in wrapper\n", + " estimator._validate_params()\n", + " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 436, in _validate_params\n", + " validate_parameter_constraints(\n", + " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n", + " raise InvalidParameterError(\n", + "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "272 fits failed with the following error:\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n", + " estimator.fit(X_train, y_train, **fit_params)\n", + " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 1382, in wrapper\n", + " estimator._validate_params()\n", + " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 436, in _validate_params\n", + " validate_parameter_constraints(\n", + " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n", + " raise InvalidParameterError(\n", + "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n", + "\n", + " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", + "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_search.py:1108: UserWarning: One or more of the test scores are non-finite: [ nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan -15.43366587 -11.6164328 -11.27435849\n", + " -14.5820768 -11.23554016 -11.36335203 -15.76857583 -12.44221896\n", + " -12.12038863 -14.66678277 -11.81629708 -11.61178998 -15.03447951\n", + " -12.10550901 -11.79386245 -15.61337517 -12.82035289 -12.67222122\n", + " -17.47154562 -13.5015248 -13.25917603 -17.47154562 -13.5015248\n", + " -13.25917603 -14.9627059 -13.17550421 -13.18977974 -15.43366587\n", + " -11.6164328 -11.27435849 -14.5820768 -11.23554016 -11.36335203\n", + " -15.76857583 -12.44221896 -12.12038863 -14.66678277 -11.81629708\n", + " -11.61178998 -15.03447951 -12.10550901 -11.79386245 -15.61337517\n", + " -12.82035289 -12.67222122 -17.47154562 -13.5015248 -13.25917603\n", + " -17.47154562 -13.5015248 -13.25917603 -14.9627059 -13.17550421\n", + " -13.18977974 nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan -15.91594522 -12.16781458\n", + " -12.06318706 -16.250399 -11.91238305 -12.01762806 -16.13582335\n", + " -12.50536945 -12.63605136 -15.5718352 -12.82225453 -12.11801577\n", + " -14.72627438 -12.28481043 -12.25241906 -16.73890135 -12.97095\n", + " -13.08337804 -16.24138596 -13.48690631 -13.3459846 -16.24138596\n", + " -13.48690631 -13.3459846 -16.35859837 -13.62628501 -13.44813366\n", + " -15.91594522 -12.16781458 -12.06318706 -16.250399 -11.91238305\n", + " -12.01762806 -16.13582335 -12.50536945 -12.63605136 -15.5718352\n", + " -12.82225453 -12.11801577 -14.72627438 -12.28481043 -12.25241906\n", + " -16.73890135 -12.97095 -13.08337804 -16.24138596 -13.48690631\n", + " -13.3459846 -16.24138596 -13.48690631 -13.3459846 -16.35859837\n", + " -13.62628501 -13.44813366 nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan -15.91293058\n", + " -11.5947586 -11.45373592 -14.44348788 -11.32279156 -11.47162727\n", + " -15.49815865 -12.46546805 -12.09982189 -14.27964486 -11.78431829\n", + " -11.5571342 -14.91128126 -12.11213898 -11.79477435 -15.61337517\n", + " -12.82248465 -12.66730308 -17.47154562 -13.5015248 -13.25917603\n", + " -17.47154562 -13.5015248 -13.25917603 -14.9627059 -13.17550421\n", + " -13.18977974 -15.91293058 -11.5947586 -11.45373592 -14.44348788\n", + " -11.32279156 -11.47162727 -15.49815865 -12.46546805 -12.09982189\n", + " -14.27964486 -11.78431829 -11.5571342 -14.91128126 -12.11213898\n", + " -11.79477435 -15.61337517 -12.82248465 -12.66730308 -17.47154562\n", + " -13.5015248 -13.25917603 -17.47154562 -13.5015248 -13.25917603\n", + " -14.9627059 -13.17550421 -13.18977974 nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " -15.43366587 -11.6164328 -11.27435849 -14.5820768 -11.23554016\n", + " -11.36335203 -15.76857583 -12.44221896 -12.12038863 -14.66678277\n", + " -11.81629708 -11.61178998 -15.03447951 -12.10550901 -11.79386245\n", + " -15.61337517 -12.82035289 -12.67222122 -17.47154562 -13.5015248\n", + " -13.25917603 -17.47154562 -13.5015248 -13.25917603 -14.9627059\n", + " -13.17550421 -13.18977974 -15.43366587 -11.6164328 -11.27435849\n", + " -14.5820768 -11.23554016 -11.36335203 -15.76857583 -12.44221896\n", + " -12.12038863 -14.66678277 -11.81629708 -11.61178998 -15.03447951\n", + " -12.10550901 -11.79386245 -15.61337517 -12.82035289 -12.67222122\n", + " -17.47154562 -13.5015248 -13.25917603 -17.47154562 -13.5015248\n", + " -13.25917603 -14.9627059 -13.17550421 -13.18977974]\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meilleurs paramètres trouvés : {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 50}\n", + "MSE : 15.536399122952776\n", + "R² : 0.8255571826701679\n" + ] + } + ], "source": [ - "# Login using e.g. `huggingface-cli login` to access this dataset\n", - "splits = {'food': 'food.parquet', 'beauty': 'beauty.parquet'}\n", - "df = pd.read_parquet(\"hf://datasets/openfoodfacts/product-database/\" + splits[\"food\"])" + "### Random forest \n", + "\n", + "X = work_df.drop(\"nutriscore_score\", axis=1) \n", + "y = work_df[\"nutriscore_score\"]\n", + "\n", + "from scripts.rf import random_forest_GS\n", + "best_rf, X_train, X_test, y_train, y_test, y_pred = random_forest_GS(X, y)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 14, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "### Autres méthodes" + "\n", + "from scripts.rf import plot_learning_curve, plot_mse\n", + "\n", + "plot_learning_curve(best_rf, X, y)\n", + "plot_mse(best_rf, X_train, X_test, y_train, y_test)\n" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 26, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "Pour des détails complets sur les différentes options pour charger les données, consultez la [page dédiée du projet](https://world.openfoodfacts.org/data)" + "import shap\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Take the optimized model\n", + "model = best_rf # ou grid_search.best_estimator_ si tu gardes grid_search\n", + "\n", + "# Initialize SHAP explainer\n", + "explainer = shap.TreeExplainer(model)\n", + "\n", + "# Compute SHAP values\n", + "shap_values = explainer(X_train) \n", + "shap.summary_plot(shap_values.values, X_train, feature_names=X_train.columns)\n", + "shap.summary_plot(shap_values.values, X_train, feature_names=X_train.columns, plot_type=\"bar\")\n", + "\n" ] } ], From f58135cc2984ba25e05be0ebfed6456c153a6c65 Mon Sep 17 00:00:00 2001 From: DanielaMPinzon <160845437+DanielaMPinzon@users.noreply.github.com> Date: Mon, 8 Sep 2025 15:13:45 +0200 Subject: [PATCH 3/6] Add files via upload --- scripts/feature_selection_sfs.py | 69 +++++++++++++++++++++++++------- 1 file changed, 55 insertions(+), 14 deletions(-) diff --git a/scripts/feature_selection_sfs.py b/scripts/feature_selection_sfs.py index d4de759..fa7acaf 100644 --- a/scripts/feature_selection_sfs.py +++ b/scripts/feature_selection_sfs.py @@ -1,22 +1,63 @@ - +import matplotlib.pyplot as plt +from matplotlib.widgets import Lasso +import numpy as np from sklearn.feature_selection import SequentialFeatureSelector from sklearn.neighbors import KNeighborsRegressor +from sklearn.model_selection import cross_val_score +from sklearn.linear_model import Lasso +import pandas as pd + +# Function to run feature selection for multiple n +def select_features(X, y, n_features_list=None): + if n_features_list is None: + n_features_list = list(range(24, 10, -1)) # default + + estimator = Lasso(alpha=0.01, random_state=42, max_iter=1000) + results = [] + + for n_feat in n_features_list: + sfs = SequentialFeatureSelector( + estimator, + n_features_to_select=n_feat, + direction="backward", # use backward elimination + cv=5, # cross-validation to evaluate performance + n_jobs=-1 + ) + sfs.fit(X, y) + + # Evaluate performance with CV + X_selected = sfs.transform(X) + score = np.mean(cross_val_score(estimator, X_selected, y, cv=5)) + + selected_features = X.columns[sfs.get_support()] + print(f"n={n_feat}, CV score={score:.4f}, Features: {list(selected_features)}") + + results.append((n_feat, score, selected_features)) + + return results, sfs + + +# Function to plot results +def plot_results(results): + n_feat = [r[0] for r in results] + scores = [r[1] for r in results] + + plt.figure(figsize=(8,5)) + plt.plot(n_feat, scores, marker='o') + plt.xlabel("Number of Selected Features") + plt.ylabel("Cross-validated R² score") + plt.title("Feature Selection with SequentialFeatureSelector (KNN)") + plt.gca().invert_xaxis() # decreasing n_features + plt.grid(True) + plt.show() -def select_features(X, y, n_features=10): - knn = KNeighborsRegressor(n_neighbors=3) - sfs = SequentialFeatureSelector(knn, n_features_to_select=n_features, direction='forward') - sfs.fit(X, y) - sfs.get_support() - # Transform the data to select the features - X_selected = sfs.transform(X) - X_selected.shape - # keep the selected feature names - selected_features = X.columns[sfs.get_support()] - print("Selected features:", selected_features) +# Example usage: +# results, = select_features(X, y, list(range(24, 10, -1))) +# plot_results(results) + - return sfs, X_selected, selected_features if __name__ == "__main__": # Example usage - sfs, X_selected, selected_features = select_features(X, y, n_features=10) \ No newline at end of file + results, X_selected = select_features(X, y, list(range(24, 10, -1))) \ No newline at end of file From e606fccc2e619b4d2a4e6f0956b703b14d251c25 Mon Sep 17 00:00:00 2001 From: DanielaMPinzon <160845437+DanielaMPinzon@users.noreply.github.com> Date: Mon, 8 Sep 2025 15:16:16 +0200 Subject: [PATCH 4/6] Add files via upload --- scripts/reg_Lasso.py | 144 +++++++++++++++++++++++++++++-------------- 1 file changed, 98 insertions(+), 46 deletions(-) diff --git a/scripts/reg_Lasso.py b/scripts/reg_Lasso.py index 1d1ee66..4cb85f0 100644 --- a/scripts/reg_Lasso.py +++ b/scripts/reg_Lasso.py @@ -1,63 +1,115 @@ -from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve -from sklearn import linear_model -from sklearn.metrics import mean_squared_error, r2_score -import matplotlib.pyplot as plt -import numpy as np - - -def linear_reg_lasso(X, y): - # train-test split - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - # applying Lasso regression - reg_lasso = linear_model.Lasso() - reg_lasso.fit(X_train, y_train) - # make predictions - y_pred = reg_lasso.predict(X_test) - - print("Lasso Regression") - print("paramètres Lasso : ", reg_lasso.get_params()) - - # halving grid search pour optimiser les hyperparamètres +def linear_reg_lasso(X_train, y_train, X_test, y_test): + from sklearn.linear_model import Lasso + from sklearn.model_selection import GridSearchCV + from sklearn.metrics import mean_squared_error, r2_score + import numpy as np + + # defining the model + lasso = Lasso(random_state=42) + param_grid = { 'alpha': [0.01, 0.1, 1.0, 10.0], - 'max_iter': [1000, 5000], + 'max_iter': [1000, 5000, 10000], 'tol': [1e-4, 1e-3, 1e-2] } - grid_search = GridSearchCV(reg_lasso, param_grid, cv=5, scoring='neg_mean_squared_error') + grid_search = GridSearchCV(lasso, param_grid, cv=5, scoring='neg_mean_squared_error') grid_search.fit(X_train, y_train) - y_Grid_pred = grid_search.predict(X_test) - best_regLasso = grid_search.best_params_ - # Meilleurs paramètres - print("Meilleurs paramètres trouvés : ", best_regLasso) + best_model = grid_search.best_estimator_ + y_pred = best_model.predict(X_test) + + print("Best parameters:", grid_search.best_params_) + print("CV RMSE:", np.sqrt(-grid_search.best_score_)) + print("Test RMSE:",(mean_squared_error(y_test, y_pred))) + print("Test R²:", r2_score(y_test, y_pred)) + + + # Get coefficients + coefs = best_model.coef_ + intercept = best_model.intercept_ + + coef_df = pd.DataFrame({ + "Feature": X_train.columns, + "Coefficient": coefs + }).sort_values(by="Coefficient", ascending=False) - return reg_lasso, X_train, X_test, y_train, y_test, y_pred, y_Grid_pred, best_regLasso + print("Intercept:", intercept) + print(coef_df) + return best_model, y_pred -# Courbe d'apprentissage / Learning curve (Lr): -def plot_learning_curve(model, X, y, cv=5): +def plot_learning_curve(best_model, X_train, y_train, y_test, y_pred): + from sklearn.model_selection import learning_curve + from sklearn.metrics import mean_squared_error + import numpy as np + import matplotlib.pyplot as plt + train_sizes, train_scores, test_scores = learning_curve( - model, X, y, cv=cv, n_jobs=-1, train_sizes=np.linspace(0.1, 1.0, 10) + best_model, X_train, y_train, cv=5, n_jobs=-1,shuffle=True, + random_state=42, scoring='neg_mean_squared_error', train_sizes=np.linspace(0.1, 1.0, 10) ) - train_scores_mean = train_scores.mean(axis=1) - test_scores_mean = test_scores.mean(axis=1) - - # Plot learning curve - plt.figure() - plt.plot(train_sizes, train_scores_mean, label="Training score") - plt.plot(train_sizes, test_scores_mean, label="Cross-validation score") - plt.xlabel("Training examples") - plt.ylabel("Score") - plt.title("Learning Curve") - plt.grid() + + train_score_mean = -train_scores.mean(axis=1) + val_score_mean = -test_scores.mean(axis=1) + + # RMSE (for interpretability) + train_rmse_mean = np.sqrt(train_score_mean) + val_rmse_mean = np.sqrt(val_score_mean) + + # ----- Plot RMSE learning curve ----- + plt.figure(figsize=(8, 5)) + plt.plot(train_sizes, train_rmse_mean, 'o-', label='Training RMSE') + plt.plot(train_sizes, val_rmse_mean, 'o-', label='Validation RMSE') + plt.xlabel('Training set size') + plt.ylabel('RMSE') + plt.title('Learning Curve (RMSE) - Lasso') + plt.grid(True) plt.legend() plt.show() - return train_sizes, train_scores_mean, test_scores_mean + # Loss function + loss = mean_squared_error(y_test, y_pred) + rmse = np.sqrt(loss) + print("loss (RMSE):", rmse) + + return train_sizes, train_rmse_mean, val_rmse_mean + + +def plot_learning_curve(best_model, X_train, y_train, y_test, y_pred): + from sklearn.model_selection import learning_curve + from sklearn.metrics import mean_squared_error + import numpy as np + import matplotlib.pyplot as plt + + train_sizes, train_scores, test_scores = learning_curve( + best_model, X_train, y_train, cv=5, n_jobs=-1,shuffle=True, + random_state=42, scoring='neg_mean_squared_error', train_sizes=np.linspace(0.1, 1.0, 10) + ) + + train_score_mean = -train_scores.mean(axis=1) + val_score_mean = -test_scores.mean(axis=1) + + # RMSE (for interpretability) + train_rmse_mean = np.sqrt(train_score_mean) + val_rmse_mean = np.sqrt(val_score_mean) + + # ----- Plot RMSE learning curve ----- + plt.figure(figsize=(8, 5)) + plt.plot(train_sizes, train_rmse_mean, 'o-', label='Training RMSE') + plt.plot(train_sizes, val_rmse_mean, 'o-', label='Validation RMSE') + plt.xlabel('Training set size') + plt.ylabel('RMSE') + plt.title('Learning Curve (RMSE) - Lasso') + plt.grid(True) + plt.legend() + plt.show() + + # Loss function + loss = mean_squared_error(y_test, y_pred) + rmse = np.sqrt(loss) + print("loss (RMSE):", rmse) + + return train_sizes, train_rmse_mean, val_rmse_mean -if __name__ == "__main__": - # Example usage - reg_lasso, X_train, X_test, y_train, y_test, y_pred, y_Grid_pred, best_regLasso = linear_reg_lasso(X, y) - train_sizes, train_scores_mean, test_scores_mean = plot_learning_curve(reg_lasso, X_train, y_train) \ No newline at end of file From 5ae6b750325a1332a000936affe18b528c730371 Mon Sep 17 00:00:00 2001 From: DanielaMPinzon <160845437+DanielaMPinzon@users.noreply.github.com> Date: Mon, 8 Sep 2025 15:16:42 +0200 Subject: [PATCH 5/6] Add files via upload --- notebooks/project_starter.ipynb | 8416 +++++++++++++++++++++++++------ 1 file changed, 6773 insertions(+), 1643 deletions(-) diff --git a/notebooks/project_starter.ipynb b/notebooks/project_starter.ipynb index 90ee0b4..fad169c 100644 --- a/notebooks/project_starter.ipynb +++ b/notebooks/project_starter.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -51,28 +51,17 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_49241/1388454853.py:2: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n" + "/tmp/ipykernel_9642/3882293860.py:4: DtypeWarning: Columns (12,13,14,15,16,17,24,25,26,27,28,32,33,34,36,37,52,73) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df_test = pd.read_csv(path, sep='\\t', encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True, skiprows=range(1, 10001), nrows=5000) # skip rows 1–10000, keep header (row 0)\n" ] - } - ], - "source": [ - "path = \"https://static.openfoodfacts.org/data/en.openfoodfacts.org.products.csv.gz\"\n", - "df = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ + }, { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { @@ -139,8 +128,8 @@ }, { "name": "abbreviated_product_name", - "rawType": "object", - "type": "unknown" + "rawType": "float64", + "type": "float" }, { "name": "generic_name", @@ -435,7 +424,7 @@ { "name": "environmental_score_grade", "rawType": "object", - "type": "string" + "type": "unknown" }, { "name": "nutrient_levels_tags", @@ -480,7 +469,7 @@ { "name": "last_image_datetime", "rawType": "object", - "type": "string" + "type": "unknown" }, { "name": "main_category", @@ -495,12 +484,12 @@ { "name": "image_url", "rawType": "object", - "type": "string" + "type": "unknown" }, { "name": "image_small_url", "rawType": "object", - "type": "string" + "type": "unknown" }, { "name": "image_ingredients_url", @@ -1153,26 +1142,34 @@ "type": "float" } ], - "ref": "8b1eca09-f6a2-4943-bfc7-a52d26fc5277", + "ref": "292ffd4f-df53-4e5e-9e5c-d8ad2d81fda6", "rows": [ [ "0", - "54", - "http://world-en.openfoodfacts.org/product/000000000054/limonade-artisanale-a-la-rose", + "12409", + "http://world-en.openfoodfacts.org/product/00012409/bagnat-thon-crous", "kiliweb", - "1582569031", - "2020-02-24T18:30:31Z", - "1733085204", - "2024-12-01T20:33:24Z", + "1573212227", + "2019-11-08T11:23:47Z", + "1581586728", + "2020-02-13T09:38:48Z", + "neuni", + "1743316740", + "2025-03-30T06:39:00Z", + "Bagnat thon", null, - "1740205422", - "2025-02-22T06:23:42Z", - "Limonade artisanale a la rose", null, + "296,5g", null, null, null, null, + "Crous", + "xx:crous", + "crous", + "Sandwichs, Sandwichs au poisson, Sandwichs au thon", + "en:sandwiches,en:fish-sandwiches,en:tuna-sandwiches", + "Sandwiches,Fish sandwiches,Tuna sandwiches", null, null, null, @@ -1188,21 +1185,61 @@ null, null, null, + "en:fr", + "en:france", + "France", + "PAIN BAGNAT 40,5% : Farine de BLE tendre et BLE dur, eau, huiles végétales [olive, colza], levure, sel, émulsifiants : E472e, agent de traitement de la farine E300, levure désactivée. Garniture 59,5% : THON au naturel 28,3% (THON, eau, sel), tomate 17%, salade batavia 17%, OEUF dur 15%, MAYONNAISE 14,2% (huile de colza 68,5%, eau, vinaigre, jaune d'OEUF frais 5%, MOUTARDE de Dijon [eau, graine de MOUTARDÉ, vinaigre sel antioxydant : DISULFITE de potassium, acidifiantacide citrique], sei/ sucre, amidon modifié, épaississant: gomme xanthane, acidifiant : E330, antioxydant : E385, colorants : lutéine et extrait de paprika, arômes), tapenade noire 8,5% (olives noires 80%, huile d'olive vierge extra 0%, câpres, pâte d'ANCHOIS [ANCHOIS, huile d'olive] ail, basile jus concentré de citron, herbes de Provence, poivre). (% exprimés sur le garniture)", + "fr:pain-bagnat,en:water,en:vegetable-oil,en:oil-and-fat,en:vegetable-oil-and-fat,en:yeast,en:salt,en:emulsifier,en:flour-treatment-agent,en:deactivated-yeast,en:filling,en:tomato,en:vegetable,en:fruit-vegetable,fr:salade-batavia,en:hard-boiled-egg,en:egg,en:boiled-egg,en:mayonnaise,en:sauce,fr:tapenade-noire,en:soft-wheat-flour,en:cereal,en:flour,en:wheat,en:cereal-flour,en:wheat-flour,en:durum-wheat,en:olive-oil,en:colza-oil,en:rapeseed-oil,en:e472e,en:e300,en:tuna-in-brine,en:fish,en:tuna,en:canned-tuna,en:vinegar,fr:jaune-d-oeuf-frais,en:egg-yolk,en:dijon-mustard,en:mustard,fr:sei,en:sugar,en:added-sugar,en:disaccharide,en:modified-starch,en:starch,en:thickener,en:acid,en:antioxidant,en:colour,en:flavouring,en:black-olive,en:olive,en:extra-virgin-olive-oil,en:virgin-olive-oil,en:capers,en:plant,en:anchovy-paste,en:oily-fish,en:anchovy,en:garlic,en:root-vegetable,en:onion-family-vegetable,fr:basile-jus-concentre-de-citron,en:herbes-de-provence,en:herb,en:pepper,en:seed,en:mustard-seed,en:condiment,en:spice,fr:vinaigre-sel-antioxydant,fr:acidifiantacide-citrique,en:e415,en:e330,en:e385,en:e161b,en:e160c,en:e224", + "en:may-contain-palm-oil,en:non-vegan,en:non-vegetarian", null, null, null, null, null, + "296,5", null, + "off", + "9.0", null, + "en:e14xx,en:e160c,en:e161b,en:e224,en:e300,en:e330,en:e385,en:e415,en:e472e", + "E14XX - Modified Starch,E160c - Paprika extract,E161b - Lutein,E224 - Potassium metabisulphite,E300 - Ascorbic acid,E330 - Citric acid,E385 - Calcium disodium ethylenediaminetetraacetate,E415 - Xanthan gum,E472e - Mono- and diacetyltartaric acid esters of mono- and diglycerides of fatty acids", + "0.0", + "a", + "4.0", + "Composite foods", + "Sandwiches", + "en:sandwiches", + "en:composite-foods,en:sandwiches", + "Composite foods,Sandwiches", + "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", + "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", + "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", null, - "en:fr", - "en:france", - "France", + "45.0", + "c", + "en:fat-in-moderate-quantity,en:saturated-fat-in-low-quantity,en:sugars-in-low-quantity,en:salt-in-moderate-quantity", + "296.5", null, null, + "1.0", + "bottom-25-percent-scans-2020,bottom-20-percent-scans-2020,top-85-percent-scans-2020,top-90-percent-scans-2020,top-country-fr-scans-2020,bottom-25-percent-scans-2021,bottom-20-percent-scans-2021,top-85-percent-scans-2021,top-90-percent-scans-2021,top-country-fr-scans-2021,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-10000-re-scans-2024,top-50000-re-scans-2024,top-100000-re-scans-2024,top-country-re-scans-2024", + "0.6875", + "1581586582.0", + "2020-02-13T09:36:22Z", + "en:tuna-sandwiches", + "Tuna sandwiches", + "https://images.openfoodfacts.org/images/products/000/000/001/2409/front_fr.3.400.jpg", + "https://images.openfoodfacts.org/images/products/000/000/001/2409/front_fr.3.200.jpg", + "https://images.openfoodfacts.org/images/products/000/000/001/2409/ingredients_fr.7.400.jpg", + "https://images.openfoodfacts.org/images/products/000/000/001/2409/ingredients_fr.7.200.jpg", + "https://images.openfoodfacts.org/images/products/000/000/001/2409/nutrition_fr.5.400.jpg", + "https://images.openfoodfacts.org/images/products/000/000/001/2409/nutrition_fr.5.200.jpg", null, + "213.0", + "891.0", null, + "10.2", + "1.1", null, null, null, @@ -1215,32 +1252,20 @@ null, null, null, - "unknown", null, - "unknown", - "unknown", null, null, null, - "en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-to-be-completed, en:packaging-to-be-completed, en:quantity-to-be-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-to-be-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts to be completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands to be completed,Packaging to be completed,Quantity to be completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", null, null, - "unknown", null, null, null, null, null, null, - "0.1625", - "1733085204.0", - "2024-12-01T20:33:24Z", null, null, - "https://images.openfoodfacts.org/images/products/invalid/front_en.6.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.6.200.jpg", null, null, null, @@ -1250,6 +1275,8 @@ null, null, null, + "20.9", + "0.9", null, null, null, @@ -1265,12 +1292,16 @@ null, null, null, + "1.7", null, null, + "8.6", null, null, null, + "1.1", null, + "0.44", null, null, null, @@ -1310,11 +1341,13 @@ null, null, null, + "180.375", null, null, null, null, null, + "0.0", null, null, null, @@ -1326,17 +1359,42 @@ null, null, null, + null + ], + [ + "1", + "1241000224", + "http://world-en.openfoodfacts.org/product/0001241000224/threptin", + "smoothie-app", + "1723262866", + "2024-08-10T04:07:46Z", + "1723478958", + "2024-08-12T16:09:18Z", + "krishanti", + "1738830508", + "2025-02-06T08:28:28Z", + "Threptin", null, null, + "275 g", null, null, null, null, + "Threptin", + "threptin", + "Threptin", + "Snacks,Sweet snacks,Biscuits and cakes,Biscuits and crackers,Biscuits", + "en:snacks,en:sweet-snacks,en:biscuits-and-cakes,en:biscuits-and-crackers,en:biscuits", + "Snacks,Sweet snacks,Biscuits and cakes,Biscuits and crackers,Biscuits", null, null, null, null, null, + "No gluten", + "en:no-gluten", + "No gluten", null, null, null, @@ -1344,6 +1402,12 @@ null, null, null, + "India", + "en:india", + "India", + "Casein, Sucrose, Precooked Rice Flour, Edible Vegetable Fat, Bengal Gram, Raising Agent [500 (ii)], Natural Colour (150 c), Emulsifier, Vitamins, Acidity Regulator (525) and Antioxidant (304).", + "en:casein,en:protein,en:animal-protein,en:milk-proteins,en:sucrose,en:added-sugar,en:disaccharide,en:sugar,en:rice-flour,en:flour,en:rice,en:vegetable-fat,en:oil-and-fat,en:vegetable-oil-and-fat,en:bengal-gram,en:raising-agent,en:natural-colours,en:colour,en:emulsifier,en:vitamins,en:acidity-regulator,en:antioxidant,en:500,en:150-c,en:525,en:304,en:ii", + "en:may-contain-palm-oil,en:non-vegan,en:vegetarian-status-unknown", null, null, null, @@ -1352,13 +1416,47 @@ null, null, null, + "4.0", null, + "en:e150,en:e304,en:e500,en:e525", + "E150 - Caramel,E304 - Fatty acid esters of ascorbic acid,E500 - Sodium carbonates,E525 - Potassium hydroxide", null, + "unknown", + "4.0", + "Sugary snacks", + "Biscuits and cakes", + "en:biscuits-and-cakes", + "en:sugary-snacks,en:biscuits-and-cakes", + "Sugary snacks,Biscuits and cakes", + "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", + "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", + "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", null, null, null, + "en:fat-in-moderate-quantity,en:saturated-fat-in-high-quantity,en:sugars-in-high-quantity", + "275.0", null, null, + "1.0", + "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-10000-in-scans-2024,top-50000-in-scans-2024,top-100000-in-scans-2024,top-country-in-scans-2024", + "0.6875", + "1723262876.0", + "2024-08-10T04:07:56Z", + "en:biscuits", + "Biscuits", + "https://images.openfoodfacts.org/images/products/000/124/100/0224/front_en.3.400.jpg", + "https://images.openfoodfacts.org/images/products/000/124/100/0224/front_en.3.200.jpg", + "https://images.openfoodfacts.org/images/products/000/124/100/0224/ingredients_en.11.400.jpg", + "https://images.openfoodfacts.org/images/products/000/124/100/0224/ingredients_en.11.200.jpg", + "https://images.openfoodfacts.org/images/products/000/124/100/0224/nutrition_en.13.400.jpg", + "https://images.openfoodfacts.org/images/products/000/124/100/0224/nutrition_en.13.200.jpg", + null, + "438.0", + "1833.0", + null, + "14.0", + "7.0", null, null, null, @@ -1370,31 +1468,12 @@ null, null, null, - null - ], - [ - "1", - "63", - "http://world-en.openfoodfacts.org/product/000000000063/m-amp-m-white-fitpiggy", - "kiliweb", - "1673620307", - "2023-01-13T14:31:47Z", - "1750061386", - "2025-06-16T08:09:46Z", - "bodysupport", - "1750061386", - "2025-06-16T08:09:46Z", - "M&M white", null, null, - "80 gram", null, null, null, null, - "Fitpiggy", - "xx:fitpiggy", - "fitpiggy", null, null, null, @@ -1413,61 +1492,35 @@ null, null, null, - "en:fr", - "en:france", - "France", - "Weizenmehl, Rapsöl, Speisesalz, 1,7% Meersalz, Fefe, Gerstenmaizextrakt, Säureregulator: Natrium - hydroxid; Backtriebnittel: Ammoniumcarbonate. Das Produkt kann Spuren von Sesam enthalten. DURCHSCHNITTLICHE NÄHRWERTE - pro %RM* 100g pro 100g Brennwert kJ/kcal 1630/385 19% Fett 3,7g 5% davon: - gesättigte Fettsäuren 0,6g 3% Kohlenhydrate 74,0g 28% davon: - Zucker 2,4g 3% Ballaststoffe 3,9g 12,0g 24% Eiweiß 3,9g 65% Salz *Referenzmenge für einen durchschnittlichen Erwachsenen (8400 kJ/2000 kcal) AP EDEKA KUNDEN UND ERNÄHRUNGSSERVICE 2509 (", - "en:weizenmehl,en:rapsol,en:speisesalz,en:meersalz,en:fefe,en:gerstenmaizextrakt,en:saureregulator,en:hydroxid,en:backtriebnittel,en:pro-rm-100g-pro-100g-brennwert-kj,en:kcal-1630,en:385-19-fett-3-7-5-davon,en:gesattigte-fettsauren-0-6-3-kohlenhydrate-74-28-davon,en:zucker-2-4-3-ballaststoffe-3-9-12-24-eiweiss-3-9-65-salz-referenzmenge-fur-einen-durchschnittlichen-erwachsenen,en:ap-edeka-kunden-und-ernahrungsservice-2509,en:sodium,en:minerals,en:ammoniumcarbonate,en:das-produkt-kann-spuren-von-sesam-enthalten,en:durchschnittliche-nahrwerte,en:8400-kj,en:2000-kcal", - "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", + "48.0", + "30.0", + "30.0", null, null, null, null, null, - "80 gram", - "80.0", null, - "0.0", null, null, null, null, - "unknown", null, - "unknown", - "unknown", null, null, null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", null, null, - "unknown", null, - "80.0", + "30.0", null, null, - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-us-scans-2024,top-100000-us-scans-2024,top-country-us-scans-2024", - "0.6625", - "1746257766.0", - "2025-05-03T07:36:06Z", null, null, - "https://images.openfoodfacts.org/images/products/invalid/front_en.12.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.12.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.8.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.8.200.jpg", null, null, null, - "359.0", - "1502.0", null, - "19.0", - "17.3", null, null, null, @@ -1503,10 +1556,9 @@ null, null, null, - "26.0", - "1.0", null, null, + "0.0", null, null, null, @@ -1523,13 +1575,25 @@ null, null, null, - "17.0", null, + null + ], + [ + "2", + "1241380141", + "http://world-en.openfoodfacts.org/product/0001241380141/formula-1", + "zoneblockscommunity", + "1471281610", + "2016-08-15T17:20:10Z", + "1728042357", + "2024-10-04T11:45:57Z", + "fix-code-bot", + "1737547478", + "2025-01-22T12:04:38Z", + "formula 1", null, null, - "1.2", null, - "0.48", null, null, null, @@ -1555,6 +1619,9 @@ null, null, null, + "Italy", + "en:italy", + "Italy", null, null, null, @@ -1569,49 +1636,46 @@ null, null, null, - "0.0", null, null, + "unknown", null, + "unknown", + "unknown", null, null, null, + "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-to-be-completed, en:packaging-to-be-completed, en:quantity-to-be-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", + "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-to-be-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", + "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands to be completed,Packaging to be completed,Quantity to be completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", null, null, + "unknown", null, null, null, null, + "1.0", + "top-country-fr-scans-2019", + "0.2625", + "1559645420.0", + "2019-06-04T10:50:20Z", null, null, + "https://images.openfoodfacts.org/images/products/000/124/138/0141/front_en.3.400.jpg", + "https://images.openfoodfacts.org/images/products/000/124/138/0141/front_en.3.200.jpg", null, null, + "https://images.openfoodfacts.org/images/products/000/124/138/0141/nutrition_fr.5.400.jpg", + "https://images.openfoodfacts.org/images/products/000/124/138/0141/nutrition_fr.5.200.jpg", null, - null - ], - [ - "2", - "114", - "http://world-en.openfoodfacts.org/product/000000000114/chocolate-n3-jeff-de-bruges", - "kiliweb", - "1580066482", - "2020-01-26T19:21:22Z", - "1751035658", - "2025-06-27T14:47:38Z", - "teolemon", - "1751035658", - "2025-06-27T14:47:38Z", - "Chocolate n3", null, null, - "80 g", null, + "0.0", null, null, null, - "Jeff de Bruges", - "xx:jeff-de-bruges", - "jeff-de-bruges", null, null, null, @@ -1620,9 +1684,6 @@ null, null, null, - "Green Dot,Made in France", - "en:green-dot,en:made-in-france", - "Green Dot,Made in France", null, null, null, @@ -1630,9 +1691,6 @@ null, null, null, - "France", - "en:france", - "France", null, null, null, @@ -1649,48 +1707,29 @@ null, null, null, - "unknown", null, - "unknown", - "unknown", null, + "0.0", null, null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", null, null, - "unknown", null, - "80.0", null, null, - "1.0", - "bottom-25-percent-scans-2022,bottom-20-percent-scans-2022,top-85-percent-scans-2022,top-90-percent-scans-2022,top-country-fr-scans-2022,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-country-fr-scans-2024", - "0.475", - "1737247860.0", - "2025-01-19T00:51:00Z", null, null, - "https://images.openfoodfacts.org/images/products/invalid/front_fr.21.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_fr.21.200.jpg", null, null, - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.5.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.5.200.jpg", - "2415.0", null, - "2415.0", null, - "44.0", - "28.0", null, null, null, null, null, null, + "0.0", null, null, null, @@ -1720,8 +1759,6 @@ null, null, null, - "30.0", - "27.0", null, null, null, @@ -1740,13 +1777,10 @@ null, null, null, - "7.1", null, null, null, - "0.025", null, - "0.01", null, null, null, @@ -1759,6 +1793,21 @@ null, null, null, + null + ], + [ + "3", + "12416", + "http://world-en.openfoodfacts.org/product/00012416/le-bollygood-canderel", + "kiliweb", + "1576148496", + "2019-12-12T11:01:36Z", + "1729149601", + "2024-10-17T07:20:01Z", + "llegris", + "1743314725", + "2025-03-30T06:05:25Z", + "Le bollygood", null, null, null, @@ -1766,6 +1815,9 @@ null, null, null, + "Canderel", + "xx:canderel", + "canderel", null, null, null, @@ -1784,6 +1836,9 @@ null, null, null, + "France", + "en:france", + "France", null, null, null, @@ -1800,43 +1855,42 @@ null, null, null, + "unknown", null, + "unknown", + "unknown", null, null, null, - null - ], - [ - "3", - "105", - "http://world-en.openfoodfacts.org/product/0000000105/paleta-gran-reserva-sierra-nevada-advocare", - "kiliweb", - "1572117743", - "2019-10-26T19:22:23Z", - "1738073570", - "2025-01-28T14:12:50Z", + "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-to-be-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", + "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", + "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity to be completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", null, - "1743653496", - "2025-04-03T04:11:36Z", - "Paleta gran reserva - Sierra nevada-", null, + "unknown", null, - "750ml", null, null, null, + "1.0", + "bottom-25-percent-scans-2020,top-80-percent-scans-2020,top-85-percent-scans-2020,top-90-percent-scans-2020,top-100000-fr-scans-2020,top-country-fr-scans-2020,top-75-percent-scans-2021,top-80-percent-scans-2021,top-85-percent-scans-2021,top-90-percent-scans-2021,top-100000-fr-scans-2021,top-country-fr-scans-2021,bottom-25-percent-scans-2022,bottom-20-percent-scans-2022,top-85-percent-scans-2022,top-90-percent-scans-2022,top-country-fr-scans-2022,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-ma-scans-2024,top-100000-ma-scans-2024,top-country-ma-scans-2024", + "0.375", + "1675082379.0", + "2023-01-30T12:39:39Z", null, - "AdvoCare", - "xx:advocare", - "advocare", - "Bebidas y preparaciones de bebidas, Bebidas", - "en:beverages-and-beverages-preparations,en:beverages", - "Beverages and beverages preparations,Beverages", null, + "https://images.openfoodfacts.org/images/products/000/000/001/2416/front_fr.16.400.jpg", + "https://images.openfoodfacts.org/images/products/000/000/001/2416/front_fr.16.200.jpg", null, null, + "https://images.openfoodfacts.org/images/products/000/000/001/2416/nutrition_fr.18.400.jpg", + "https://images.openfoodfacts.org/images/products/000/000/001/2416/nutrition_fr.18.200.jpg", null, + "193.0", + "808.0", null, + "5.2", + "1.8", null, null, null, @@ -1847,55 +1901,21 @@ null, null, null, - "Spanien, Germany", - "en:germany,en:spain", - "Germany,Spain", - "Thiamin, Biotin, Chromium, Garcinia cambogia fruit extract, Taurine, Green coffee fruit extract, Caffeine, Inositol, Citric Acid, Natural , Artificial Flavors, Sucralose, Spirulina Extract, Beta Carotene", - "en:thiamin,en:biotin,en:vitamins,en:chromium,en:minerals,en:garcinia-cambogia-fruit-extract,en:taurine,en:green-coffee-fruit-extract,en:caffeine,en:inositol,en:e330,en:natural,en:artificial-flavouring,en:flavouring,en:e955,en:spirulina-concentrate,en:algae,en:spirulina,en:e160ai,en:e160a", - "en:may-contain-palm-oil,en:vegan-status-unknown,en:vegetarian-status-unknown", null, null, null, null, null, - "5g", - "5.0", null, - "2.0", null, - "en:e330,en:e955", - "E330 - Citric acid,E955 - Sucralose", null, - "unknown", - "4.0", - "Beverages", - "Artificially sweetened beverages", - "en:artificially-sweetened-beverages", - "en:beverages,en:artificially-sweetened-beverages", - "Beverages,Artificially sweetened beverages", - "en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts to be completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", null, null, - "unknown", null, - "750.0", null, null, - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-500-az-scans-2024,top-1000-az-scans-2024,top-5000-az-scans-2024,top-10000-az-scans-2024,top-50000-az-scans-2024,top-100000-az-scans-2024,top-country-az-scans-2024", - "0.675", - "1738073557.0", - "2025-01-28T14:12:37Z", - "en:beverages", - "Beverages", - "https://images.openfoodfacts.org/images/products/invalid/front_es.21.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_es.21.200.jpg", null, null, - "https://images.openfoodfacts.org/images/products/invalid/nutrition_es.5.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_es.5.200.jpg", null, null, null, @@ -1906,6 +1926,8 @@ null, null, null, + "26.0", + "1.2", null, null, null, @@ -1921,12 +1943,16 @@ null, null, null, + "1.7", null, null, + "9.5", null, null, null, + "1.3", null, + "0.52", null, null, null, @@ -1984,13 +2010,31 @@ null, null, null, + null + ], + [ + "4", + "12421", + "http://world-en.openfoodfacts.org/product/00012421/sirop-pur-sucre-banane-kiwi-super-u", + "openfoodfacts-contributors", + "1633542300", + "2021-10-06T17:45:00Z", + "1633542303", + "2021-10-06T17:45:03Z", null, + "1743281810", + "2025-03-29T20:56:50Z", + "sirop pur sucre banane kiwi", null, null, + "70 cl", null, null, null, null, + "super u", + "xx:super-u", + "super-u", null, null, null, @@ -2003,13 +2047,15 @@ null, null, null, - "0.0113351004464306", null, null, null, null, null, null, + "en:france", + "en:france", + "France", null, null, null, @@ -2021,42 +2067,38 @@ null, null, null, - null - ], - [ - "4", - "2", - "http://world-en.openfoodfacts.org/product/00000002/filets-de-poulet-blanc-x2-solo", - "kiliweb", - "1722606455", - "2024-08-02T13:47:35Z", - "1749171851", - "2025-06-06T01:04:11Z", - "altroconsumo", - "1749171851", - "2025-06-06T01:04:11Z", - "Filets de poulet blanc x2", null, null, - "240-400 g", + null, + null, + null, + "unknown", + null, + "unknown", + "unknown", + null, + null, + null, + "en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-uploaded", + "en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-uploaded", + "To be completed,Nutrition facts to be completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be uploaded", + null, + null, + "unknown", + null, + "700.0", + null, + null, + null, + null, + "0.3", + null, + null, + null, null, null, null, null, - "SoLo, selbstgemacht 2 Liter", - "xx:solo,xx:selbstgemacht-2-liter", - "solo,selbstgemacht-2-liter", - "Protein powders", - "en:dietary-supplements,en:bodybuilding-supplements,en:protein-powders", - "Dietary supplements,Bodybuilding supplements,Protein powders", - "île d’Orléans,Québec,Canada", - "en:canada,en:quebec,fr:ile-d-orleans", - "Canada,Québec,fr:ile-d-orleans", - "Ancenis", - "ancenis", - "Organic, EU Organic, French meat, Bee Friendly, French poultry, AB Agriculture Biologique, en:no-additives", - "en:organic,en:eu-organic,en:french-meat,en:bee-friendly,en:french-poultry,en:no-additives,fr:ab-agriculture-biologique", - "Organic,EU Organic,French meat,Bee Friendly,French poultry,No additives,AB Agriculture Biologique", null, null, null, @@ -2064,61 +2106,23 @@ null, null, null, - "Brasilien, Germany", - "en:brazil,en:germany", - "Brazil,Germany", - "48% Tomatenpulver, Stärke, Zucker, jodiertes Speisesalz, WEIZENMEHL, Würze, Maiskeimöl, Kaliumchlorid, Kräuter (Basilikum, Thymian, Oregano), Hefeextrakt, Zwiebeln, Gewürze (Knoblauch, Pfeffer), Rote-Bete-Pulver, Aromen, Speisesalz. Kann ROGGEN, GERSTE, HAFER, EI, SOJA, MILCH, SELLERIE, SENF enthalten. Kochsalzersatz, gewonnen aus natürlichen Kaliummineralien. NUTRI-SCORE berechnet pro 100 g zubereitetes Gericht. Unilever Deutschland GmbH Konsumenten - Postfach 57 05 50 22774 Hamburg Ene Fett da Fe Kol da Ball Elw Salz 196 Erw service Tel.: 0800 5858 555 E (gebührenfrei) ww Unilever www.knorr.de", - "en:tomatenpulver,en:starke,en:zucker,en:jodiertes-speisesalz,en:weizenmehl,en:wurze,en:maiskeimol,en:kaliumchlorid,en:krauter,en:hefeextrakt,en:zwiebeln,en:gewurze,en:rote-bete-pulver,en:aromen,en:speisesalz,en:kann-roggen,en:gerste,en:hafer,en:ei,en:soya,en:milch,en:sellerie,en:senf-enthalten,en:kochsalzersatz,en:gewonnen-aus-naturlichen-kaliummineralien,en:nutri-score-berechnet-pro-100-g-zubereitetes-gericht,en:unilever-deutschland-gmbh-konsumenten,en:postfach-57-05-50-22774-hamburg-ene-fett-da-fe-kol-da-ball-elw-salz-196-erw-service-tel,en:basilikum,en:thymian,en:oregano,en:herb,en:knoblauch,en:pfeffer,en:0800-5858-555-e,en:ww-unilever-www-knorr-de,en:gebuhrenfrei", - "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", null, null, null, null, null, - "35gm", - "35.0", null, - "0.0", null, null, null, null, - "not-applicable", null, - "unknown", - "unknown", null, null, null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-validated, en:packaging-photo-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-validated,en:packaging-photo-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos validated,Packaging photo selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", null, null, - "unknown", - "en:fat-in-low-quantity,en:saturated-fat-in-low-quantity,en:sugars-in-moderate-quantity,en:salt-in-moderate-quantity", - "400.0", - "org-le-picoreur-bodin-bio", - "en:energy-value-in-kcal-does-not-match-value-in-kj,en:nutrition-sugars-plus-starch-greater-than-carbohydrates,en:energy-value-in-kj-does-not-match-value-computed-from-other-nutrients", - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-10000-in-scans-2024,top-50000-in-scans-2024,top-100000-in-scans-2024,top-country-in-scans-2024", - "0.8", - "1749171849.0", - "2025-06-06T01:04:09Z", - "en:protein-powders", - "Protein powders", - "https://images.openfoodfacts.org/images/products/invalid/front_en.120.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.120.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.122.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_en.122.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.75.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.75.200.jpg", - "392.0", - "141.0", - "392.0", null, - "2.7", - "0.6", null, null, null, @@ -2134,9 +2138,7 @@ null, null, null, - "0.0", null, - "0.0", null, null, null, @@ -2152,10 +2154,6 @@ null, null, null, - "0.0", - "0.0", - "0.9", - "6.2", null, null, null, @@ -2171,23 +2169,17 @@ null, null, null, - "2.2", null, null, - "30.0", null, null, null, - "0.4", null, - "0.16", null, - "0.0", null, null, null, null, - "0.0", null, null, null, @@ -2199,11 +2191,9 @@ null, null, null, - "0.0", null, null, null, - "0.0001", null, null, null, @@ -2220,7 +2210,6 @@ null, null, null, - "0.052506232193732", null, null, null, @@ -2291,16 +2280,16 @@ " \n", " \n", " 0\n", - " 54\n", - " http://world-en.openfoodfacts.org/product/0000...\n", + " 12409\n", + " http://world-en.openfoodfacts.org/product/0001...\n", " kiliweb\n", - " 1582569031\n", - " 2020-02-24T18:30:31Z\n", - " 1733085204\n", - " 2024-12-01T20:33:24Z\n", - " NaN\n", - " 1740205422\n", - " 2025-02-22T06:23:42Z\n", + " 1573212227\n", + " 2019-11-08T11:23:47Z\n", + " 1581586728\n", + " 2020-02-13T09:38:48Z\n", + " neuni\n", + " 1743316740\n", + " 2025-03-30T06:39:00Z\n", " ...\n", " NaN\n", " NaN\n", @@ -2315,16 +2304,16 @@ " \n", " \n", " 1\n", - " 63\n", - " http://world-en.openfoodfacts.org/product/0000...\n", - " kiliweb\n", - " 1673620307\n", - " 2023-01-13T14:31:47Z\n", - " 1750061386\n", - " 2025-06-16T08:09:46Z\n", - " bodysupport\n", - " 1750061386\n", - " 2025-06-16T08:09:46Z\n", + " 1241000224\n", + " http://world-en.openfoodfacts.org/product/0001...\n", + " smoothie-app\n", + " 1723262866\n", + " 2024-08-10T04:07:46Z\n", + " 1723478958\n", + " 2024-08-12T16:09:18Z\n", + " krishanti\n", + " 1738830508\n", + " 2025-02-06T08:28:28Z\n", " ...\n", " NaN\n", " NaN\n", @@ -2339,16 +2328,16 @@ " \n", " \n", " 2\n", - " 114\n", - " http://world-en.openfoodfacts.org/product/0000...\n", - " kiliweb\n", - " 1580066482\n", - " 2020-01-26T19:21:22Z\n", - " 1751035658\n", - " 2025-06-27T14:47:38Z\n", - " teolemon\n", - " 1751035658\n", - " 2025-06-27T14:47:38Z\n", + " 1241380141\n", + " http://world-en.openfoodfacts.org/product/0001...\n", + " zoneblockscommunity\n", + " 1471281610\n", + " 2016-08-15T17:20:10Z\n", + " 1728042357\n", + " 2024-10-04T11:45:57Z\n", + " fix-code-bot\n", + " 1737547478\n", + " 2025-01-22T12:04:38Z\n", " ...\n", " NaN\n", " NaN\n", @@ -2363,16 +2352,16 @@ " \n", " \n", " 3\n", - " 105\n", - " http://world-en.openfoodfacts.org/product/0000...\n", + " 12416\n", + " http://world-en.openfoodfacts.org/product/0001...\n", " kiliweb\n", - " 1572117743\n", - " 2019-10-26T19:22:23Z\n", - " 1738073570\n", - " 2025-01-28T14:12:50Z\n", - " NaN\n", - " 1743653496\n", - " 2025-04-03T04:11:36Z\n", + " 1576148496\n", + " 2019-12-12T11:01:36Z\n", + " 1729149601\n", + " 2024-10-17T07:20:01Z\n", + " llegris\n", + " 1743314725\n", + " 2025-03-30T06:05:25Z\n", " ...\n", " NaN\n", " NaN\n", @@ -2387,16 +2376,16 @@ " \n", " \n", " 4\n", - " 2\n", - " http://world-en.openfoodfacts.org/product/0000...\n", - " kiliweb\n", - " 1722606455\n", - " 2024-08-02T13:47:35Z\n", - " 1749171851\n", - " 2025-06-06T01:04:11Z\n", - " altroconsumo\n", - " 1749171851\n", - " 2025-06-06T01:04:11Z\n", + " 12421\n", + " http://world-en.openfoodfacts.org/product/0001...\n", + " openfoodfacts-contributors\n", + " 1633542300\n", + " 2021-10-06T17:45:00Z\n", + " 1633542303\n", + " 2021-10-06T17:45:03Z\n", + " NaN\n", + " 1743281810\n", + " 2025-03-29T20:56:50Z\n", " ...\n", " NaN\n", " NaN\n", @@ -2415,201 +2404,3775 @@ "" ], "text/plain": [ - " code url creator \\\n", - "0 54 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", - "1 63 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", - "2 114 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", - "3 105 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", - "4 2 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", + " code url \\\n", + "0 12409 http://world-en.openfoodfacts.org/product/0001... \n", + "1 1241000224 http://world-en.openfoodfacts.org/product/0001... \n", + "2 1241380141 http://world-en.openfoodfacts.org/product/0001... \n", + "3 12416 http://world-en.openfoodfacts.org/product/0001... \n", + "4 12421 http://world-en.openfoodfacts.org/product/0001... \n", "\n", - " created_t created_datetime last_modified_t last_modified_datetime \\\n", - "0 1582569031 2020-02-24T18:30:31Z 1733085204 2024-12-01T20:33:24Z \n", - "1 1673620307 2023-01-13T14:31:47Z 1750061386 2025-06-16T08:09:46Z \n", - "2 1580066482 2020-01-26T19:21:22Z 1751035658 2025-06-27T14:47:38Z \n", - "3 1572117743 2019-10-26T19:22:23Z 1738073570 2025-01-28T14:12:50Z \n", - "4 1722606455 2024-08-02T13:47:35Z 1749171851 2025-06-06T01:04:11Z \n", + " creator created_t created_datetime \\\n", + "0 kiliweb 1573212227 2019-11-08T11:23:47Z \n", + "1 smoothie-app 1723262866 2024-08-10T04:07:46Z \n", + "2 zoneblockscommunity 1471281610 2016-08-15T17:20:10Z \n", + "3 kiliweb 1576148496 2019-12-12T11:01:36Z \n", + "4 openfoodfacts-contributors 1633542300 2021-10-06T17:45:00Z \n", "\n", - " last_modified_by last_updated_t last_updated_datetime ... \\\n", - "0 NaN 1740205422 2025-02-22T06:23:42Z ... \n", - "1 bodysupport 1750061386 2025-06-16T08:09:46Z ... \n", - "2 teolemon 1751035658 2025-06-27T14:47:38Z ... \n", - "3 NaN 1743653496 2025-04-03T04:11:36Z ... \n", - "4 altroconsumo 1749171851 2025-06-06T01:04:11Z ... \n", + " last_modified_t last_modified_datetime last_modified_by last_updated_t \\\n", + "0 1581586728 2020-02-13T09:38:48Z neuni 1743316740 \n", + "1 1723478958 2024-08-12T16:09:18Z krishanti 1738830508 \n", + "2 1728042357 2024-10-04T11:45:57Z fix-code-bot 1737547478 \n", + "3 1729149601 2024-10-17T07:20:01Z llegris 1743314725 \n", + "4 1633542303 2021-10-06T17:45:03Z NaN 1743281810 \n", "\n", - " water-hardness_100g choline_100g phylloquinone_100g beta-glucan_100g \\\n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", + " last_updated_datetime ... water-hardness_100g choline_100g \\\n", + "0 2025-03-30T06:39:00Z ... NaN NaN \n", + "1 2025-02-06T08:28:28Z ... NaN NaN \n", + "2 2025-01-22T12:04:38Z ... NaN NaN \n", + "3 2025-03-30T06:05:25Z ... NaN NaN \n", + "4 2025-03-29T20:56:50Z ... NaN NaN \n", "\n", - " inositol_100g carnitine_100g sulphate_100g nitrate_100g acidity_100g \\\n", - "0 NaN NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN NaN \n", + " phylloquinone_100g beta-glucan_100g inositol_100g carnitine_100g \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", "\n", - " carbohydrates-total_100g \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", + " sulphate_100g nitrate_100g acidity_100g carbohydrates-total_100g \n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", "\n", "[5 rows x 214 columns]" ] }, - "execution_count": 31, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2. Pre-processing " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 2.1 Data curation " + "# select rows from 10001 till 20000\n", + "path = \"https://static.openfoodfacts.org/data/en.openfoodfacts.org.products.csv.gz\"\n", + "#df_test = pd.read_csv(path, skiprows=10000, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n", + "df_test = pd.read_csv(path, sep='\\t', encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True, skiprows=range(1, 10001), nrows=5000) # skip rows 1–10000, keep header (row 0)\n", + "df_test.head()" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/daniela/Documents/PROJECTS/DESU_Data_Science/N_Rocher/OFF_Project/OFFProject/scripts/Data_filter_Jess.py:58: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " num_keep.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n" + "/tmp/ipykernel_9642/1388454853.py:2: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n" ] } ], "source": [ - "filtered_df = dfj.filter_nutriscore_data(df)\n", - "cat_df = dfj.categorical_filter(filtered_df, cat_keep= True)\n", - "num_df = dfj.numerical_filter(filtered_df, num_drop= True)\n", - "final_df = dfj.final_df(cat_df, num_df)\n", - "final_df.head()\n", - "target = df['nutriscore_score']\n", - "final_df = final_df.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 2.2 Enconding " + "path = \"https://static.openfoodfacts.org/data/en.openfoodfacts.org.products.csv.gz\"\n", + "df = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 68, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "array(['unknown', 'Beverages', nan, 'Fruits and vegetables',\n", - " 'Composite foods', 'Sugary snacks', 'Cereals and potatoes',\n", - " 'Salty snacks', 'Fat and sauces', 'Milk and dairy products',\n", - " 'Fish Meat Eggs', 'Alcoholic beverages'], dtype=object)" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "code", + "rawType": "int64", + "type": "integer" + }, + { + "name": "url", + "rawType": "object", + "type": "string" + }, + { + "name": "creator", + "rawType": "object", + "type": "string" + }, + { + "name": "created_t", + "rawType": "int64", + "type": "integer" + }, + { + "name": "created_datetime", + "rawType": "object", + "type": "string" + }, + { + "name": "last_modified_t", + "rawType": "int64", + "type": "integer" + }, + { + "name": "last_modified_datetime", + "rawType": "object", + "type": "string" + }, + { + "name": "last_modified_by", + "rawType": "object", + "type": "unknown" + }, + { + "name": "last_updated_t", + "rawType": "int64", + "type": "integer" + }, + { + "name": "last_updated_datetime", + "rawType": "object", + "type": "string" + }, + { + "name": "product_name", + "rawType": "object", + "type": "string" + }, + { + "name": "abbreviated_product_name", + "rawType": "object", + "type": "unknown" + }, + { + "name": "generic_name", + "rawType": "object", + "type": "unknown" + }, + { + "name": "quantity", + "rawType": "object", + "type": "unknown" + }, + { + "name": "packaging", + "rawType": "object", + "type": "unknown" + }, + { + "name": "packaging_tags", + "rawType": "object", + "type": "unknown" + }, + { + "name": "packaging_en", + "rawType": "object", + "type": "unknown" + }, + { + "name": "packaging_text", + "rawType": "object", + "type": "unknown" + }, + { + "name": "brands", + "rawType": "object", + "type": "unknown" + }, + { + "name": "brands_tags", + "rawType": "object", + "type": "unknown" + }, + { + "name": "brands_en", + "rawType": "object", + "type": "unknown" + }, + { + "name": "categories", + "rawType": "object", + "type": "unknown" + }, + { + "name": "categories_tags", + "rawType": "object", + "type": "unknown" + }, + { + "name": "categories_en", + "rawType": "object", + "type": "unknown" + }, + { + "name": "origins", + "rawType": "object", + "type": "unknown" + }, + { + "name": "origins_tags", + "rawType": "object", + "type": "unknown" + }, + { + "name": "origins_en", + "rawType": "object", + "type": "unknown" + }, + { + "name": "manufacturing_places", + "rawType": "object", + "type": "unknown" + }, + { + "name": "manufacturing_places_tags", + "rawType": "object", + "type": "unknown" + }, + { + "name": "labels", + "rawType": "object", + "type": "unknown" + }, + { + "name": "labels_tags", + "rawType": "object", + "type": "unknown" + }, + { + "name": "labels_en", + "rawType": "object", + "type": "unknown" + }, + { + "name": "emb_codes", + "rawType": "object", + "type": "unknown" + }, + { + "name": "emb_codes_tags", + "rawType": "object", + "type": "unknown" + }, + { + "name": "first_packaging_code_geo", + "rawType": "object", + "type": "unknown" + }, + { + "name": "cities", + "rawType": "float64", + "type": "float" + }, + { + "name": "cities_tags", + "rawType": "object", + "type": "unknown" + }, + { + "name": "purchase_places", + "rawType": "object", + "type": "unknown" + }, + { + "name": "stores", + "rawType": "object", + "type": "unknown" + }, + { + "name": "countries", + "rawType": "object", + "type": "string" + }, + { + "name": "countries_tags", + "rawType": "object", + "type": "string" + }, + { + "name": "countries_en", + "rawType": "object", + "type": "string" + }, + { + "name": "ingredients_text", + "rawType": "object", + "type": "unknown" + }, + { + "name": "ingredients_tags", + "rawType": "object", + "type": "unknown" + }, + { + "name": "ingredients_analysis_tags", + "rawType": "object", + "type": "unknown" + }, + { + "name": "allergens", + "rawType": "object", + "type": "unknown" + }, + { + "name": "allergens_en", + "rawType": "float64", + "type": "float" + }, + { + "name": "traces", + "rawType": "object", + "type": "unknown" + }, + { + "name": "traces_tags", + "rawType": "object", + "type": "unknown" + }, + { + "name": "traces_en", + "rawType": "object", + "type": "unknown" + }, + { + "name": "serving_size", + "rawType": "object", + "type": "unknown" + }, + { + "name": "serving_quantity", + "rawType": "float64", + "type": "float" + }, + { + "name": "no_nutrition_data", + "rawType": "object", + "type": "unknown" + }, + { + "name": "additives_n", + "rawType": "float64", + "type": "float" + }, + { + "name": "additives", + "rawType": "float64", + "type": "float" + }, + { + "name": "additives_tags", + "rawType": "object", + "type": "unknown" + }, + { + "name": "additives_en", + "rawType": "object", + "type": "unknown" + }, + { + "name": "nutriscore_score", + "rawType": "float64", + "type": "float" + }, + { + "name": "nutriscore_grade", + "rawType": "object", + "type": "string" + }, + { + "name": "nova_group", + "rawType": "float64", + "type": "float" + }, + { + "name": "pnns_groups_1", + "rawType": "object", + "type": "string" + }, + { + "name": "pnns_groups_2", + "rawType": "object", + "type": "string" + }, + { + "name": "food_groups", + "rawType": "object", + "type": "unknown" + }, + { + "name": "food_groups_tags", + "rawType": "object", + "type": "unknown" + }, + { + "name": "food_groups_en", + "rawType": "object", + "type": "unknown" + }, + { + "name": "states", + "rawType": "object", + "type": "string" + }, + { + "name": "states_tags", + "rawType": "object", + "type": "string" + }, + { + "name": "states_en", + "rawType": "object", + "type": "string" + }, + { + "name": "brand_owner", + "rawType": "object", + "type": "unknown" + }, + { + "name": "environmental_score_score", + "rawType": "float64", + "type": "float" + }, + { + "name": "environmental_score_grade", + "rawType": "object", + "type": "string" + }, + { + "name": "nutrient_levels_tags", + "rawType": "object", + "type": "unknown" + }, + { + "name": "product_quantity", + "rawType": "float64", + "type": "float" + }, + { + "name": "owner", + "rawType": "object", + "type": "unknown" + }, + { + "name": "data_quality_errors_tags", + "rawType": "object", + "type": "unknown" + }, + { + "name": "unique_scans_n", + "rawType": "float64", + "type": "float" + }, + { + "name": "popularity_tags", + "rawType": "object", + "type": "unknown" + }, + { + "name": "completeness", + "rawType": "float64", + "type": "float" + }, + { + "name": "last_image_t", + "rawType": "float64", + "type": "float" + }, + { + "name": "last_image_datetime", + "rawType": "object", + "type": "string" + }, + { + "name": "main_category", + "rawType": "object", + "type": "unknown" + }, + { + "name": "main_category_en", + "rawType": "object", + "type": "unknown" + }, + { + "name": "image_url", + "rawType": "object", + "type": "string" + }, + { + "name": "image_small_url", + "rawType": "object", + "type": "string" + }, + { + "name": "image_ingredients_url", + "rawType": "object", + "type": "unknown" + }, + { + "name": "image_ingredients_small_url", + "rawType": "object", + "type": "unknown" + }, + { + "name": "image_nutrition_url", + "rawType": "object", + "type": "unknown" + }, + { + "name": "image_nutrition_small_url", + "rawType": "object", + "type": "unknown" + }, + { + "name": "energy-kj_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "energy-kcal_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "energy_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "energy-from-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "saturated-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "butyric-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "caproic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "caprylic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "capric-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "lauric-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "myristic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "palmitic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "stearic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "arachidic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "behenic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "lignoceric-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "cerotic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "montanic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "melissic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "unsaturated-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "monounsaturated-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "omega-9-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "polyunsaturated-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "omega-3-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "omega-6-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "alpha-linolenic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "eicosapentaenoic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "docosahexaenoic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "linoleic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "arachidonic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "gamma-linolenic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "dihomo-gamma-linolenic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "oleic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "elaidic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "gondoic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "mead-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "erucic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "nervonic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "trans-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "cholesterol_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "carbohydrates_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "sugars_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "added-sugars_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "sucrose_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "glucose_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fructose_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "galactose_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "lactose_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "maltose_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "maltodextrins_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "psicose_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "starch_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "polyols_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "erythritol_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "isomalt_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "maltitol_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "sorbitol_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fiber_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "soluble-fiber_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "insoluble-fiber_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "proteins_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "casein_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "serum-proteins_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "nucleotides_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "salt_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "added-salt_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "sodium_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "alcohol_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-a_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "beta-carotene_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-d_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-e_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-k_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-c_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-b1_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-b2_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-pp_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-b6_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-b9_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "folates_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-b12_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "biotin_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "pantothenic-acid_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "silica_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "bicarbonate_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "potassium_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "chloride_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "calcium_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "phosphorus_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "iron_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "magnesium_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "zinc_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "copper_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "manganese_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fluoride_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "selenium_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "chromium_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "molybdenum_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "iodine_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "caffeine_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "taurine_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "methylsulfonylmethane_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "ph_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fruits-vegetables-nuts_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fruits-vegetables-nuts-dried_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fruits-vegetables-nuts-estimate_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "collagen-meat-protein-ratio_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "cocoa_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "chlorophyl_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "carbon-footprint_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "carbon-footprint-from-meat-or-fish_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "nutrition-score-fr_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "nutrition-score-uk_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "glycemic-index_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "water-hardness_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "choline_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "phylloquinone_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "beta-glucan_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "inositol_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "carnitine_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "sulphate_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "nitrate_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "acidity_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "carbohydrates-total_100g", + "rawType": "float64", + "type": "float" + } + ], + "ref": "afe378a7-d223-441b-964e-a98c48a29936", + "rows": [ + [ + "0", + "54", + "http://world-en.openfoodfacts.org/product/000000000054/limonade-artisanale-a-la-rose", + "kiliweb", + "1582569031", + "2020-02-24T18:30:31Z", + "1733085204", + "2024-12-01T20:33:24Z", + null, + "1740205422", + "2025-02-22T06:23:42Z", + "Limonade artisanale a la rose", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "en:fr", + "en:france", + "France", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "unknown", + null, + "unknown", + "unknown", + null, + null, + null, + "en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-to-be-completed, en:packaging-to-be-completed, en:quantity-to-be-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", + "en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-to-be-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", + "To be completed,Nutrition facts to be completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands to be completed,Packaging to be completed,Quantity to be completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", + null, + null, + "unknown", + null, + null, + null, + null, + null, + null, + "0.1625", + "1733085204.0", + "2024-12-01T20:33:24Z", + null, + null, + "https://images.openfoodfacts.org/images/products/invalid/front_en.6.400.jpg", + "https://images.openfoodfacts.org/images/products/invalid/front_en.6.200.jpg", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null + ], + [ + "1", + "63", + "http://world-en.openfoodfacts.org/product/000000000063/m-amp-m-white-fitpiggy", + "kiliweb", + "1673620307", + "2023-01-13T14:31:47Z", + "1750061386", + "2025-06-16T08:09:46Z", + "bodysupport", + "1750061386", + "2025-06-16T08:09:46Z", + "M&M white", + null, + null, + "80 gram", + null, + null, + null, + null, + "Fitpiggy", + "xx:fitpiggy", + "fitpiggy", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + "en:fr", + "en:france", + "France", + "Weizenmehl, Rapsöl, Speisesalz, 1,7% Meersalz, Fefe, Gerstenmaizextrakt, Säureregulator: Natrium - hydroxid; Backtriebnittel: Ammoniumcarbonate. Das Produkt kann Spuren von Sesam enthalten. DURCHSCHNITTLICHE NÄHRWERTE - pro %RM* 100g pro 100g Brennwert kJ/kcal 1630/385 19% Fett 3,7g 5% davon: - gesättigte Fettsäuren 0,6g 3% Kohlenhydrate 74,0g 28% davon: - Zucker 2,4g 3% Ballaststoffe 3,9g 12,0g 24% Eiweiß 3,9g 65% Salz *Referenzmenge für einen durchschnittlichen Erwachsenen (8400 kJ/2000 kcal) AP EDEKA KUNDEN UND ERNÄHRUNGSSERVICE 2509 (", + "en:weizenmehl,en:rapsol,en:speisesalz,en:meersalz,en:fefe,en:gerstenmaizextrakt,en:saureregulator,en:hydroxid,en:backtriebnittel,en:pro-rm-100g-pro-100g-brennwert-kj,en:kcal-1630,en:385-19-fett-3-7-5-davon,en:gesattigte-fettsauren-0-6-3-kohlenhydrate-74-28-davon,en:zucker-2-4-3-ballaststoffe-3-9-12-24-eiweiss-3-9-65-salz-referenzmenge-fur-einen-durchschnittlichen-erwachsenen,en:ap-edeka-kunden-und-ernahrungsservice-2509,en:sodium,en:minerals,en:ammoniumcarbonate,en:das-produkt-kann-spuren-von-sesam-enthalten,en:durchschnittliche-nahrwerte,en:8400-kj,en:2000-kcal", + "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", + null, + null, + null, + null, + null, + "80 gram", + "80.0", + null, + "0.0", + null, + null, + null, + null, + "unknown", + null, + "unknown", + "unknown", + null, + null, + null, + "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", + "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", + "To be completed,Nutrition facts completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", + null, + null, + "unknown", + null, + "80.0", + 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en:front-photo-selected, en:photos-uploaded", + "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", + "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", + null, + null, + "unknown", + null, + "80.0", + null, + null, + "1.0", + "bottom-25-percent-scans-2022,bottom-20-percent-scans-2022,top-85-percent-scans-2022,top-90-percent-scans-2022,top-country-fr-scans-2022,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-country-fr-scans-2024", + "0.475", + "1737247860.0", + "2025-01-19T00:51:00Z", + null, + null, + "https://images.openfoodfacts.org/images/products/invalid/front_fr.21.400.jpg", + "https://images.openfoodfacts.org/images/products/invalid/front_fr.21.200.jpg", + null, + null, + "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.5.400.jpg", + "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.5.200.jpg", + "2415.0", + null, + "2415.0", + null, + "44.0", + "28.0", + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + null, + 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"en:thiamin,en:biotin,en:vitamins,en:chromium,en:minerals,en:garcinia-cambogia-fruit-extract,en:taurine,en:green-coffee-fruit-extract,en:caffeine,en:inositol,en:e330,en:natural,en:artificial-flavouring,en:flavouring,en:e955,en:spirulina-concentrate,en:algae,en:spirulina,en:e160ai,en:e160a", + "en:may-contain-palm-oil,en:vegan-status-unknown,en:vegetarian-status-unknown", + null, + null, + null, + null, + null, + "5g", + "5.0", + null, + "2.0", + null, + "en:e330,en:e955", + "E330 - Citric acid,E955 - Sucralose", + null, + "unknown", + "4.0", + "Beverages", + "Artificially sweetened beverages", + "en:artificially-sweetened-beverages", + "en:beverages,en:artificially-sweetened-beverages", + "Beverages,Artificially sweetened beverages", + "en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", + "en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", + "To be completed,Nutrition facts to be completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", + null, + null, + "unknown", + null, + "750.0", + null, + null, + "1.0", + "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-500-az-scans-2024,top-1000-az-scans-2024,top-5000-az-scans-2024,top-10000-az-scans-2024,top-50000-az-scans-2024,top-100000-az-scans-2024,top-country-az-scans-2024", + "0.675", + "1738073557.0", + "2025-01-28T14:12:37Z", + "en:beverages", + "Beverages", + "https://images.openfoodfacts.org/images/products/invalid/front_es.21.400.jpg", + "https://images.openfoodfacts.org/images/products/invalid/front_es.21.200.jpg", + null, + null, + "https://images.openfoodfacts.org/images/products/invalid/nutrition_es.5.400.jpg", + 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"en:organic,en:eu-organic,en:french-meat,en:bee-friendly,en:french-poultry,en:no-additives,fr:ab-agriculture-biologique", + "Organic,EU Organic,French meat,Bee Friendly,French poultry,No additives,AB Agriculture Biologique", + null, + null, + null, + null, + null, + null, + null, + "Brasilien, Germany", + "en:brazil,en:germany", + "Brazil,Germany", + "48% Tomatenpulver, Stärke, Zucker, jodiertes Speisesalz, WEIZENMEHL, Würze, Maiskeimöl, Kaliumchlorid, Kräuter (Basilikum, Thymian, Oregano), Hefeextrakt, Zwiebeln, Gewürze (Knoblauch, Pfeffer), Rote-Bete-Pulver, Aromen, Speisesalz. 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data visualization\n", + "plt.figure(figsize=(10, 6))\n", + "sns.histplot(df['nutriscore_score'].dropna(), bins=30, kde=True)\n", + "plt.title('Distribution of Nutriscore Score')\n", + "plt.xlabel('Nutriscore Score')\n", + "plt.ylabel('Frequency')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Pre-processing " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.1 Data curation " + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/daniela/Documents/PROJECTS/DESU_Data_Science/N_Rocher/OFF_Project/OFFProject/scripts/Data_filter_Jess.py:58: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " num_keep.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n" + ] + } + ], + "source": [ + "# aplication of the curation process to the training data set:\n", + "filtered_df = dfj.filter_nutriscore_data(df)\n", + "cat_df = dfj.categorical_filter(filtered_df, cat_keep= True)\n", + "num_df = dfj.numerical_filter(filtered_df, num_drop= True)\n", + "final_df = dfj.final_df(cat_df, num_df)\n", + "final_df.head()\n", + "target = df['nutriscore_score']\n", + "final_df = final_df.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.2 Enconding " + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['unknown', 'Beverages', nan, 'Fruits and vegetables',\n", + " 'Composite foods', 'Sugary snacks', 'Cereals and potatoes',\n", + " 'Salty snacks', 'Fat and sauces', 'Milk and dairy products',\n", + " 'Fish Meat Eggs', 'Alcoholic beverages'], dtype=object)" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['pnns_groups_1'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " pnns_groups_1 PNNS_pro\n", + "6 unknown NA\n", + "9 unknown NA\n", + "11 Fruits and vegetables Plant_based\n", + "12 unknown NA\n", + "14 Composite foods Processed\n", + "... ... ...\n", + "9934 Composite foods Processed\n", + "9943 Sugary snacks Snacks\n", + "9949 Composite foods Processed\n", + "9963 Cereals and potatoes Plant_based\n", + "9986 Sugary snacks Snacks\n", + "\n", + "[1207 rows x 2 columns]\n" + ] + } + ], + "source": [ + "pnns_mapping = {\"unknown\" : 'NA', \n", + " 'Beverages' : 'Drinks',\n", + " 'nan' : 'NA',\n", + " 'Fruits and vegetables' : 'Plant_based',\n", + " 'Composite foods' : 'Processed',\n", + " 'Sugary snacks' : 'Snacks',\n", + " 'Salty snacks' : 'Snacks',\n", + " 'Cereals and potatoes' : 'Plant_based',\n", + " 'Fat and sauces' : 'Processed', \n", + " 'Milk and dairy products' : 'Animal_based',\n", + " 'Fish Meat Eggs' : 'Animal_based',\n", + " 'Alcoholic beverages' : 'Drinks'\n", + "}\n", + "\n", + "final_df['PNNS_pro'] = final_df['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n", + "print(final_df[['pnns_groups_1', 'PNNS_pro']])" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "filtered_df = one_hot_encode_column(final_df, 'PNNS_pro')\n", + "filtered_df.head()\n", + "filtered_df = filtered_df.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "code", + "rawType": "float64", + "type": "float" + }, + { + "name": "additives_n", + "rawType": "float64", + "type": "float" + }, + { + "name": "nutriscore_score", + "rawType": "float64", + "type": "float" + }, + { + "name": "energy_100g", + "rawType": "float64", + 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codeadditives_nnutriscore_scoreenergy_100gfat_100gsaturated-fat_100gtrans-fat_100gcholesterol_100gcarbohydrates_100gsugars_100g...vitamin-c_100gcalcium_100giron_100gfruits-vegetables-nuts-estimate-from-ingredients_100gPNNS_pro_Animal_basedPNNS_pro_DrinksPNNS_pro_NAPNNS_pro_Plant_basedPNNS_pro_ProcessedPNNS_pro_Snacks
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96.0NaN4.01520.011.02.000.00.0125.00.98...NaNNaNNaNNaN0.00.00.00.01.00.0
117.00.04.04.01.01.00NaNNaN1.01.00...NaNNaNNaN0.00.00.00.01.00.00.0
128.01.06.01510.02.00.50NaNNaN6.71.70...0.0714290.1785710.0089290.00.00.01.00.00.00.0
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NaN NaN NaN \n", + "12 1.70 ... 0.071429 0.178571 0.008929 \n", + "14 0.24 ... 0.090000 NaN NaN \n", + "\n", + " fruits-vegetables-nuts-estimate-from-ingredients_100g \\\n", + "6 0.0 \n", + "9 NaN \n", + "11 0.0 \n", + "12 0.0 \n", + "14 NaN \n", + "\n", + " PNNS_pro_Animal_based PNNS_pro_Drinks PNNS_pro_NA PNNS_pro_Plant_based \\\n", + "6 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "11 0.0 0.0 0.0 1.0 \n", + "12 0.0 0.0 1.0 0.0 \n", + "14 0.0 0.0 1.0 0.0 \n", + "\n", + " PNNS_pro_Processed PNNS_pro_Snacks \n", + "6 0.0 1.0 \n", + "9 1.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "14 0.0 0.0 \n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "df['pnns_groups_1'].unique()" + "filtered_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.3 Imputing" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 147, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " pnns_groups_1 PNNS_pro\n", - "6 unknown NA\n", - "9 unknown NA\n", - "11 Fruits and vegetables Plant_based\n", - "12 unknown NA\n", - "14 Composite foods Processed\n", - "... ... ...\n", - "9946 Composite foods Processed\n", - "9955 Milk and dairy products Animal_based\n", - "9961 Sugary snacks Snacks\n", - "9970 Sugary snacks Snacks\n", - "9990 Fat and sauces Processed\n", - "\n", - "[1208 rows x 2 columns]\n" + ": shape of df with only numeric features=(2296, 25)\n" ] + }, + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "code", + "rawType": "float64", + "type": "float" + }, + { + "name": "additives_n", + "rawType": "float64", + "type": "float" + }, + { + "name": "nutriscore_score", + "rawType": "float64", + "type": "float" + }, + { + "name": "energy_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "saturated-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "trans-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "cholesterol_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "carbohydrates_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "sugars_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fiber_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "proteins_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "salt_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "sodium_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-a_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-c_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "calcium_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "iron_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Animal_based", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Drinks", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_NA", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Plant_based", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Processed", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Snacks", + "rawType": "float64", + "type": "float" + } + ], + "ref": "21e0d7e7-a980-48a9-8be6-5f403c8f6e74", + "rows": [ + [ + "6", + "4.0", + "0.0", + "15.0", + "2401.0", + "12.0", + "10.5", + "0.0", + "0.0", + "13.0", + "9.0", + "36.0", + "23.0", + "0.3", + "0.12", + "0.030258428792000004", + "0.058185714", + "0.176374286", + "0.010164708552", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "1.0" + ], + [ + "9", + "6.0", + "1.8", + "4.0", + "1520.0", + "11.0", + "2.0", + "0.0", + "0.01", + "25.0", + "0.98", + "9.0", + "22.0", + "0.95", + "0.38", + "0.030258428792000004", + "0.058185713999999986", + "0.171798016", + "0.008664708552", + "20.400223270165018", + "0.0", + "0.0", + "0.0", + "0.0", + "1.0", + "0.0" + ], + [ + "11", + "7.0", + "0.0", + "4.0", + "4.0", + "1.0", + "1.0", + "0.129619454", + "0.011300083200000002", + "1.0", + "1.0", + "1.0", + "1.0", + "1.0", + "0.4", + "0.030258428792000004", + "0.058185714", + "0.164718016", + "0.015298708552", + "0.0", + "0.0", + "0.0", + "0.0", + "1.0", + "0.0", + "0.0" + ], + [ + "12", + "8.0", + "1.0", + "6.0", + "1510.0", + "2.0", + "0.5", + "0.11299435", + "0.0167446326", + "6.7", + "1.7", + "10.714286", + "76.0", + "1.5", + "0.6", + "0.00023214286", + "0.07142857", + "0.17857143", + "0.008928571", + "0.0", + "0.0", + "0.0", + "1.0", + "0.0", + "0.0", + "0.0" + ], + [ + "14", + "9.0", + "0.6", + "-11.0", + "293.0", + "0.5", + "0.06", + "0.129619454", + "0.0097602602", + "2.0", + "0.24", + "88.0", + "18.0", + "0.275", + "0.11", + "0.030258428792000004", + "0.09", + "0.164718016", + "0.006798708552", + "20.00029130415483", + "0.0", + "0.0", + "1.0", + "0.0", + "0.0", + "0.0" + ] + ], + "shape": { + "columns": 25, + "rows": 5 + } + }, + "text/html": [ + "
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codeadditives_nnutriscore_scoreenergy_100gfat_100gsaturated-fat_100gtrans-fat_100gcholesterol_100gcarbohydrates_100gsugars_100g...vitamin-c_100gcalcium_100giron_100gfruits-vegetables-nuts-estimate-from-ingredients_100gPNNS_pro_Animal_basedPNNS_pro_DrinksPNNS_pro_NAPNNS_pro_Plant_basedPNNS_pro_ProcessedPNNS_pro_Snacks
64.00.015.02401.012.010.500.0000000.00000013.09.00...0.0581860.1763740.0101650.0000000.00.00.00.00.01.0
96.01.84.01520.011.02.000.0000000.01000025.00.98...0.0581860.1717980.00866520.4002230.00.00.00.01.00.0
117.00.04.04.01.01.000.1296190.0113001.01.00...0.0581860.1647180.0152990.0000000.00.00.01.00.00.0
128.01.06.01510.02.00.500.1129940.0167456.71.70...0.0714290.1785710.0089290.0000000.00.01.00.00.00.0
149.00.6-11.0293.00.50.060.1296190.0097602.00.24...0.0900000.1647180.00679920.0002910.00.01.00.00.00.0
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" + ], + "text/plain": [ + " code additives_n nutriscore_score energy_100g fat_100g \\\n", + "6 4.0 0.0 15.0 2401.0 12.0 \n", + "9 6.0 1.8 4.0 1520.0 11.0 \n", + "11 7.0 0.0 4.0 4.0 1.0 \n", + "12 8.0 1.0 6.0 1510.0 2.0 \n", + "14 9.0 0.6 -11.0 293.0 0.5 \n", + "\n", + " saturated-fat_100g trans-fat_100g cholesterol_100g carbohydrates_100g \\\n", + "6 10.50 0.000000 0.000000 13.0 \n", + "9 2.00 0.000000 0.010000 25.0 \n", + "11 1.00 0.129619 0.011300 1.0 \n", + "12 0.50 0.112994 0.016745 6.7 \n", + "14 0.06 0.129619 0.009760 2.0 \n", + "\n", + " sugars_100g ... vitamin-c_100g calcium_100g iron_100g \\\n", + "6 9.00 ... 0.058186 0.176374 0.010165 \n", + "9 0.98 ... 0.058186 0.171798 0.008665 \n", + "11 1.00 ... 0.058186 0.164718 0.015299 \n", + "12 1.70 ... 0.071429 0.178571 0.008929 \n", + "14 0.24 ... 0.090000 0.164718 0.006799 \n", + "\n", + " fruits-vegetables-nuts-estimate-from-ingredients_100g \\\n", + "6 0.000000 \n", + "9 20.400223 \n", + "11 0.000000 \n", + "12 0.000000 \n", + "14 20.000291 \n", + "\n", + " PNNS_pro_Animal_based PNNS_pro_Drinks PNNS_pro_NA PNNS_pro_Plant_based \\\n", + "6 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "11 0.0 0.0 0.0 1.0 \n", + "12 0.0 0.0 1.0 0.0 \n", + "14 0.0 0.0 1.0 0.0 \n", + "\n", + " PNNS_pro_Processed PNNS_pro_Snacks \n", + "6 0.0 1.0 \n", + "9 1.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "14 0.0 0.0 \n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "pnns_mapping = {\"unknown\" : 'NA', \n", - " 'Beverages' : 'Drinks',\n", - " 'nan' : 'NA',\n", - " 'Fruits and vegetables' : 'Plant_based',\n", - " 'Composite foods' : 'Processed',\n", - " 'Sugary snacks' : 'Snacks',\n", - " 'Salty snacks' : 'Snacks',\n", - " 'Cereals and potatoes' : 'Plant_based',\n", - " 'Fat and sauces' : 'Processed', \n", - " 'Milk and dairy products' : 'Animal_based',\n", - " 'Fish Meat Eggs' : 'Animal_based',\n", - " 'Alcoholic beverages' : 'Drinks'\n", - "}\n", - "\n", - "final_df['PNNS_pro'] = final_df['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n", - "print(final_df[['pnns_groups_1', 'PNNS_pro']])" + "imputed_df = Imputing.knn_impute_numeric(filtered_df, n_neighbors=5)\n", + "imputed_df.head()" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "filtered_df = one_hot_encode_column(final_df, 'PNNS_pro')\n", - "filtered_df.head()\n", - "filtered_df = filtered_df.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])" + "##### 2.4 Scaling" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [ - "### maybe we should reduce pnns_groups by : plant_based; animal; processed" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ": shape of df with only numeric features=(2296, 24)\n" + ] + }, { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { @@ -2745,146 +6308,146 @@ "type": "float" } ], - "ref": "a267ec65-d71b-41a1-a29f-6be3b4dd45d5", + "ref": "0d45cec1-9080-483e-8114-43d5af163d5d", "rows": [ [ "6", - "4.0", - "0.0", + "-0.13533113794283022", + "-0.15384615384615385", "15.0", - "2401.0", - "12.0", - "10.5", - "0.0", - "0.0", - "13.0", - "9.0", - "36.0", - "23.0", - "0.3", - "0.12", - null, - null, - null, - null, + "0.6105666808691946", + "-0.1828836020712541", + "0.3435181172204681", "0.0", + "-0.31128177892797976", + "-0.42524806188477393", + "-0.33089132972689367", + "7.355875299760192", + "0.5270967741935483", + "-0.24849397447601054", + "-0.2484939759036145", "0.0", + "-0.10051365369360706", + "-0.0684941848741461", "0.0", + "-0.7993902047507119", "0.0", "0.0", + "-0.5", "0.0", - 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null, - null, + "-1.6348530038346822", + "-0.5827241769418667", + "-0.795722398593283", + "7.796610114438984", + "0.9464415381329611", + "-0.700162151231853", + "-0.8656898645254284", + "19.82589928057554", + "0.20451612903225794", + "-0.25602409491467754", + "-0.25602409638554224", "0.0", + "0.3791073083460255", + "-0.15720381957669743", + "-0.4282874004079488", + "-0.3996892807331566", "0.0", - "1.0", "0.0", + "2.0", "0.0", + "-0.33333333333333337", "0.0" ] ], @@ -2938,122 +6501,122 @@ " \n", " \n", " 6\n", - " 4.0\n", - " 0.0\n", + " -0.135331\n", + " -0.153846\n", " 15.0\n", - " 2401.0\n", - " 12.0\n", - " 10.50\n", - " 0.0\n", - " 0.00\n", - " 13.0\n", - " 9.00\n", + " 0.610567\n", + " -0.182884\n", + " 0.343518\n", + " 0.00000\n", + " -0.311282\n", + " -0.425248\n", + " -0.330891\n", " ...\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " -0.100514\n", + " -0.068494\n", + " 0.000000\n", + " -0.799390\n", " 0.0\n", " 0.0\n", + " -0.5\n", " 0.0\n", - " 1.0\n", + " -0.333333\n", + " 5.0\n", " \n", " \n", " 9\n", - " 6.0\n", - " NaN\n", + " -0.135331\n", + " 0.538462\n", " 4.0\n", - " 1520.0\n", - " 11.0\n", - " 2.00\n", - " 0.0\n", - " 0.01\n", - " 25.0\n", - " 0.98\n", + " -0.327865\n", + " -0.217652\n", + " -0.584024\n", + " 0.00000\n", + " 0.977335\n", + " -0.125342\n", + " -0.820513\n", " ...\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 0.0\n", + " -0.100514\n", + " -0.103322\n", + " -0.190859\n", + " -0.391697\n", " 0.0\n", " 0.0\n", + " -0.5\n", " 0.0\n", - " 1.0\n", + " 1.333333\n", " 0.0\n", " \n", " \n", " 11\n", - " 7.0\n", - " 0.0\n", + " -0.135331\n", + " -0.153846\n", " 4.0\n", - " 4.0\n", - " 1.0\n", - " 1.00\n", - " NaN\n", - " NaN\n", - " 1.0\n", - " 1.00\n", + " -1.942693\n", + " -0.565340\n", + " -0.693147\n", + " 7.79661\n", + " 1.144866\n", + " -0.725154\n", + " -0.819292\n", " ...\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 0.0\n", - " 0.0\n", + " -0.100514\n", + " -0.157204\n", + " 0.653246\n", + " -0.799390\n", " 0.0\n", " 0.0\n", + " -0.5\n", " 1.0\n", - " 0.0\n", + " -0.333333\n", " 0.0\n", " \n", " \n", " 12\n", - " 8.0\n", - " 1.0\n", + " -0.135331\n", + " 0.230769\n", " 6.0\n", - " 1510.0\n", - " 2.0\n", - " 0.50\n", - " NaN\n", - " NaN\n", - " 6.7\n", - " 1.70\n", + " -0.338517\n", + " -0.530571\n", + " -0.747708\n", + " 6.79661\n", + " 1.846459\n", + " -0.582699\n", + " -0.776557\n", " ...\n", - " 0.071429\n", - " 0.178571\n", - " 0.008929\n", - " 0.0\n", - " 0.0\n", + " 0.099131\n", + " -0.051773\n", + " -0.157285\n", + " -0.799390\n", " 0.0\n", - " 1.0\n", " 0.0\n", + " 2.0\n", " 0.0\n", + " -0.333333\n", " 0.0\n", " \n", " \n", " 14\n", - " 9.0\n", - " NaN\n", + " -0.135331\n", + " 0.076923\n", " -11.0\n", - " 293.0\n", - " 0.5\n", - " 0.06\n", - " NaN\n", - " NaN\n", - " 2.0\n", - " 0.24\n", + " -1.634853\n", + " -0.582724\n", + " -0.795722\n", + " 7.79661\n", + " 0.946442\n", + " -0.700162\n", + " -0.865690\n", " ...\n", - " 0.090000\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 0.0\n", + " 0.379107\n", + " -0.157204\n", + " -0.428287\n", + " -0.399689\n", " 0.0\n", - " 1.0\n", " 0.0\n", + " 2.0\n", " 0.0\n", + " -0.333333\n", " 0.0\n", " \n", " \n", @@ -3062,79 +6625,133 @@ "" ], "text/plain": [ - " code additives_n nutriscore_score energy_100g fat_100g \\\n", - "6 4.0 0.0 15.0 2401.0 12.0 \n", - "9 6.0 NaN 4.0 1520.0 11.0 \n", - "11 7.0 0.0 4.0 4.0 1.0 \n", - "12 8.0 1.0 6.0 1510.0 2.0 \n", - "14 9.0 NaN -11.0 293.0 0.5 \n", + " code additives_n nutriscore_score energy_100g fat_100g \\\n", + "6 -0.135331 -0.153846 15.0 0.610567 -0.182884 \n", + "9 -0.135331 0.538462 4.0 -0.327865 -0.217652 \n", + "11 -0.135331 -0.153846 4.0 -1.942693 -0.565340 \n", + "12 -0.135331 0.230769 6.0 -0.338517 -0.530571 \n", + "14 -0.135331 0.076923 -11.0 -1.634853 -0.582724 \n", "\n", " saturated-fat_100g trans-fat_100g cholesterol_100g carbohydrates_100g \\\n", - "6 10.50 0.0 0.00 13.0 \n", - "9 2.00 0.0 0.01 25.0 \n", - "11 1.00 NaN NaN 1.0 \n", - "12 0.50 NaN NaN 6.7 \n", - "14 0.06 NaN NaN 2.0 \n", + "6 0.343518 0.00000 -0.311282 -0.425248 \n", + "9 -0.584024 0.00000 0.977335 -0.125342 \n", + "11 -0.693147 7.79661 1.144866 -0.725154 \n", + "12 -0.747708 6.79661 1.846459 -0.582699 \n", + "14 -0.795722 7.79661 0.946442 -0.700162 \n", "\n", " sugars_100g ... vitamin-c_100g calcium_100g iron_100g \\\n", - "6 9.00 ... NaN NaN NaN \n", - "9 0.98 ... NaN NaN NaN \n", - "11 1.00 ... NaN NaN NaN \n", - "12 1.70 ... 0.071429 0.178571 0.008929 \n", - "14 0.24 ... 0.090000 NaN NaN \n", + "6 -0.330891 ... -0.100514 -0.068494 0.000000 \n", + "9 -0.820513 ... -0.100514 -0.103322 -0.190859 \n", + "11 -0.819292 ... -0.100514 -0.157204 0.653246 \n", + "12 -0.776557 ... 0.099131 -0.051773 -0.157285 \n", + "14 -0.865690 ... 0.379107 -0.157204 -0.428287 \n", "\n", " fruits-vegetables-nuts-estimate-from-ingredients_100g \\\n", - "6 0.0 \n", - "9 NaN \n", - "11 0.0 \n", - "12 0.0 \n", - "14 NaN \n", + "6 -0.799390 \n", + "9 -0.391697 \n", + "11 -0.799390 \n", + "12 -0.799390 \n", + "14 -0.399689 \n", "\n", " PNNS_pro_Animal_based PNNS_pro_Drinks PNNS_pro_NA PNNS_pro_Plant_based \\\n", - "6 0.0 0.0 0.0 0.0 \n", - "9 0.0 0.0 0.0 0.0 \n", - "11 0.0 0.0 0.0 1.0 \n", - "12 0.0 0.0 1.0 0.0 \n", - "14 0.0 0.0 1.0 0.0 \n", + "6 0.0 0.0 -0.5 0.0 \n", + "9 0.0 0.0 -0.5 0.0 \n", + "11 0.0 0.0 -0.5 1.0 \n", + "12 0.0 0.0 2.0 0.0 \n", + "14 0.0 0.0 2.0 0.0 \n", "\n", " PNNS_pro_Processed PNNS_pro_Snacks \n", - "6 0.0 1.0 \n", - "9 1.0 0.0 \n", - "11 0.0 0.0 \n", - "12 0.0 0.0 \n", - "14 0.0 0.0 \n", + "6 -0.333333 5.0 \n", + "9 1.333333 0.0 \n", + "11 -0.333333 0.0 \n", + "12 -0.333333 0.0 \n", + "14 -0.333333 0.0 \n", "\n", "[5 rows x 25 columns]" ] }, - "execution_count": 41, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "filtered_df.head()" + "work_df = Scaling.scaler_numeric(imputed_df, target_col='nutriscore_score') \n", + "work_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "##### 2.3 Imputing" + "##### 2.5 Feature Selection --> Using the Lasso Estimator" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = work_df.drop(\"nutriscore_score\", axis=1) \n", + "y_train = work_df[\"nutriscore_score\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "n=23, CV score=-0.2037, Features: ['code', 'additives_n', 'energy_100g', 'fat_100g', 'saturated-fat_100g', 'trans-fat_100g', 'cholesterol_100g', 'carbohydrates_100g', 'sugars_100g', 'proteins_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'vitamin-c_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", + "n=22, CV score=0.2849, Features: ['code', 'additives_n', 'energy_100g', 'saturated-fat_100g', 'trans-fat_100g', 'cholesterol_100g', 'carbohydrates_100g', 'sugars_100g', 'proteins_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'vitamin-c_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", + "n=21, CV score=0.3910, Features: ['code', 'additives_n', 'energy_100g', 'saturated-fat_100g', 'trans-fat_100g', 'cholesterol_100g', 'carbohydrates_100g', 'sugars_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'vitamin-c_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", + "n=20, CV score=0.4338, Features: ['code', 'additives_n', 'energy_100g', 'saturated-fat_100g', 'trans-fat_100g', 'cholesterol_100g', 'sugars_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'vitamin-c_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", + "n=19, CV score=0.4687, Features: ['code', 'additives_n', 'energy_100g', 'saturated-fat_100g', 'trans-fat_100g', 'cholesterol_100g', 'sugars_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", + "n=18, CV score=0.5000, Features: ['additives_n', 'energy_100g', 'saturated-fat_100g', 'trans-fat_100g', 'cholesterol_100g', 'sugars_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", + "n=17, CV score=0.5047, Features: ['additives_n', 'energy_100g', 'saturated-fat_100g', 'cholesterol_100g', 'sugars_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", + "n=16, CV score=0.5065, Features: ['additives_n', 'saturated-fat_100g', 'cholesterol_100g', 'sugars_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", + "n=15, CV score=0.5074, Features: ['additives_n', 'saturated-fat_100g', 'cholesterol_100g', 'sugars_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", + "n=14, CV score=0.5083, Features: ['additives_n', 'saturated-fat_100g', 'cholesterol_100g', 'sugars_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Drinks', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", + "n=13, CV score=0.5083, Features: ['additives_n', 'saturated-fat_100g', 'cholesterol_100g', 'sugars_100g', 'salt_100g', 'vitamin-a_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Drinks', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", + "n=12, CV score=0.5076, Features: ['additives_n', 'saturated-fat_100g', 'sugars_100g', 'salt_100g', 'vitamin-a_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Drinks', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", + "n=11, CV score=0.5067, Features: ['additives_n', 'saturated-fat_100g', 'sugars_100g', 'salt_100g', 'vitamin-a_100g', 'calcium_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Drinks', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", + "n=10, CV score=0.5229, Features: ['additives_n', 'saturated-fat_100g', 'sugars_100g', 'vitamin-a_100g', 'calcium_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Drinks', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", + "n=9, CV score=0.5177, Features: ['additives_n', 'saturated-fat_100g', 'sugars_100g', 'vitamin-a_100g', 'calcium_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", + "n=8, CV score=0.5122, Features: ['additives_n', 'saturated-fat_100g', 'sugars_100g', 'vitamin-a_100g', 'calcium_100g', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", + "n=7, CV score=0.5010, Features: ['additives_n', 'saturated-fat_100g', 'sugars_100g', 'vitamin-a_100g', 'calcium_100g', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", + "n=6, CV score=0.4927, Features: ['additives_n', 'saturated-fat_100g', 'sugars_100g', 'vitamin-a_100g', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scripts import feature_selection_sfs\n", + "from feature_selection_sfs import select_features, plot_results\n", + "# Example usage:\n", + "n = list(range(X_train.shape[1] -1 , 5, -1)) # from number of features down to 5\n", + "\n", + "results, sfs = select_features(X_train, y_train, n)\n", + "plot_results(results)\n" ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 138, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ": shape of df with only numeric features=(2298, 25)\n" - ] - }, { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { @@ -3144,116 +6761,41 @@ "rawType": "int64", "type": "integer" }, - { - "name": "code", - "rawType": "float64", - "type": "float" - }, { "name": "additives_n", "rawType": "float64", "type": "float" }, - { - "name": "nutriscore_score", - "rawType": "float64", - "type": "float" - }, - { - "name": "energy_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fat_100g", - "rawType": "float64", - "type": "float" - }, { "name": "saturated-fat_100g", "rawType": "float64", "type": "float" }, - { - "name": "trans-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "cholesterol_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "carbohydrates_100g", - "rawType": "float64", - "type": "float" - }, { "name": "sugars_100g", "rawType": "float64", "type": "float" }, - { - "name": "fiber_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "proteins_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "salt_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "sodium_100g", - "rawType": "float64", - "type": "float" - }, { "name": "vitamin-a_100g", "rawType": "float64", "type": "float" }, - { - "name": "vitamin-c_100g", - "rawType": "float64", - "type": "float" - }, { "name": "calcium_100g", "rawType": "float64", "type": "float" }, - { - "name": "iron_100g", - "rawType": "float64", - "type": "float" - }, { "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g", "rawType": "float64", "type": "float" }, - { - "name": "PNNS_pro_Animal_based", - "rawType": "float64", - "type": "float" - }, { "name": "PNNS_pro_Drinks", "rawType": "float64", "type": "float" }, - { - "name": "PNNS_pro_NA", - "rawType": "float64", - "type": "float" - }, { "name": "PNNS_pro_Plant_based", "rawType": "float64", @@ -3270,151 +6812,76 @@ "type": "float" } ], - "ref": "27fead0f-7d2d-4a0b-b95e-cc97b088394d", + "ref": "7e387743-c218-4d4c-8a85-1805062a8253", "rows": [ [ - "6", - "4.0", - "0.0", - "15.0", - "2401.0", - "12.0", - "10.5", - "0.0", - "0.0", - "13.0", - "9.0", - "36.0", - "23.0", - "0.3", - "0.12", - "0.030258428792000004", - "0.058185714", - "0.176374286", - "0.010164708552", - "0.0", - "0.0", - "0.0", + "0", + "-0.15384615384615385", + "0.3435181172204681", + "-0.33089132972689367", "0.0", + "-0.0684941848741461", + "-0.7993902047507119", "0.0", "0.0", - "1.0" + "-0.33333333333333337", + "5.0" ], [ - "9", - "6.0", - "1.8", - "4.0", - "1520.0", - "11.0", - "2.0", - "0.0", - "0.01", - "25.0", - "0.98", - "9.0", - "22.0", - "0.95", - "0.38", - "0.030258428792000004", - "0.058185713999999986", - "0.171798016", - "0.008664708552", - "20.400223270165018", - "0.0", + "1", + "0.5384615384615384", + "-0.5840244483367047", + "-0.8205128193483832", "0.0", + "-0.10332172747570254", + "-0.3916967383282756", "0.0", "0.0", - "1.0", + "1.3333333333333335", "0.0" ], [ - "11", - "7.0", - "0.0", - "4.0", - "4.0", - "1.0", - "1.0", - "0.129619454", - "0.011300083200000002", - "1.0", - "1.0", - "1.0", - "1.0", - "1.0", - "0.4", - "0.030258428792000004", - "0.058185714", - "0.196138016", - "0.015798708552000003", - "0.0", - "0.0", + "2", + "-0.15384615384615385", + "-0.6931471031081368", + "-0.819291818127382", "0.0", + "-0.15720381957669743", + "-0.7993902047507119", "0.0", "1.0", - "0.0", + "-0.33333333333333337", "0.0" ], [ - "12", - "8.0", - "1.0", - "6.0", - "1510.0", - "2.0", - "0.5", - "0.11299435", - "0.0167446326", - "6.7", - "1.7", - "10.714286", - "76.0", - "1.5", - "0.6", - "0.00023214286", - "0.07142857", - "0.17857143", - "0.008928571", - "0.0", - "0.0", - "0.0", - "1.0", + "3", + "0.23076923076923075", + "-0.7477084304938528", + "-0.7765567753923394", + "-0.9864855078758132", + "-0.051772897392910376", + "-0.7993902047507119", "0.0", "0.0", + "-0.33333333333333337", "0.0" ], [ - "14", - "9.0", - "0.6", - "-11.0", - "293.0", - "0.5", - "0.06", - "0.129619454", - "0.0097602602", - "2.0", - "0.24", - "88.0", - "18.0", - "0.275", - "0.11", - "0.030258428792000004", - "0.09", - "0.171798016", - "0.008664708552", - "20.00029130415483", - "0.0", + "4", + "0.0769230769230769", + "-0.795722398593283", + "-0.8656898645254284", "0.0", - "1.0", + "-0.15720381957669743", + "-0.3996892807331566", "0.0", "0.0", + "-0.33333333333333337", "0.0" ] ], "shape": { - "columns": 25, + "columns": 10, "rows": 5 } }, @@ -3437,24 +6904,13 @@ " \n", " \n", " \n", - " code\n", " additives_n\n", - " nutriscore_score\n", - " energy_100g\n", - " fat_100g\n", " saturated-fat_100g\n", - " trans-fat_100g\n", - " cholesterol_100g\n", - " carbohydrates_100g\n", " sugars_100g\n", - " ...\n", - " vitamin-c_100g\n", + " vitamin-a_100g\n", " calcium_100g\n", - " iron_100g\n", " fruits-vegetables-nuts-estimate-from-ingredients_100g\n", - " PNNS_pro_Animal_based\n", " PNNS_pro_Drinks\n", - " PNNS_pro_NA\n", " PNNS_pro_Plant_based\n", " PNNS_pro_Processed\n", " PNNS_pro_Snacks\n", @@ -3462,266 +6918,155 @@ " \n", " \n", " \n", - " 6\n", - " 4.0\n", - " 0.0\n", - " 15.0\n", - " 2401.0\n", - " 12.0\n", - " 10.50\n", - " 0.000000\n", - " 0.000000\n", - " 13.0\n", - " 9.00\n", - " ...\n", - " 0.058186\n", - " 0.176374\n", - " 0.010165\n", - " 0.000000\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 1.0\n", - " \n", - " \n", - " 9\n", - " 6.0\n", - " 1.8\n", - " 4.0\n", - " 1520.0\n", - " 11.0\n", - " 2.00\n", + " 0\n", + " -0.153846\n", + " 0.343518\n", + " -0.330891\n", " 0.000000\n", - " 0.010000\n", - " 25.0\n", - " 0.98\n", - " ...\n", - " 0.058186\n", - " 0.171798\n", - " 0.008665\n", - " 20.400223\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " -0.068494\n", + " -0.799390\n", " 0.0\n", - " 1.0\n", " 0.0\n", + " -0.333333\n", + " 5.0\n", " \n", " \n", - " 11\n", - " 7.0\n", - " 0.0\n", - " 4.0\n", - " 4.0\n", - " 1.0\n", - " 1.00\n", - " 0.129619\n", - " 0.011300\n", - " 1.0\n", - " 1.00\n", - " ...\n", - " 0.058186\n", - " 0.196138\n", - " 0.015799\n", + " 1\n", + " 0.538462\n", + " -0.584024\n", + " -0.820513\n", " 0.000000\n", + " -0.103322\n", + " -0.391697\n", " 0.0\n", " 0.0\n", - " 0.0\n", - " 1.0\n", - " 0.0\n", + " 1.333333\n", " 0.0\n", " \n", " \n", - " 12\n", - " 8.0\n", - " 1.0\n", - " 6.0\n", - " 1510.0\n", - " 2.0\n", - " 0.50\n", - " 0.112994\n", - " 0.016745\n", - " 6.7\n", - " 1.70\n", - " ...\n", - " 0.071429\n", - " 0.178571\n", - " 0.008929\n", + " 2\n", + " -0.153846\n", + " -0.693147\n", + " -0.819292\n", " 0.000000\n", - " 0.0\n", + " -0.157204\n", + " -0.799390\n", " 0.0\n", " 1.0\n", - " 0.0\n", - " 0.0\n", + " -0.333333\n", " 0.0\n", " \n", " \n", - " 14\n", - " 9.0\n", - " 0.6\n", - " -11.0\n", - " 293.0\n", - " 0.5\n", - " 0.06\n", - " 0.129619\n", - " 0.009760\n", - " 2.0\n", - " 0.24\n", - " ...\n", - " 0.090000\n", - " 0.171798\n", - " 0.008665\n", - " 20.000291\n", + " 3\n", + " 0.230769\n", + " -0.747708\n", + " -0.776557\n", + " -0.986486\n", + " -0.051773\n", + " -0.799390\n", " 0.0\n", " 0.0\n", - " 1.0\n", + " -0.333333\n", " 0.0\n", + " \n", + " \n", + " 4\n", + " 0.076923\n", + " -0.795722\n", + " -0.865690\n", + " 0.000000\n", + " -0.157204\n", + " -0.399689\n", " 0.0\n", " 0.0\n", + " -0.333333\n", + " 0.0\n", " \n", " \n", "\n", - "

5 rows × 25 columns

\n", "" ], "text/plain": [ - " code additives_n nutriscore_score energy_100g fat_100g \\\n", - "6 4.0 0.0 15.0 2401.0 12.0 \n", - "9 6.0 1.8 4.0 1520.0 11.0 \n", - "11 7.0 0.0 4.0 4.0 1.0 \n", - "12 8.0 1.0 6.0 1510.0 2.0 \n", - "14 9.0 0.6 -11.0 293.0 0.5 \n", - "\n", - " saturated-fat_100g trans-fat_100g cholesterol_100g carbohydrates_100g \\\n", - "6 10.50 0.000000 0.000000 13.0 \n", - "9 2.00 0.000000 0.010000 25.0 \n", - "11 1.00 0.129619 0.011300 1.0 \n", - "12 0.50 0.112994 0.016745 6.7 \n", - "14 0.06 0.129619 0.009760 2.0 \n", - "\n", - " sugars_100g ... vitamin-c_100g calcium_100g iron_100g \\\n", - "6 9.00 ... 0.058186 0.176374 0.010165 \n", - "9 0.98 ... 0.058186 0.171798 0.008665 \n", - "11 1.00 ... 0.058186 0.196138 0.015799 \n", - "12 1.70 ... 0.071429 0.178571 0.008929 \n", - "14 0.24 ... 0.090000 0.171798 0.008665 \n", + " additives_n saturated-fat_100g sugars_100g vitamin-a_100g calcium_100g \\\n", + "0 -0.153846 0.343518 -0.330891 0.000000 -0.068494 \n", + "1 0.538462 -0.584024 -0.820513 0.000000 -0.103322 \n", + "2 -0.153846 -0.693147 -0.819292 0.000000 -0.157204 \n", + "3 0.230769 -0.747708 -0.776557 -0.986486 -0.051773 \n", + "4 0.076923 -0.795722 -0.865690 0.000000 -0.157204 \n", "\n", - " fruits-vegetables-nuts-estimate-from-ingredients_100g \\\n", - "6 0.000000 \n", - "9 20.400223 \n", - "11 0.000000 \n", - "12 0.000000 \n", - "14 20.000291 \n", - "\n", - " PNNS_pro_Animal_based PNNS_pro_Drinks PNNS_pro_NA PNNS_pro_Plant_based \\\n", - "6 0.0 0.0 0.0 0.0 \n", - "9 0.0 0.0 0.0 0.0 \n", - "11 0.0 0.0 0.0 1.0 \n", - "12 0.0 0.0 1.0 0.0 \n", - "14 0.0 0.0 1.0 0.0 \n", - "\n", - " PNNS_pro_Processed PNNS_pro_Snacks \n", - "6 0.0 1.0 \n", - "9 1.0 0.0 \n", - "11 0.0 0.0 \n", - "12 0.0 0.0 \n", - "14 0.0 0.0 \n", + " fruits-vegetables-nuts-estimate-from-ingredients_100g PNNS_pro_Drinks \\\n", + "0 -0.799390 0.0 \n", + "1 -0.391697 0.0 \n", + "2 -0.799390 0.0 \n", + "3 -0.799390 0.0 \n", + "4 -0.399689 0.0 \n", "\n", - "[5 rows x 25 columns]" + " PNNS_pro_Plant_based PNNS_pro_Processed PNNS_pro_Snacks \n", + "0 0.0 -0.333333 5.0 \n", + "1 0.0 1.333333 0.0 \n", + "2 1.0 -0.333333 0.0 \n", + "3 0.0 -0.333333 0.0 \n", + "4 0.0 -0.333333 0.0 " ] }, - "execution_count": 55, + "execution_count": 138, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "imputed_df = knn_impute_numeric(filtered_df, n_neighbors=5)\n", - "imputed_df.head()" + "from sklearn.feature_selection import SequentialFeatureSelector\n", + "from sklearn.linear_model import Lasso\n", + "# Application of the feature selector to the training data set:\n", + "n_selected_features = 10 # specify the number of features you want to select\n", + "# use the n best features\n", + "estimator = Lasso(alpha=0.01, random_state=42, max_iter=1000)\n", + "sfs = SequentialFeatureSelector(\n", + " estimator,\n", + " n_features_to_select=n_selected_features,\n", + " direction=\"backward\", # use backward elimination\n", + " cv=5, # cross-validation to evaluate performance\n", + " n_jobs=-1\n", + " )\n", + "sfs.fit(X_train, y_train)\n", + "\n", + "selected_features = sfs.get_support() # Transform the data to select the features\n", + "X_train_selected = sfs.transform(X_train)\n", + "\n", + "# merge selected features with their names\n", + "X_train_selected_df = pd.DataFrame(X_train_selected, columns=X_train.columns[selected_features])\n", + "X_train_selected_df.head()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "##### 2.4 Scaling" + "##### Apply all pre-processing steps to the test data set" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 148, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - ": shape of df with only numeric features=(2298, 24)\n" + "/home/daniela/Documents/PROJECTS/DESU_Data_Science/N_Rocher/OFF_Project/OFFProject/scripts/Data_filter_Jess.py:58: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " num_keep.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n" ] }, - { - "ename": "NameError", - "evalue": "name 'pd' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[63]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m#work_df = scaler_numeric(imputed_df, 'nutriscore_score')\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m work_df = Scaling.scaler_numeric(imputed_df, \u001b[33m'\u001b[39m\u001b[33mnutriscore_score\u001b[39m\u001b[33m'\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/PROJECTS/DESU_Data_Science/N_Rocher/OFF_Project/OFFProject/scripts/Scaling.py:16\u001b[39m, in \u001b[36mscaler_numeric\u001b[39m\u001b[34m(df, target_col)\u001b[39m\n\u001b[32m 13\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m: shape of df with only numeric features=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mX_numeric.shape\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 15\u001b[39m \u001b[38;5;66;03m#scale the numerical values and put the scaled numerical data into a dataframe\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m16\u001b[39m scaler = RobustScaler()\n\u001b[32m 17\u001b[39m X_scaled = scaler.fit_transform(X_numeric)\n\u001b[32m 18\u001b[39m X_scaled_df = pd.DataFrame(X_scaled, columns=X_numeric.columns, index=X_numeric.index)\n", - "\u001b[31mNameError\u001b[39m: name 'pd' is not defined" - ] - } - ], - "source": [ - "#work_df = scaler_numeric(imputed_df, 'nutriscore_score')\n", - "work_df = Scaling.scaler_numeric(imputed_df, 'nutriscore_score')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def scaler_numeric(df, target_col=''):\n", - "\n", - " #separate the nutriscore and the rest of the values to do the scaling\n", - " X = df.drop([target_col], axis = 1)\n", - " y = df[target_col]\n", - "\n", - " #select only the numerical variables to do the scaling\n", - " X_numeric = X.select_dtypes(include=['float','int'])\n", - " print(f\": shape of df with only numeric features={X_numeric.shape}\")\n", - "\n", - " #scale the numerical values and put the scaled numerical data into a dataframe\n", - " scaler = RobustScaler()\n", - " X_scaled = scaler.fit_transform(X_numeric)\n", - " X_scaled_df = pd.DataFrame(X_scaled, columns=X_numeric.columns, index=X_numeric.index)\n", - "\n", - " #combine the scaled df with the nutriscore\n", - " X_non_numeric = X.select_dtypes(exclude=['float','int'])\n", - " X_processed = pd.concat([X_scaled_df, X_non_numeric], axis=1)\n", - " scaled_df = pd.concat([X_processed, y], axis=1)\n", - "\n", - " # Ensure the column order is the same as the original dataframe\n", - " scaled_df = scaled_df[df.columns]\n", - "\n", - " return scaled_df\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - ": shape of df with only numeric features=(2298, 24)\n" + ": shape of df with only numeric features=(3680, 26)\n", + ": shape of df with only numeric features=(3680, 25)\n" ] }, { @@ -3813,6 +7158,11 @@ "rawType": "float64", "type": "float" }, + { + "name": "potassium_100g", + "rawType": "float64", + "type": "float" + }, { "name": "calcium_100g", "rawType": "float64", @@ -3859,151 +7209,156 @@ "type": "float" } ], - "ref": "10242e7e-a9c1-4695-9cd6-45205c0ed31f", + "ref": "ed4259a3-2793-4a45-be83-29099b26d64b", "rows": [ [ - 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" 6\n", - " -0.135337\n", - " -0.250\n", - " 15.0\n", - " 1.218021\n", - " -0.376506\n", - " 0.797872\n", - " 0.00000\n", - " -0.311282\n", - " -0.463580\n", - " -0.258114\n", - " ...\n", - " -0.100514\n", - " 0.022831\n", - " 0.000000\n", - " -0.713668\n", + " 0\n", + " -3.699528\n", + " 3.083333\n", " 0.0\n", + " 0.165907\n", + " 0.049020\n", + " -0.272774\n", + " 0.0\n", + " 1.756421\n", + " 0.003831\n", + " -0.432384\n", + " ...\n", + " -1.217262\n", + " -0.912360\n", + " 0.350367\n", + " 23.950075\n", " 0.0\n", - " -0.5\n", " 0.0\n", " -1.0\n", + " 0.0\n", " 4.0\n", + " 0.0\n", " \n", " \n", " 9\n", - " -0.135337\n", - " 0.875\n", - " 4.0\n", - " -0.418765\n", - " -0.419933\n", - " -0.709220\n", - " 0.00000\n", - " 0.977335\n", - " -0.093210\n", - " -0.642886\n", - " ...\n", - " -0.100514\n", - " -0.011996\n", - " -0.189521\n", - " -0.349693\n", + " -3.699528\n", + " 0.583333\n", + " 13.0\n", + " 3.174111\n", + " 2.039216\n", + " 1.749871\n", " 0.0\n", + " 1.756421\n", + " 0.199234\n", + " 0.806050\n", + " ...\n", + " -1.217262\n", + " -0.850658\n", + " -0.908866\n", + " -0.629354\n", " 0.0\n", - " -0.5\n", " 0.0\n", " 1.5\n", + " 0.0\n", " -1.0\n", + " 0.0\n", " \n", " \n", - " 11\n", - " -0.135337\n", - " -0.250\n", - " 4.0\n", - " -3.235300\n", - " -0.854196\n", - " -0.886525\n", - " 7.79661\n", - " 1.144866\n", - " -0.833951\n", - " -0.641927\n", - " ...\n", - " -0.100514\n", - " 0.173243\n", - " 0.711839\n", - " -0.713668\n", + " 15\n", + " -1.396009\n", + " -0.666667\n", + " 18.0\n", + " 1.920693\n", + " -1.200980\n", + " 0.564076\n", " 0.0\n", + " 1.173333\n", + " 3.838442\n", + " 12.964413\n", + " ...\n", + " -0.186553\n", + " -0.998936\n", + " -0.677943\n", + " -0.629354\n", " 0.0\n", - " -0.5\n", - " 5.0\n", + " 1.0\n", " -1.0\n", + " 0.0\n", " -1.0\n", + " 0.0\n", " \n", " \n", - " 12\n", - " -0.135337\n", - " 0.375\n", - " 6.0\n", - " -0.437343\n", - " -0.810770\n", - " -0.975177\n", - " 6.79661\n", - " 1.846459\n", - " -0.658025\n", - " -0.608343\n", - " ...\n", - " 0.099131\n", - " 0.039553\n", - " -0.156182\n", - " -0.713668\n", + " 24\n", + " -3.699527\n", + " -0.250000\n", + " 18.0\n", + " 7.271650\n", + " 4.558824\n", + " 4.848173\n", " 0.0\n", + " 1.756421\n", + " 0.393359\n", + " 3.322064\n", + " ...\n", + " -1.217262\n", + " -0.912360\n", + " -0.464959\n", + " 2.777358\n", " 0.0\n", - " 2.0\n", " 0.0\n", " -1.0\n", + " 0.0\n", " -1.0\n", - " \n", - " \n", - " 14\n", - " -0.135337\n", - " 0.125\n", - " -11.0\n", - " -2.698374\n", - " -0.875909\n", - " -1.053191\n", - " 7.79661\n", - " 0.946442\n", - " -0.803086\n", - " -0.678389\n", - " ...\n", - " 0.379107\n", - " -0.011996\n", - " -0.189521\n", - " -0.356829\n", + " 2.5\n", + " \n", + " \n", + " 44\n", + " -3.699527\n", + " 1.416667\n", + " 37.0\n", + " 5.220602\n", + " 2.352941\n", + " 3.818837\n", " 0.0\n", + " 1.756421\n", + " 1.478927\n", + " 0.902135\n", + " ...\n", + " -1.217262\n", + " -0.912360\n", + " -0.464959\n", + " 0.747962\n", " 0.0\n", - " 2.0\n", " 0.0\n", " -1.0\n", + " 1.0\n", " -1.0\n", + " 0.0\n", " \n", " \n", "\n", - "

5 rows × 25 columns

\n", + "

5 rows × 26 columns

\n", "" ], "text/plain": [ " code additives_n nutriscore_score energy_100g fat_100g \\\n", - "6 -0.135337 -0.250 15.0 1.218021 -0.376506 \n", - "9 -0.135337 0.875 4.0 -0.418765 -0.419933 \n", - "11 -0.135337 -0.250 4.0 -3.235300 -0.854196 \n", - "12 -0.135337 0.375 6.0 -0.437343 -0.810770 \n", - "14 -0.135337 0.125 -11.0 -2.698374 -0.875909 \n", + "0 -3.699528 3.083333 0.0 0.165907 0.049020 \n", + "9 -3.699528 0.583333 13.0 3.174111 2.039216 \n", + "15 -1.396009 -0.666667 18.0 1.920693 -1.200980 \n", + "24 -3.699527 -0.250000 18.0 7.271650 4.558824 \n", + "44 -3.699527 1.416667 37.0 5.220602 2.352941 \n", "\n", " saturated-fat_100g trans-fat_100g cholesterol_100g carbohydrates_100g \\\n", - "6 0.797872 0.00000 -0.311282 -0.463580 \n", - "9 -0.709220 0.00000 0.977335 -0.093210 \n", - "11 -0.886525 7.79661 1.144866 -0.833951 \n", - "12 -0.975177 6.79661 1.846459 -0.658025 \n", - "14 -1.053191 7.79661 0.946442 -0.803086 \n", + "0 -0.272774 0.0 1.756421 0.003831 \n", + "9 1.749871 0.0 1.756421 0.199234 \n", + "15 0.564076 0.0 1.173333 3.838442 \n", + "24 4.848173 0.0 1.756421 0.393359 \n", + "44 3.818837 0.0 1.756421 1.478927 \n", "\n", - " sugars_100g ... vitamin-c_100g calcium_100g iron_100g \\\n", - "6 -0.258114 ... -0.100514 0.022831 0.000000 \n", - "9 -0.642886 ... -0.100514 -0.011996 -0.189521 \n", - "11 -0.641927 ... -0.100514 0.173243 0.711839 \n", - "12 -0.608343 ... 0.099131 0.039553 -0.156182 \n", - "14 -0.678389 ... 0.379107 -0.011996 -0.189521 \n", + " sugars_100g ... potassium_100g calcium_100g iron_100g \\\n", + "0 -0.432384 ... -1.217262 -0.912360 0.350367 \n", + "9 0.806050 ... -1.217262 -0.850658 -0.908866 \n", + "15 12.964413 ... -0.186553 -0.998936 -0.677943 \n", + "24 3.322064 ... -1.217262 -0.912360 -0.464959 \n", + "44 0.902135 ... -1.217262 -0.912360 -0.464959 \n", "\n", " fruits-vegetables-nuts-estimate-from-ingredients_100g \\\n", - "6 -0.713668 \n", - "9 -0.349693 \n", - "11 -0.713668 \n", - "12 -0.713668 \n", - "14 -0.356829 \n", + "0 23.950075 \n", + "9 -0.629354 \n", + "15 -0.629354 \n", + "24 2.777358 \n", + "44 0.747962 \n", "\n", " PNNS_pro_Animal_based PNNS_pro_Drinks PNNS_pro_NA PNNS_pro_Plant_based \\\n", - "6 0.0 0.0 -0.5 0.0 \n", - "9 0.0 0.0 -0.5 0.0 \n", - "11 0.0 0.0 -0.5 5.0 \n", - "12 0.0 0.0 2.0 0.0 \n", - "14 0.0 0.0 2.0 0.0 \n", + "0 0.0 0.0 -1.0 0.0 \n", + "9 0.0 0.0 1.5 0.0 \n", + "15 0.0 1.0 -1.0 0.0 \n", + "24 0.0 0.0 -1.0 0.0 \n", + "44 0.0 0.0 -1.0 1.0 \n", "\n", " PNNS_pro_Processed PNNS_pro_Snacks \n", - "6 -1.0 4.0 \n", - "9 1.5 -1.0 \n", - "11 -1.0 -1.0 \n", - "12 -1.0 -1.0 \n", - "14 -1.0 -1.0 \n", + "0 4.0 0.0 \n", + "9 -1.0 0.0 \n", + "15 -1.0 0.0 \n", + "24 -1.0 2.5 \n", + "44 -1.0 0.0 \n", "\n", - "[5 rows x 25 columns]" + "[5 rows x 26 columns]" ] }, - "execution_count": 65, + "execution_count": 148, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "work_df = scaler_numeric(imputed_df, target_col='nutriscore_score') \n", - "work_df.head()" + "# Aplication of all the preprocessing process to the test data set:\n", + "target_test = df_test['nutriscore_score']\n", + "\n", + "#curation\n", + "filtered_test_df = dfj.filter_nutriscore_data(df_test)\n", + "cat_test_df = dfj.categorical_filter(filtered_test_df, cat_keep= True)\n", + "num_test_df = dfj.numerical_filter(filtered_test_df, num_drop= True)\n", + "final_test_df = dfj.final_df(cat_test_df, num_test_df)\n", + "final_test_df = final_test_df.drop(columns=['nutrition-score-fr_100g'])\n", + "# encodage\n", + "final_test_df['PNNS_pro'] = final_test_df['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n", + "filtered_test_df = encoding_func.one_hot_encode_column(final_test_df, 'PNNS_pro')\n", + "filtered_test_df = filtered_test_df.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])\n", + "# imputation \n", + "imputed_test_df = Imputing.knn_impute_numeric(filtered_test_df, n_neighbors=5)\n", + "# scaling\n", + "work_test_df = Scaling.scaler_numeric(imputed_test_df, target_col='nutriscore_score')\n", + "work_test_df.head()" ] }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 149, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selected features: Index(['saturated-fat_100g', 'carbohydrates_100g', 'sugars_100g', 'salt_100g',\n", - " 'sodium_100g', 'vitamin-a_100g',\n", - " 'fruits-vegetables-nuts-estimate-from-ingredients_100g',\n", - " 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA'],\n", - " dtype='object')\n" - ] - }, { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { @@ -4257,12 +7617,12 @@ "type": "integer" }, { - "name": "saturated-fat_100g", + "name": "additives_n", "rawType": "float64", "type": "float" }, { - "name": "carbohydrates_100g", + "name": "saturated-fat_100g", "rawType": "float64", "type": "float" }, @@ -4272,107 +7632,107 @@ "type": "float" }, { - "name": "salt_100g", + "name": "vitamin-a_100g", "rawType": "float64", "type": "float" }, { - "name": "sodium_100g", + "name": "calcium_100g", "rawType": "float64", "type": "float" }, { - "name": "vitamin-a_100g", + "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g", "rawType": "float64", "type": "float" }, { - "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g", + "name": "PNNS_pro_Drinks", "rawType": "float64", "type": "float" }, { - "name": "PNNS_pro_Animal_based", + "name": "PNNS_pro_Plant_based", "rawType": "float64", "type": "float" }, { - "name": "PNNS_pro_Drinks", + "name": "PNNS_pro_Processed", "rawType": "float64", "type": "float" }, { - "name": "PNNS_pro_NA", + "name": "PNNS_pro_Snacks", "rawType": "float64", "type": "float" } ], - "ref": "4c77ba6e-3858-4fdc-8d46-d80569dde80c", + "ref": "cd027cb4-8788-4e6d-9e56-77a4a040fd74", "rows": [ [ "0", - "0.7978723539528367", - "-0.46358024632489264", - "-0.2581140394332294", - "-0.7375875193926286", - "-0.7375875238701466", - "0.0", - "-0.713668045138673", + "3.0833333333333335", + "-0.27277406073082855", + "-0.43238434163701067", + "-1.1086363883287199e-06", + "-0.9123596396904581", + "23.95007487669774", "0.0", "0.0", - "-0.5" + "4.0", + "0.0" ], [ "1", - "-0.7092198446287235", - "-0.09320987595452225", - "-0.6428862705844275", - "-0.5307129184842322", - "-0.53071292170592", + "0.5833333333333334", + "1.7498713329902211", + "0.8060498220640568", + "-1.1086363883287199e-06", + "-0.8506575120308837", + "-0.6293544771515514", "0.0", - "-0.3496933585983946", "0.0", - "0.0", - "-0.5" + "-1.0", + "0.0" ], [ "2", - "-0.8865248091677305", - "-0.833950616695263", - "-0.6419267388359208", - "-0.5147994876451247", - "-0.5147994907702101", - "0.0", - "-0.713668045138673", - "0.0", + "-0.6666666666666667", + "0.5640761708697887", + "12.9644128113879", + "3.1995246167166846", + "-0.998936170212766", + "-0.6293544771515514", + "1.0", "0.0", - "-0.5" + "-1.0", + "0.0" ], [ "3", - "-0.975177291437234", - "-0.6580246907693371", - "-0.6083431276381853", - "-0.3556651792540506", - "-0.3556651814131128", - "-0.9864357615942851", - "-0.713668045138673", + "-0.25000000000000006", + "4.848172928461142", + "3.322064056939502", + "-1.1086363883287199e-06", + "-0.9123596396904581", + "2.7773578380042756", "0.0", "0.0", - "2.0" + "-1.0", + "2.5" ], [ "4", - "-1.0531914758343972", - "-0.8030864191643988", - "-0.6783889452791765", - "-0.7455442348121822", - "-0.7455442393380014", + "1.4166666666666667", + "3.8188368502315995", + "0.902135231316726", + "-1.1086363883287199e-06", + "-0.9123596396904581", + "0.7479622137833136", "0.0", - "-0.35682882520766857", - "0.0", - "0.0", - "2.0" + "1.0", + "-1.0", + "0.0" ] ], "shape": { @@ -4399,135 +7759,128 @@ " \n", " \n", " \n", + " additives_n\n", " saturated-fat_100g\n", - " carbohydrates_100g\n", " sugars_100g\n", - " salt_100g\n", - " sodium_100g\n", " vitamin-a_100g\n", + " calcium_100g\n", " fruits-vegetables-nuts-estimate-from-ingredients_100g\n", - " PNNS_pro_Animal_based\n", " PNNS_pro_Drinks\n", - " PNNS_pro_NA\n", + " PNNS_pro_Plant_based\n", + " PNNS_pro_Processed\n", + " PNNS_pro_Snacks\n", " \n", " \n", " \n", " \n", " 0\n", - " 0.797872\n", - " -0.463580\n", - " -0.258114\n", - " -0.737588\n", - " -0.737588\n", - " 0.000000\n", - " -0.713668\n", + " 3.083333\n", + " -0.272774\n", + " -0.432384\n", + " -0.000001\n", + " -0.912360\n", + " 23.950075\n", " 0.0\n", " 0.0\n", - " -0.5\n", + " 4.0\n", + " 0.0\n", " \n", " \n", " 1\n", - " -0.709220\n", - " -0.093210\n", - " -0.642886\n", - " -0.530713\n", - " -0.530713\n", - " 0.000000\n", - " -0.349693\n", + " 0.583333\n", + " 1.749871\n", + " 0.806050\n", + " -0.000001\n", + " -0.850658\n", + " -0.629354\n", " 0.0\n", " 0.0\n", - " -0.5\n", + " -1.0\n", + " 0.0\n", " \n", " \n", " 2\n", - " -0.886525\n", - " -0.833951\n", - " -0.641927\n", - " -0.514799\n", - " -0.514799\n", - " 0.000000\n", - " -0.713668\n", + " -0.666667\n", + " 0.564076\n", + " 12.964413\n", + " 3.199525\n", + " -0.998936\n", + " -0.629354\n", + " 1.0\n", " 0.0\n", + " -1.0\n", " 0.0\n", - " -0.5\n", " \n", " \n", " 3\n", - " -0.975177\n", - " -0.658025\n", - " -0.608343\n", - " -0.355665\n", - " -0.355665\n", - " -0.986436\n", - " -0.713668\n", + " -0.250000\n", + " 4.848173\n", + " 3.322064\n", + " -0.000001\n", + " -0.912360\n", + " 2.777358\n", " 0.0\n", " 0.0\n", - " 2.0\n", + " -1.0\n", + " 2.5\n", " \n", " \n", " 4\n", - " -1.053191\n", - " -0.803086\n", - " -0.678389\n", - " -0.745544\n", - " -0.745544\n", - " 0.000000\n", - " -0.356829\n", + " 1.416667\n", + " 3.818837\n", + " 0.902135\n", + " -0.000001\n", + " -0.912360\n", + " 0.747962\n", " 0.0\n", + " 1.0\n", + " -1.0\n", " 0.0\n", - " 2.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " saturated-fat_100g carbohydrates_100g sugars_100g salt_100g \\\n", - "0 0.797872 -0.463580 -0.258114 -0.737588 \n", - "1 -0.709220 -0.093210 -0.642886 -0.530713 \n", - "2 -0.886525 -0.833951 -0.641927 -0.514799 \n", - "3 -0.975177 -0.658025 -0.608343 -0.355665 \n", - "4 -1.053191 -0.803086 -0.678389 -0.745544 \n", + " additives_n saturated-fat_100g sugars_100g vitamin-a_100g calcium_100g \\\n", + "0 3.083333 -0.272774 -0.432384 -0.000001 -0.912360 \n", + "1 0.583333 1.749871 0.806050 -0.000001 -0.850658 \n", + "2 -0.666667 0.564076 12.964413 3.199525 -0.998936 \n", + "3 -0.250000 4.848173 3.322064 -0.000001 -0.912360 \n", + "4 1.416667 3.818837 0.902135 -0.000001 -0.912360 \n", "\n", - " sodium_100g vitamin-a_100g \\\n", - "0 -0.737588 0.000000 \n", - "1 -0.530713 0.000000 \n", - "2 -0.514799 0.000000 \n", - "3 -0.355665 -0.986436 \n", - "4 -0.745544 0.000000 \n", + " fruits-vegetables-nuts-estimate-from-ingredients_100g PNNS_pro_Drinks \\\n", + "0 23.950075 0.0 \n", + "1 -0.629354 0.0 \n", + "2 -0.629354 1.0 \n", + "3 2.777358 0.0 \n", + "4 0.747962 0.0 \n", "\n", - " fruits-vegetables-nuts-estimate-from-ingredients_100g \\\n", - "0 -0.713668 \n", - "1 -0.349693 \n", - "2 -0.713668 \n", - "3 -0.713668 \n", - "4 -0.356829 \n", - "\n", - " PNNS_pro_Animal_based PNNS_pro_Drinks PNNS_pro_NA \n", - "0 0.0 0.0 -0.5 \n", - "1 0.0 0.0 -0.5 \n", - "2 0.0 0.0 -0.5 \n", - "3 0.0 0.0 2.0 \n", - "4 0.0 0.0 2.0 " + " PNNS_pro_Plant_based PNNS_pro_Processed PNNS_pro_Snacks \n", + "0 0.0 4.0 0.0 \n", + "1 0.0 -1.0 0.0 \n", + "2 0.0 -1.0 0.0 \n", + "3 0.0 -1.0 2.5 \n", + "4 1.0 -1.0 0.0 " ] }, - "execution_count": 97, + "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# feature selection - selecting top 10 features using Sequential Feature Selector (SFS)\n", - "X = work_df.drop(\"nutriscore_score\", axis=1) \n", - "y = work_df[\"nutriscore_score\"]\n", "\n", - "from scripts import feature_selection_sfs\n", - "# use the function to select features\n", - "sfs, X_selected, selected_features = feature_selection_sfs.select_features(X, y, n_features=10)\n", + "# Apply the feature selection process to the test dataset\n", + "# make sure to drop the same columns as in training if any were dropped\n", + "work_test_df = work_test_df.reindex(columns=work_df.columns, fill_value=0)\n", "\n", - "# merge selected features with their names\n", - "X_selected_df = pd.DataFrame(X_selected, columns=selected_features)\n", - "X_selected_df.head()" + "y_test_df = work_test_df['nutriscore_score']\n", + "X_work_test_df = work_test_df.drop(\"nutriscore_score\", axis=1)\n", + "X_test_selected = sfs.transform(X_work_test_df)\n", + "\n", + "X_test_selected_df = pd.DataFrame(X_test_selected, columns=X_work_test_df.columns[sfs.get_support()])\n", + "X_test_selected_df.head()\n" ] }, { @@ -4546,135 +7899,248 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 150, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "((2298, 24), (2298,))" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "(3680, 10)\n", + "(2296, 10)\n" + ] } ], "source": [ - "# using the selected features for modeling\n", - "X = X_selected_df.drop(\"nutriscore_score\", axis=1) \n", - "y = work_df[\"nutriscore_score\"]\n", - "X.shape, y.shape" + "print(X_test_selected_df.shape)\n", + "print(X_train_selected_df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [], + "source": [ + "def linear_reg_lasso(X_train, y_train, X_test, y_test):\n", + " from sklearn.linear_model import Lasso\n", + " from sklearn.model_selection import GridSearchCV\n", + " from sklearn.metrics import mean_squared_error, r2_score\n", + " import numpy as np\n", + "\n", + " # defining the model \n", + " lasso = Lasso(random_state=42)\n", + "\n", + " param_grid = {\n", + " 'alpha': [0.01, 0.1, 1.0, 10.0],\n", + " 'max_iter': [1000, 5000, 10000],\n", + " 'tol': [1e-4, 1e-3, 1e-2]\n", + " }\n", + "\n", + " grid_search = GridSearchCV(lasso, param_grid, cv=5, scoring='neg_mean_squared_error')\n", + " grid_search.fit(X_train, y_train)\n", + "\n", + " best_model = grid_search.best_estimator_\n", + " y_pred = best_model.predict(X_test)\n", + "\n", + " print(\"Best parameters:\", grid_search.best_params_)\n", + " print(\"CV RMSE:\", np.sqrt(-grid_search.best_score_))\n", + " print(\"Test RMSE:\",(mean_squared_error(y_test, y_pred)))\n", + " print(\"Test R²:\", r2_score(y_test, y_pred))\n", + "\n", + "\n", + " # Get coefficients\n", + " coefs = best_model.coef_\n", + " intercept = best_model.intercept_\n", + "\n", + " coef_df = pd.DataFrame({\n", + " \"Feature\": X_train.columns,\n", + " \"Coefficient\": coefs\n", + " }).sort_values(by=\"Coefficient\", ascending=False)\n", + "\n", + " print(\"Intercept:\", intercept)\n", + " print(coef_df)\n", + "\n", + " return best_model, y_pred\n", + "\n", + "\n", + "def plot_learning_curve(best_model, X_train, y_train, y_test, y_pred):\n", + " from sklearn.model_selection import learning_curve\n", + " from sklearn.metrics import mean_squared_error\n", + " import numpy as np\n", + " import matplotlib.pyplot as plt\n", + " \n", + " train_sizes, train_scores, test_scores = learning_curve(\n", + " best_model, X_train, y_train, cv=5, n_jobs=-1,shuffle=True,\n", + " random_state=42, scoring='neg_mean_squared_error', train_sizes=np.linspace(0.1, 1.0, 10)\n", + " )\n", + "\n", + " train_score_mean = -train_scores.mean(axis=1)\n", + " val_score_mean = -test_scores.mean(axis=1)\n", + "\n", + " # RMSE (for interpretability)\n", + " train_rmse_mean = np.sqrt(train_score_mean)\n", + " val_rmse_mean = np.sqrt(val_score_mean)\n", + "\n", + " # ----- Plot RMSE learning curve -----\n", + " plt.figure(figsize=(8, 5))\n", + " plt.plot(train_sizes, train_rmse_mean, 'o-', label='Training RMSE')\n", + " plt.plot(train_sizes, val_rmse_mean, 'o-', label='Validation RMSE')\n", + " plt.xlabel('Training set size')\n", + " plt.ylabel('RMSE')\n", + " plt.title('Learning Curve (RMSE) - Lasso')\n", + " plt.grid(True)\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + " # Loss function\n", + " loss = mean_squared_error(y_test, y_pred)\n", + " rmse = np.sqrt(loss)\n", + " print(\"loss (RMSE):\", rmse)\n", + "\n", + " return train_sizes, train_rmse_mean, val_rmse_mean\n", + "\n", + "\n", + "def plot_learning_curve(best_model, X_train, y_train, y_test, y_pred):\n", + " from sklearn.model_selection import learning_curve\n", + " from sklearn.metrics import mean_squared_error\n", + " import numpy as np\n", + " import matplotlib.pyplot as plt\n", + " \n", + " train_sizes, train_scores, test_scores = learning_curve(\n", + " best_model, X_train, y_train, cv=5, n_jobs=-1,shuffle=True,\n", + " random_state=42, scoring='neg_mean_squared_error', train_sizes=np.linspace(0.1, 1.0, 10)\n", + " )\n", + "\n", + " train_score_mean = -train_scores.mean(axis=1)\n", + " val_score_mean = -test_scores.mean(axis=1)\n", + "\n", + " # RMSE (for interpretability)\n", + " train_rmse_mean = np.sqrt(train_score_mean)\n", + " val_rmse_mean = np.sqrt(val_score_mean)\n", + "\n", + " # ----- Plot RMSE learning curve -----\n", + " plt.figure(figsize=(8, 5))\n", + " plt.plot(train_sizes, train_rmse_mean, 'o-', label='Training RMSE')\n", + " plt.plot(train_sizes, val_rmse_mean, 'o-', label='Validation RMSE')\n", + " plt.xlabel('Training set size')\n", + " plt.ylabel('RMSE')\n", + " plt.title('Learning Curve (RMSE) - Lasso')\n", + " plt.grid(True)\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + " # Loss function\n", + " loss = mean_squared_error(y_test, y_pred)\n", + " rmse = np.sqrt(loss)\n", + " print(\"loss (RMSE):\", rmse)\n", + "\n", + " return train_sizes, train_rmse_mean, val_rmse_mean\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 152, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Lasso Regression\n", - "paramètres Lasso : {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': 1000, 'positive': False, 'precompute': False, 'random_state': None, 'selection': 'cyclic', 'tol': 0.0001, 'warm_start': False}\n", - "Meilleurs paramètres trouvés : {'alpha': 1.0, 'max_iter': 1000, 'tol': 0.0001}\n", - "Mean Squared Error: 53.21311486260403\n", - "R^2 Score: 0.4793291186543984\n" + "Best parameters: {'alpha': 0.01, 'max_iter': 1000, 'tol': 0.0001}\n", + "CV RMSE: 7.423040951562572\n", + "Test RMSE: 3254.303640585438\n", + "Test R²: -51.49091602408616\n", + "Intercept: 1.947423862727594\n", + " Feature Coefficient\n", + "8 PNNS_pro_Processed 5.705126\n", + "6 PNNS_pro_Drinks 3.919226\n", + "1 saturated-fat_100g 3.252060\n", + "0 additives_n 2.519466\n", + "9 PNNS_pro_Snacks 1.821796\n", + "4 calcium_100g 0.907920\n", + "2 sugars_100g 0.779826\n", + "5 fruits-vegetables-nuts-estimate-from-ingredien... -2.853652\n", + "7 PNNS_pro_Plant_based -3.554252\n", + "3 vitamin-a_100g -4.226487\n" ] }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss (RMSE): 57.04650419250454\n" + ] } ], "source": [ - "from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n", - "from sklearn import linear_model\n", - "from sklearn.metrics import mean_squared_error, r2_score\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# train-test split\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "\n", - "# applying Lasso regression \n", - "reg_lasso = linear_model.Lasso()\n", - "reg_lasso.fit(X_train, y_train)\n", - "# make predictions\n", - "y_pred = reg_lasso.predict(X_test)\n", - "\n", - "\n", - "print(\"Lasso Regression\")\n", - "print(\"paramètres Lasso : \", reg_lasso.get_params())\n", - "\n", - "# halving grid search pour optimiser les hyperparamètres\n", - "param_grid = {\n", - " 'alpha': [0.01, 0.1, 1.0, 10.0],\n", - " 'max_iter': [1000, 5000],\n", - " 'tol': [1e-4, 1e-3, 1e-2]\n", - "}\n", - "\n", - "grid_search = GridSearchCV(reg_lasso, param_grid, cv=5, scoring='neg_mean_squared_error')\n", - "grid_search.fit(X_train, y_train)\n", - "y_Grid_pred = grid_search.predict(X_test)\n", - "best_regLasso = grid_search.best_params_\n", - "\n", - "# Meilleurs paramètres\n", - "print(\"Meilleurs paramètres trouvés : \", best_regLasso)\n", - "\n", - "# Courbe d'apprentissage / Learning curve (Lr):\n", - "def plot_learning_curve(model, X, y, cv=5):\n", - " train_sizes, train_scores, test_scores = learning_curve(\n", - " model, X, y, cv=cv, n_jobs=-1, train_sizes=np.linspace(0.1, 1.0, 10)\n", - " )\n", - " train_scores_mean = train_scores.mean(axis=1)\n", - " test_scores_mean = test_scores.mean(axis=1)\n", - "\n", - " return train_sizes, train_scores_mean, test_scores_mean\n", - "\n", - "train_sizes, train_scores_mean, test_scores_mean = plot_learning_curve(reg_lasso, X, y)\n", "\n", + "##### Using the function\n", + "best_reg_Lasso, y_pred_Lasso = linear_reg_lasso(X_train_selected_df, y_train, X_test_selected_df, y_test_df)\n", "\n", - "# Estimate the loss function\n", - "mse = mean_squared_error(y_test, y_Grid_pred)\n", - "print(f\"Mean Squared Error: {mse}\")\n", - "r2 = r2_score(y_test, y_Grid_pred)\n", - "print(f\"R^2 Score: {r2}\") \n", - "\n", - "\n", - "# Plot learning curve\n", - "plt.figure() \n", - "plt.plot(train_sizes, train_scores_mean, label=\"Training score\")\n", - "plt.plot(train_sizes, test_scores_mean, label=\"Cross-validation score\")\n", - "plt.xlabel(\"Training examples\")\n", - "plt.ylabel(\"Score\")\n", - "plt.title(\"Learning Curve\")\n", - "plt.grid()\n", - "plt.legend()\n", - "plt.show()\n" + "train_sizes, train_rmse, test_rmse = plot_learning_curve(best_reg_Lasso, X_train_selected_df, y_train, y_test_df, y_pred_Lasso)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 153, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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cuBA3NzcAevXqRZ8+fdiwYYMauKSlpXHp0iUUReGjjz6ie/fuaj0Gg4EJEyaQnp5OaGgoVapUUbd5e3sTHBzMoUOHrBYZuJuwsDDc3d3x8PAgMTGRtLQ0wsLCWLhwIYqiMHbsWJvzaNSoEVOmTEGrvTVUZtasWRw6dIhZs2bRqVMntbxJkya8+uqr/PTTT4wbNw4wL4iwcuVKXnzxRV599VWresaOHau+h7NmzeKff/4hODjYKrDq0qUL/fr1Y/369bz66qsALFmyhAYNGhAcHIxGc+tK6csvv6z+vmnTJq5du8aqVavsZoAspk6dSnJyMitWrLAajti0aVNee+01duzYQd++fQH45JNPiIiI4Ntvv+Wxxx4DoF+/fjg5ObF69Wqr16KwxcfH4+7ujouLC2BeGENRFDWwys7OJiUlxSqjePXqVasM0+2PY2Nj8fX1VV/zwjhGdmYmYCftorF/xTzB2f7FCBP2AxqHXMo1OGJ3nJrRkK/z+K+8H0X1GHknGRd7JHARQjyyIiIiMJlMHDx40Gqyu4ODA23atGHixIlUrFiRX375Bbg15MYSuOzcuZPBgwcTExNDcnIyQUFBVvWHh4fj5+eHl5cXAI8//ji///47BoMBR0dHNbDJOS+mTJkyhIaGEhoayu+//87mzZvZvHkzWq2WJ554gnfeeSfPV9AjIiLQaDRMnz5dDVoAPDw8qF+/PsePH1fLIiMjURSFrl27WgUtANu2bSMyMpJ33nnHKmgBaN68OcHBwVy8ePGeAxeDwcC5c+fIysqyWWygdu3azJs3jzZt2ljtW7FiRSZNmmQVbFy9epW1a9fSpUsXm456kyZNcHBw4OLFi4C5A/LNN9/QsmVLq6FZFpaOSWRkJBs3bmT48OFUqlRJDWwBPD098fb2Jjo6Wi1LTk4mIyODmJgYq2Ajp+TkZAD+/vvvXAOXvXv38ueffzJ16lQ1mLOoWrUqYM5GWdr466+/8tRTT6lBi0WrVq1YvXp1kRomVqpUKavHt68W5+zsbDMM8vaO3+2Pc2bRCusYTq65jBXLJfFRNsv+pJgsDxf7T8hlSJgWvf3d//3bKIqv1aN8DFEwJHARQjyyLBmP8ePH4+/vj1arpUSJElSrVk0dipNzP0tmxN/fn8qVK7Nr1y4GDx5sN3OSmppKTEyMVUc6KCiI3bt3c/ToUVq1akVYWBilSpWiXLlyVu2qXLky7777Lu+88w4XLlzg4MGDrFy5ku3bt+Pm5sa0adPydJ46nY7GjRtTrVo1m20mk8kqmLEEU71797bZd8eOHTg7O1tNor9dzrruJioqiqysLHr27EmPHj3QaDS4uLjg5+dnk106f/48WVlZdO3a1WYi/q5duzCZTPTr18/mGFqt1uocd+/eTUZGBoMGDbpj23777TcURSEkJISQkBC7++QMIEeNGsWnn37K008/zWOPPUZgYCBPPvmkVQfoqaeeYtu2bXz00UcEBwfTvn17goKCCAgIUPfZunUrYM6kfPLJJ3aPa+k0bdu2DUVRGDx4sM0+xn/nVxSlwOWRk8sFc/ds+4GLNpcxZ7ndJsaII472ghfvEvfQOFHUyRwX+yRwEUI8snKuFHanCe86nQ4XFxeqV6+ulgUFBREaGsrNmzftTsy3LLOcs6xdu3Y4OTmxa9cuGjduzMWLF9VJ2/ZoNBqqV69O9erVad68OUOGDOHYsWN5OseUlBSuXLlitWKahcFgQKfTWbVRp9Ph4OBA06ZNbfa3ZDxuvxcNwNmz5hvk1a5d+57bZnnd2rZte8fXwdIuwKqTf/uxc74/FlFRUSiKgr+/v1U9t8+puV1ERASlS5dmxowZue6TM7PSr18/AgMD2bNnDwcOHODrr7/m66+/5uOPP1bvo+Ln58fq1as5fPgwBw4cYOfOnaxatYoOHTowZ84c9bi1a9dWh7XZU7NmTcD8+rm7u9sNSE+fNt8jRAKXB89k5zYrgJr5uF0FfSr2hpaVuxBvd39n7B8gDW9cuGm7YfXkXBokihcJXOyRwEUI8ciyZDzuFLQoioJOp6N27dpWk847derEsmXL2L17t3qvFkvn2FI3WA8D8/DwoEWLFuzZs4fu3btjNBpt7vuSG0uWIa/DxCwddXvLGG/evJmkpCS6detmtX+1atXsBieA3cUCFEVh8+bN+Pr62qwOdieW1yjn65Ybe/e7sbCMQ7fXtg0bNgCow/gsE+U1ucw/sEhNTUWr1d41oMqpbNmyDBgwgAEDBnD+/HkGDRrEhg0brG4A6ezsTPv27Wnfvj1vvvkm48eP5/fff+fmzZuUKVOG1NRUKlaseE/HtSzkYK/tGzZsoGTJkjbZPFHw7rRC+Nkvy/LVL4l8uVVPXT9YM9GHkJBIbqxqyO0dU690+wFKMu64YrsKmaGmL5yzE7g0qpmX5gtRrOSWgRRCiP+0zMxMLl68aLOk7e2io6NJS0uz6TA3bNgQX19fdu7cqU7Mt8yPAPvLI4O5Ax0XF8ePP/4IWAc2f/75J9l2hpGYTCaCg4MBbOad3I2lw3/06FGr8ps3bxIcHEzNmjXVoV+Wm0HmFkzVqFGD6Ohobt607ix9++23hIeHM2rUKKu5J/fSNhcXF5v5MrntW758eXx8fOy2C7DJRh0+fJhVq1bRs2dP9RiWLMmBAwds6tHrbw27qVSpEjdu3LB53cA8DCvn3JOEhASbfVxcXDCZTPj6+gLmuTW3swRabm5uakBauXJldDodFy5csNu+lJRbHVg/Pz/S0tI4deqU1X7z5s0jOTlZsi0PiXKnO1ACr/fwQTe/LBsm31re3KnBJat96nTwtnvzSYBYStotL3f2Ixj7pHWhftXdmiuKCQWN3Z9HnWRchBCPpIiICIxG412v9luyAvY6gUFBQfz4448oikK7du1snufr60vJktadjo4dO/LJJ5+wc+dOwDqwmTlzJmlpaXTq1Al/f3+cnZ2JjY1l27ZtREdH0717d5577rk8n6e3tzfx8fFMmDCBwMBA4uLiWLt2LYqiMHv2bDUbc+7cOQwGQ66By4svvsiBAwcYPXq0unLVnj17+OOPPxg6dCh9+vS553YZjUYiIiKoXr36XZdPtmS9WrRoYXf7U089xfLly5kxYwYRERGUK1eOU6dOsWXLFho3bqwubwzmIV2rVq1i5syZnDlzhlq1apGcnMypU6eoVKkSEyZMAGDQoEHs3LmTcePG0bdvX2rVqkVaWhoxMTHs3buXN998k6CgIGJiYujfvz+BgYHUq1eP0qVLc+XKFTZs2ICnpycjRowAYMqUKSQnJ9O2bVsqVqxIVlYWe/bs4dixY7z55ptq0Dts2DAmTpzISy+9xFNPPUXlypVJTEwkJiaGXbt2sWDBAvX96devH1u2bGHSpEkMGjQId3d3tm/frgY9Erg8HKZ7X/1b5dMgiRGfBVhlCXd/9zfGKNsljssM98fRwxfDV0fUMq+IN8y/zHvF/CPEI0ICFyHEI8mSEblb4HKnIUpBQUGsXLkSsO4kpqenc/nyZbura/n4+NCsWTOOHDmCt7e31cozo0aN4uDBgxw7dozt27eTmZmJj48PDRs2ZOLEiXlaZjhn++vUqcOECROYNWsWn3/+OW5ubrRt25bRo0dbTR7PLUtk0ahRI+bMmUNwcDBffvklrq6u1KtXjzlz5tidQ3MnFy5cIDMz856GicXExNjNell4eHjw9ddfM2/ePJYvX46iKFSuXJmxY8fy3HPPWQ2TK1++PKGhoSxatIjff/+ddevW4e3tTf369a2GzNWtW5fQ0FAWL17M3r17WbduHV5eXvj5+dGnTx91ro2joyP9+vXjxIkT/PXXX+j1eipUqEDXrl0ZNmyYushAx44d2b9/P5s3byY5OZlSpUrh7+/PokWLrObtBAYGsnjxYpYuXcqvv/5KQkICPj4+VKlShaFDh6rzW8C8YtrMmTNZsmQJ33zzDSVLlqRt27b07t2bDz/8ME/zjUT+FVRHqtO559ihWWZT3iSko/mXL+/9woAQ/1UaRZE7FQkhxH9RdnY27du359lnn2XixImF3Rwh/pOeWWlgbYz9bcok67BGr9erq9SNGDHC7rysP9puJuVwHCWfrEjzLV1stotHQ5ZmtN1yF+Xrh9ySokUyLkII8R9lGfp1t3k8Qoj8Uwp4tnCrA70KtkJRLMl8FvskcBFCiGIoMTERgyG3dVjNTpw4Adzbql0FKT4+HpPJ/j0pLDw8PHJduUyI4kQj41aEeGgkcBFCiGLojTfeUBcOyM2AAQPQarV272/yIA0YMICkpKQ77jNu3DiGDh36kFokxIOjucuqYkLkj2Rc7JHARQghiqHJkyer9yTJja+vL5MnP/yb0c2ePdtqaWF7qlat+pBaI8SDdZdbAgkhCpAELkIIUQzd7c7vhalZs2aF3QQhHhoZKSYeBJnjYp8ELkIIIYQQ+WSQ/qV4IOSDZU8Br4UhhBBCCPHoCKxU2C0Q4tEhgYsQQgghRD692U4Gr4iCp6Cx+/Ook8BFCCGEEOI+eDrYluknSCdTiIImgYsQQgghxH1InuDImRegsjsMqgPKJEccHexEM0KI+yL5TSGEEEKI+1S/nCPRowu7FUL8t0ngIoQQQgghRBEi81nsk6FiQgghhBBCiCJPMi5CCCGEEEIUKZJxsUcCFyGEEEIIIYoQGSpmnwwVE0IIIYQoYCvOGCg538D4HYbCbooQ/xmScRFCCCGEKECaz24FK/NOwLwTBpRJ0uUS904yLvZJxkUIIYQQooD8Gmk/w1J6nmRehLhfErgIIYQQQhSQHhvsl8frH247hPgvksBFCCGEEEIIUeTJgEshhBBCCCGKEJnjYp9kXIQQQgghhBBFnmRchBBCCCGEKFIk42KPZFyEEEIIIYQQRZ5kXIQQQgghhChCZI6LfRK4CCGEEEIIUYRI4GKfBC5CCCGEEA/BvrB0Pv27L6Bh2QeJHJhZtrCbJESxInNchBBCiCJs/vz5BAQEkJiYWNhNEffDaOSV4GzMXS8N15Kh1hs3Cq7+tAyoOQoen1JwdYpCpMnl59EmGRchhHiIUlNT6dSpE4qiqGVubm74+fnx9NNP8+yzzwIQGRnJ888/D8Crr77KyJEjrepRFIVOnTpRpUoVQkND1fK33nqL3bt34+bmxvbt23F1dbV63vLly5k7dy5z584lMDBQLb9+/To//PADBw8e5MqVK2i1WkqVKkXt2rXp1q0bnTt3LvDXorAYjUY2bdrEL7/8wtmzZ0lPT8fHx4dKlSrRtGlTXnrpJZvXrTBFRETg6+uLj49PYTdF3AeP1OwHV3n7KbAvwvx71A3QPA2/z4D2DR/cMYUoBBK4CCHEQxQeHo6iKHTu3Jn27dsDcPPmTVavXs2nn36KyWRi4MCBhIeHA+Dg4MCuXbtsApfLly+TmppK3bp1bep3cHAgIyODw4cP07FjR6vtOp0OwOp5x44dY+LEiQD06NGDGjVqoNfruXjxIrt376ZMmTL/mcAlLS2NCRMmcPz4cR5//HGGDx+Op6cnV65c4ejRo/z000+MHj26sJtpRafT0bChdECLO22OixU5bTl6g57Ny2IyKmi0oNHk46q6JWjJqcM0UNblvS5RJNj/tAgJXIQQ4iGyBCTdu3enQ4cOannLli0ZMmQIO3futApcunfvzubNm7l8+TKVKlVS9w8LCwOgXr16allSUhJXr16lU6dO/Pnnn+zcudMmcAkLC6Ns2bKUKVMGgIyMDKZMmYK7uzvfffcdZctaj7mfNGkSSUlJBfcC3COj0YiiKDg6FuzX1FdffcWxY8f48MMP6dmzp832a9euodUWnVHU169fJyEhgTp16hR2U8R90jva/1xduZjJzMmHrMre3dEGk9HELtfvwWAua3OpH+6VvABQsgwoadloS5V4oG0WoqiRwEUIIR4iS0DSoEEDq3JLIJGWlgaYA4wSJUowfPhwNm/ezI4dOxg+fLhNPTkzJ5ZgplGjRjg7O7Nv3z70ej1OTk6AOUiJjo6mbdu26nP++OMP4uPjGTp0qE3QAuDo6Ejp0qXzdI6xsbH06tWLESNG4OTkxG+//cbVq1cpXbo0PXv2ZOTIkVYBydSpU9m3bx+rV6/m22+/Zf/+/SQkJLBp0yYqVKhAWloay5YtY+fOncTGxuLu7k6HDh0YO3Ysnp6eeWrb1q1b8fHxsRu0APj6+lo9HjJkCHq9njlz5vDNN99w+PBhsrKyaNy4Me+88w4VKlSw2v/HH3/kwIEDnDt3jsTEREqVKkWbNm0YM2YMJUuWtNpXURR+/fVXNmzYQEREBNnZ2fj6+tKmTRvefPNNtFqtmiGrXbu21fNCQkL45ptv6NmzJ5MnT8bFxYUrV66wbNkyjhw5wrVr13B2dqZixYr07t2bgQMH5ul1EgXP4GQ/cIldeNlmwvHMzodotdM6i3Ko8s90VoaR/tl+0j7ag5KchVP7anj98AwOD6bJohDJqmL2SeAihBAPUXh4OL6+vmqgYnHokPmKa4MGDTCZTERGRlKnTh2qVatGzZo12bVrl03g4uTkRK1atazKwBzM+Pn58dtvv/HHH3+oc1l0Oh0mk8kq2MnIyAAgKirKKsi5HxER5g7Xxo0bKVGiBE899RQuLi5s3ryZxYsX4+7uztChQ9X9dTod7u7uDB8+nGbNmjFq1CgSExOpUKECsbGxjBo1iqSkJPr160eVKlWIjIxk7dq1REREsHTpUhwc7q3bpigKmZmZaLVaoqOjqVKlyh33NxgMREVFUalSJV566SWCgoIYPXo0ERERrFmzhk8++YT58+er+8fFxRESEkLHjh1p06YNLi4unDp1ivXr1xMTE8PChQut6p46dSq7d++mRYsWjBo1Sg1U/vzzTzXrc3vgkpiYyLRp0zh69CjvvPMOffv2BeDixYsMGzYMb29vevbsSbly5UhKSuL06dPExsbe0+sjHiynbJPdcrvd01yGle3QLOMxzqqP9XsvkPLKBnzuv3lCFAsSuAghxEOSnp5OdHQ0rVu3VleISkhI4I8//uDrr7/G29ubESNGcOHCBTIyMtRhYEFBQQQHBxMbG0v58uUBc5BSo0YNq0AjZ+Di7OyMi4sLu3btUgMXS0YmZ+DSokULPD09OXDgAN27dycwMJCAgABat25tE1zdK0tnu2LFiixcuBA3NzcAevXqRZ8+fdiwYYMauKSlpXHp0iUUReGjjz6ie/fuaj0Gg4EJEyaQnp5OaGioVaDh7e1NcHAwhw4dslpk4E40Gg0dO3Zk+/btDBgwgObNm9OyZUtatmxJ/fr1bfY/f/482dnZxMbGsmTJEvz9/a22nTlzxmp/T09PtmzZYpVN6t+/P87Ozqxfv560tDTc3d0B+OKLL9i9ezdTp06lf//+VvVkZWVZvZbu7u74+flx4sQJ3n33XRwdHVm6dKnV8LEff/yR7Oxsvv/+e7y8vO7p9RAPl3O24Z731Zhym+FgtCnJ/u2snf1E8ScZF3uKzkBeIYT4j4uIiMBkMnHw4EE6d+5M586dGTBgAF988QVNmzYlJCSEihUrqgGIpWMaFBQEwM6dOwGIiYkhOTnZan4LmAMXPz8/vLy8cHV15fHHH+f333/HYDCo28F6XkyZMmUIDQ2lX79+aLVaNm/ezAcffECPHj2YOnUqKSkp+TpPjUbD9OnT1aAFwMPDg/r163P16lW1LDIyEkVR6Nq1q1XQArBt2zYiIyMZPXq0TXakefPmgDnTkBcffvghEyZMoGbNmhw5coSvvvqKF154gf79+3P06FGrfS0B2IsvvmgVtAA4OTnh4uJiVebs7KwGLZmZmSQmJpKYmIiPjw+KoqDX6wE4e/Ysq1evpk+fPjZBC2BVr06no3bt2ixfvpxRo0ZRp04dli9fbjPnJTk5GaPRyD///JOn1+Nhio+PtwrKUlNTrT5f2dnZxMXFWT0n52fF3uPY2FirFfqKwjHAfmbFMZdgxG6pyX4d9jjULHVP9RbF1+pROoYoGJJxEUKIh8SS8Rg/fjz+/v5otVpKlChBtWrV1CvxOfezZEb8/f2pXLkyu3btYvDgwXYzJ6mpqcTExNCpUye1LCgoiN27d3P06FFatWpFWFgYpUqVoly5clbtqly5Mu+++y7vvPMOFy5c4ODBg6xcuZLt27fj5ubGtGnT8nSeOp2Oxo0bU61aNZttJpPJKpixBFO9e/e22XfHjh04OzvTo0ePXI+Vs6574ezszODBgxk8eDCJiYn89ddfbNq0iQMHDvDWW2+xceNGPDw81PMA6Nq1q009Z8+epUaNGlZlYWFh6hyT2xc08PLyUpcz3rp1KyaTicGDB9+xrSkpKVy5coXr169z/PhxXn75ZV555RW7q04NGjSII0eO8Prrr1O1alXat29P586dbeZSFaZSpaw72JbX2cLZ2dlmPtXtc4huf2zJQBalY+R2Tdgt0zZbAqAHXG4rU5zsd8/qrW6H42w9hj8vmwucHPD4tCv022uz7+2fkqL4Wj1Kx8grmeNinwQuQgjxkORcKexOE951Oh0uLi5Ur15dLQsKCiI0NJSbN2/anZhvWWY5Z1m7du1wcnJi165dNG7cmIsXL9KqVatcj6vRaKhevTrVq1enefPmDBkyhGPHjuXpHC2d7ZwrplkYDAZ0Op1VG3U6HQ4ODjRt2tRm/3PnzlGxYkW791Q5e9Y8PCbnpPW88vHxUTNfzz//PJGRkZw9e5YmTZqobStdujQVK1a0el5cXBw3b960Cqj27NnD5MmTqVq1Ki+88AJVqlTBzc0NjUbD1KlTbc65RIkS1KxZ847tswROvXr1YsuWLYSHh2MymezO6WnQoAHr169n//79HDx4kPXr1/P9998zcOBAJk2alN+XSBQo+xmX7JJaXBKsMyxdx1XCyTeTmyui1TKHSi74PeOP0rcGWZvCMV1JwaVnHRyql7y9SvEfIIGLfRK4CCHEQ2LJeNwpaFEURR0elLOD2qlTJ5YtW8bu3bvVe7XkHL5kb3lkDw8PWrRowZ49e+jevTtGo9Hmvi+5cXZ2Bsjzql2Wzra9ZYw3b95MUlIS3bp1s9q/WrVqud7w0d5iAYqisHnzZnx9fe3OTckPy3FyXkmNiIigcePGNvvay3jNnz8fX19fQkNDrc7lxIkTJCcnW+2blpZ2T/fqsLyWAwcOpEmTJnzwwQd89tlnTJ482e7+JUqUoGvXrnTt2pXU1FReeukl1q1bJ4FLEZHt7Ihzpu08l4Zv1KBXXQ++efEEzq4OvLGiOY7ODtC7MvxgW4/GyQHXp4tOJk2Ih0nmuAghxEOQmZnJxYsXrVYBsyc6Opq0tDSbAKNhw4b4+vqyc+dOdWJ+zrkQ9rIwYM7UxMXF8eOPPwLWgc2ff/5Jdrbt3bxNJhPBwcEANvNO7sbS2b59vsjNmzcJDg6mZs2aaqZCr9cTFRWVazBVo0YNoqOjuXnzplX5t99+S3h4uLoS1724cOECly9ftrtt7969hIeHU7t2bfX9sdzg8/Z5RGD/tbYsnJAzaImPj2fGjBmA9evu5+dHWloaJ06csKnbMg8GzK+ls7Mz1apVo1evXrz88susXr2a77//3uo58fHxNvU4OzujKIrNsEBReJLc7F8r7t24BD7l3Ji8uQ0T1rQ0By0FoYJ3wdQjRBEiGRchhHgIIiIiMBqNNpO8b2e5mm/vhoNBQUH8+OOPKIpCu3btbJ7n6+trc6+Qjh078sknn6gT+3N2tmfOnElaWhqdOnXC398fZ2dnYmNj2bZtG9HR0XTv3p3nnnsuz+fp7e1NfHw8EyZMIDAwkLi4ONauXYuiKMyePVvNxpw7dw6DwZBr4PLiiy9y4MABRo8eTb9+/XBycmLPnj388ccfDB06lD59+txzu7Zv387ixYtp1aoVzZo1o1SpUiQmJnL8+HH2799P+fLl+eSTT9T9LQGYvbZZVvqqXLmyWta8eXMOHTrEzJkzadCgAdHR0WzevFldmS1n4PL888+zY8cOJkyYwNNPP03lypWJj4/nr7/+IjAwkEGDBqnHqVmzpvp6jRo1ipiYGObPn0+FChXo3LkzAC+88ALlypWjRYsWlC9fnpSUFLZu3crFixetzkkUrkx3F0jItCn39na3s3ceKeug/Ai49u/cqp5NYfP791+vEEWMBC5CCPEQWK7S3y1wuVOHOSgoiJUrVwLWgU16ejqXL1+2uyywj48PzZo148iRI3h7e1tNGB01ahQHDx7k2LFjbN++nczMTHx8fGjYsCETJ06852WGb29/nTp1mDBhArNmzeLzzz/Hzc2Ntm3bMnr0aKtJr7lliSwaNWrEnDlzCA4O5ssvv8TV1ZV69eoxZ84cu3No7qRdu3akp6dz9OhRVq5cSVJSkjqP6LXXXuPZZ5+lRIlbdyG/W+BSp04dq+Fe06dPZ9asWWzbto0dO3bQuHFjPv/8c0JCQrhy5Qp+fn7qvvXr11dvIPnLL7+QlJREyZIlady4Me3btwfMqxRduHCBXr16WR37/fffJzY2lunTp1OuXDkaNWpEjx49OHr0KGvXriUtLY2yZctSv359pk2bds9DA8VD4KBFC5jUuS4afng9b4tL3FFsSMHVJQqdzHGxT6MoudzlSAghhMiD7Oxs2rdvz7PPPsvEiRMLuzlCFArNZ7nfryV7nEJIiDnAGDFiRIHc8FX8N93QvGe3vKzy8UNuSdEiGRchhBAFwjL0627zeIQQQtyZZFzsk8BFCCHEXSUmJqo3ssyNZbL53YbDFbT4+HhMd7lhn4eHR64rlwkhhCgeJHARQghxV2+88Ya6cEBuBgwYgFartbr/zMMwYMAAmxs+3m7cuHEMHTr0IbVICCHul2Rc7JHARQghxF1NnjyZtLS0O+7j6+ub6z1GHqTZs2dbLSNsT9WqVR9Sa4QQ4v7JBHT7JHARQghxVw0bNizsJuSqWbNmhd0EIYQQD4EELkIIIYQQQhQhMjnfvnu75bAQQgghhBBCFCIJXIQQQgghCkiz0oXdAvFfoKCx+/Ook8BFCCGEEKKAHB1hfxR+9viH2w4h/oskcBFCCCGEKECpb1g/3vMMODnKtGKRF5pcfh5t8lckhBBCCFGA3F0cUSYVdiuE+O+RwEUIIYQQQogiROaz2CdDxYQQQgghhBBFnmRchBBCCCGEKEKUwm5AESWBixBCCCGEEEWIDBWzT4aKCSGEEEIIIYo8ybgIIYQQQhSUaq/AxZu3HndqCLtmFF57RDElGRd7JOMihBBCCFEQdp20DloAdp+G7OzCaY8Q/zESuAghhBBCFIQnPrRf7jLwrk/ddV6Pf7CeRccNBdwoURwpaOz+POpkqJgQQgghRCHSfKpXfx+9XWH0dj3K206F2CIhiibJuAghhBBCFJLvTvwbtGg0t36AFkv1d3iW+K9Tcvl51EngIoQQQghRSEZsQw1WVBoNf920u7sQjzQZKiaEEEIIUVgUZAEpYUPms9gngYsQQgghRGFRAEW5Lesig4IedRK42CdDxYQQQgghCpNNH1U6rULYIxkXIYQQQohCZSdQkaTLI06CV3sk4yKEEEIIUahuj1IkahHCHglchBDiERAVFUVAQADLly8v7Kbkav/+/QQEBLBr167CbooQD5cJ8zwX9Qe54P6Ik+WQ7ZOhYkII8QgICwsDoG7dumpZamoqP/zwAwEBATRv3rywmqYKDw8HoE6dOoXckru7fPkyGzduJDw8HJ1OR1xcHN26dWPmzJm5PufUqVMsXbqUU6dOodfradq0KRMmTKB69eo2+2ZlZREaGsqvv/5KbGwsZcqU4emnn2bYsGFobl86VxR/t7+lGqSXKoQdknERQohHwJNPPsmBAwesApSTJ08SHBxMQkJCIbbsluHDh3PgwAH8/PwKuyl3tXXrVtauXYter6dGjRqAdVB4u82bN/Pyyy9z48YNXnnlFYYNG8bJkyd57bXXyMzMtNo3NTWVl19+mZCQENq2bcubb75J1apV+eqrr/jpp58e6HmJIkIurz/yFDR2fx51knERQohHgIODAw4ODlZlp0+fBqBBgwaF0SQbjo6OODoWj6+lQYMGMXLkSAAWLFjAkSNHqFevnt19w8LCmDlzJq1ateLzzz9Xz7F8+fJ88MEHbN68mWeeeUbd/8MPP0Sn07Fw4UI10OzXrx9PP/00ISEhDBw48AGfnRBCFE3F4xtCCCGEjRkzZrBx40a2bt1KmTJlrLZdvXqV/v37061bN6ZPn07Xrl2pW7cu8+fPJyUlhU6dOqn79u7dW/39p59+ombNmpw9e5a1a9dy9OhRrl27BkDVqlUZPnw4QUFBVseaOnUq+/btY82aNXzzzTfs37+frKwsWrRowbRp0/Dy8mL16tWsXbuWy5cv4+fnx6RJk2jZsqVaR0ZGBh06dODpp59mypQpAKSlpdGxY0cGDBhAUFAQISEhnD59GicnJ4KCgpg0aRLOzs739Frt37+fX3/9ldOnT3Pz5k3c3d2pV68eY8aMydfQtBIlSqi/322I2xdffIGDgwPvvvuuVWAWEBAAwNmzZ9Wyv/76i927d/Pcc89ZZce0Wi1NmzZl06ZNxMfHU6pUKXXbxYsX+eabb/jjjz/Q6/UEBAQwdepUxo4di5OTE6GhoXk+v4cudDes2AfeJWBCb2hd9IcLFhjFBGhBm+NqumWei3hkSXbFPglchBCimPL39wfMHd/bA5evvvoKrVbL6NGjuXHjBvHx8WrHWlEUZsyYwaeffkqFChUYOnSo+ryqVasCsHbtWi5cuMATTzyBr68v8fHxbNiwgSlTprBo0SKaNWumPken0+Hp6cnLL79M69atefXVV/nzzz/ZuXMn8+fPJy0tjevXr9OnTx8yMjIIDQ1l6tSpbNmyBVdXVwAiIiIwmUxWnf+IiAgURSEsLIw9e/bQp08fnnjiCXbu3Mm6devw8/Nj2LBh9/Raff3111SpUoV+/frh4+PDxYsXWbduHa+//jpr1qzB29s7H++AWXh4OH5+fnh6etps0+l0HDt2jH79+uHr62u1zXLucXFxatnKlSvRarVW74mFm5ubur8lcAkPD+fVV1/F2dmZ/v37U65cOXbu3Mn48eOJjo6mR48e+T6vh+bLLTB2ya3HG47AkU+hUdXCa9ND8s2fekCxM8dFAxqJXIS4nQQuQghRTFkCl3PnztG6dWu1/J9//mHbtm2MHDmScuXKsX//fuBWRsDLy4vAwECmTZtGQECA3c7t2LFj1Y6yRa9evejZsyf79u1TA5e0tDQuXbqEVqu1GdrUs2dPNmzYQN++fVmyZIk6qTw7O5ulS5cSHR1N7dq1AXMHP2cbc5ZlZGSwatUqPDw81HZ06tRJHep2LxYvXmxzPnXq1OG9997j77//JjAw8J7ryik2NpaEhIRcFzfYsWMHAN26dbPZlpKSAoC7uztgPs+DBw/SpEkTypcvf9f9s7KymDx5Mt7e3ixdupTSpUsD5tf+ueeeIysrq1gsdMCCrdaPs/SwZAfMfalw2vMQTd+lBxzMgYoQOUjGxT6ZnC+EEMVUzsAlp7lz51KmTBleeOEF4NZQppyTx+2V5WTp5BuNRlJSUkhMTFTnoGRlZan7RUZGoigK/fv3txna5ObmhpeXF2+//bbVSliWACTnnBudToeDgwM1a9a0KgN499131ecA6nArS8biXljOR6/Xk5SURGJiolpnzvPJq7u9jqdOncLBwYGGDRvabDt//jwAlSpVAswBp16v57HHHrNb1/nz53FxcaFcuXIA/Pzzz8TExPDWW2+pQQuYX1dLYGkJDIuC+Ph4q9c6NTXVHIwpdjILJnPZ1atXrYpvfxwbG4uS4/m5HuNf2dnZVhmuB3EMe4y3PbYEpupfhd3buNwqLIzzkGMU7DHySpZDtk8yLkIIUUx5e3tTrlw5oqKi1LI9e/Zw7Ngxpk2bpnbWdTodHh4eVqt13anDnZWVxU8//cTWrVs5d+4cRqN1t6tKlSo29XTu3Nlqn+zsbK5cuULXrl1xcXGx2nb+/HkcHBzUDrulnurVq1vtq9PpqFChAo0aNbJ6/qVLl8jKylKHtQEkJCRYtdPR0REfHx8Abt68yXfffcfevXu5cuWKzfnmPJ+8ulvgcvnyZXx9fe0GWSdPngRQA5VLly4BUK1aNZt909LSiIyMpFGjRmrgtm3bNsqWLZtrtkir1arBbVGQc14O3ApgGdUV3vzu1gYnR3jxCQAqVKhg9ZzbH9+emcr1GP9ydna2CvIexDHscbjtcWxsLAAfBDnx6k4jNjdu0QCKbbBv8TDOQ45RsMcQBUMCFyGEKMb8/f05fvw4iqJgNBqZP38+tWvXplevXuo+Op2O2rVrW2U9wsPDcXV1tekk6/V6xowZw+nTp+nduzeDBg2iZMmSODo6sn//flauXGnVSbdkSurXr29VT2RkJAaDwSbosDynRo0aapBiMBiIioqyGk6l1+uJioqyWkQgZ9sBdRWvzMxMnnzySavApWXLlixcuJCYmBhefPFFjEYj/fr1w9/fH09PT7RaLUuXLuX06dN276Nyr+4WuKSmploFaBaKorBjxw5KliypZkdSU1MB7M6V2bVrF0ajkSeeeEIt0+l0Vgsc5HTu3DmqVKliMzyuSJrYBzzd4Ie94OMOb/aBJvl/T4qTUS3/DVxkqJiwIZ8JeyRwEUKIYszf358DBw5w9epV9u3bR3R0NF9//TVarXkkcEpKCleuXKFDhw5Wz9PpdPj7+9sskbxz505OnjzJlClTrJboBVi2bBlardZq+JFOp6Nq1ao2GYXcOvTZ2dlERUXRvXt3tezcuXPo9Xqr+Rjnzp3DYDDYXWLYXt3z58+32scynOq7774jLi6OVatWqfdbAfN8kvDwcGrVqnVfSzCHhYVRvnx5Nbtzu5IlS5KRkWFTvnPnTmJiYhg5cqR6fMtV3dv3NxqNrFixAg8PD3U+ksFgQK/Xq+9zTtHR0fz99982WbAi7eUu5p9HkcPt+RghRG5kjosQQhRjlqFAp06dYvHixbRr144WLVqo2+1NegeIiYmhYsWKNvVZxmXfnolZsWIFf/75J1WrVlWXArZkRXILLhwcHGyGKp09exaDwWB3Er69Mnt163Q6ypYtqw7NcHV1pVWrVlY/lizK1atXcXJyssp6GAwGPvjgA9LS0nK998q9uHnzJnFxcXesw9/fn5iYGKvx7jdv3uSLL76gfPnyDBkyxGpfMC+JnNN3331HZGQkr7zyirr6maOjI76+vpw+fdrqBpaZmZl8+OGHNiu0iSLO3jwf8UiTG1DaJxkXIYQoxiyd3S+++ILk5GTGjRtntT23zIefnx+HDx9m5cqVeHt7U7FiRZo0aUKzZs3QaDTMnDmTZ599FkVROHDgAMnJyTb1WLIi9oZJhYeHU61atXvKxOh0OjQajU0mx167Ldtym8B+u4CAAA4fPsz48ePp3LkzCQkJ/Prrr+oQqjvd7d6eq1evsmXLFvV3gMTERBYvXgyY5/rkDPqGDRvGnj17GDt2LP379yc9PZ3Vq1ej1+v56quvrMbK+/v707ZtWzZs2ICTkxO1a9fm8OHD7Ny5k6eeeopBgwZZteXpp59mwYIFvPrqq/To0YPMzEw2bNigTiqWwKWYsdy7RfqmQuRKAhchhCjGqlatiouLC3FxcTz77LM2mRKdToezs7NN+QcffMCcOXNYsGABmZmZjBgxgiZNmtC4cWOmT5/OkiVL+PLLLylfvjw9evSgZcuWvPjii/e0MpnBYODs2bN07drVpr06nQ6tVmuTXfHz87PqxFvKbp/vcfXqVZKSku454BgyZAhJSUn88ssvnDp1ipo1a/LSSy+RkJBAWFhYngOXQ4cOsWjRIquy48ePc/z4cQDatWtnta1hw4Z88sknfPvtt8ybN4+SJUsSGBjIyJEjbe7rAuabis6ZM4etW7eyefNmatasyYwZM+wuWT106FCys7PZvHkz8+bNo0aNGgwfPpzIyEh++uknuyuZiSJKyeV38ciS7Ip9GkWR/KQQQgjxX6DX6+nXrx+VK1fm66+/LuzmPHo0T+e+TVmHXq8nJCQEgBEjRuDk5ITmU33uT3nbqaBbKIoJneZzu+V1lIkPuSVFi8xxEUIIIYqh2+8/YzKZmD17NrGxseo9fIQQxZPcx8U+GSomhBDikZeWlmZ39a+cct4bprBduXKFF198kV69euHn50dcXBy7d+9Gp9MxbNgw2rRpU9hNFELcBxkqZp8ELkIIIR55wcHBLF++/I771KhRg1WrVj2kFt2ZZdWwTZs2kZSUhJubG/Xq1eOzzz6jY8eOhd08kVcarC+n3/5YCAFI4CKEEELw9NNP3zVLYVkGuiioVKkS8+bNK+xmiIKi0diuJiZTkB9pknGxTwIXIYQQj7wqVapQpUqVwm6GEEKIO5DJ+UIIIYQQheTzDthmVxSF5uUKpTmiiJDJ+fZJ4CKEEEIIUUgmtHL6t1eqWP3/r+GyFLIQt5PARQghhBCiEClvO/FzH6jupRDcWe7fIsxzXOz9POokcBFCCCGEKAj7P7Jfnvz9XZ/6VB0nokY5MbKpBC1C5EYCFyGEEEKIgtC2AVQtbV3WpjZ4uhdOe0SxJRkX+2RVMSGEEEKIgnIhuLBbIMR/lgQuQgghhBBCFCGygph9ErgIIYQQQghRhMiwMPtkjosQQgghhBCiyJOMixBCCCGEEEWIZFzsk4yLEEIIIYQQosiTwEUIIYQQogBcTjJQ9isDTnP0PLvBQJZBpliL/FFy+XnUSeAihBBCCHGfTl8zUPlbhZuZYFA0rI5Q8JhrKOxmCfGfIoGLEEIIIcR9avS9Apoc8xI0GgwKbIsyFl6jRLElN6C0TwIXIYQQQoj7lcs4nuCTpofbDiH+w2RVMSGEEEKIB+RmWmG3QBRPkl2xRwIXIYQQQogHJDajsFsgiiMZFmafDBUTQgghhHhA4nMELtnpBjKWVCVjSTUWBu4huOeeuzw5BRZvg+sJD7SNQhQXknERQgghhLhfCnZH98Rn3vo9uOt+QKvulnk1k80Oy3A2Qd1vWlLllXq3dvYfA2djzb+/vAj8SsLlJQ+o8aKokaWP7ZOMixBCCCHE/cplZI/lVi6nfrlis1uJVD0mF/M15LBRf3Lz4L+ByoF/bgUtFjEJsPNEwbVXiGJIAhchhBBCiIKgKOYfy+8mqOJhfvjrJ5FWu5aJSaFyZDyuGeZ7vWiAY21/M28MfM9+/Z1nPIBGi6JIlkO2TwIXIYQQQoj7ZRnbo7HuXHatfut3k9bc7dKYFEpdTc21CiGEfTLHRQghhBCiINx2A0q0Cv/cND9UAIOjIxgNaBQTilw8F3cgQax9knERQghRqPbs2UNAQAA7d+685+fs3LmTF154gQ4dOhAQEMDatWsfYAuFyL+DFwy8PDySLEctGqMRrdGEVgORzcoTXbuU2kFVAJN0V4W4I8m4CCFEEXH16lU2btxIp06dqF27dmE3B4DU1FR++OEHAgICaN68+QM5RmSkeex/nTp17mn/Y8eOMWXKFFq1asWECRNwdnamZcuWeTpmREQEu3fvpk+fPlSoUCHPbdbpdGzduhWdTodOpyMpKYkRI0bw2muv5fqc/fv38/333xMeHo5Go6F169ZMnDiRcuXK2eybnJzMkiVL2LVrFzdv3qRixYoMGzaMPn365Lmt4iHRoM5rwfTv71pwzDaSatTioNHgZDTemqWg0ZDm48oNP0/KxaSgATQSuIh/mWQ+i12ScRFCiCLiwIEDBAcHo9frC7spqpMnTxIcHExCwoO7j0RkZCQeHh74+fnd0/6rV6/GxcWF2bNn89RTT9GjRw/KlCmTp2Nu3bqV4OBgXF1d89NkVq1axS+//IKjoyOVKlUC7hx4LVmyhPHjx6PRaHjjjTfo378/e/bsYeLEiSiKdWc1NjaWoUOHsn79ep588kkmTpyIh4cHM2bM4Pfff89Xe8VDYE6ZgNEE2QbIMkKGEUWj4ZqTEw4m+0FJSqmcn0HprApxJ5JxEUKIIuL06dM4Ozs/kGyLoijo9XqcnZ3z3CaABg0aFHibLCIjI6lduzYazb112k6cOEGjRo1wc3PL9zFPnz5NxYoVKVmyZL6eP3HiRN5//30A3n33Xc6cOUO9evXs7rt3716+/vpr+vXrx7vvvquWu7q68s0333Do0CEef/xxAEwmE2+//TaJiYmEhIRQo0YNAHr06EGPHj0ICQmhQ4cO+WqzeMA0mNc+NgGOWvMcF5OCUQ9hpT0JumD/8+2gN6m/lySFa5r3cMcXd67ZD2M0T5v/7+UGpTzAQQuZ2VDeB5rVhFoVQG8EJy3UrwIHwmDjEUjNhNKeUK8SDGkPGXpISoPeLaCMl/Ux5myAVQegfX2Y/hx4uIHRCJ+sg2PnYHAH6N8GYuLgl2NwPRFKuoNnCUjPgs6N4Uo8nLkEberAhj/MbWhTB2YNBTcX2PU3nL1q3vdaIpy6CIF1zW3/YS84OcILHSE1A45GQSt/aFrj3t+P389A2GXo1BDq3NtFEQAuXoffTkDVstClMWgL5xq/rCBmnwQuQgiRT2lpaaxatYpt27Zx5coVTCYTZcuWpWnTpmqn9tdff2XHjh3odDri4+Px9vamSZMmvP7662qGQafTMXjwYLXeNm3aAKDRaNizZw8pKSn06tXL7lCkZcuW8eWXX7JmzRqqVasGwJo1a/jf//7HokWLOHr0KL/88gtXr15l2rRp9OrVi/379/Prr79y+vRpbt68ibu7O/Xq1WPMmDFq1iAlJYVOnTqpx+ndu7f6+08//UTNmjUBOHXqFMuXL+fEiROkpaVRuXJlBg4cSN++fW0CkePHj7N48WJOnTqFi4sLvXv35sUXXyQmJobAwMC7vt4ffvghmzZtAuDGjRsEBAQAsHTpUh577DF+/PFHDhw4wLlz50hMTKRUqVK0adOGMWPGqAHKnj17mDRpklqnpQ4fHx927Nhx1zZYuLu7q7+Hh4fj5eVlN2NkMBj4/PPPKVu2LBMmTLDaZhl6FxkZqQYumzdv5p9//mH8+PFq0GI5Xr169dRAMqczZ84QHBzM8ePH0Wg0dOzYkUmTJtGrVy9atWrFrFmz7vm8RAHQaqx/d9Rw3dWZhBIlcMvMwtF4K1BBUfC9mASAC1nU4gqupOKMHhMl0JJuHoGGnVxMcob5xyImAY6ev3Pbom/C8fOwYt+tMk832D4dWv17waTqKIi+Yf79z7Pw9Ta4sAgajYfYRHP5z3+ag4wj5yDLTobY0ujbHY2CxTsgqJE54LnTvgD/W2f9+L1n4KNBdz5HgMFf3DpHjQa+Ggljut/9eWsPwcDPwWA0P+78GPz6Pjg63P25BUwGDdongYsQQuRDdnY2L7/8MjExMfTp04fq1auTlZVFVFQUFy9eBMxXz+fPn0/r1q157rnn8PDwICIigvXr1xMREcFPP/2Eo6MjPj4+TJs2jRkzZtCiRQs1SHB2dsbd3Z0jR44AULduXZt2hIWF4ebmRpUqVdQynU4HwJw5c3Bzc2PAgAFoNBoaN24MwNdff02VKlXo168fPj4+XLx4kXXr1vH666+zZs0avL29URSFGTNm8Omnn1KhQgWGDh2q1l+1alUA1q5dy6xZs2jQoAFDhw7Fzc2NvXv38vHHH5OZmcnAgQPV52zdupXp06dTu3ZtRo8eDZiHfJ05cwaTyXRP81u6d++Oj48P33//PQMGDKBRo0YA1K5dm7i4OEJCQujYsSNt2rTBxcWFU6dOsX79emJiYli4cCEA1apVY+zYscyfP58uXbrQrl07ALy9ve96fHvS09O5dOlSrvN/9u/fz+XLlxkzZgwlSpSw2mbJGMXFxallP/74I+7u7jzzzDM2dbm5uZGZmUlqaioeHuabgxw8eJA333wTX19fhgwZgpeXF5s2bWLs2LGkpqbe87whUQBMYDfE0JhnriS5uuDr5IRJY8TRaB5CZnTQ4vRvxsWIAxr0OJH9b1/eFSPOaDCiIRsNWQ+m3SkZ8O4K2PEB/LjvVtBikZYJz352K2ix2B+ee5136nVn6m8FLXfb93afrINXu4Ff6dz3ORhuHZgpCkz9AYYHQQmX3J+nKDAx5FbQArDjFGz405xdEkWCBC5CCJEPe/fuJSIigrlz5+aaLVAUhfXr1+PiYv1lWbZsWRYuXMjFixepWbMmvr6+6vCw9u3b06NHD6v9w8PNHQR7Q5HCwsLw9/dHm2M4gyVwadSoEVOmTLHaBrB48WKbYVZ16tThvffe4++//yYwMBAvLy8CAwOZNm0aAQEBNm06evQos2bNYujQobzxxhtq+TPPPMPIkSP5/vvv1cAlKiqKjz76iLZt2zJ79mwcHMxXL5944gn69esH2A/KbteyZUsiIiIA6Nevn9WQOq1Wy5YtW3B0vPW11r9/f5ydnVm/fj1paWm4u7tTrVo1zp83X5Xu1q0bHTt2vOtx70Sn02EymXJtvyWL061bN5ttycnJwK3sTXR0NBEREfTs2dPu3Jvk5GS0Wq0aAMXFxfHee+9Rp04dFi5cqJb37NmTvn37Ave+4MHDEB8fj7u7u/r3kJqaiqIoeHp6AuaLASkpKZQufatTevXqVavFE25/HBsbi6+vr5rdK8xjgCu5zVEplZGN/t/PveLogP7fK/gOevMqYwAGHInHG0+ScjxTi4IWc1T0gAIXAF0MqampOO8+hd3BpOExD+7YeWE0EffnP5R6KjDX9yPz1Hls/nqS0+FqAtQsD+TynvuUMmekbqeLKZDPVV7JUDH7ZHK+EELkg6XTeebMGYxGo919HBwc1C+6rKwsEhMTSUxMxMvLPJ48Oztb3dcSnOSWVfH29qZixYpW5UlJScTExFh1Tg0GA+fOnaNixYpMmjTJJmiBW1f69Xo9SUlJJCYmqlfws7JudY7u1KYvvvgCPz8/Bg8erJ6X5cff359r166pdX399dcYjUbeeustNWgB8PX1pVq1ajg7O6vD3O5Gp9Ph7OxsNYwKzNkpS9CSmZmptsXHx0ed33Mv55VXYWFhd6zr1KlTlC5d2u4wMksAVblyZXVfgMcee8xuXRcuXKBixYrqe7ps2TJSUlJ47733rLI5Hh4e1K9fHyhagUupUqWsgngPDw+14wfm9zBnxw+w6fjd/rh8+fJWQxIL9Rh36FElaB1Id3JCYzJZlZe/mIg2R8bBhANGO9eUNWTblBWoLo3x8PDA+aUu9re/2tW27B7npNmV36eW9KB0t5Z3fD9cuzc3z/vJqWZ5qOGrPrT7nru5wON2/l66NC6Qz5UoGJJxEUKIfOjcuTNr164lODiYtWvXEhgYSMeOHWnbtq3aOY+OjiYkJIRDhw5x86btlTxLhxXMHXKNRmN3Yn54eHiuAQ1Yd07Pnz9PVlYWXbt2tTsR/+bNm3z33Xfs3buXK1eu2GzPOeQstw7+hQsX1G1du9rp0AAuLi64uLiQnp7O/v37CQoKsvtFbjQaqVmzphp0pKenk56ebrWPj4+Puj08PNxq/5yvxbJlyzhy5AhJSUlW27y8vPDx8bE6Lx8fH8qXL2+37XlxpyDIYDBw5cqVXIeRnTx5ErgVqFy6dAnAbhB34cIFkpKS1LkwiqKwfft2GjVqRK1atezWX7p06Tyvtibuwx2GPJk0GsI93WmTrcfk6AAaDeUuJlL2SrJVBQ4oZFECDWk4oAcUtGSiJZ8rDTpowGinYQ7mxZcxmswT0GcPM5e3rA1DOsDyHKvXdW8K054zT6BftA1MCrg6wdyX4LtdcDji1r5a84IE+JU2DzFLTAN3V/PvFj2aQZ8WMGW5eXul0uYJ/fGp4OsDGVm35u6U9DAvdHAjGSqXgcVj7jzcC6BqOVj6Gkz8DuJSwL8CrJhwb4HWd2/Ac3PM84A83eCD56CF/92f9wBIxsU+CVyEECIfvLy8CA0N5ejRo+zfv589e/awceNGGjRoQHBwMBEREYwZMwYvLy+eeeYZqlWrhoeHBxqNhtmzZ2MwGNQsB5g7wJUrV7aa9A3mQCMuLs5qcryFpeObM3CxDBOzTDrPKSYmhhdffBGj0Ui/fv3w9/fH09MTrVbL0qVLOX36NNWrV7dqk6urq01H2jJca/z48fj72/9Stwx1OnfuHHq93u6qZKmpqVy4cMHq3GbOnMlvv/1mtd+WLVvw9fUlMzOT6Ohom3uZ7Nmzh8mTJ1O1alVeeOEFqlSpgpubGxqNhqlTp9oEFbkFgvkRHh6Ou7u7VcBnkZaWBqBm2HLKyMhg//791K5dW11OOTU1Ndf9La/JE088AZiHid24cYOgoCC77YqKiipS2ZZHQm79zCxzRlZRFBSt1pxhURTi/LzQKgpecRm4ZhoADeeoSAmycOUSWgz/3tvlNpVLQ4ta8FRL84T6m8ng6mwOQnzczSuEZWabH5f1Ns9huRoPbs7g4gjJmebOfKbevF85H+v6vx8Hc1+EQzoIaggl/h14tWAUfDbcPAemjvkzy6iu5rqdHSEj23zsm8lQqQxk6+F6kjmISc2E09HQqIp5hTKAYZ1u7as3mOfQ+JUyzzW5cN2cNalaznweVxOgYklwuMdJ8i90gucC4ca/x7/X7JB/RTg2x7xaWikPcxZGFCkSuAghRD45ODjQsmVLWrZsybhx4/j444/ZtGkT4eHhfPPNN5hMJpYvX251tf/q1atER0erHVAwT+KPjIykffv2NseIiooCsLmqbjKZ2LFjBw4ODuoKX3ArcLHXMf/uu++Ii4tj1apVVkOtMjIyCA8Pp1atWlaZDJ1Oh7+/v9XwLrjVwa5VqxatWrW642uUkWG+cnr7vUoAVq5cidFotOpgDxo0yCow0Wq1+Pr6qu25fX+A+fPn4+vrS2hoqNXckBMnTpCcnGz1WtwpEMyrzMxMLl68SOPGje0u5ezp6YmTk5P6GuS0Zs0aMjIyeO6559Qyy8pnt++fnp7OunXr8PPzU+dTZWaar2DbGwr4559/cu3aNbp3v4dVlETBsmQcTIq5A55lBL0JraLQKCnF6nOiaDVcr+KDwVFLxYu3soQVlBm3ljy2JzrY+nHtuyz16+Jku9wxgLMTeJWwLQfzssm9bC9+4OZyK2hRG1zK+nGlMrfqt/zu6WZeCjknV+db250czRkVi5o5srOODtbb7pVLjuPn1Z0m/z8ksqqYfTLHRQgh8ighIcGmI+7g4IBGo0Gj0VCuXDmuXr1KyZIlrVarysjI4L333rOZzB0XF0dmZqbdeRCWTuztndkFCxZw/vx5atSoYTUkTKfTUb58eatgyeLq1as4OTmpV/jBPJzpgw8+IC0tzWbyf0xMjM28Grg1xO32zEjO87GwnNPhw4et9jl//jzLly8HrDNGDRo0oFWrVupPixYt1G25DcuKjY2lfPnyVkFLfHw8M2bMAKwXNYiJMU8ytndeeRUREYHRaMw1e6PVaqlRowZnzpyxev/Onz/P4sWLqVevHr169VLLLdkryypyFl988QVxcXFMmDBBDSzLly+Pg4MDR48etfosJiYm8r///Q8oWvNbHhlazFf3HbTmIVr/vjedb8TjY1IwaLWYNBpMWo3aMU3zkqv6QtwrybgIIUQezZ07lxMnTtChQwcqV66MoigcPnyYvXv3MmjQIMqXL09AQADr1q1jypQptG7dmuvXr7Nx40b1qnrOznTJkiUpUaIE27Ztw9fXlxIlSlCzZk3q1KlD3bp1cXJyYuHChcTHx+Pg4MC+ffvUQCln51RRFHQ6nVVnP6eAgAAOHz7M+PHj6dy5MwkJCfz666/qZP3bO+B+fn4cPnyYlStXqosDNGnShObNm/PYY4+xadMmbty4weOPP46zszPXrl3j5MmTeHh48Pnnn6t1tGzZksOHD/P222/TunVrYmJiWLNmDc7OzmRkZOQ63Ox24eHhODg42GSfmjdvzqFDh5g5cyYNGjQgOjqazZs3q/M7cr7Wlsnta9asQavV4uzsTMOGDe0O9bInMjJSvXu9Jbt1+fJlFi9eDMBTTz1lNa9kxIgRTJkyhddee40ePXpw48YNfvrpJ3x8fKxWWANo164dNWvWZPHixWRmZlK+fHl27tzJn3/+yauvvmq1ApqjoyN9+vTh559/ZuzYsXTo0IHExETWrVunBnASuBQCjQYcFPPlcjdHcHfEV6vnyVIOpPx2E0WrwaT595qxA2j1Bkqk3loQwyTzGsS/ZI6LfRrFXv5eCCFErrZs2cL27duJiIggISEBLy8vqlevznPPPafetDEtLY05c+bw+++/o9frqVevHsOGDePQoUOsXLmSXbt2Wc1l2LdvHwsWLCA6Oprs7GzeffdddangHTt2sHDhQq5du0bFihXp27cvTZs2ZdiwYUycOJFBg8w3ZLt8+TJPPfUUr776KiNHjrRpt8FgYMGCBfzyyy+kpaVRs2ZNBg4cSEJCAp9//jnLly+3Cl7+/vtv5syZw9mzZ8nMzLS6AWZ6ejorVqxg+/btXLlyBa1WS5kyZWjSpAlPPfWUeo8VMGeoZs+ezR9//EF2dja1atVi8ODBfP/996SmprJ27dp7et0HDRqEwWBg1apVVuU3b95k1qxZ/Pnnn2i1Who3bszIkSMJCQnh6NGj7N6922qIzsaNG/nuu++4cuUKBoOBr776itatW99TGxYsWEBISEiu23fv3m212hCY71fzww8/cO3aNcqWLUtQUBAvvvii3bksV69e5bPPPuPIkSNotVrq1q3L0KFDadu2rc2+mZmZzJs3j927d5OWlkadOnV44YUX2LRpEydOnGDbtm12h7CJB0MzW28OWG7rVTlr4FSHFNa9fRqbGRqKgv/xGNxTzZPvFaCbMuzOQ8WUdblvE/8Zv2uW2i3voLz4kFuSf+np6WzYsIGoqCh8fHwYMmRIvu+ZZSGBixBCCPEfkZycTPfu3enWrRvTpk0r7OY8UjSz/l35yzII/98gxtMR/n4yg+/H/o2zwXbp9IYHL+JoMKlPkcBFQPEPXM6ePUvnzp2Jjo5Wb5575MgRmjVrxt69e2nSpIndizd3I3NchBBCiGLGYDDY3D8oOzub6dOnYzAYGDx4cCG17BFnmeOi0Zgn6mvM886r1Pc0z0W67VqxW0qWGrQAaNz+zZB5W98gVlXR58G0WxQ5Si4/xcX48ePx9vYmIiKCpKQkq7l43333Xb4vrMgcFyGEEI+8lJQUq5tv2uPi4mIzDKywnDhxgpkzZ/Lkk09Srlw5rl+/ztatW7l8+TJvv/221Upz4iHRYLvsrsbc0dJoNAyZ04C1Y45hcnIEjQaNyYRTVjZZrg64ZJqD0KAE87BPzsyHSi/bHuPCNw/0FIQoKAcPHmTJkiXUqlXL5iJLp06d+OKLL/JVrwQuQgghHnkfffQRu3btuuM+gYGBzJ079+E06C5KlChB+fLlWb16NSkpKXh6etKoUSPee++9XG94KR6wXKYT6f/ts1VvWhKf8g4kX8lW78+id9Jg0oB3B18CtnXBwfnfWTB+pWHLVOj5ya2KfpoITk4P8gxEEVLcJ+ebTCZ14ZfbVahQgejo6HzVK4GLEEKIR94rr7xC//7977iPZUW4oqB+/fp8/fXXhd0McQ/Sc1xsHraqLYveWIPpeEk0aKjRsSw9jne3v4hCjxYyn0UUW61atWLdunU8+eSTNts0Go3d+1vdCwlchBBCPPJq1apls8yyEAXh9nkJLs1SoFkKI0aMwEkyKCIXxWk+iz3vv/8+HTt2pG7durzxxhtW2/744497XoL+djI5XwghhBDiAXmsbGG3QIiHLzAwkPfee49JkybRoEEDNBoNBw4c4Msvv2TWrFl3zXDnRjIuQgghhBAPyNQ2hd0CURwV9zkuAB988AEBAQFMmzYNRVEYN24cYJ6cP2XKlHzVKYGLEEIIIcR9KusKNzIVm5XFnq4jw8HEo6tXr1706tWLa9eucenSJUqVKkWNGjXyXZ8ELkIIIYQQ9yn2dUccZhvIOTvhtcaF1x5RvBX3jMvNmzcpU6aM+tjX1xdfX9/7rlcCFyGEEEKI+6TVaFDeduLAZQNHr8FrTR1w0BbvzqcoPKa771KklStXjkqVKtGsWTOaNWtG8+bNadasGRUqVLiveiVwEUIIIYQoIG0rOdK2UmG3QojC9dVXX3Hs2DGOHj3KL7/8gsFgQKPRUK5cOTWQmTFjRp7r1SiKUtxXXBNCCCGEKPL0ej0hISEAshyyuKPfHELtlnczvvCQW3L/srKyOHXqFL/99hvBwcEkJCSg0WhISkrKc12yHLIQQgghhBDigXBxcaFFixa89957nDhxgoYNG7J9+/Z81SWBixBCCCGEEEWIorH/U9yVLFmS//u//+P//u//8vV8CVyEEEIIIYQQD0WTJk3Yt29fvp4rk/OFEEIIIR6QqXsMpBlg7hMOhd0UUYwoxXxFOk9PT5o2baquJta8eXPq1auHRqNh7969GI3GfNUrgYsQQgghRAE7etVAwA+3Hn95wkhI58JrjxAP06uvvsrRo0cJCQlh3rx5aDQa3Nzc8PX1JTo6mv79++erXglchBBCCCEKWM6gxWLEDvjG6+G3RRQ/SjGfzDF79mz194iICI4cOcLx48e5evUqI0aMYPz48fmqVwIXIYQQQgghihDFofgNFTt58iQNGzbEwcF6WGTt2rWpXbs2gwcPvu9jSOAihBBCCCGEuC9NmzbFzc2NJk2aEBAQQIsWLWjRogV16tQpsGNI4CKEEEIIIUQRYiqGk/N//vlnjh49yl9//cXKlSv58ssv0Wg0eHh40Lx5c1q0aKEGNNWqVcvXMSRwEUIIIYQQQtyXvn370rdvX/XxxYsX+euvvzhy5Ah//fUXwcHBzJ49G41GQ+nSpbl+/XqejyGBixBCCCGEEEVIcZ+cD1C1alWqVq1qtYJYZGQkR44c4ejRo/mqUwIXIYQQQgghxAPn7++Pv78/gwYNytfzJXARQgghhHhQFOXf/wN2rqJ/33wj2UkG8wNHeDGsHxpN8ZvfIApWcb8BJcCGDRv4/fffcXd3p2HDhgQEBFCzZs37qlMCFyGEEEKIB8GkQM4bhJusN4c8tg5Teo4CAyz1/5mXzj79MFonxAOzYMECxo4dS+XKlbl06ZJa7u3trU7U/7//+7881/sfGEEnhBBCCFHEWIIWkwJ6k/ozKnbIrV3Sc3+6eLQpGvs/xcWCBQt48803OXv2LIqi8NVXXzF27FgyMzM5fvw48+fPz1e9ErgIIYQoklJSUggICODTTz+9p/3nzJlDQEAAiYmJatmUKVPo0KEDimW4zl3MmzfPpg4h8kXBPEzMqFiX4ZDLE4T477hw4QJPPvmkOuyxZcuWfPHFF/zxxx94eXnle3K+BC5CCCGKpMjISIB7vnlZZGQkvr6++Pj4qGVhYWHUqVPHas7Anj17WLx4sd1gJjw8nPLly1vVIUS+KJizLXY2TI0MIv56ttUoMiFyUrQauz/Fhbu7O1qtFgcHB0qUKKFeDGrUqBGjR49m2rRp+apXAhchhBBFUn4Cl9v3Xb16NQsXLrQqCwkJYdu2bXYnQM+fP5+ff/45ny0WIofc+pgK1L3pxMdjLxDWqDbXy5bEKrxRFDJ18Q+hgaIoM2ns/xQXNWvWJCYmBoAqVaqo/54DBAQEsHPnznzVK5PzhRBCFEmRkZE4Ojre0yo0169fJykpySZwcXZ2tnqs1+uJiIjgySeftFuPk5NT/hssRE4aQKuxzbpoNDRPTkWL+ar6tQplUTQayl2PV2Odf+quxJEM/LiOFgVtJUe8LuvM22uUg6h/b9xXtQxcvGn+vUY5GNwBzl6FkNfh/HW4HAedG4PuMkTfhC5NIC4ZridDvUoQnwKxiVC/MiSkwM0U8K8I2XrI1INXCUhKM+9Tx+/f3xOgTiXrc9IbID0LvN0fzGspip1u3bqxZ88eBg8eTLdu3fj2228ZPnw4rq6uHD58GJPJdPdK7JDARQghRIFLSkpi5cqVHD58mMuXL5ORkYGvry+9e/fmhRdewMHh1jj/q1evsmjRIg4cOEB2djatWrVi8uTJREZGUqNGDZtgYsOGDfz0009cvHiRsmXL8tJLL1GqVCnAOjuzZs0a/ve///Hdd9/RsGFD5syZw8qVKwHYtGkTmzZtAuCJJ55g1qxZHDhwgHHjxvHJJ5/QpUsXtmzZwvTp0/n4449tAh2j0cjzzz+PwWBg1apVODqav07PnTtHaGgoR44cITExkQoVKtC7d2+GDBmi7gOQlpbGqlWr2LZtG1euXMFkMlG2bFmaNm3K+++/f8+v89mzZxk4cCDjx4+nQoUKrFixgoiICNzd3enTpw9jxoy557pEAdMAjhowWAcuJbL06DzdqZmWjk9KOr434nE0GMhydcZJb8AjNRMT4EvCv7NhNCiXs1FwQIPxVtACt4IWMJd/tNr8+8r9eW+rpZmuTuDoAKmZUNoD4lLN5Y5aMPzb2fR0g9+mQZs6sPBXeH8lxKdCu/qwfBxUKZu34wsbxWlYmD0ffvghKSkpALz55pssW7aMatWqUbVqVY4ePcqQIUPuUoN9ErgIIYQocIcPH2bXrl0EBgbSq1cvDAYDO3fuZMGCBRiNRkaOHAmYO/qvvPIKzs7OPPvss5QqVYpdu3YxceJEoqKi6NKli1W9n3zyCWvXrqVTp04888wz3Lhxg88++0wNWOrWravuq9PpcHBwoFatWgB07NiR1NRUNm3axCuvvEKlSuarxjVq1FD3h1vBj7+/v9rG223YsIGoqCg+++wzNSDZu3cvU6dOxc/Pj2eeeQZvb2+OHTvGV199RXx8PBMnTgQgOzubl19+mZiYGPr06UP16tXJysoiKiqKixcv5ul1trR5165dJCYm0qNHD7p3787GjRtZunQp/v7+Nq+heIi0GnDVgl4BRcE9U49vcgbnPN256ubCiLPRuGVkoTUY0QBGrZYMFyc8tSm4mPQ5KnIghUp4k7fPxz3LGVtl6oF/j20JWuBW0AKQkgG9/w+2vg+vBd8q3/cPjPgKdn74YNopihVPT08AKlWqxP79+/noo4+Ijo5m/PjxfPDBB/mqUwIXIYQQBa59+/Z069bNqmzAgAEMHDiQvXv3MnLkSLKzs3nrrbfw8PDgu+++o2TJkgA8/fTTPP/882RmZlplUDZs2MDatWsZN24cQ4cOVctr1qzJlClT8Pb2pnz58mq5TqejatWquLq6AtC8eXN2794NwPPPP69+qebc393dncqVKwNQvXp1HB0dbQKX9PR0vvnmG5o1a0bHjh0B8wo677zzDp06deLDDz9UM0r9+/dHq9Xy008/8frrr+Ps7MzevXuJiIhg7ty5BAYG5vs1trQZwM3NjUWLFqlD49q2bUvv3r05ffq0BC6FSVHM924xKWhMClqjgqIBjQLpjo5c9PakUcqtNZEdTCYUrQZXTaZtVUWtyxaXApvtrAy162/zUDNnGXZ5P4rT0sf2LFmyhJdeekl9XL9+fTXjfT9kcr4QQogC5+bmBoCiKKSmppKYmEhKSgolS5YkKysLgHXr1hEdHc3o0aPVoAVAq9XSsmVL4Fb2Q6/X8/XXX1O9enUGDx5sdaxWrVoBULt2bbXMYDBw7tw5mzkv4eHh+Pn52QQtYA4C/P391Un7Tk5OVKtWzSZwCQ0NJT4+ngkTJqhlCxYswMnJiTFjxpCSkkJiYqL6U6tWLYxGI1euXAEgOTkZgDNnzmA03t+6UjqdDq1Wy/Tp063m81iG17m4uNxX/QUpPj5efe8BUlNT1aEkYM5ExcXFWT3n6tWrd3wcGxtrtTpckTuGAmSbQAFFoyHFzZkbHm7qvgZ7w4EUUHKd2V+EODqQUNbVplipVBqczEFWkXs/CvEYj5pRo0bRvHlz9u3bV6D1FrHwXQghxH/Bjh07WL16NWfOnCEz0/rqcadOnQDYtm0b5cqVo2vXrjbPNxqNaDQadbjW0aNHuXnzJi+//DJardZmX7Ce33L+/HmysrKsyhRFITIyktatW9scLzU1lZiYGNq2bWtV7u/vz9atW8nMzMTV1ZUbN26wfPlyevToQb169QDzfJX9+/ej1+vp06dPrq+JJVjq3Lkza9euJTg4mLVr1xIYGEjHjh1p27at1dyfexEREUGTJk0oV66cVfnZs2cBqFatWp7qe5As85AsPDw8rB47OztTunRpq7IKFSrc8XHODFvRO4be+h4u/0p1caRcCmgUhUpJKTbbNShkGt2AtNvK9SjkvljZQzexNyVH9oAfDsMhc+YPrRbNrKHwb/BftN6Pwj1GXil2Vj0sTo4ePcqECRPo2LEjzzzzDLNnz6ZKlSr3Xa8ELkIIIQrUvHnz+P7773n88ccZP348vr6+ODs7c+3aNWbMmKEGEzqdjlatWtkEIgCnT5+mUqVKagchPDwcgAYNGtjdF6wDF8sQqpxzXi5evEhaWppVWc79FUWx2ebv78+vv/5KVFQU9evXV5dWfu2119R9zp8/j16v54UXXlCzP7fTaDRqx8bLy4vQ0FCOHj3K/v372bNnDxs3bqRBgwYEBwfbrISWm5iYGFJSUmjUqJHNNsvrZQmuRCHI5Z6nWkUhSwNVMrPwTk6z3UGBdNxJJBNvUjHnX4y4E33vx+5QDxIzICEVRnWFBVshNQNe7QrHzsP1JHjtSVhzyLxi2Jt94PQluHQTRnczl11NMK9Itvx3OH8NXukMO05DVCy83h3a/fu3+PtHsPGIedWyHs3Mq4+JR17jxo3ZtWsXP//8M5MnT6Zu3bq8+eabTJ06lRIlSuS7XglchBBCFJhr166xfPlyunTpwieffGK1bfHixYC5M20wGMjOzrZ7E8hjx47xzz//0LlzZ7UsIyMj12MuX74csB+43C2YudP+YD1B38nJiS1btvDiiy9aZThSU80TmCtXrpxr4HI7BwcHWrZsScuWLRk3bhwff/wxmzZtIjw8nMcee+ye6oiIiADsByfh4eG4ublRtWrVe6pLPEA5V+zCvDpy1ys38FIUnOwFNxrAw5GSG0fg3anyrXKj0TxB3uW2uSOpGealiMv55N6Gd56xX/5KN/vlOf3v1nwynmhiu93JEfq3uXs9Ik+K0z1b7qRfv3706tWLefPmMXPmTEJCQvjkk0+s5inmhcxxEUIIUWCuXbuGoig2Q5ROnTpFSEgIYA4cHB0d8fX15e+//yYt7dZV5/T0dObMmQNYBxF+fuaruIcOHbKq99dff+Wvv/7CxcXFqpOu0+ls5rJYboZWsWJFm3brdDqcnJzUFcYscgYu8+bNo1SpUgwbNsxqH8vqZNu2bbMbiOUc+56QkGCzj4ODAxqNBo1GYzPk607uFIiFh4fj7+9vN5slHiYNOGjMq4tpMf/uqCWkcnkc3HJ7ioZmKaOsgxYABwfboAXAw+3OQYsolhStxu5PceTk5MSkSZOIjIykd+/evPTSS3aH7N4LybgIIYQoMDVr1sTHx4cffvgBjUZDqVKlOHPmDH/88Qc+Pj4A6pCpp556ikWLFjFq1Cj69OlDeno669evJz3dvMpSzsAlKCiIefPm8c0335CYmEi1atU4ceIEe/bswdXVlVq1aqnzQxRFQafTqRP8LSzBz8KFC2nXrh1arZb27dvj4eGBTqejZs2aVvdaAShTpgylSpXi119/JS4ujvfee09deMCiUqVKdOnShe3btzN8+HA6d+6Mh4cH165dIywsjJs3b/LDDz8AMHfuXE6cOEGHDh2oXLkyiqJw+PBh9u7dy6BBg2zG1t+JTqfD09NTDZwsLPN1Hn/88XuuSzxAGg3mG7L82+lUFBbVXcOLn44gtJ6uMFsmxEORnZ1NWFgYf//9N15eXlSvXp0jR47kqy4JXIQQQhQYd3d35s6dy5w5c1i2bBmenp4EBgYSEhLCwIEDadq0qbrv8OHDyc7OZsuWLcydO5eKFSvSq1cvkpOTWbFihdUqYR4eHixcuJDPPvuMtWvX4uLiwmOPPcYXX3zB6NGjrfaNiYkhLS3NZthXly5dOH36NNu2bWPXrl1otVp+//13srOzOX/+PL169bJ7TrVq1eLPP//E398/18n3H330EY0bN2bTpk0sWbIEo9FI6dKladiwIc8995y6X8uWLUlKSmLHjh0kJCSoX+KzZ89WFy24VzqdLk/zdUQRYVLnrguRq+K+HPLMmTP5+++/+fvvv4mMjMRoNKIoCm5ubjRo0IAXX3wxX/VqFHt5bSGEEEIIkW+azwzmCS2WFa8VBTQww/0nfF0zGDFiBKH1Ntl97ktnn354DRVF0orKq+yWD7r07ENuSf5otVoqVqxI48aNrX5q1659X0NYJeMihBBCCPEgWOa2gDpB39f11kITA//ozo+tfrV6Ssd5zR9S40RRVtyXQ75+/TplypQp8HolcBFCCCGKkMTERAwGwx33cXNzw93d/SG1SBQIzW3LiwHupd146ezTRG25REZCFg2G1CqctglRwB5E0AISuAghhBBFyhtvvEFYWNgd9xkwYACTJ09+SC0SD1qNnpXvvpN4pPxXlkMuaBK4CCGEEEXI5MmTrZaItsfX1/chtUYIIYoOCVyEEEKIIqRhw4aF3QQhRCEr7nNcHhS5M5UQQgghhBCiyJOMixBCCCGEEEVIcb+Py4MigYsQQgghhBBFiOk/MFTsypUrLFu2jKioKHx8fHj77bcpW7bsfdUpgYsQQgghRAHrUBF+v2JdVsmtcNoixMN29OhRgoKCMBqNVKlSBZ1Ox/PPP0/ZsmVZtWoVjz/+OJUqVcpzvTLHRQghhBCigO0Z5Mgngbcej20CUa8UWnNEMaNo7P8UF2+++SZNmzblypUrnDp1CkW5dQ+jvXv38vHHH+erXsm4CCGEEEI8AFNaOzKl9a3Her2+8BojxEN07NgxfvzxR7y8vDAajVbb2rZty8yZM/NVrwQuQgghhBBCFCHFfTlkZ2dnDAaD3W3ly5cnJiYmX/XKUDEhhBBCCCFEgWnfvj3Lli2zu02v19tkYe6VBC5CCCGEEEIUIYpGY/enuPjwww/ZuHEjb7zxBqmpqVbbdu/eTbVq1fJVrwQuQgghhBBCiALTqFEjFi1axKJFi6hevToajYZVq1bx5ptvMmfOHF544YV81SuBixBCCCGEEEVIcV9VDOCll17ir7/+omPHjri6uvLpp5/y1VdfMXToUMaNG5evOmVyvhBCCCHEA/bDG8eIOZUK1AEgqUcGZSo5FW6jRJGlaItZlJJDdnY2jRo14ttvv6VDhw6sW7cORVGIi4vD09MTFxeXfNctGRchhBBCiAfot891XD5lGeevATQsGXyiEFskxIPj7OzMtWvXrO7dotFoKFOmzH0FLSCBixBCCCHEA3ViwzXsXT/PTM166G0RxUNxn5wfFBTEkSNHCrxeCVyEEEIIIR4yDRC297r6OCLewLcnDWQaTIXXKCEKyLRp0/jqq6/4+++/C7RemeMihBBCCFEINI7moTQunxvI/jdeGbXdRPeqJn4ZIF20R1lxnuMC0LJlS1xdXWnVqhWvvPIK/fr1o0WLFpQoUeK+6pWMixBCCCFEIXAp4UDfNbeCFotfL4LRJJkXUXx98MEHdOvWjfLlyzN//nyCgoLw9vamYcOGDB8+nAULFuSrXgnnhRBCCCEeIHvXzhVA46Bh4wVAUf4tAP6dx9B3rYnNA+T68iOrGM1nseedd95Rf09ISODYsWMcPXqUo0ePcuDAAZYvX85rr72W53olcBFCCCGEeMg0gBYFsoxgylHorAWthsNXCrFxQhSgkiVL8sQTT/DEE0+oZcnJyfmqS0J5IYQQQogCkL5wP0kOb5GkeYukdl/dcV8FeHyn562gxVKYbQIFTMYH2VJR1Clajd2f4mLNmjVERUXlut3Lyytf9UrGRQghhBDiPqV+tB3jtG23CvZfJMllMt5Zs9RRYDlpgDijk+0lZMX8H20x6qQKcbtnn30WjUaDt7c3TZo0oXnz5jRr1oxmzZpRp06dfNcrgYsQQgghxH2yClosbp91f7s7zGPQK7luEo+A4nTPFnvOnDmjzmk5evQo33zzDampqWg0Gtzd3WnSpAl79+7Nc735Hir2119/8corrxAUFERAQABffXXnlGhezJs3j4CAABITEwusTmFf9+7dGTNmzD3tu2bNGgICAggLC3vArRL344UXXmDgwIGF3YyHIiMjg3nz5tG3b19at25Ny5YtyczMLOxm3bPVq1cTEBBAeHh4YTelwOzZs4eAgAB27typlv0Xz1MIW/YijdyjD3NiJZftGg0OBdEkUWwpGq3dn+KiXr16DBkyhC+++IK9e/eSlJTE3r176dmzJ87Oznh6euar3nxlXC5dusQbb7yBv78/r7/+Oq6urjRs2DBfDbAnPDyc8uXL4+Pjo5YdPXqUv/76i8GDB+Ph4VFgxyqOUlNT+eGHHwgICKB58+b5richIYEbN27QrVu3e9pfp9Ph4OBAzZo1833MoioiIoLdu3fTp08fKlSoUNjNuauVK1fi6elJr169rMoNBgNnz56lS5cuhdQyM0VRWLJkCbVq1aJjx44P7DizZ89my5YtvPDCC1SrVg1XV1dcXV0f2PEKWlhYGI6OjtSoUaOwm1JgIiMjAayGAjzs89y8eTMpKSk8//zzD+wYOp2OrVu3otPp0Ol0JCUlMWLEiDuukrN//36+//57wsPD0Wg0tG7dmokTJ1KuXDmbfZOTk1myZAm7du3i5s2bVKxYkWHDhtGnT58Hdk4in+ZsgP/9DJSxu/lUl19AcbfJrmgg98DFZMJUjDqpQtyNRqMhMDCQwMBABg0axFNPPZWvevL1V7F+/Xr0ej2ffPIJTz/9ND169KBKlSr5aoA98+fP5+eff7YqW7NmDStWrLjvG9f8F5w8eZLg4GASEhLuqx6dTgdA7dq173n/6tWr4+zsfF/HLYq2bt1KcHBwsej0ZmRkMHfuXM6cOWOzzdHRkd27d/P+++8XQstuuXDhAosWLSI6OvqBHSM1NZVff/2V7t2789prr9GzZ0+rFUuKg3feeYe9e/f+p/6mIiMj8fDwwM/PTy172Of55Zdfcvjw4Qd6jFWrVvHLL7/g6OhIpUqVAO44bnvJkiWMHz8ejUbDG2+8Qf/+/dmzZw8TJ05Eua3zGhsby9ChQ1m/fj1PPvkkEydOxMPDgxkzZvD7778/0PMSeeTUHyYtg5vJuBGLlmyrzQoaknZcwSnLYPNUBdDmlpDRau2voSweGcV9cv6dvPPOO8ybNy9fz81XxuX48eP4+flZfTHZk5WVhYuLS57rd3Jysik7ffo0devWRauVKxCnT58GoEGDBvdVj2XYxr0ELkajkXPnztG5c+f7OmZRdfr0aSpWrEjJkiULuyl3debMGYxGY67vf37+5gpaQX1G73YMvV5PixYt7rqv0WhEURQcHYvWtL6i0J78/judm8jISGrXro0mx9Xlh3meV65cIS4u7oF+9gAmTpyoXiB49913OXPmDPXq1bO77969e/n666/p168f7777rlru6urKN998w6FDh3j88ccBMJlMvP322yQmJhISEqJmqXr06EGPHj0ICQmhQ4cOD/TcxD0KiwbDrcjDmXQcySKFKtx+XdjRYMSAdd9GA7gZjKQ5ONwqcPj3F5MJjYP0d0Tx9ddff/HYY4/ZvWDl5+fHyZMn81Vvnr5Nvv32W7799lv1cUBAAACvvfYaCxYs4PXXX6dMmTL8+OOPREVF0a1bN959913at29Pp06dmDlzplV927dvZ+rUqXz55Ze0adMGgAMHDjBu3Dg++eQTunTpwooVK/j8888BuHr1qnrM+vXrExoaCpjT7ytXriQqKorExES8vLyoU6cOY8aMoW7durmez5YtW5g+fToff/wxTz75pNU2o9HI888/j8FgYNWqVeoX77lz5wgNDeXIkSMkJiZSoUIFevfuzZAhQ2y+nM+cOUNwcDDHjx9Ho9HQsWNHJk2aRK9evWjVqhWzZs2y2v/XX3/l559/RqfTodFoqF27NqNHj6Zp06YApKSk0KlTJ3X/3r17q7//9NNP1KxZk7Nnz7J27VqOHj3KtWvXAKhatSrDhw8nKCjI6ng6nQ5nZ2fc3d358MMP2b9/PwaDgSZNmvDWW29RsWJFdd/z58+TlZVlE+QYDAbWrVvHli1biIqKwtnZmQYNGjB27Fhq1aplte/JkycJDQ1Fp9MRFxeHh4cH1atX56WXXqJVq1a5vk8AZ8+eZeDAgYwfP54KFSqwYsUKIiIicHd3p0+fPjbzdPr27Uvp0qVZunSpVfnp06cZPnw4U6dOVa94Tpo0Sd1u+Xz5+PiwY8eO+253Qb9GgwYNIiIiAjDflfaDDz4A4O233+bZZ59l/vz5hIaGsm3bNkqVKgWglq1Zs4a1a9eyc+dOkpKSaNSoEe+//z5+fn789ttv/PDDD5w7d46yZcsyevRomyGEJ06cYOPGjZw4cYIbN27g7OxMzZo1eeWVV9TXDaBTp06kpKQAMGrUKLV87ty5BAYGAnn7O7L3erZr1w69Xg/A9OnTmT59OlWqVGHdunVMnTqVffv2sXr1ar799lv2799PQkICmzZtokKFCqSlpbFs2TJ27txJbGws7u7udOjQgbFjx1qNubX8W/Txxx+TmJjIunXriImJwc/Pj4kTJ9K6dWvOnDnDt99+y4kTJ3BxcaFXr1688cYbVp323Jw/f54BAwbwxhtvMGzYMCDvn3OAw4cPExISwpkzZ3Bzc6N79+4MGzaMbt26MXToUMaNG2d1PHv/Tk+fPh2AU6dOsXz5ck6cOEFaWhqVK1dm4MCB9O3b1+acjh8/zuLFizl16hQuLi707t2bF198kZiYGPV9zu08Le/jvfxdpKWl0bFjRwYMGEBQUBAhISGcPn0aJycngoKCmDRpkvrF+Oabb6oZiZzfV5bXQa/Xs379ejZt2kRMTAyZmZmULl2ahg0b8u677+Lu7n7X980i577h4eF4eXnZvZhnMBj4/PPPKVu2LBMmTLDaZhnqGxkZqQYumzdv5p9//mH8+PFWQ+vc3d2pV6+eelEgp7x+14g8uhIPn/4M/1yGjg1gQm9wc4GgaTa7ajHiRBpplCQBH7JwwVGrp2RcClc9rDP66Y6O1EhI429fJ3DQmAMXE+Y0jEaL6S7z+sV/W3GfnN+yZUucnJyoX78+zZo1U1cV8/Pz49tvv304c1zatGmDq6sr8+fPp0uXLrRr1w4wXyEC+O2330hNTaVXr17069cPPz8/zp07R3Z2tt0rUf/88w9gnV63DF+ylDVt2pShQ4fy/fffM2DAABo1agSgzkNYtmwZX375JW3btmXYsGG4uLgQGxvL3r17bdLvt/P39wfMnajbbdiwgaioKD777DO1I7V3716mTp2Kn58fzzzzDN7e3hw7doyvvvqK+Ph4Jk6cqD7/4MGDvPnmm/j6+jJkyBC8vLzYtGkTY8eOJTU11eqcFUVh+vTpbN26laCgIF577TWys7PZsGEDr776KsuWLaNu3booisKMGTP49NNPqVChAkOHDlXrqFq1KgBr167lwoULPPHEE/j6+hIfH8+GDRuYMmUKixYtolmzZlavtaenJ6+88gqtW7dmzJgx6HQ61q1bx8WLF60CttvfF4Ds7GzGjRvHiRMn6N69O7179yY5OZm1a9cycuRIfvzxR8qXLw+YPxvvvfcejRs3ZuDAgXh4eHDjxg0OHTpEWlraHd+nnMfftWsXiYmJ9OjRg+7du7Nx40aWLl2Kv7+/Oq8jJSWFmJgY2rZta1OPZWEBy3lUq1aNsWPH2nymvb29C6TdBf0ajRgxghUrVhAWFsa0abe+NC2Bg06no2zZsmrQYilzcXFh8uTJ1KpVixdffJGIiAjWrVvH//3f/+Hv78/Bgwfp0aMHXbt2JTQ0lA8++IDGjRurbQPz3xqYr/yWKVOG2NhYfv75Z8aNG8fKlSupUqUKBoOBt956i2+//ZbMzEzGjh2rPr9JkyZA3v6O7DEYDLz//vusXr3a6nUoU6aMer7u7u4MHz6cZs2aMWrUKDU4io2NZdSoUSQlJdGvXz+qVKlCZGQka9euJSIigqVLl+Lw79VPy2duxYoVODk50bdvXzIyMli2bBmTJ09m6tSpfPHFF/Tp04d27dqxYcMGQkNDqVev3j3NMbL3N5WXzzmYh+3OnDkTf39/Ro4ciZOTE6tXr1Y7t/bqtvfvNJj/7Zg1axYNGjRg6NChuLm5sXfvXj7++GMyMzOtFnzYunUr06dPVy+ugHkC/pkzZzCZTHf8Nx3y9ncRERGBoiiEhYWxZ88e+vTpwxNPPMHOnTtZt24dfn5+akDUr18/TCYT+/bt4+2331bnQ1qyL5MmTeLPP/+kV69e9OnTB0VRuHjxIocPH873MOT09HQuXbqU63zD/fv3c/nyZcaMGWNzDDc3NwDi4uLUsh9//BF3d3eeeeYZm7rc3NzIzMwkNTVVPbe8fNeIfMjSQ/v34Fys+fH2k3AsCn6YALG2N9BTACNablAWPU4oQExZbzQmEyVSMkj3cFXnuiiKwhWvEuagxTIESIN53oui4PAfGRYkHk2bNm1S56dv3bqVkJAQqwtgOfsweZGnwKVRo0bcuHEDgG7duqmTbi3j1JycnFi5cqXV5PkNGzYA2M18hIWF2e1kubu7U7lyZcC8KsGxY8cA85dSziv+JpOJpUuX0rlzZ/73v/9Z1W35Mr2T6tWr4+joaBO4pKen880339CsWTP1HC9cuMA777xDp06d+PDDD9XOTf/+/dFqtfz000+8/vrrODs7ExcXx3vvvUedOnVYuHCh+mXVs2dP+vbtC1h/iS9ZsoTffvuNzz77jPbt26vlvXv3pk+fPqxYsYIZM2bg5eVFYGAg06ZNIyAggB49etic09ixY9UvQ4tevXrRs2dP9u3bpwYu6enpXL58GUVRmDdvnnq1D8xfjt9//z2HDx9Wr5za63zMmjWLf/75h+DgYKvFGbp06UK/fv1Yv349r776qnqODRo0IDg42OqD+/LLL+f+BuVgOb6bmxuLFi1Sr7C2bduW3r17c/r0abVDZxkCl1uw7ODgoF7RrVatGufPnwesP9MW99vugn6NunTpwtKlS6lRo4bd91+n09kslKHT6cjKymLEiBFWmcWIiAj++OMPHBwc+OGHH9Qhmh4eHnz88cfqIhkW//d//2fz2WrTpg0vvfQShw8fpkqVKjg6OtKjRw9mz55N8+bNbdqYl7+j3Li6utKjRw9CQ0NtXoe0tDQuXbqEoih89NFHdO/eXd1mMBiYMGEC6enphIaGWs3L8/b2Jjg4mEOHDtl85uvWrcvUqVPV98QSvHz55ZcsX76csmXLAtCqVSv69evHqVOn8hS45Py3MS+f87NnzzJr1iw6dOjA//73P/UiQ6dOnXj66acB+4GLvX+njx49yqxZsxg6dChvvPGGWv7MM88wcuRIvv/+ezVwiYqK4qOPPqJt27bMnj1bfQ+feOIJ+vXrl+s55SzLy9+F5fkZGRmsWrVKbXevXr3o1KmTVQYiMDCQdevW4eXlxbPPPmv1ev/zzz8cOHCAyZMnM2DAALvvSX7odDpMJlOu2X1L5tbeIiiWO0dbsjfR0dFERETQs2dPu/PtkpOT0Wq16ndKXr9rRD5sPX4raLFY9wcstz/XyPyvhBH9v92rDGcnspyd0AAlbybjmZiG0VFLYilPSrhAhqP2VtCiVqABxbpYPIKK+fvfpk0bevbsqT6OiYnh+PHjXL16lXr16lll5vMizwMoLZ3C27+YNBoNM2bMsFnxy7J6yu3/qCuKQnh4uM0/qjqdDn9/f6uOW3h4OM7OzjYr0uj1ejIyMtQxzXnl5OREtWrVbAKX0NBQ4uPjrdL6CxYswMnJiTFjxpCSkkJiYqL6U6tWLYxGI1euXAHMV6ZTUlJ47733rK6weXh4UL9+feDWl0lcXBwhISF069aNxx57zKpeRVHw8/Pj0qVLVq8F2A8E4dYVPKPRqLbT0dERR0dHsrKy1P0iIiIwmUwEBQVZBS2Amqm4fPmyWqbT6ahYsaKa2ouMjGTjxo08++yzVKpUyardnp6eeHt7W03MTk5OJi4ujpiYmNzfkDvQ6XRotVqmT59u1am1dLZzjtG3ZFXsBS5hYWFUrVrVqlNwp9f0ftr9IF6j7OxsoqKi7Lb12rVrJCYmWv1NWcratWtnMxzSw8MDBwcH3n//fat5ZZa/4duHbFk+WwaDgeTkZBITE9XPd3b2rQmply9fJiUlxW4b8/J3dCdZWVmcP3/e5hiRkZEoikLXrl2tghaAbdu2ERkZyejRo20WE7FcLb948aJaptPp8PHxYdKkSVb/Hllen3HjxqlBS87ye53PodPp8PX1tVo9MS+f82+++QYHBwfeffddq2NWrFgRPz8/XFxcrM7zTv9Of/HFF/j5+TF48GCr9yQxMRF/f3+uXbum/vvx9ddfYzQaeeutt9SgBcDX15dq1arh7OxMtWrVcj3PvP5dWAKXd99916rdlnO+vYOv0+nsdtYtQYJOp7P6vN4vy783uf2bfOrUKUqXLm13GJnloonlQt2pU6cAeOyxx+zWdeHCBSpWrKjO9czLd01REB8fb/U9lJqaqg4rBfO/I7d/l1+9evWOj2NjY61GVxT4MYx2xmspCvqMLNvyf5nusJCxo8GIS6Ze7ZN6Z+nNGRYHDThrwUkLTuatJpOx4M6Dh/BayTHuWOejpmzZsvz111/qYz8/P3r16sXLL7+c76AF8jE5Pzw8HB8fH6srsZarvDm/rHLuX7lyZZsvyujoaJs0dmpqqt0hPjqdjlq1atl0CFxcXHjppZdYvHgxPXv2pHnz5rRr145u3bqpk6yNRqPN6luurq5qe/z9/dm6dSuZmZm4urpy48YNli9fTo8ePdSOb1paGvv370ev199xKUpPT08URWH79u00atTIZv6CRenSpdVhLXv27CErK4tffvmFX375xe7+OQOLO3Wys7Ky+Omnn9i6dSvnzp3DaDRabc/ZibHMkxg0aJBNPZYvxZxX1yMjI62GQvz2228oikJISAghISF2251z/OKoUaP49NNPefrpp3nssccIDAzkySeftPoc3bx50+r5zs7OeHl5qe1t0qSJzbKhZ8+eBbD67IWHh+Pi4mLzeczMzOT8+fN07drVqtzeZ7og2v0gXqPIyEiMRqPd99/y2cj5N/X/7N1neBRVG4DhZ3dTSUhCQggh1NCk9yKgVEFAEaRZQJqIolItKGADLCBSRFBBEaSDKIiNDtJL6JAESGghAZKQ3rbM9yPfDpnsJhBKirz3da2yZ87OnJndzZ53TrOm2ZttKzw8nLp162oq39Z0uNX9EDK/mz///DObN2/m8uXLavdQK2vFK+sxs5cxL98jyJyuO+tn2MHBQVP5NZvNNpUy67Gzjv+y2rx5M05OTnZbqqysn3nr36KnnnrKpvUnPDwcJycnTeuoNR201y23z3RISIhNBfVOP+fWa9m+ffscJ5SoWrWqJrDI6e/0hQsX1OuW/bth5ezsjLOzMykpKezatYt27drZnTbcbDZTuXJlzd/q7OeZ1+9FSEgI/v7+ajdhq8uXL5Oenq653nFxcVy7ds3ueTRq1IjmzZvz22+/sXnzZlq2bKmOv7yX2c5y+5tsMpm4evVqjt3IrINTrdfHepPK3m/phQsXiI+PV38P8vpbUxhk7V0B2NQLnJyc8PHx0aRl/5xlf5797/Z9P0bnBlDWB65kqZR2bojj4A7w5g9kZ8GAmWI4YMaEA64ZRlzTM0h1vvUZS3d2xOic2QoTkJTOdU9XzIasrS460KNZs6NIXCs5Rq77zKuiPsZFURSbuoLV2bNn+eabb5g5c2ae95vnwCX73azIyEji4+M1g3OtzGYzZ8+etfmBh1t3lrJ2/QoJCUFRFM0PQFpaGhcvXsyxojNs2DC6du3K9u3b2bVrF9OnT+ebb77h66+/pn79+hw8eJA33nhD85pXX32Vl19+Gcj8cf/rr78ICwujZs2azJ07F0AzF394eDhGo5GXXnopx8HYOp0OHx8foqOjuXHjhs1AeKuwsDDN9QsNDUWv1zN79uwcB/RmrcQEBwfj4uJi88NmNBoZPnw4J0+e5Omnn+aFF16gRIkSODg4qJMX2Ou+Ya9Vwtr1wrrt6tWrJCQkaN6r0NBQfHx8+OSTT+yWGVCnCIXMbn6tWrVi+/bt7N69m3nz5jFv3jwmT57ME088wcWLF+nZs6fm9c888wwTJ04kIiKCxMREm4qL9XpkP4/g4GCqVatmE+iePHkyx8puTndL76Xc9/saZT1fe+XNretR9muXWwUvODgYd3d3tWwJCQkMHDiQ69ev06NHD2rWrImXlxd6vZ7ffvuNTZs2aY6ZUxnz8j1KS0vjySef1AQuTZs2Vb+fOR3DutaQdUKLrM6fP0+ZMmXsdsGxBgbWz7j1b1FOn7kqVarY7Cd7mXL7bFj/bmYtf14+52FhYRiNRrufg7S0NCIiItSuQpD732nrTYxRo0ap4/6ys57r+fPnMRqNdmfsSkpK4sKFC5qg0d555uV7YTQaCQsL00xKYmXvu599DFtWjo6OzJkzh+PHj/Pvv/+yY8cO/vnnHwICAli0aJGm5SsvgoODcXNzs7skgHVsmjVYzSo1NZVdu3ZRrVo19XyTkpJyzP/PP/8At25CxMTE5Om3RtwlV2fYMQkmrYZTl6FtbZjQG4o5Qyl3uJ6kZrWgJ4kyGNDhyw1uUoI0nCkTncCFUt4kF3fB6OxIoldm61icsxMmnQ53o4l4l2zVMVmAUhRB69evJzg4mEaNGqHT6XKs18bGxvL1118/+MDlxo0bxMTEaPqs2assWUVGRpKWlmb3x9D6R/h2gzhzu8NsVbZsWfr160e/fv04ePAgr732Gn/88Qf169cnMDCQb775RpM/6wKKWQfoOzo68scffzB48GBNsGD9MSlXrtxtZ5Gyrtptb9rmAwcOcO3aNU0XlqSkJHQ6HQ0aNLijKUmtXemy3kkF2LJlC8eOHWPcuHE2gzoXLVqEXq+3CRL1er1NOU0mE2vWrCEwMFBTiQNsWsf0ev0dzapl5evrS+/evenduzfh4eG88MILrFu3jieeeAJPT0+b98l6F99asbIXZAUHB+Pq6qredTUajVy5csXuHU57n7no6GhiYmLs3qG/13Lf72sEt943e1NYWydbyDobnHXMWPZKVW7dW6xdOK1/cNasWcOlS5eYNWuWpjXUYrHwxRdf4O3tjZ+fn+aY9u705uV7BJmzoWWV9TuZ03UICQlRF6K0x95U64qisGHDBvz8/NTuNTn9XbPeSMkaFFgFBwfj6Oio/n3J7bNh7zuVl895bn9n/v77b0wmk8333d75wK33pUqVKrd9X1JTUwHsTnyyfPlymxsD9/q34/z585hMphyvCdx+PE12devWpW7durz++ut8++23LFiwgH379tl0pbwT1s9DvXr17P5AFy9eHEdHR/W6ZbVmzRpSU1Pp27evmmZtPcuePyUlRZ2IwNrFIq+/NeIeBJaGhW/apq97Hx59X32qx4Ib10jGH2fAnUSu4wUWHW5pJi5Vu9U6qgAR7m6kGXQ4Ge3flZZZxR5uRXHNlvDwcMaPH6+2tPTo0YMmTZrQqFEj9VGqVCnOnTtn9wbNncjTGJe8/lBY//impKRo0tesWcO+fftsFikLCQnB0dFRM5bF2t8/e//g9PR09Qc3K+tddmtFqlSpUjRr1kzzyFqhyhq4zJo1C29vb82UnXDr7t/GjRvt/mBn7ddYunRpDAYDhw8f1uSNi4tTJxDI+iNerlw5zGYzmzZtstkvZEalWUVERGgqplbWvpTZW2KWLVvGgQMHqFChgtoH2mQyERYWhsVi0fQ/BPjpp5+4evWqpsXJ3kKVZcuW5caNGxw+fNimLGazmbi4OPW5vYUynZ2dsVgs6vvk5eVl8z5ZzzO3z1hwcDBVq1ZVf7zT09OxWCw2P/y7du1SJ4rIeh7Wz5e9a3qv5b7f18haXm9vb7sVc3t9+61p2StVOY0DsrbEZL3W9j5biqIwY8YMLl26ZPO+WKcMzi4v3yMXFxeb61qpUiV1e3BwMOXLl9d0Z7Tenc+p0hoYGMilS5dsum99//33BAcHM2zYMPVzFBISgoODg00XnJCQEMxmc44V6cqVK6vBUV4/03n5nFuvb/bv7+XLl9VgyV4AYW/f1mDKGthnl/V9sR43++KO4eHhLFmy5I6Om5fvRW4tw9YZ9LJ2z8jp9yI+Ph6TyXYBQOvvhb1uonciNDQ01xtrer2ewMBATp06pfmbFB4ezoIFC6hRowZPPfWUmm79PTp48KBmPzNmzCAmJobRo0drypyX3xrxADR/JHNsShYmXFD+f0/YnVSqcgGAiDLaLp06oFbMTYJ93LnhYedGi6JgLnr1VnEfKTqd3UdhNnLkSBISEvj3339RFIVGjRpx9epVpkyZQteuXfH398fX15dBgwbZ7Y11J/LU4pJTH/qc5q8vV64cnp6erFixAsi8A3nw4EFu3LhB8eLFbQbhh4SE2PSPtlZ2Fi1axPXr13FwcKBp06ZcuHCBUaNG0aZNG6pWrYqnpyfh4eGsW7eOsmXL3vGsMSVLlsTb25u//vpLnaEl+8xJZcuW5YknnmDTpk0MHDiQDh064O7uzrVr1zhz5gzR0dEsXboUyPwh7NatG7/++isjRoygdevW6hoQ1spm1uv37LPPsnr1aiZNmkRQUBA1a9bEaDQSGRnJrl27ePbZZ+nXr5+aPyAggH379rF8+XI8PT0pU6YM9evXp2HDhuh0OqZMmUKfPn1QFIXdu3erA1Kz/rCGhYWRkZFBrVq1mDBhAv369cPLy0vtPjFo0CDNAmchISF4enpqftxfeOEFtmzZwsiRI3nmmWeoUqUKycnJREREsHPnTsaOHUu7du2IiIigZ8+etGrViho1auDj48PVq1dZt24dxYsXZ9CgQbd9j6wtCVm7VcGtcQhZxwBZ1z3ZtGkTJUqUwN/fnxMnTnDy5En8/f2xWCzqVMeAOtB1zZo16PV6nJycqF27NgaD4Z7L/SCuUUBAAAcOHOCbb75RWxbat29PfHw8UVFRmrEs1rScutlknb0vazpoPy+NGzfm119/5d1336V79+6kpKSwZcsW9Y5K9kplQEAAhw8f5qeffqJUqVKUKFGCRx99NE/fo9yYTCbOnz9vc17Wu/M5VSIHDx7M7t27ee211+jRoweOjo5s376d/fv3079/f0131JCQEAIDA23GPuTURc165z23lrvs+8n+ncrL57xMmTI0b96cnTt38v7779OkSROuXr3Kr7/+ipubGwkJCZqgK7e/040aNaJu3br8/vvv3LhxgxYtWuDk5MS1a9c4duwY7u7u6lpaAQEBNG3alH379vHOO+/QvHlzIiIiWLNmDU5OTqSmpmpa2O2d551+L6zXxN71tm7LPkbIen7Tpk2jbt26GAwGOnXqxIoVK1i7di3t2rWjQoUKODg4cOzYMf755x/atm2rTtV9J86ePauuFWMt35UrV1iwYAEA3bt319wcGzRoEOPGjeP111+nS5cu3Lhxg5UrV+Ll5aWZlQ3gscceo3LlyixYsIC0tDRKly7Nli1bOHDgAK+++qpm1sO8/taIB8T0C7w6FxbvJDHVFwvaIMSFdFxqepLgbX+6bR0KOS7ckPuKDkIUSq6urrRo0YJnn32WTz75hJo1a2I2mzl58iSHDx/m+PHjeHl5qWuM5VWeAhd73U5CQ0Nz/OPo4uLCF198wVdffcWyZcsoWbIkTz75JOPHj6dLly428/qHh4dr7j4B1K5dmzfeeIM1a9YwZcoUzGYza9eupUSJEnTq1Iljx46xc+dOIHMg1PPPP88LL7yQp4VtqlSpwoEDB6hatWqOY2kmTZpEvXr1+P333/nhhx8wm83qwmVZm/ohcxE0BwcHtm3bxtGjR6levTrjxo3j999/JzExUVNZLFWqFEuXLmX+/PkcPnyYv//+m2LFilG6dGlat25ts1L9Rx99pI7jSUtLY9CgQdSvX5969erx4Ycf8sMPP/D1119TunRpunTpQtOmTRk8eLDdO7ujRo3i2LFjrFq1Sp3VacqUKTbTdtq7k//II4+wePFiFixYwM6dO9UpSAMCAujWrZval97BwYEePXpw9OhRDh06hNFoxN/fn44dOzJgwIA7GjgaEhKSY8Ul+5goyJy29/PPP2ft2rV4enrStm1bfv75Z3r06KFZxwYyu2ZNmDCBn376ic8//xyTycScOXOoVKnSPZf7QVyjYcOGERMTw5o1a0hMTKRu3bq0b98+1zVB7qQ7WNb07K/p1KkTUVFRrFq1ihkzZlCuXDm1cjZu3Dib/Y8dO5YvvviChQsXkpyczJNPPqkuMJuX71FOrIF39uPebsa9OnXqMH36dObPn8/XX3+Ni4sLNWrUYPr06ZpA3fq3KGuX2KzHsNcSc+7cudt2ac3K3t/NvH7OJ0+ezLRp09i3bx+7d++mbt26fPnll0ybNo1q1appWuVy+zut1+uZM2cOy5YtY9OmTcybNw+9Xk/JkiWpX78+3bt31+SfMmUK06ZNY//+/ezdu5cqVaowceJEfv75Zzw8PDQ3fuwd906/F9ZzDwgIsPl7bm/sDMBzzz1HWFgYW7duZe3atfj6+tK5c2dq1arF+fPn2bVrF+vXr8fFxYXy5cszYcKEXCdrsGfjxo02kwrs3LlT/R3K/jnu0KED7777LkuXLmX69On4+vrSvXt3Bg8ebNNVQq/XM3PmTL788ktWrlyJXq/nkUcesemmaZWX3xrxAH07HL4djkX3ls0mHdDkVG+2tt5pf3Zbi5K56KS9TfezjKLIKeytK7ezZs0a9d8Gg4F69epRr169e96vTrndKo3ivkhISKBz58506tTprhfdEUKI3Fy4cIFevXppJiARDxf5rSk48bq37aZ7KtP4IofA5f02jTA7O2ZOg5w1g6LgqdcRNzbPcyiJ/4gZj26xmz56r+0soQ+TPK/jInJnMplspiHOyMjgww8/xGQy8eKLLxZQyYQQ/xVZ1xOwSkpKYuLEibi7u6sLUIr/LvmtKXz0k+1MJ+6Y+11z8/+nPralo4jfcBf3qCiOccnqxo0b9OjRA29vb8qVK0fnzp2ZOHEi69atu+s1/eAupkMWuTt69ChTpkzhySefpFSpUly/fp2///6bK1eu8M4772hmNBNCiLuxceNGVqxYQdu2bfHx8SEiIoLff/+dhIQEPvvsM5s1B8TtJSYm2g0Is3J2ds5TN+QHSX5rCp/i458gxasYxjd/yxyf0rICnrveyPU1pYyJXHctYbtKug7pKyaKtLfffptDhw7xxhtvMHnyZIxGI//884/aRd3Pz++OFpzOTgKX+8w6PmX16tUkJiZSvHhx6tSpw4QJE3JchEwIIfLCx8cHZ2dnli1bRnJyMl5eXjRq1IiBAwfanSpb3N6kSZPYunVrrnlatWp1V+sOPAjyW1M4FXu9JbxuOx4pp/vku7pbqLbNzgYFdFJDe6gVpdYVezZt2sTkyZN56aWXmDx5Mn/++ScGg4ERI0Zw9epVuwtj3wn5WtxnNWvWZN68eQVdDCHEf1iLFi00s4yJe/fKK6/YLBianXWdlcJAfmv+I0yAnbV40MGjZW2ThSgqYmNjNdP4G41GGjZsyNatW2nZsqVmlsS8kMBFCCHEQ69KlSo2s8UJ8aCZDQpdK8AfF223re8pw5AfZkW9xaVkyZIkJCSg0+nw9fVV1wQzGAyMHDmSr776iueeey7P+5VvhRBCCCFEAbCYLWzo7YBTtjpq5/JgsNcSIx4aRX1wfqNGjQgNDQUybwzt2rVL3ebr68upU6fuar/S4iKEEEIIUQAc/j+QJX2sA2djTGy9BAPq6HFxkKBFFG3Dhw8nKCgIyFwAesyYMTRu3JgaNWowdepUzcLEeSGBixBCCCFEAShX89a4qao+DlT1KcDCiEJF0Red1hV7OnbsSMeOmVOEv/TSS6xatYpevXqh0+nQ6XT88MMPd7VfCVyEEEIIIR6gKq29Obcj1ibd1cO5AEojRP5ydHTk77//ZuPGjVy6dIlmzZpRt27du9qXBC5CCCGEEA/Qs5/U5vsX9xN3JZ3MRV4U+n1Xv4BLJQqzojSeJTeKonD16lW8vLzo1KnTPe9POlEKIYQQQjxgryxtxujNj+L1YgheL4biW9mtoIskxANjNBoZO3Ysbm5ulC9fHk9PT44dO3bP+5XARQghhBBCiEKkqM8q9vHHHzNv3jzGjBnDkiVLsFgsmM1mAHr16sXChQvvar8SuAghhBBCCCHum2XLljFp0iQmT55Mnz59NNsee+wxVqxYcVf7lTEuQgghhBBCFCJFqXXFnsjISBo2bGh3W2BgIKdPn76r/UqLixBCCCGEEOK+CQwM5MSJE3a3eXl5ER0dfVf7lcBFCCGEEEKIQqSoj3EZPHgwn376KefPn7fZdvHiRXx87m7RIglchBBCCCHySXiyB8MSBuI0C3RfmkhMMxV0kUQhVNQDl5EjR+Lv70/Tpk2ZMmWKuvDkzZs3mTFjBk2bNr2r/eoURVHuc1mFEEIIIUQ2RqMRp1kA2gqo8pYMORZaUzrstZs+fvOj+VySuxcfH8+QIUNYu3YtkNlFLCkpCScnJ3bv3k29evXyvE/5pgghhBBC5AN7QQuA11cm4sZIlUzcUpRaV3Li6enJmjVrOHHiBH/++SeXL1/G29ubF198kerVq9/VPuVbIoQQQghRgOItBV0CIe5d3759mT59OmXLltWk16lThzp16tyXY8gYFyGEEEIIIQoRRWf/UZitXr2aq1evatJymlnsbkngIoQQQgghhLivzGYz9evXJygo6L7tU7qKCSGEEEIIUYj8F8a4ANzvOcCkxUUIIYQQQghR6EmLixBCCCGEEIVIUW1xuXHjxgPdvwQuQgghhBBCFCKWIhq4dOvWDQ8PD+rVq0edOnXQ6XRERETQsGHD+7J/CVyEEEIIIYQQ9+SPP/4gKCiIoKAgDh8+zM6dOwHo3r07bm5u1K9fn0aNGtGwYUMaNmxIrVq18nwMCVyEEEIIIYQoRBQ7C5UWdp07d6Zz587q89jYWDWIsQY0s2fPRlEUdDodZrM5z8eQwEUIIcQD995777Fnzx62b9+Orgh0gejcuTOVKlVi7ty5BV2UuxYSEsKLL77IqFGj6NevX0EXRwjxkPH29qZDhw506NBBTUtISODw4cMcOXLkrvYpgYsQokhKSkqibdu2mqkWXV1dCQgI4Nlnn6VPnz4AnD17lueffx6AV199lZdfflmzH0VRaNu2LeXLl2fx4sVq+ttvv822bdtwdXVl06ZNuLi4aF63ZMkSZs6cycyZM2nVqpWafv36dZYuXcqePXu4evUqer0eb29vqlWrRqdOnTR/wIu6F198kZCQEPW5g4MDfn5+tGnThqFDh+Lu7q5uCwkJoVq1avkStCiKwg8//ECVKlVo06ZNnl9/8+ZNbty4QadOne5/4fLR2bNnAahevXoBl+QhkJ4Ors+DAvgVh6hF9vOZLWAw5GvRRNFUVAfn346Hhwdt27albdu2d/V6CVyEEEVScHAwiqLQoUMHHn/8cQCio6NZvXo1U6dOxWKx8NxzzxEcHAyAwWBg69atNoHLlStXSEpK4pFHHrHZv8FgIDU1lX379tlUgK0V9qyvCwoKYsyYMQB06dKFwMBAjEYjFy9eZNu2bZQsWfI/E7gYjUbCwsKoXLkyAwYMACAlJYW///6bpUuXcvXqVaZNm6amX7lyhUcffTRfynbhwgW+/fZbRowYcVevt7631apVu5/Fynfnzp0Div55FAkuz9/697VE0D0LylpNlipv3sC9hCtJHhK4CHG3JHARQhRJ1oCkc+fOtG7dWk1v2rQp/fr1Y8uWLZrApXPnzmzYsIErV65QtmxZNf+ZM2cAqFGjhpoWHx9PZGQkbdu25cCBA2zZssUmcDlz5gy+vr6ULFkSgNTUVMaNG4ebmxs//fQTvr6+mvxvvfUW8fHx9+8C3CGz2YyiKDg43N8/9+fPn8doNNKsWTO6dOmipj/99NM89dRT7Ny5E6PRiKOjI6GhoVgslny783/y5EmAuxr4Cbc+W0W9wh8aGoq/vz+enp4FXZT/Nt2z9tOHz4W5w4HMoAXAKcOUX6USRdx/tcXlXkngIoQokqyVy+yVU2sgkZycDGQGGMWKFWPgwIFs2LCBzZs3M3DgQJv9ZG05sQYzderUwcnJiX///VethENmkHLp0iVatmypvmb//v3ExsbSv39/m6AFMrtR+fj45Okco6KieOqppxg0aBCOjo78888/REZG4uPjQ9euXXn55Zc1Acl7773Hv//+y+rVq/n+++/ZtWsXN2/e5Pfff8ff35/k5GQWLVrEli1biIqKws3NjdatWzNixAiKFy+ep7JZr1HNmjU16c7Oznh4eHDz5k3S0tJwdHS024KRkZHB0qVL2bt3LxcvXiQpKYmSJUvSrl07XnnlFVxdXdW8O3fuZMyYMXz++eckJiayZs0aLly4QIkSJejfvz99+/ZV87Zt25bExEQAhg0bpqZn79KXm5CQEJycnHBzc+Pjjz9m165dmEwm6tevz9tvv02ZMmU0+f/66y82b95MSEgIsbGxeHp6Ur9+fd544w0CAgI0eY8dO8bixYsJCQkhJiYGd3d3KlWqxJAhQ2jWrJma7/z58yxevJiDBw8SFxeHv78/Tz/9NP369bMJQs+dO8d3333HwYMHAWjXrh1jx47l3Llz1K1b947OWTwA8zargYuVs/n+riIuxMNGAhchRJEUHByMn5+fGqhY7d27F8gMaCwWC2fPnqV69epUrFiRypUrs3XrVpvAxdHRkSpVqmjSIDOYCQgI4J9//mH//v1qxTckJASLxaIJdlJTUwEICwvTBDn3IjQ0FID169dTrFgxunfvjrOzMxs2bGDBggW4ubnRv39/NX9ISAhubm4MHDiQhg0bMmzYMLXSGxUVxbBhw4iPj6dHjx6UL1+es2fP8ssvvxAaGsqPP/6IIQ99763XqHbt2pr0y5cvc+XKFcqXL68GQyEhITg4OFC5cmVNWX/99Vdat25N+/bt0ev17N27l59//pmEhAQmTpxocx2WLVuGyWSiY8eOuLi4sHLlSqZNm0atWrWoXbs2JpOJt99+m++//560tDRNV7H69evf8bmFhIRQvHhxXnnlFZo3b87w4cMJCQlh7dq1XLx4kVWrVqnBg8ViYfbs2TRv3py+ffvi7u5OaGgov/32G6GhoaxcuVLN+88//zBhwgTq1avHc889h7u7Ozdu3GDv3r1qoA2Zgdp7771HQEAAvXr1wtPTk6CgIObMmUNsbKzaHRHg0KFDjBw5Ej8/PwYOHIirqyt//PEHo0ePJjY2Vsa3FDKuaSYcTBZMDvqCLooo5KTFxT4JXIQQRU5KSgqXLl2iefPmxMXFAZkDqvfv38+8efPw9PRk0KBBXLhwgdTUVLUbWLt27Zg/fz5RUVGULl0ayKyABwYGagKNrIGLk5MTzs7ObN26VQ1crK0NWQOXJk2aULx4cXbv3k3nzp1p1aoVjRs3pnnz5jbB1Z2ytlSUKVOGuXPnqq0QTz31FN26dWPdunVq4JKcnMzly5dRFIVJkyZppqQ0mUyMHj2alJQUFi9eTPny5dVtnp6ezJ8/n717995xi4T1Gri5ueHu7k5cXBzJycmcOXOGuXPnoiiKJmgICQmxucZVq1Zl3bp1msH6vXv35vXXX2fHjh2awMV6HSpUqMDEiRPR6zMrfVWqVGHYsGGcPHmS2rVr4+DgQJcuXZg2bRqNGjXSdGG7U9bxOIqiMGvWLFq0aKFuc3V15eeff2bfvn3qtVIUhd9++w1nZ2fNfnx9fZk7dy4XL15UA7YffviBWrVqMX/+fM15Dx06VP33hQsXeP/992nbti0ff/yxGkz27NkTvV7PypUreeONN3ByciI2NpZ3332XypUr8/3336sTSHTr1o1nnnkGKFwD82NjY3Fzc1OvVVJSEoqiqAFuRkYGiYmJmpbJyMhI/P39c3weFRWFn5+fej0L4hgW4PZhiALo0AFlriUSXcKVdCcDZgfrzQIjimIo0POQYzzYY4j7Q0J+IUSRYx0zsWfPHnWqxd69ezNjxgwaNGjAwoULKVOmjBqAWCtv7dq1A2DLli0AREREkJCQoBnfApmBS0BAAB4eHri4uNCiRQt27NiByWRSt4N2XEzJkiVZvHgxPXr0QK/Xs2HDBj766CO6dOnCe++9p3Zfyut56nQ6PvzwQ03XKXd3d2rWrElkZKSadvbsWRRFoWPHjpqgBWDjxo2cPXuW1157TRO0ADRq1AiAixcv3nG5TCYT58+fJzk5Wb3+zzzzDOPGjcPFxYVZs2apY4JMJhNhYWE2FWgXFxe1UpCSkkJcXBxxcXF4e3uTkZGhyWttAXn33XfVoAVQA6GsM75duXKFxMREm8kW7pT1s9WuXTtN0AKoXQOvXLmiphkMBrUyk56erp6Hh4cHgOZcEhISiImJISIiIsfjf/PNNzg6OjJ8+HASExPV/cXFxVGlShXMZjNXr14FYNGiRcTHxzN27FjNNXB1daVOnTpA4Rqn4+3trQnw3N3dNV0UnZycbLpTZq/4ZX9eunRpTRBYEMe4s4rUrdfHuTuT4uqYJWgBcCzw85BjPNhj5JWis/942EmLixCiyLG2eIwaNYqqVaui1+spVqwYFStWxM3NzSaftRJbtWpVypUrx9atW3nxxRfttpwkJSURERGhmaqxXbt2bNu2jcOHD9OsWTPOnDmDt7c3pUqV0pSrXLlyjB8/nvfff58LFy6wZ88eli9fzqZNm3B1deWDDz7I03mGhIRQr149KlasaLPNYrFoghlrMPX000/b5N28eTNOTk65tkBk3dfthIWFkZ6eTteuXenSpQs6nQ5nZ2cCAgJsWpesXeeyBy4HDhxg6dKlHD16VNNNCrStBImJiVy9epWnnnrKZkpq66xZWa+PvTFLeWHtlvbCCy/YbLMGTVmv1aVLl1i4cCF79+4lOjra5jXlypVT/z1s2DCmTp3Ks88+S926dWnVqhVPPvmk2vqXnJzMrl27MBqNdOvWLccyWitMGzdupE6dOtSrV88mj8ViwdPTU923KBzSHfQkeDiDdAMSt2GRz4hdErgIIYqcrDOF5TbgPSQkBGdnZypVqqSmtWvXjsWLFxMdHW23kmudZjlr2mOPPYajoyNbt26lXr16XLx4UTOQOjudTkelSpWoVKkSjRo1ol+/fgQFBeXpHK0V9qwzplmZTCZCQkI0ZQwJCcFgMNCgQQOb/OfPn6dMmTI2FX+4uylzrdetZcuWuV4Ha7my73/16tV88cUX1KlTh1dffVUtW0ZGBmPHjrU5L7AdS2Mth16v1+z7XgMX6/Gyt8LBrdnKrNtOnjzJ8OHD8fDwoFevXlSsWBF3d3d0Oh3Tpk3DZDJp1rLp0aMHrVq1Yvv27ezevZt58+Yxb948Jk+ezBNPPEF4eDhGo5GXXnopx+uq0+nw8fEhOjqaGzdu0L59e5s8ZrOZ06dPF6puYv9pns4Qn26bfvnW4qUlXSE6FWI8XSRoEeIeSOAihChyrC0euQUtiqKoix5mHXTetm1bFi1axLZt29S1WqpWrarZN2grru7u7jRp0oTt27fTuXNnzGbzHVeMnZycAPI8a5e1Am1vGuMNGzYQHx+vWSAxJCSEihUr2g1OALuTBSiKwoYNG/Dz87OZHSw31muU9brlJCQkBJ1OpwYXGRkZfP3119StW5f58+dr3ps///wTi8Wiufa5BRLBwcGUL1+eYsWKafL7+Pjc07givV6v6ZIGmcHimjVrCAwMVM9l3rx5WCwWlixZgpeXl5o3MjKSS5cu2Q0qfH196d27N7179yY8PJwXXniBdevW8cQTT5CUlARkttLcLiBMS0sD0CzAavXXX38RExNzV2N8xF2IW25/SuSyt1q79k31pd7YG6S6SrVL3BkZnG+fjHERQhQpaWlpXLx4UTMLmD2XLl0iOTnZJsCoXbs2fn5+bNmyRR2Yn7Xvck537Nu1a0dMTAwrVqwAtBXpAwcO2IzLgMzuOvPnzwewGXdyO9YK++HDhzXp0dHRzJ8/n8qVK6sVU+tikDkFU4GBgVy6dMmmK9P3339PcHAww4YNs6mo365szs7ONuNlcspbpkwZteUhISGBlJQUypUrpwlaLl26xIwZMwBsWlwcHBxsgiTrOJvs5xwREWEzBfGdso7HsVgsHDp0SLPtp59+4urVq7z++utqWmRkJCVKlNCsk5KamsqECRNsZp27efOmzfGcnZ2xWCz4+fkBqOsLbdy40W5AEhMTo/67dOnSGAwGDh48iMViUdOjo6OZN28eULgG5v/nKWvh7/HQphZc+8Fm8UmAY9N9wSDVLiHuhYT+QogiJTQ0FLPZfNu7/dZWAXuVt3bt2rFixQoUReGxxx6zeZ2fnx8lSpTQpLdp04bPPvtMHdiftVI6ZcoUkpOTadu2LVWrVsXJyYmoqCg2btzIpUuX6Ny5s2atkTs9T09PT2JjYxk9ejStWrUiJiaGX375BUVRmDZtmtoac/78eUwmU46By+DBg9m9ezevvfYaPXr0wNHRke3bt7N//3769++f63iK7MxmM6GhoVSqVOm20ycrikJoaKim9aBkyZJUqFCBv//+G29vb8qVK8e5c+fYtGkTHh4eJCQkaN5b64xk1pYrq/DwcNLT023OOSAggMOHD/PTTz9RqlQpSpQowaOPPnpH5xYWFkZGRga1atViwoQJ9OvXDy8vL/7991927NjBoEGDNF33GjduzNq1axk3bhzNmzfn+vXrrF+/Xv3sWIPbiIgIevbsSatWrahRowY+Pj5cvXqVdevWUbx4cQYNGgRkBi5PPPEEmzZtYuDAgXTo0AF3d3euXbvGmTNniI6OZunSpUBmS9xTTz3FunXreP3112nfvj2xsbHq5wPuvrucuEudGmU+hLgPpMXFPglchBBFirVF5HaBi7XFwl7lrV27dixfvhzQBjbWqXDtTQvs5eVFw4YNOXjwIJ6enpoZY4YNG8aePXsICgpi06ZNpKWl4eXlRe3atRkzZkyephnOWv7q1aszevRovvjiC7766itcXV1p2bIlr732mmbQ9e3GddSpU4fp06czf/58vv76a1xcXKhRowbTp0+3O4YmNxcuXCAtLe2OuolduXKF5ORkm/Ez06dPZ+rUqaxZswZnZ2eaNGnC/Pnzef/99zUtYBkZGVy4cIGuXbva7Dun93fs2LF88cUXLFy4kOTkZJ588sk7Dlys+xw1ahTHjh1j1apV6mxeU6ZM0XTNAxg5ciQmk4kdO3awd+9eatSowfjx49m7dy/BwcFq2RwcHOjRowdHjx7l0KFDGI1G/P396dixIwMGDNB0a5s0aRL16tXj999/54cffsBsNuPj40Pt2rVtgt+33noLBwcHtm/fzvHjx6lQoQJDhw7l8OHD/Pvvv3fUIiaEEEWJTrHXHi2EEKLAZGRk8Pjjj9OnTx/NYoNCiKJN96WRrFMjZ6W8JfeSxS1v9ThhN/3LX+vkc0kKF+lsKYQQhYy169ftxvEIIYQQDxMJ74UQIh/FxcWpC1nm5OjRo8Cdzdp1P8XGxmoGetvj7u6e48xlhVVGRgYJCQm3zefj46NZdE4IIQqKLDZpnwQuQgiRj95880114oCc9O7dG71er1l/Jj/07t2b+Pj4XPOMHDmS/v3751OJ7o/9+/czevTo2+bbvHmzZlpjIYQoKEoOXQofdjLGRQgh8tHJkydtVorPzs/PT7MafH4JCgrCaDTmmqdChQpFbjX2uLg4deB9bho1amR33Rwh7hcZ4yLu1JhnT9pN/2qt7WK8DxP5lgghRD6ytwJ8YdGwYcOCLsID4eXlddsFHYUQojCxSLdVu2RwvhBCCCGEEKLQkxYXIYQQQgghChFZgNI+aXERQgghhMgHTgDYDi0eUze/SyJE0SSBixBCCCFEPkgaaT99ekfpACO0FJ3O7uNhJ4GLEEIIIUQ++c5jITU4DygMryOziQmRF/JtEUIIIYTIR6M8djBoUCCOjlINE/ZZpHHFLmlxEUIIIYQQQhR6EuoLIYQQQghRiMh4FvskcBFCCCGEEKIQsSCBiz3SVUwIIYQQQghR6EmLixBCCCFEPgm/Uo6UFDcyMsw4OjoWdHFEISVdxeyTwEUIIYQQ4gG7EZPOsLei0OlqAfDSG9doWVPHmLfLF3DJhCg6pKuYEEIIIcQDNvTta+iy3kXX6Thw3MSFM8kFVyhRaFl09h8POwlchBBCCCEeMHsVLkWnY83Mi/leFiGKKukqJoQQQgiRDyz/f8D/AxkdxEWlF1yBRKFlkTEudkngIoQQQgjxgKlBy/8rpGZFwaTToTdIBVWIOyVdxYQQQgghHpCbKSZ0n6Zrghb+/2+TXo9f1WIFVTRRiCk6nd3Hw05aXIQQQgghHhDvry2g16EDFDvbUxNN+V0kUQTIQHz7pMVFCCGEEOJBUQCHLNUtRcl8ADrg+g1zgRRLiKJIWlyEEEIIIR4Ufeat8xSDDnezgnojXVEwAwYZ4yLsyPJJEVlIi4sQQgghxAMWV8wZPZmtLNaHV0oqTsisYkLcKQlchBBCCCEeFIsFFAWDomABTP9/WMic8rbEuWiSolIKtoyi0LHodHYfDzsJXIQQQgghHhgdKFA83YQ5y+xQZp2ODAcDzhlGNgw/UNCFFKJIkDEuQgghRB7t2LGDPXv2EBwczLlz50hPT2fOnDk0b97cbn6LxcKqVav47bffuHz5Mh4eHjz55JMMHz4cR0dHm/xhYWHMnz+fQ4cOkZKSwiOPPMKIESOoV6/egz41cb/pdaAoOJotNpuMegMAGSeiSQ2KxLWhf36XThRS0rpin7S4CCGEEHk0a9Ys9u/fT+nSpfHy8gLgkUcesZvXZDIxevRovvrqK2rUqMHYsWNp0KABP//8M19//bVN/n379jFgwACCg4N56aWXeO2117hy5QpvvPEG0dHRD/K0xIOgKGBUyNDbVrmKGY2YHAzUvBbBjUYLiGi2oAAKKETRIS0uQggh/tPS09Nxdna+r/tcsmQJxYplLhz45JNPagKY7L7++mt2797NpEmT6Ny5MwDPPvsssbGxrFmzhqFDh1K8eHEAoqKieO+99yhXrhwLFixQj1GzZk1eeeUVli9fzptvvnlfz0U8YBYFzAqhnsWoH5N4axFKRaFUcjJpbq6USoyjODfRH4jArPsTQwlXKOEG7z4LZ6PAwQHa1oLHa2VOrXzxBpT3BUcHyDDC1Vgo4Q7ODmC0QHFXSE7LzGuygJtL5jEzjGC2gKud74PRBOlGiIiBqmXATqBl19mrUL4kODvdn+tVGJjMmdfCet1yE5MAJy9B69r3tQiyjot9ErgIIYTIVXJyMqtWrWLjxo1cvXoVi8WCr68vDRo0YOLEiWzYsIGPPvrIblepESNGEBISwj///KNJ37dvHwsXLuTUqVO4urrSuXNnBgwYQKdOnejfvz8jR45U8/71119s3ryZkJAQYmNj8fT0pH79+rzxxhsEBASo+VJTU2ndujU9evSgVatWLFq0iDNnzlC7dm2+++47bt68yZIlS9i5cydRUVHo9Xr8/Pxo06YNw4cPz9M1sQYU0dHRREdH06ZNG7v5Ll26xIoVK3jsscfUoMWqUaNGHDp0iIsXL1K7dmalZ968eSQlJTFhwgT1GAD169fHYDBw9uxZm2P8888/LF26lPPnz+Pp6UmvXr1o0aIF/fr145133qFPnz55Ojdxn1ky12y57FkMD5ORMsnpeKSb8E1KwSstHb3OQmmuYF2iUqEY3EyBm6kw7Ptb+/l8beb/dTp1HRicDJBhZx2Y4i6QmPb/IEmBDvWgsh8s3gEZJniuFXz3KhT7fwDz2S/w4QowZtnXG53h66E5n9d3/8Dw79Xzo3MD+HPiXV2iQmX6OpjyC8QlQ6f6sOhNKOVlP2/l1yDs2q3nU1+Ct7vnQyEfXhK4CCGEyFFGRgZDhw4lIiKCbt26UalSJdLT0wkLC+PixYsAhISEAPa7SgUHB9uk//bbb0yZMoWqVavy8ssv4+joyOrVqzl58iQA1atXV/NaLBZmz55N8+bN6du3L+7u7oSGhvLbb78RGhrKypUrcXDI/CkLDQ3FYrFw4sQJNm/eTI8ePejatSvFixcnLi6Ofv36YTKZePrppylbtixJSUmEhIRw5cqVu74+wcHBOZ47wOrVqzGbzQwcONBmm6urKwAxMTEAxMXF8ffff9O0aVNq1qypyavX63F2dlbzWn377bcsWLCAhg0bMnz4cDIyMli+fDm7du0CtNdSFBCDHgyATsep0iU4rSi0vBKLYjDgajLR4PKZ/wctADoUXDGThgHbMTHAraAF7ActkBm0ZM276RhsyrJ9yQ4I8IbP+8NfQfD+Utt9zPkrsxJe3tf+MV77LnNxTau/jsAve6Hno/bzFwXbTsBbi249//sIvD4fVr9tm/edRdqgBeCdxfctcLHIOi52SeAihBAiRzt37iQ0NJSZM2fSqlUru3mCg4Px9/e36SoVFRVFbGyspvJ87tw5vvjiC1q3bs3nn3+uBh1t27bl2WefBbSVbUVR+O2332y6evn6+jJ37lwuXrxI5cqVgVsBVGJiIitWrMDX91aF6+eff+batWusWrWKwMDAu7wa9s8dcg5ctmzZgr+/v91B9QkJCQC4ubkBsG3bNsxmM506dbLJazKZSElJUfNCZqvVggUL6NOnD2+//Ta6/3dBatCgAS+//DJ6vZ6qVave2wmK+yPLQGtFpyPU251SKbHEuzjR+uyh7JlRcAZSH2yZ/jicGbj8cTjnPFN+yWyZyW7DQW3QYvXluqIduNi7Fjldn0Xb7afvDYFH7/2GgSKD8+2SwflCCCFyZK1cnzp1CrPZ9u6uoiiEhobarbifPn0a0AYi3333HQaDgfHjx6tBC0CZMmUICAjA2dmZ8uXLq+kGg0ENWtLT04mLiyMuLg4PDw8gs0XIyhq4vP/++5qgJet5nDhxIg9nf3tnzpwB7AcukZGRXL9+nTp16th9bXh4OADlypUD4Pjx4wDUrVvXJu+FCxc0eSFz7Iyfnx+jR49WgxaAevXq4ezsTNmyZTXdzQpabGws6em3FltMSkoiMTFRfZ6RkWHTohQZGZnr86ioKJQsLRCF9RgoSubYkgwzGC2k/n/8iIPJiKspg2yZgRxaUu6jjACvzPOomEOLCpBQN0DzXL029SvZzZ9cqWSReD9yPEbFUrYnVbGU3WOYynnb5gWoXdbuMcT9IS0uQgghctShQwd++eUX5s+fzy+//EKrVq1o06YNLVu2xGAwcPHiRZKTk6lRo4bNa7NX6pOTk9m1axft27enRIkSdo9XtWpVDAaD+vzSpUssXLiQvXv32p1RK2tFPjg4GF9fX7tTEnfv3p2NGzcyadIk5s+fz+OPP067du1o3Lhx3i5INsHBwZQsWZKSJUvabLt8+TIAFStWtNmmKAonTpzAz88PPz8/Nb9er9cEblZHjx4FUIOgixcvEhISwqBBg2ymU9bpdCiKUui6iXl7ayt67u7umudOTk74+Pho0vz9/XN9Xrp06cJ/DB2ZA+ZN1sq2QrolczFKo2IgTe+Ii8WobtNhxoHswcw90uvAyw1ik/5faBecJr0Izs7w8hOwYAuERGhfU8INj9ef1iSp16ZsSahVDk5dvrXR0YDb4lGa1qVC+X7kdowBbeG7jZmD7QEcDPBZP/vH2PIxeL2kSadKaSjuZvcYeSWD8+2TwEUIIUSOPDw8WLx4MYcPH2bXrl1s376d9evXU6tWLebPn692lbIXuBw7dgw3Nzd1AH1YWBhGo9Fu60RaWhoRERE888wzatrJkycZPnw4Hh4e9OrVi4oVK+Lu7o5Op2PatGmYTCa1kmIymQgLC6N9+/aa1gergIAAVq9ezb59+9i9ezdbtmxh1apVtG7dmunTp9/VtYmLi+PatWs89thjdrcnJWVWEq2tQ1kdPnyY6Ohonn/+eU1+d3d39HZmc9q4cSMGg0GdBCC363758mUyMjIKXeDyUDNp+1Vl6PWccHPijRMRzG/SnZf3b8CJNBQUnInLeT8fPweOevj1AJQuAc82gzV74WwkVPPPnN2rlBc8UhbCouBKDJQrCT2agZ8XrNoNaUbo9SiU+X9l3MsNgr7M3DZ/E0TehCfqwbd2uohldXIWfLISlu6E2uXh55GZs58VZcVd4cAX8Ms+uBYH3ZpkzrBmj6c73FwEnafAheswpANMfiFfi/swKuKfMCGEEA+awWCgadOmNG3alJEjRzJ58mR+//13goOD1e5O2cdSXLt2jaNHj1KvXj01kEhLyxwwbK9i/vfff2MymahWrZqaNm/ePCwWC0uWLNGMn4mMjOTSpUu0b99eTTt//nyOQZGVk5MTjz/+OI8//jhjx45l1KhR7Nixg+joaLstJreTWzcxuHUnODXVdqzCkiVLMBgM9OzZU5P/0qVLNnlPnTrFkSNH6Ny5s3odcruWGzZsAGRgfmHnF5+EQVHQmxQinMtRPD0NHRkEJH8NxW4zDe97vW79e2C7Oz/oKx3tpxdzztxPXvYF8EHfzMd/iasz9Gt9Z3m9isPezx9IMWQBSvtkjIsQQgi7bt68qelLDplBjE6nQ6fTUapUKbVSnpKSouZJS0vjgw8+wGKxaCrP1paXQ4e0g5EvX77MN998A2gr25GRkZQoUQJPT081LTU1lQkTJmCxWDQBQ24zm8XGxtqkWbtXubq6qmuo5NXtBuZXrFgRR0dHDh48qEn/66+/2LVrF3369NF0I6tSpQoZGRlqtzDIvK6ffvopbm5uvPbaa2p6Ttfy5MmTLFmyBJDApdDQ6cCgrYQ6mC10D7mQuRmI8vAAZz3+6R/ePmgR4iEmLS5CCCHsmjlzJkePHqV169aUK1cORVHYt28fO3fu5IUXXqB06dLqmItx48bx1FNPqVP6Wvt3Z608lylThubNm7Nz507ef/99mjRpwtWrV/n1119xc3MjISGBKlWqqPkbN27M2rVrGTduHM2bN+f69eusX79eHR+TtZuUNXCxV1kfN24cCQkJtGzZkjJlypCens727dsJCgpi7NixeVqc0jrLGmTOAgaZwcPZs2dxdHRkwIABal4vLy+6d+/O6tWrmThxIg0bNuTUqVOsX7+eFi1aaNaqAXjhhRf45ZdfeO+993jxxRcBWLt2LTdu3GDq1KmaPvMNGjQgMDCQVatWkZGRQc2aNTl//jx//vmn2jUte798UUAsCjjrIcMCZoUSqekMPXASlyyTXQT7+9P2+isFWEhR2Mh0yPbplOy304QQQgjgjz/+YNOmTYSGhnLz5k08PDyoVKkSffv2pW3btmq+uXPnsn79epKSkqhevTr9+/fn6tWrfPXVVyxdulQTTMTFxTFt2jT27duHyWSibt26DBkyhGnTpqHX6/n555/VvMnJyUyfPp0dO3ZgNBqpUaMGAwYMYO/evSxfvpytW7eqlfSXX36ZGzdusG7dOpvzWLZsGbt27eL8+fMkJCTg7e1N1apV6devX54H5w8ePFid/Su7atWqsWzZMk1aeno6s2fPZuPGjaSkpFC+fHmeeeYZ+vTpY7eb16FDh/j6668JDQ2lePHiNGnShCFDhtidwjkyMpIvvviCo0ePYjAYaNSoEUOHDmX48OE0aNCAqVOn5uncxIOhm2rMHBwP+CYkM/LfExRPTFarpelOjlSq5EDPpY8XXCFFodNj8GW76b/+WM5u+sNCAhchhBAF6sKFC/Tq1YtXX32Vl19+uaCLU6Tt2bOHESNG8PHHH9O1a9eCLo4AdNOMYAHMChUSUqgRm4RXShql4xJAsVAh4joDf30cr0rut92XeHh0G2I/cFn/w8MduEhXMSGEEPkiPT3dpltWUlISEydOxN3dXV2AUtxeWloaLi7asRDR0dF89tlnBAQE0KFDhwIqmbBhVsACpZPTaHQ9cz2hDCcnLpUqiRloUKeYBC3ChgzOt08CFyGEEPli48aNrFixgrZt2+Lj40NERAS///47CQkJfPbZZwU6JiM2NhaLxZJrHnd3d5tgoaAsWrSIw4cP06JFCzw8PAgPD2f9+vUAfPPNN3katyMesP9/rMolptls0isKJap75W95hCjCJHARQgiRL3x8fHB2dmbZsmUkJyfj5eVFo0aNGDhwoGYa5ILQu3dv4uPjc80zcuRI+vfvn08lyl25cuXYuXMnCxcuJD09nZIlS/LEE08wcOBAypYtW9DFE3aY7dxB1wGpZtu8QsgClPbJGBchhBAPvaCgIIxGY655KlSoYLNqtxC38/PxDF7aAPWuxxOYkKoOylcAFIUnq+t4ZVz5AiyhKIy6DL1iN/3P+Q/3jQlpcRFCCPHQa9iwYUEXQfxH9a/rRKdAE/3HpGPi1gJ6CqDT6SheXJbUE7ZkOmT7JHARQgghhHiASrk74GKyoOh0ZB1JpVMUinsYCqxcQhQ1ErgIIYQQQjxgesCiKCjWsS6Kgg4oX6VYQRZLFFL2xkQJCVyEEEIIIfKFA6AoCgr/D2SAOs09CrZQQhQh0rFSCCGEEOIBq1Du/+NayKx8KYCLY+Y4FyGys+jsPx52ErgIIYQQQjxgMz8uT+d2zphRMKPQqK6B5d/JbGJC5IV0FRNCCCGEyAcD+3qjpCwEYNCgQQVcGlGYmWVWMbskcBFCCCGEEKIQMUvcYpd0FRNCCCGEEEIUetLiIoQQQgghRCFikUkb7JIWFyGEEEIIIUShJy0uQgghhBBCFCKyAKV9ErgIIYQQQuSTNu9fpHiMgehhk3HAjNvKPhTrU7egiyVEkSBdxYQQQggh8sHNob/iFuOMggMKBow4kdh3dUEXSxRCphweDzsJXIQQQggh8kH6omCb1TnMGIh5fmmBlEeIokYCFyGEEEKIfGB/1IIOVpzK55KIws6s09l9POxkjIsQQgghRD5Q1P/eqoDqMRdQaURhZpIYxS4JXIQQQggh8oEeE3oUFAz/f26WwEWIPJDARQghhBAiH5hxxJlUdFmCFaUAyyMKL1MOHQsfdjLGRQghhBAiX1jQkRmsSMAiRN5Ji4sQQgghRD4wYCIdRwyY0aNgQYfu/8GMEFkZ5UNhlwQuQgghhBD5QEGPM8YsgYqSpQ1GCHE7ErgIIYQQQjxgh5edp6TeAZ3FqEnXS9Ai7DDK1Md2yRgXIYQQ99WAAQPo27fvXb22Y8eOjB49+j6XSIiC82uwEd1UIzunn+VmsWI22yVsEeLOSYuLEEKI+8ZsNnPu3Dnat2+f59dGRUURGxvLI4888gBKdv8tX76c06dPExISwsWLFwHYsWMHLi4udvMnJCTwww8/sHXrVqKjoylTpgwDBgygW7dudvPv2rWLn3/+meDgYHQ6Hc2bN2fMmDGUKlXqgZ2TuL8u3zTx7HpwiUvhikdx9lesRvFzx/FKS1bzyH11YY/x9lkeShK4CCGEuG8uXrxIeno61apVy/Nr/fz82L17Nw4Ohf+n6dq1a8yaNYvAwEBq1KhBeHg4lSpVyjFoiYqKYtiwYcTFxdGnTx9KlSrFhg0b+OSTT/D09KR169aa/D/88APz5s2jcePGvPnmm0RGRrJ06VKuXLnCzz//jE66kRQJ5ecrkGpEb1HQGSDF2YnN1epROToSj7QU/BLjcDem4UREQRdViCKh8P86CCGEKDJCQkIA7qrVRKfT4ezsfL+L9EB4e3vz77//4ujoyMWLF/njjz+oUaOG3bwWi4V33nmHuLg4Fi5cSGBgIABdunShS5cuLFy4UBO47Ny5k3nz5tGjRw/Gjx+vpru4uPDdd9+xd+9eWrRo8WBPUNwnCjgYSPFxY/qTzXBPy2Dg7hMYDZnVr0o3Iml+JRQXUiEtDbIGvooCN5OghDtIoPrQSZH33C4JXIQQ4iF38+ZNlixZwr///ktERAROTk4EBgby/PPP06FDB6Kioli5ciUHDhwgMjISo9FIQEAAffr04dlnn9Xsyxq4ZG9xud0xAN577z127drFjh070Oszh2A+88wz+Pj48OOPP2r2d/LkSQYOHMh7771Hz549AQgPD6d379688cYblCpVimXLlhEeHk7JkiV57bXXePLJJ7l06RLz5s3jwIEDKIpCmzZtePfdd/McMDk6Oqr/Dg4OBnIO1jZs2MDp06cZNWqUGrQAuLm5UaNGDU6ePKmmmUwmvvrqK3x9fW3G+jRq1AiAs2fPagKXjIwMFi9ezJ9//klUVBRly5Zl+PDhREREMGPGDFauXEnlypXzdH7iPlEUcDaoT5NcnFjavBYfrd+FXgFnYxquRGPAAq4v5L4vgx5WjYXENPhlL5TyhDHdoGa5zO0Lt8DHqyAlHfq0hDlD7e/nWDjM3ACxSfBcK3j+sbs7t5tJMH09HD4PzarC2GeguOvd7UvYSJW4xS4JXIQQ4iEWFhbG8OHDSUxMpFu3brzwwgvcvHmTvXv3EhYWBsDff//N4cOHadmyJaVLlyYlJYU//viDTz/9FBcXF7p06aLuLyQkBH9/fzw8PPJ0DOtrq1atqgYtiYmJRERE0LJlS5tynzlzBoDq1atrXg+wdetW0tPT6dy5Mw4ODixatIgPP/wQg8HAtGnT6NSpE8OHD2fLli2sX7+eChUqMGDAgLu+hrcLXFasWIGbmxu9evWy2ebq6kpaWhpJSUm4u7uza9curly5wvDhwymWbSC3q2tmpTAmJkZNM5lMjBw5kkOHDvHkk0/y/PPPc/nyZcaPH0/VqlVxdnamYsWKd31u4h7ZuWt+082FOFcXfBKSaHV9Ny6k3Nm+zBboOU2btnovHP8KDp6Dwd/cSv/mL4hJhOVjtPlDr0KL9zODG4D1B+FGAozomoeTIjMge3ISHDib+fzvI7D9FGyflLf9CJFHErgIIcRDKikpiTfffBMHBwdWrFhBuXLl1G2DBg0iPT2zctO3b18GDhyoeW3Pnj3p0qULO3bs0AQuoaGhNGjQIM/HSE5O5vLlyzRr1kzdbg0I7HXBOn36NAaDgSpVqqhp1sDF29ubL7/8Uh0r4+Liwueff87kyZP58ccf1daHrl270rZtW06cOJGHq2YrODgYvV6vCaKsLl26RGhoKF27drU7/iUhIQG9Xq8GKZs3bwagU6dOdvNCZkuN1XfffcfBgwf5/PPP1ZYrAE9PT+bNm0etWrUwGAw2+xL5xKLYzt9qtrC7ZAleuXweF+UOg5acJKTAou2ZLTDZ/brPNu2HzbeCFquv/8x74LI/9FbQYrXjFJy4CHUq5G1fwq4MmbbBLpkOWQghHlI//PAD165d45NPPtEEFFbW7lPWO/0Wi4WkpCTi4uJIT0+nePHiZGRkqPkjIyOJj4/XdBO702OcPXsWRVE0lX9rq4q9wOXMmTNUqFBBEwyEhIRgMBiYOHGiZoC/u7s7kBkoZe0y5eLigsFguOeKfUhICOXKlbNpIQE4fvw4AHXr1rX72gsXLlCmTBm1len48eP4+PgQEBBgkzc8PBxAvY4xMTEsW7aMDh06aIIWgKZNmwK2XfYKUmxsrBqoQmZQm5iYqD7PyMjQtCZB5mcqt+dRUVEoyq0JhQvdMXQ6SM78jugtFh4LvcyQ7cdwRof5ftVLM0xgsdgkKxbtRMtRUVEoGSY7rzfe/jz+T702RrPdokRfvVa4348CPIa4P6TFRQghHkKKovDXX39Rs2ZNGjZsmGM+i8XCb7/9xvr16wkJCcFo1E7S2aZNG/Xf1hYPa/Bxp8cA+92tgoOD7XZ1SktLIzw8nI4dO2rSQ0JCaNCgAT4+Ppr0CxcuANhU7qOiokhLS6NChbu/Q3zlyhUSEhJ49NFH7W6/fPkygN3uWhcuXCA+Pl4dr2Iymbh69ao6liW7Y8eOAbeCoB07dpCenk737t1zLJ+9VqCC4u3trXluDSitnJycbN47f3//XJ+XLl26cB/DAiRmQIqR586c59Er19V8yW7epOqK4XovrS7OjtDvcahWBgbM1mzSPdlA87x06dIwoC188zcYswQwL3e4/Xn8n3ptWj4CNcrCmSu3NjaoRMlOTTX5C937UYDHyDNpcLFLAhchhHgIxcTEEB0dTbt27XLNN378eDZt2sQTTzxB9+7dKVmyJI6Ojpw+fZpvvvlGE2hkD1zu9BjW1zo4OGhaRIKDg6lWrZrN9MgnT57EbDZrKuXW1p46derY7PvMmTOUKFGCsmXLatJz64p2p263j6SkJADNmB+rf/75B0Bd8yY5OTnHvKmpqezatYtq1aqp55Hbsc+fPw/c3exu4j4yZ961L5ZmpGnEdc2mZGdXQj3qUT3+NM7E31k99eO+4OoMq/eAnxeM6wGPlM18xCTA579CWgY81RiWjLJ9ff1K8M9EmLYOYhMzB+fntZsYgF4Pmz7MnAzg0DloVg0+urtFZ4XICwlchBDiIWStJOe2HsixY8fYtGkTL730EiNGjNBs27hxI6CtNIeEhODp6anerbyTY2R9bWBgoDpbl9Fo5MqVK3ZbH6wVfnsD8+21MAQHB9utwN9uUP2duN0+SpQoAWQGHlmlpKSwdu1aAgICaNWqFQDFixfH0dHRJi/AmjVrSE1NpW/fW5XDtLQ0ALWbWVYbNmxAr9drxgCJAuAAOr0Og8WCQbHdbNI7kow/OjJwTvwOst7JvxwNh89Bt6aZgUJWb3e33dnobpmP22lbJ/NxrwJ84PvX7n0/wj6ZDtkuGeMihBAPIT8/PxwdHdm/fz8mk7bfu6IomEwmtY929m5OW7du5ffff8fV1VXTzSokJEQzpuJOjgGZQUpYWJgm6EhPT8disdhU4nft2sW6desA7fgNa+CSvfXhxo0bxMTE5Bi4eHp63lOXDuuq9jl1yapatSoABw8e1KTPmDGDmJgYRo8erbYo6fV6AgMDOXXqlOa8w8PDWbBgATVq1OCpp55S063jYA4dOqTZ94oVKzh69KjNGCBRAPR6FFcHEp2dOe3tpdnkZDISEJ85LiIdZ23QAlCuJHRvbhu0CPEQkxYXIYR4CLm4uNC7d2+WLVvG4MGD6dixI8WKFePy5cts3bqVRYsWUbduXZydnfnmm2+4efMmLi4uBAUFce7cOVxdXTVTF8fFxXHt2jWeeOKJPB3Dy8uL8+fPYzKZNJV/d3d3KlWqxKZNmyhRogT+/v6cOHGCkydP4u/vj8ViwdPTU80fHByMh4eHzaD23FpEgoOD72oMyMqVK9WBuadOncLV1ZWVK1cCUL58ec3Ym8cee4zKlSuzYMEC0tLSKF26NFu2bOHAgQO8+uqrmjFCkDmBwLhx43j99dfp0qULN27cYOXKlXh5eTFt2jTNRAJPP/00ixYt4uOPPyY0NBRfX1/27dtHaGhojrOcifx17CUd9RY7ggV+qlWVp89fpOOFS/ikJFEn8gKOFjMKYKRUQRdViCJBAhchhHhIjRw5kooVK7J27Vrmz58PZA4offLJJ/Hy8sLLy4svvviCOXPm8N133+Hj40O7du0YMWIEzzzzjN3xLdlnsbrdMSDn4OLTTz/l888/Z+3atXh6etK2bVt+/vlnevToYTPYPzQ01G5F3TozWfZ9W8ffZJ3K+U6kpaXx1VdfYTZrZ1X69ttvAXj++ec1gYter2fmzJl8+eWXrFy5Er1ezyOPPMKsWbPsrk/ToUMH3n33XZYuXcr06dPx9fWle/fuDB482GbsS5kyZZg9ezazZs1i8eLFeHl50bZtW/r06cOrr75qd7yPyF91SzswpZWR8buccNOZGL1tJ1VSrmvGs1gAg3SAEdlJVzG7dErW+d6EEEIIUaR9/fXXLF68mPXr19/7zEbivvn7o+NU/fgvQKEYyRgwY8QRE464k4KP8mlBF1EUIrqxcXbTlele+VqOwkZCfCGEEKIIsg7Oz+rw4cMsXbqUjh07StBSyDz5UV0c1FDFjAFwxIwTGbd9rXgI6XJ4POSkq5gQQoiHnslkIi4u7rb5vLy8bKZnLihjxozB29ubWrVqodPpOHXqFP/88w8VK1bk7bffLujiCTtcSUHPrfqnAQsGbBePFELYVzj++gohhBAF6MKFCzz33HO3zbd48WJq1qyZDyW6vUceeYRt27axbds2IHOWsSFDhtC/f3+KFStWwKUTOZGb5uLOyCfFHhnjIoQQ4qGXmprK8ePHb5uvdu3auLm55UOJxH/RNd37Od4xljEuIivdW/F205UvPe2mPyykxUUIIcRDz9XVlWbNmhV0McR/nNxDF3dMPix2yeB8IYQQQoh8IHVRIe6NtLgIIYQQQghRmEiUa5e0uAghhBBC5IMcBxV3rZyfxRCiyJLARQghhBAiH6Q3qWgTvCiAz4YhBVEcUajJQi72SOAihBBCCJEP/Lb2J8nZgEJmwGIBnOd3K+BSiUJJ4ha7ZIyLEEIIIUQ+0Dk7sHF2OQAGDRqEo6NjAZdIiKJFAhchhBBCCCEKFWlesUe6igkhhBBCCCEKPWlxEUIIIYQQojCRBhe7pMVFCCGEEEIIUehJi4sQQgghhBCFibS42CWBixBCCCFEPvCYBWkMAhRWrISt/Qq6REIULdJVTAghhBDiAdN9aSIt81+Anm1RUPxLU8EWShRispCLPRK4CCGEEELkC53m30kFVg4hiibpKiaEEEIIIURhIo0rdkngIoQQQgghRGGik8jFHukqJoQQQgghhCj0JHARQgghhBBCFHoSuAghhBBCCCEKPRnjIoQQQgghRGEiQ1zskhYXIYQQQgghRKEnLS5CCCGEEEIUKtLkYo+0uAghhBBCCCEKPQlchBBCiAdk165dNG7cmK1bt971PgYMGEDfvn3vY6mEEIWeLofHQ066igkhRCGQlJRE27ZtURRFTXN1dSUgIIBnn32WPn36AHD27Fmef/55AF599VVefvllzX4URaFt27aUL1+exYsXq+lvv/0227Ztw9XVlU2bNuHi4qJ53ZIlS5g5cyYzZ86kVatWavr169dZunQpe/bs4erVq+j1ery9valWrRqdOnWiQ4cO9/1aFJQXX3yRkJAQ9bmDgwOenp5UrlyZdu3a0a1bN5ycnPK0z+DgYACqV69+V2Uym82cO3eOdu3a3dXrhRBFlAQpdkngIoQQhUBwcDCKotChQwcef/xxAKKjo1m9ejVTp07FYrHw3HPPqRVhg8HA1q1bbQKXK1eukJSUxCOPPGKzf4PBQGpqKvv27aNNmzaa7dYKe9bXBQUFMWbMGAC6dOlCYGAgRqORixcvsm3bNkqWLPmfCVyMRiNhYWEEBgYycOBANe369evs2bOHzz//nNWrVzN37lx8fHzueL8DBw6kf//+ODs731W5Ll68SHp6+l0HPkII8V8igYsQQhQC1oCkc+fOtG7dWk1v2rQp/fr1Y8uWLZrApXPnzmzYsIErV65QtmxZNf+ZM2cAqFGjhpoWHx9PZGQkbdu25cCBA2zZssUmcDlz5gy+vr6ULFkSgNTUVMaNG4ebmxs//fQTvr6+mvxvvfUW8fHx9+8C3CGz2YyiKDg43N+fr/Pnz2M0GmncuDFdunTRbBs6dCiLFy9m9uzZTJkyha+++irXfSmKgtFoxMnJCQcHh3sqqzWglMDlP0pRiIozU9rLYH+77tls+dc++DKJQkKaXOyRMS5CCFEIWAOSWrVqadKtgURycjKQGWAUK1ZMbRXYvHmz3f1kbTmxBjN16tShVatW/PvvvxiNRnV7amoqly5d0rxm//79xMbG0rFjR5ugBTK7UeWl5QEgKiqKxo0b88033/D999/Ts2dPWrRowdNPP823336LyWTS5H/vvfdo1aoVkZGRfPzxxzzxxBM0a9aMGzduqNdk7ty59OzZk5YtW9KxY0emTJlCYmJinsoFt65R9pYqq/79+1O+fHn+/fdfYmJi1PQ1a9bQuHFjDh06xHfffcczzzxDs2bN2LhxI6mpqTRt2pTPP/9czZ+cnEyTJk2YOnUqhw4d4vXXX6d169Z06NCBTz/9lIyMDM1xrYFLtWrV1LSkpCTeffddHn30UVauXKmmHzt2jLFjx/LUU0/x6KOP8sQTT/DKK6+wf//+PF8PkX/afBjLH4fTbDdkD1pyShPiISItLkIIUQgEBwfj5+enBipWe/fuBTIDGovFwtmzZ6levToVK1akcuXKbN26VQ1irPtxdHSkSpUqmjTIrJQHBATwzz//sH//fnUsS0hICBaLRVNpT01NBSAsLAyj0Yijo+M9n2NoaCgA69evp1ixYnTv3h1nZ2c2bNjAggULcHNzo3///mr+kJAQ3NzcGDhwIA0bNmTYsGHExcXh7+9PVFQUw4YNIz4+nh49elC+fHnOnj3LL7/8QmhoKD/++CMGQw53se243VgUnU5HrVq1uHTpEhcuXFCDNmtgMX36dFxdXenduzc6nY569eoRGhqKxWLR7DM0NBRFUThz5gzbt2+nW7dutG/fni1btrB27VoCAgIYMGCA5hr4+fnh6empPh83bhwZGRnMnz+f2rVrA/DPP/8wYcIE6tWrx3PPPYe7uzs3btxg7969atArCieTBSavTaZTfWccDHKXXfyffBTsksBFCCEKWEpKCpcuXaJ58+bExcUBcPPmTfbv38+8efPw9PRk0KBBXLhwgdTUVLUbWLt27Zg/fz5RUVGULl0ayKyABwYGagKNrIGLk5MTzs7ObN26VQ1c7LU2NGnShOLFi7N79246d+5Mq1ataNy4Mc2bN7cJru6UtZJfpkwZ5s6di6urKwBPPfUU3bp1Y926dWrgkpyczOXLl1EUhUmTJtG5c2d1PyaTidGjR5OSksLixYspX768us3T05P58+ezd+9ezSQDt3PmzBkcHR2pXLlyjnmsgVDWCRSs51SnTh3GjRuHXn+rI4M16MwauFjzp6amsmrVKtzd3dVr0LZtW06ePKk5ZmhoKHXr1gVg7dq1TJ8+nQYNGjB58mS8vLzUfD/88AO1atVi/vz56HS3ajxDhw6942vwoMXGxuLm5qaO90lKSkJRFIoXLw5ARkYGiYmJmpa8yMhI/P39c3weFRWFn5+fes6F+Rhg23JpdSPBQnyKBZ/iBvUYJci57vpfv1b/xWOI+0O6igkhRAGz3pnfs2cPHTp0oEOHDvTu3ZsZM2bQoEEDFi5cSJkyZWxaBawzTW3ZsgWAiIgIEhISNONbIDNwCQgIwMPDAxcXF1q0aMGOHTvUrlnW/WZ9XcmSJVm8eDE9evRAr9ezYcMGPvroI7p06cJ77713V92xQkND0el0fPjhh2rQAuDu7k7NmjX/X7nLdPbsWRRFoWPHjpqgBWDjxo2cPXuW1157TRO0ADRq1AjIHNR+p0wmE+fPn6dy5cq5jkeJjY0FbnXfs76uTJkyvPXWW5qgBTKDFIPBoAmGrIHL+PHj1aAFUI+bdba3yMhI4uPjKV++PBMmTODzzz/npZdeYvbs2ZqgBSAhIYGYmBgiIiLu+Lzzm7e3t2aSAnd3d7XiB+Dk5GTT/TB7xS/789KlS2sCtaJyjOyqlzGoQYv1GLndcC8s5yHHuPNjiPtDWlyEEKKAWVs8Ro0aRdWqVdHr9RQrVoyKFSvi5uZmk8/aMlK1alXKlSvH1q1befHFF+22nCQlJREREUHbtm3VtHbt2rFt2zYOHz5Ms2bNOHPmDN7e3pQqVUpTrnLlyjF+/Hjef/99Lly4wJ49e1i+fDmbNm3C1dWVDz74IE/nGRISQr169ahYsaLNNovFoglmrMHU008/bZN38+bNODk52Qyizyrrvm4nLCzstjN3KYrC6dOn8fb2pkKFCgCEh4eTnp5Ox44d7U6THBwcTKVKlTQVnpCQEPz9/alTp44m7+XLl0lPT1f3bc0LsHLlSnQ6HTNnzqRFixZ2yzds2DCmTp3Ks88+S926dWnVqhVPPvmk2hInCq+Kvgam9few3eDlBHEZtulCPMSkxUUIIQpY1pnCmjVrRpMmTahVq5YmaIHMiqyzszOVKlVS09q1a8fx48eJjo62OzDfOs1y1rTHHnsMR0dHtm7dSlpaGhcvXsxxUDpkju+oVKkSL774Il9++SWQOVVyXiQmJnL16lWb1iDIbLkICQmx6VJlMBho0KCBTX5rK0f2tWgAzp07B2gHs9+OveuW3c6dO4mLi6NDhw7qnVhrYNG4cWO75xQWFqbZp3XKZeu4FHtlyHp9rPvv0aMHRqNRHSNkT48ePVi/fj1vv/027u7uzJs3j2eeeYZNmzbl+BpROGyaWIKaZe3cR765AsZmCdzb1pZZxR4msgClXRK4CCFEAbO2eOQ2S5eiKISEhFCtWjXNoHPropXbtm1T12qpWrWqZt+grRC7u7vTpEkTtm/fTnBwMGazOddKe1bWloWs3STuhLUSbq8r1oYNG4iPj6dTp06a/BUrVrQbnAB2JwtQFIUNGzbg5+dHzZo177hs1muUU4tLUlISM2bMwMXFxWbgPNgPeKzTK2fd5/nz5zGZTHaDN3vBU0hICJ6enrz77rt069aNb775ho0bN+Z4Hr6+vvTu3ZuZM2eyYsUK9Ho969aty+3URUHT6TRdkmx8OSgzWFHWwtZP8q9cQhRSErgIIUQBsrZ4ZJ0FzJ5Lly6RnJxsU0muXbs2fn5+bNmyRR2Yn7VrUk6tCe3atSMmJoYVK1YA2sDmwIEDNtPyQmZ3rvnz5wPYjDu5HWsl//Dhw5r06Oho5s+fT+XKldWuX9aWiZyCqcDAQC5dukR0dLQm/fvvvyc4OJhhw4bZjDe5Xdn0er0m4LO6cOECr7zyChEREUycOBE/Pz/N60qXLm0z3iTr+dobmG8vcAkJCcHX11cTvGZthXr//fdp0qQJH330EUePHtW89ubNmzb7c3Z2xmKxaMorhChCdDr7j4ecjHERQogCFBoaitlstltpziq3VoF27dqxYsUKFEXhscces3mdn58fJUqU0KS3adOGzz77TB3YnzVImDJlCsnJybRt25aqVavi5OREVFQUGzdu5NKlS3Tu3Jm+ffvm+Tw9PT2JjY1l9OjRtGrVipiYGH755RcURWHatGlqa4y1ZSKnwGXw4MHs3r2b1157jR49euDo6Mj27dvZv38//fv3p1u3bndcLrPZTGhoKB4eHmzbtg3IDJxiYmI4evQo+/bto3jx4kybNk2zaKe1BaxJkyZ29xsSEoJOp9N0WcuthSYkJESdPQwgLi6Oa9eu8cQTTwCZLVVTp05l8ODBjB07loULF1K+fHkiIiLo2bMnrVq1okaNGvj4+HD16lXWrVtH8eLFGTRo0B1fCyGEKOwkcBFCiAJkbRG5XeCSW6W3Xbt2LF++HNAGNikpKVy5csXutMBeXl40bNiQgwcP4unpqZkBZ9iwYezZs4egoCA2bdpEWloaXl5e1K5dmzFjxuRpmuGs5a9evTqjR4/miy++4KuvvsLV1ZWWLVvy2muvaQaR327MSZ06dZg+fTrz58/n66+/xsXFhRo1ajB9+nRat26dp3JduHCBtLQ00tLS+OCDD9DpdBQrVgwvLy+qVavGO++8Q9euXW0G+0dERNhtAct6vgEBAZqZw6xp2bvZWWcPy95NDLRjddzd3Zk1axYDBw5k5MiRLFy4EAcHB3r06MHRo0c5dOgQRqMRf39/OnbsyIABA+566mohhCiMdErWCemFEEKI+ywjI4PHH3+cPn36MGbMmIIujhAFQvelyW668pbcQxa2dJ+k2U1XPrA/7u9hIWNchBBCPFDWrl+3G8cjhBBC5EbCfCGEEHctLi5OXcgyJ9bB5LfrDne/xcbGYrFYcs3j7u6e48xlQghRcGQgvj0SuAghhLhrb775pjpxQE569+6NXq/XrD+TH3r37k18fHyueUaOHEn//v3zqURCCCHuhYxxEUIIcddOnjxJcnJyrnn8/PyoWLFi/hQoi6CgIIxGY655KlSoIKvLi3whY1xEXugmpdtNVyY6201/WMi3RQghxF2ztwp8YdGwYcOCLoIQQoj7SAbnCyGEEEIIIQo9aXERQgghhBCiMJGx+XZJi4sQQgghhBCi0JPARQghhBDiAcvs4pJ1PiSFKY8WTFmEKKqkq5gQQgghxANmfMuBneFGWv9iRoeZhOGOuBeTapgQeSHfGCGEEEKIfPBoWfjOYxEAzo6DCrg0olDTySAXe6SrmBBCCCGEEKLQkxYXIYQQQgghChNpcLFLWlyEEEIIIYQQhZ4ELkIIIYQQQohCTwIXIYQQQgghRKEnY1yEEEIIIYQoTGSMi10SuAghhBBCCFGoSORij3QVE0IIIYQQQhR60uIihBBCCCFEYSINLnZJi4sQQgghhBCi0JPARQghhBBCCFHoSeAihBBCCCGEKPRkjIsQQgghhBCFiYxxsUtaXIQQQgghhBCFngQuQgghhBBCiEJPuooJIYQQQghRmEhXMbukxUUIIYQQQghR6EngIoQQQgghRBE3btw4XFxcMJlMBV2UB0YCFyGEEEIIIYq448ePU6NGDRwc/rsjQSRwEUIIIYQQojDR6ew/cnH8+HHq1q2bTwUsGBK4CCGEEEIIUYTdvHmTiIgI6tSpk2Oeffv20bFjRzw9PfH39+f1118nOTlZ3d6oUSPGjh2rPt+/fz86nY5BgwapaUeOHEGn03HixIkHcyK3IYGLEEIIIYQQhYkuh0cOjh07BpBji8uGDRt47LHHqFixIr/88gtffPEFa9as4fXXX1fzeHl5kZiYqD6fNWsW7u7u3Lx5U02bN28erVu3zjVAepD+u53ghBBCCFEoKIqiqRA9rIxGI6mpqQAkJCTg6OhYwCUS+al48eLobtPd624dP34csB+4xMbG0r9/f8aOHcvnn3+upsfFxTF27Fi+//57nJyc8PLyIikpCYDIyEjWrl3Lm2++ycGDB4HMz+yyZctYtGjRAzmHOyGBixBCCCEeqMTERDw9PQu6GIXKqFGjCroIIp/duHGDkiVL3lFe5a28VdGPHz9OyZIlKV26tM22xYsXYzQaGTdunCY9MDAQk8nEtWvXKFeuHF5eXty4cQOAuXPn0r59exo1asSmTZvU/Xh7e9O9e/c8le1+ksBFCCGEEA9U8eLFiY+P16QlJSXRtWtX/vjjD9zd3QuoZPlPzvvhOm+4de7Ozs4P7Bi5Dczfvn07TZo0wcvLS5N+5coVHBwc8PX1BTK7ioWHh5Oens53333H4sWLURSFuLg4AL799ltee+01DAbDAzuP25HARQghhBAPlE6nw8PDQ5Om1+sxGAx4eHg8VBVZOe+H67zh1rk/qG5iFouFU6dO8corr9jdfuPGDcqXL2+Tvn37dpo2bYqLiwuA2lVs+fLllChRgk6dOrFnzx7i4uLYuXMn586dY+jQoQ/kHO6UBC5CCCGEEEIUUefOnSMlJSXHAfPly5fn/PnzmrQjR46wevVqFi9erKZZA5fZs2fzxhtvoNPp8PT0JCEhgW+++Ybnn3/+jru6PSgSuAghhBBCCFFEWQfmX79+nd9++02zrXLlyrz00kt06dKFcePG0alTJ06fPs1HH33E888/z4svvqjm9fLyIjQ0lGLFijFw4EAAPDw8UBSFX375hf379+fXKeVIAhchhBBC5DsnJyeGDh2Kk5NTQRclX8l5P1znDQ/+3K1rqrz33ns222bMmMGoUaNYsGAB06ZNY9asWVSsWJH33nvPZoKIEiVKYDabGThwIMWLFwdQJ9Vo2rQpjRo1eiDlzwudoihKQRdCCCGEEEIIIXIjC1AKIYQQQgghCj0JXIQQQgghhBCFngQuQgghhMhXJpOJNWvW8Oabb9K5c2cef/xxBg8ezNGjR23yKorCkiVLeOaZZ2jRogWDBw/m3Llz+V/o+yQ8PJzRo0fTunVrOnbsyHfffcd/qdf+rl27mDBhAs888wwtW7akV69eNgPGrfleeuklWrZsSY8ePdiyZUv+F/YBMRqNDBgwgMaNG/P7779rtv2Xzzs/SOAihBBCiHwVFhbG999/T/Xq1ZkwYQKffvopRqORN998kytXrmjyfvLJJ/zwww/079+fadOmkZGRwbvvvovFYimg0t+9c+fOMWjQIHQ6HV988QXPP/88CxYs4O+//y7oot03kydPxtHRkREjRjBjxgxq1KjB5MmT1dXXAX7//XfGjh1LkyZN+Oqrr6hduzbvv/8+kZGRBVjy+2fq1KmcPXsWgCpVqqjp//XzzheKEEIIIUQ+io+PV9LT0zVp586dUxo1aqQsXbpUTdu0aZPSqFEjZf/+/WrakSNHlEaNGikHDx7Mt/LeD2azWXnuueeUoUOHKmazWU1/7bXXlKFDhxZgye4fk8mk3Lx50yatY8eOypgxYxRFUZTIyEilZcuWyty5c9U8qampyuOPP658++23+VncB2LdunVKu3btlM8//1xp2rSpkpaWpijKf/+884u0uAghhBAiX3l4eNhMDWswGADIyMhQ077//ntatGhB06ZN1bTKlSsD2LTMFHZbt27l7NmzjBgxAr3+VvWrcuXKREREFGDJ7h+DwYCXl5dNmk6nw2g0ArBo0SJcXFzUdUIAXFxcCAgIKHLvaXbBwcFMmzaNKVOmkJycTLly5XB2dgb+2+ednyRwEUIIIUSBW716NTqdjhYtWgCZlcCwsDC6deumyWc2mwFIT0/P9zLeiz///JPAwEBq166tSTebzaSlpRVQqR68rVu3Eh0dTYsWLTCZTGzcuJEnnngCV1dXTT6z2Vzk3tOs4uLieOeddxgyZAjNmzfn7NmzVK1aFeA/fd75TQIXIYQQQhSodevWsWrVKvr27Uu1atUAOHDgAICmtQUyK4iAzZ39wkxRFA4dOkSzZs1stt28ebNInUteBAcHM3nyZGrWrEnPnj0JCQkhPj7e7nWIi4srstfBYrEwYcIEatSowcCBAzEajYSHh6vjW/6r510QHAq6AEIIIYQo2pKSkoiOjr5tvjJlyth0EVu5ciVffvklTz31FGPGjFHTw8LCKF26NB4eHpr8oaGhwK0uY0VBZGQkKSkp6h34rM6ePVukzuVOHT16lNGjR1OmTBlmz56No6MjYWFhADbXISYmhpiYmCJ7HebNm8f169f56aefgMzPrslkUs/zv3reBUECFyGEEELck23btvHxxx/fNt/SpUupXr26+vy7775j/vz59OvXj5EjR6LT6dRtcXFxeHt72+xj//79+Pj4EBgYeH8Knw+srUTZz+fatWtcvHiR5557rgBK9eDs2rWLd999l9q1azN9+nTc3d2BnK/D/v37AdvWtaJg3759rFy5km+//Raz2UxiYiInTpwAwN/fn7S0tP/keRcUCVyEEEIIcU+efvppnn766TvOb7FYmDZtGqtXr+aNN97QDFi2cnJy0gxiB0hJSWH79u107drVZlthZm1lyl7mP//8EycnJ9q1a1cQxXog/vzzTz7++GMee+wxPv30U00LW27XoXr16lSqVClfy3o/bN++nZSUFF566SWbbc8//zyDBg2iZMmSwH/rvAuKBC5CCCGEyDcmk4kPPviALVu2MGHCBLp37243X9myZTl8+DAmkwkHh8zqysKFC8nIyKB///75WOJ75+/vj8FgICwsTJ18ICYmhiVLlvDss8/abVkqipYvX85XX33F008/zfjx49WZ4qzKli0LZHadqlGjBpDZYrFv3z6+/PLLfC/v/dCrVy86dOigSZs+fbq6lk2FChXU7o3/pfMuKBK4CCGEECLfjBkzhj179vDSSy9RuXJltVsNQKVKldRuRd26dWPJkiVMmTKFLl26sHfvXpYuXcqHH36Ir69vQRX/rri5udGhQwcWL15MqVKlcHBw4Ntvv8XPz4/XX3+9oIt3XyxatIivv/6apk2b0qNHD06fPq1u8/X1pXTp0jRr1ozSpUvzxRdfMGzYMCIiIvjmm2/o0aMHbdq0KbjC34OsC0xaXb9+nc6dO9O4cWMASpQo8Z8774KiUxRFKehCCCGEEOK/Lzo6mieffDLH7b/99pt6Vx7gjz/+4NtvvyU2NpaqVavy8ssv06pVq/wo6n138+ZNJk+ezL59+3B3d6ddu3a8/vrraqBW1PXs2ZOLFy/a3Za1Ze306dN89tlnnDt3jtKlS9OzZ09efPFFzfimouzatWt07drVpjXxv37e+UUCFyGEEEIIIUShV3RGtgkhhBBCCCEeWhK4CCGEEEIIIQo9CVyEEEIIIYQQhZ4ELkIIIYQQQohCTwIXIYQQQgghRKEngYsQQgghhBCi0JPARQghhBBCCFHoSeAihBBCCCGEKPQkcBFCCCFEvitdujRPP/10QRcDgBs3blCtWjVGjBihpr3zzjvodDpiYmIKsGSiMIiLi0On0/Hmm2/e1euTk5MxGAx3/fr/quXLl+Ps7ExERMQdv0YCFyGEEOI+iIuL48MPP6RBgwa4u7vj6upKxYoV6dq1K999950m75gxY9DpdOzevdvuvn777Td0Oh2ffPJJjsd777330Ol0lCtXLsc81uNYH3q9noCAAPr06cPZs2fv7kTvg8jISK5du0aDBg0KrAxWGRkZdOvWDQ8PD7788ks1/fDhw5QvXx4fH58CLN2DcezYMT766CMuXrxY0EUpEo4fPw5w15/X48ePY7FYCvzzvmjRImbNmpXvx123bh2TJk1CURRNeu/evfHw8GD27Nl3vC+H+104IYQQ4mFz+vRpOnbsSGxsLM8//zyDBg3CYDBw7tw51q1bB8CwYcPU/EFBQTg4ONCwYUO7+9u/fz9AjtvPnz/PjBkzqFq1KmfPniUmJsZuBdt6nB9//BGdTkdGRgY7d+5k0aJFHDhwgJMnT+Lu7n6vp59nR44cAaB+/fr5fuzs3nrrLYKCgggKCsLJyUlN/+uvvwqwVA/WsmXLmDp1Kq+//npBF6VIuNfApbB83t99910aNWrEyJEj8/W4n376KUlJSUycOFGT7uDgQJcuXVi2bBmff/45Op3utvuSwEUIIYS4B4qi0KdPHxITEzlw4AC1a9fWbP/qq6+IjIzU5D969Ch169bF1dXV7j6tgUujRo3sbh81ahQNGzZkyJAhvPzyyxw9epT27dvblOvo0aPUqVOH/v37q+mDBw8mNjaW33//nQMHDtCuXbu7Ou97Ya3IFfQd6O3btzNnzhzGjRtHrVq1NNuyBjH/Nfv376dixYr4+voWdFGKhGPHjuHo6GjzGblTR44cuafX3w8XLlzg2rVrNG3aNF+Pm5GRwdGjR3nhhRfsbm/SpAmLFy/m9OnTd3R9pKuYEEIIcQ+OHz/OqVOn6Nixo03QAqDT6ShTpoz6/Ny5c8THx9OsWTO7+7NYLBw6dIjSpUvj7+9vs/3vv//mjz/+YObMmdSpUweAo0eP2uSzHqdJkyY226zlSUhIyPG8Jk2ahE6nY+/evTbbEhMT8fPzo23btmrayZMneeONN6hduzaenp54enrStGlT1q5da/P6I0eO4OnpSaVKldS0wMBAWrRoYZP3wIED6HQ6m+52JpOJb775hmbNmuHu7k7JkiXp0qULJ06cyPGcsrJYLIwYMQIfHx/ef/99zba//voLnU7HqlWrbNKWLVvG7NmzqVWrFq6urtSqVYtNmzYBcPDgQbp27Yqnpyd+fn68++67Nt1jhgwZgrOzM3v27KFr1654e3tTvHhxWrduzbFjx2zKuXTpUrp3706FChVwcXEhICCA5557jvDwcLvn9ccff9C1a1d8fX1xdnamUqVKDBkyhPj4eLUL4o4dO7hw4YLahfBOApjExEQmTJjAI488gqurK35+fgwbNoy4uDhNvt9//x2dTseaNWuYP38+DRs2pFixYlSoUIE5c+bc9jhwa0zIq6++yl9//cVjjz2Gm5sbAQEBfPXVVwBERUUxdOhQSpcujYeHBz179rQ7HunatWuMGDGCwMBAnJ2dKVeuHO+++y7p6ek2eS9evMiAAQPw9fWlePHiPPvss0RFRXH8+HFq1aplE8z+9ddfdO7cGW9vb9zd3WnSpAm//fabzX6PHDlCjRo1cHZ2vu25K4rC/Pnzad68OR4eHhQvXpyuXbty+vRpTb6dO3ei0+n4/vvvbfYxfPhwDAYDKSkpAHTv3l39rn300Ufq+/72228D8PPPP6PT6VixYgUvv/wyZcuWxcXFhdq1a7N69WrNvi9duoROp7P5zgBMnToVnU5HSEgIkHmDxdnZmYyMDH766Sf1uL1791ZfYy2Xvc++PdLiIoQQQtyDpKQkIDNQSEpKum3Xq6CgIADKlSvHuXPnbLaHhYWRmJjI448/brPNaDQyatQoXnzxRZo2bUpycjI6nc5u4GI9TvY7rBkZGWzbtg29Xk/jxo1zLGfdunWBzIDk0Ucf1Wz77LPPuHHjBtOnT1fT5s2bR3BwML169aJs2bJcv36dH374gd69e7Nt2zbN+Rw5ckTTbSYuLo7w8HC6du1qU45Dhw4B2taZ9PR0unTpwq5du+jXrx+DBg0iNjaWefPm8dhjj3HixIlcx/5AZr/7EydOMGnSJJv3zF6LkDVtxowZODs78/LLL5OUlMQXX3xBr169mDdvHmPGjGHIkCE8/fTTLFiwgKlTp9KoUSP69Omj7ufo0aM4OzvTqVMnevbsyRdffMHp06f55ptv6NSpE+fOnVPLY7FYeOedd+jYsSNvvvkmnp6eHD16lAULFnD06FFOnjyJg8OtqtyoUaOYNWsWjRo1Yty4cbi7u3Pq1ClWrlzJvHnzeOSRR5g6dSrvvPMOffv25amnngK47Tiey5cv06ZNG2JiYhg6dCjVqlXj+PHjfPvttxw9epQ9e/ZgMBjU84PMlkaTycRzzz1HsWLFmD17Nm+++SZNmjTJMWi3OnbsmBrA//PPPwwePJi+ffsyY8YMxo4di4+PDxMmTKBz5858/PHHbN26lVWrVuHn58fcuXM1++nQoQMODg4MHTqUsmXLsnv3bqZNm8b58+dZs2aNmvfUqVO0bt0aZ2dn3njjDfz8/Pjll194+umnOX36NH379tWUccKECUyZMoVOnTrxwQcfYDAYWLZsGT169GDdunV069YNyAywT548yXPPPZfrOUPmd7NHjx78/fffvPjiiwwePJhr164xe/ZsWrVqxdGjRylfvjxw6/ttr1X20KFDVK9enWLFigHwyiuvYDab2bBhA3PmzMHT0xO49bfB+p6NGjWKGjVqMH78eOLi4pg5cyZ9+/alYsWK6g2Q2x3Xzc2NqlWrAtCjRw8SEhJYuHAhH330EZUrVwagZs2a6mvc3NwAiI6Ovu31AUARQgghxF1LTk5WKlasqACKh4eH0qdPH+Xbb79VwsLC7OZ/++23FeC2j4kTJ9q8durUqUqxYsWUK1euqGmBgYFK7dq1czzOtm3blBs3bihXrlxRtm3bpnTs2FEBlLFjx+Z6XuHh4QqgjBgxQpN++fJlxdXVVRkwYIAmPSkpyWYfV65cUXQ6nfLWW2+paXFxcYpOp1NGjRqlpm3ZskUBlIULF9rsY9CgQYrBYFBSUlLUtCFDhigeHh7K/v37NXnPnj2b47XLrkuXLorBYFCuXr1qs61Xr15K8eLFFYvFokkDlGHDhmnS3333XQVQypYtq0RERNiUJet5Go1GxdnZWdHpdMqaNWs0x5w0aZICKCtWrFDTTCaTkpqaalO+KVOmKIBy8uRJNW369OkKoIwfP14xm82a/Fn3sXbtWgVQfvvtt1yvT9Yy161bV/H19VVCQ0M12z788EMFUP744w81rUePHgqgDBw4UFOO7du3K4Aye/bs2x5zzpw5CqA0bNhQSUxMVNP/+usvBVAcHByULVu2qOlms1kpX768Ur9+fTUtLi5O8ff3V6pVq6Zcv35ds/8BAwYogHLq1ClFURQlLS1NqVq1qlKpUiVNXrPZrNSuXdum3D///LMCKPPmzdPsNy0tTQkMDFQee+wxNe3YsWMKoMycOfO25z18+HBFr9cra9eu1aRv27ZNAZS3335bTevfv7/i5OSkpKena/JmZGQozs7OygsvvKBJ79atm1KiRAm7x23btq0CKG+++aYm/d9//1UA5dVXX1XTJk6cqABKeHi4zX4qVaqktGjRQpM2YsQIBVBu3rxp99jr169XAOXbb7+1uz076SomhBBC3INixYqxZ88exowZg5eXF6tWreLVV18lMDCQjh07cvnyZU3+oKAgihUrxurVq+0+nnjiCcD2jmZUVBSTJk3i3XffJSAgQE2vXbs2wcHBNl1frHdG27Zti6+vL2XLlqVt27acOnWKGTNmMG3atFzPq2LFinh4eHDy5ElN+vvvv49Op2PKlCmadOudU7PZTFxcHNHR0Tg6OuLo6EhaWpqa7+jRoyiKomlxOXz4sN1zhsy7uNbuSZDZNe/HH3/kjTfeIDAwkOjoaPXh5eWFj4/PbWdMS0tLY+vWrTz66KN2u+MdOXKEevXqaQYLHzlyhJIlSzJr1ixNuvXu9ZdffqnpEmhNz9oicvr0adLT03nuuefo2bOn5pjWMUoXLlxQ0wwGAy4uLmqZY2JiiI6OpkSJEmoawPXr1/nggw9o3749kydPRq/XVu+s+4Bbn4ucJn7IbsWKFRw/fpzJkyerd9Kt2rRpA6B2DYLM6+Tl5cXcuXM15bB2s8ppXFdW1tat77//XtMaZr2mL7/8smZsll6vx93dXXOtv/76ayIjI/n6669tusJlL/f3365Zi5IAAA7YSURBVH/P2bNnmTJliiavXq9X3xfr5zUjI4N33nmH1q1b06tXL83nLzExkdq1a2s+f3c6MP/ixYt8++239O3blx49emi2tWrVCoPBoLnOhw8fpnbt2jbd106cOEF6errN+LGgoKAcx5QdO3aMChUqaGbVA2jZsiXOzs6az+Thw4fx9vamYsWKmryxsbGEh4fbPW5gYCBeXl52jx0aGgpw2xZSK+kqJoQQQtwjf39/pk+fzvTp0wkPD+eff/5hzpw5bNq0iYEDB7JlyxY1b1BQEHXq1KFXr15292UdB5C9Yjlu3DgcHBzo0aOHpouZv7+/2h0la8U/KCiIevXqqZURZ2dn/P39qVy58h3N3gNQp04dTp06pT4/cuQIS5YsYeLEiZrgKS0tja+//pqlS5dy6tQpTCaTZj9ZK7zWbilZKzhBQUG4urpSo0YNzetSU1M5ffo0zz//vJq2fPlyFEXh008/5dNPP7Vb7pwqSVYnT54kLS2Nxx57zGZbfHw8YWFhdOnSxSZt4MCBNuMUzpw5g7Ozs82aNGfOnAGgevXqapq1EjtkyBCb41osFgBNRf3s2bN89tln/P3335oJHqyqVKkCwK+//kpycjKjR4/O9bwh81qXLFnyjiuKq1evxtnZWTPBg5Xy//E71qA1Li6OCxcuMGDAAJsAxTr26JFHHrmjMlaqVMkmkLVe06xjJCCzC+X58+c136nVq1dToUIFOnbseNtyL1++nICAAJvuYJAZiOt0OurVqwfAtm3biIyMJDIyMsexQVnP0fp5v13gsnbtWiwWC0OHDrXZptfrsVgsanlTUlIICQlh8ODBNnntda2Mjo7mypUrdrurXbx4kdjYWIYPH253QgpFUTSfyaCgoBxvMGQ/rqIoHDt2jE6dOuV43v/++y86nS7HiUiyk8BFCCGEuI8qVarEq6++Su/evfH19eXff/9FURR0Oh3h4eHcvHlTHT9iz/Hjx/H19dVULPfv38/ixYtRFCXH1x49elT98bcep2/fvnTo0OGuz6VevXrs3r2b6OhoSpYsyVtvvUXp0qV555131DwZGRl06NCB/fv3M3DgQEaPHk2pUqVwdHRkw4YNzJo1SxOEHTlyBCcnJ02QYg2yst4xt5632WzWVPqOHj1K6dKl+fnnn3Mst7UvfU6srWDVqlWz2WZtEcpaZmta8+bNbfIfPnyYunXrquMJsp4TaFuRrJVYe2OLrDPJWSt+Bw4coH379nh7e/Paa69Ro0YNPDw80Ov1vPnmmxiNRrUFwhoQ2StfdkFBQXfc2gKZAUelSpXstpRYW+OslfrcyhEUFIRer79tBd5oNHLq1CmbFinIvNZ6vd5mwolTp06Rnp6uXmuLxcKpU6fU1sucym39Lh05coSOHTvatFRB5vtSuXJlPDw8gFvv4dKlSylVqpTd/Xt7e6v/PnLkCJUqVVLfq5xYA7us4z+ynp+iKOp1PnbsGGaz2W5lf8+ePQB2WzTtve+5fSaPHDlCRkaG+pmMiooiKiqKgQMH3tFxQ0NDSUxMzPHzlpiYyObNm2ncuDF+fn5282QngYsQQgjxADg5OaHX6/Hw8FBbOKwVCGsFJLtLly5x8+ZNzR1KRVEYMWIEtWvX5oMPPrB5zY0bNxg+fLhmgL71OLkFSHci6wD9pKQktm7dyg8//KDe+QVYs2YNu3fvZu7cubz22mua13/++ec2ldUjR45Qu3ZtHB0dgczA59y5c7z88ss2x1++fDmgvYsbHx+PXq+/p4DMaDQCULx4cZttuQ3Mz14Bs975tld267owWad4te4ne4BmMpmYM2cOlSpVUiv9EyZMwGKxcPjwYUqWLKnmvXjxIqGhoZrWBevscLdrScut4pkTnU6X4534n376iXLlyqmBhPUzaK9CHRQURLVq1W47ecWpU6fIyMiwW9kNCgqiatWqNu9b9u5viqJgMBjsljs1NZUVK1bQsmVLSpcujclkIj093Wb2N8icuevgwYOaFp74+Hggcxrf7F3n7LE3Vbk91vfOXpl//PFHAJ599lkAdYax7N/vlJQU1q9fT7ly5TQTLuT0+c26LftnEjInojAYDOr5W1tfsx/XYrGwatUqHBwcNDMr5nZcgCVLlpCcnGz3+5MTGeMihBBC3KVdu3blOKXwpEmTMJvN9OvXT02zVrByClzsVfwWLlzIgQMHmDRpEr169bJ5DB06FAcHB03gYj3OvQYuWe/wvvPOO9SrV8+m0mtdfT17F6CZM2eyZcsWqlevrlZW09PTOXPmjCaQSUtLw2KxqLOzWf3xxx8sWLAA0N7FrVKlClevXmXHjh025TWbzXc0O5G1UpecnGyzzdoilPXOt3UdDuv001ZHjx7N8c53UFCQzRgE65Sv2cv++eefEx4ezqeffqre9b948SK+vr6aCmhycjIvvvgiFotFc0xrC9Off/5pU46MjAz132FhYQCaaahvp1atWoSGhhIVFaVJ/+ijjwgKCuLjjz9Wy2y9Ttk/dyaTiRMnTtxRS4+1spv9mprNZo4dO5bjtdbpdGqwaTAYqF69OgcOHNCMr7JYLAwfPpzIyEg++eQTILPCXq5cOfbu3UtiYqKaNykpiVGjRgHaINbaPW/ZsmV2y3/t2jX132FhYcTHx9/RwpPWADf7Z2Pjxo3MmTOHl156SQ2UrJ/brN8Zk8nEK6+8ws2bN23GmeT2vluvd/bj7tixg+XLl/Pyyy/nelzIHPd25swZatasqelKmdtxjUYj06ZNo1y5crz00ku2FyQH0uIihBBC3KUPPviAQ4cO8cwzz9C4cWOKFy9OREQEv/76K0eOHKFDhw6aQey3awmxVmytFbyEhATef/99GjZsyDPPPGP3NQ4ODlSqVIljx46pXdIOHz6MTqezqWjnVZ06ddDpdHz22Wdcu3aNzZs323Snad26NTqdjldeeYU33ngDRVH4888/iY2NBbQV0JMnT2I0GjUVKw8PD2rUqMHKlSvx9fWlYsWK7Nu3j3379lGhQgUsFoum683o0aNZvXo1Xbp0YciQIdSpU4eEhATCw8NZt24ds2bNUu9M53ZegN21ULK3CFnTatWqZTO+JaeB7qmpqQQHBzNo0CA1LTw8nLi4OBo2bEj//v0ZPXo0vr6+/Pnnn6xfv55Ro0ZpxiC0bduW7777jj59+tCxY0euXLnCjz/+qHZPynpdhw4dypw5c3jllVc4cOAAderU4ebNm+zZs4fKlSur01ZXrFgRvV7PvHnz0Ov1uLi40KxZs1xbDt5//33++OMP2rVrxyuvvIKTkxO//fYbmzZt4u2339acY07X6fTp06SlpeUpcMle+T5z5gypqak5tsRk7c4FMHHiRPr06UPbtm158cUXMZvNLFu2jEOHDjF79mzN4P6hQ4cyceJE2rRpw+DBg0lKSmL+/PlqIJO1LH379uXTTz/l448/VqdQhszW0s2bN9OyZUtmz56d67nYM2TIEL788ksGDx7MsWPHCAgIYO/evSxatIiWLVvy7bffqnmtrXLDhg1j+PDhpKSksGbNGjXIzX68wMBAAEaMGEGLFi0wGAw8//zz6PV6jh49Sr169Zg9ezYpKSnUr1+fo0ePMn/+fJo2bcrMmTPV/TRs2BAnJyfGjx/P9evXMRgMbNiwAYPBoAkcsx93/PjxPPXUUxgMBrp164aHhwdz5swhPDyclStXaiaPuK07mntMCCGEEDbWr1+vDBkyRKlZs6bi7e2tODg4KL6+vkqnTp2UJUuWaKbNVRRF8fX1VSpWrJjj/nr27KkA6lTKY8aMUQDl999/z7UcTz31lAIo586du6Pj5EWVKlUUQHnqqadyzPPTTz8pVapUUZydnZVq1aopn3zyibJnzx4FUL766is13/z58xVA+ffffzWvP3bsmNKyZUvF1dVVCQgIUN58803lxo0bipeXl/LMM8/YHO/kyZNKnz59lAoVKihOTk6Kn5+f8uijjyoffPCBEhsbe0fnVbduXaVLly6atLS0NMXBwUEZMmSITdrgwYNt9jFw4EDF0dHRZkraffv2KYAyd+5cNe2XX35RAGXHjh3KBx98oPj7+yvOzs5KvXr1lB9//NFm3wkJCcrgwYMVHx8fxd3dXWnTpo3y119/KSNHjlQAm/M8e/as8vzzzyvly5dXnJycFH9/f6Vbt27KwYMHNfl+/PFHpWrVqoqjo6MCKBs3brzttfr999+Vxo0bK05OToqXl5fSvn17Zd26dXavnb3r9NNPPymAsnXr1tseq1WrVkpgYOAd78NsNitubm5Knz59bF7z448/KjVr1lQcHR0VHx8f5emnn1Z27txpk89oNCrjx49XypUrpzg7OyuPPPKIMmnSJGX06NEKoERGRmry37hxQxkzZoxSvXp1xdXVVSlRooRSu3Zt5Y033lCnWFYURRk/frwCKJcvX77teSuKopw+fVrp2rWr4u7urri5uSn169dXvvrqK8VoNNrknTNnjlK+fHnFxcVFadiwofLDDz+oU13/+uuvmrzJyclK//79lZIlSyqAUqZMGUVRFCU2NlYBlEmTJikrV65UHnnkEcXJyUmpWLGiMn78eM0U5FarVq1SqlWrpri4uCg1atRQvvzyS+XAgQMKoMyYMUOT12w2K6NGjVJKly6t6HQ6dVrzS5cuKR4eHkrv3r3v6LpkpVMUO536hBBCCCH+w7788ksmTJjA9evXNXfqH5QPPviASZMmERcXd9uB2kLkh61bt9K+fXvNgpkPmsVioWPHjkRERHDgwAG748xyI2NchBBCCPHQeeWVV3B1dWXhwoX5crwjR45Qvnx5CVpEoWHtynavXUrzYvLkyZw+fZq//vorz0ELgLS4CCGEEEI8YOXKlaN+/fr8/vvvBV0UIQDo378/69atIz4+/o7Xdipo0uIihBBCCPEAxcTEcOXKlXy9sy3E7VgnoigqQQtIi4sQQgghhBCiCJAWFyGEEEIIIUShJ4GLEEIIIYQQotCTwEUIIYQQQghR6EngIoQQQgghhCj0JHARQggh/td+HQsAAAAADPK3nsaOsgiAPXEBAAD2xAUAANgTFwAAYE9cAACAvQAbIrM4ij9rHAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "#%pip install shap\n", + "# SHAP \n", + "import shap\n", + "import matplotlib.pyplot as plt\n", "\n", - "# Loss function plot\n", - "plt.figure()\n", - "plt.scatter(y_test, y_Grid_pred, color='blue', alpha=0.5)\n", - "plt.plot([y.min(), y.max()], [y.min(), y.max()], 'k---', lw=2)\n", - "plt.xlabel('True Values')\n", - "plt.ylabel('Predictions')\n", - "plt.title('Lasso Regression: True vs Predicted Values')\n", - "plt.show()\n" + "# with the best model\n", + "model = best_reg_Lasso\n", + "# Use the selected features DataFrame for SHAP\n", + "explainer = shap.LinearExplainer(model, X_train_selected_df)\n", + "\n", + "# Compute SHAP values\n", + "shap_values = explainer(X_train_selected_df)\n", + "# Summary plots\n", + "shap.summary_plot(shap_values.values, X_train_selected_df, feature_names=X_train_selected_df.columns, plot_type=\"bar\")\n", + "shap.summary_plot(shap_values.values, X_train_selected_df, feature_names=X_train_selected_df.columns)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conclusion: it seams that the model is not working" ] }, { @@ -4693,11 +8159,28 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'np' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[81]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m### Decision Tree Regression\u001b[39;00m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscripts\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DecisionTree \n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m results = DecisionTree.decision_tree(X_selected, y)\n\u001b[32m 5\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTest R²: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresults[\u001b[33m'\u001b[39m\u001b[33mr2\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.3f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m, MAE: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresults[\u001b[33m'\u001b[39m\u001b[33mmae\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.3f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m, MSE: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresults[\u001b[33m'\u001b[39m\u001b[33mmse\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.3f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/PROJECTS/DESU_Data_Science/N_Rocher/OFF_Project/OFFProject/scripts/DecisionTree.py:54\u001b[39m, in \u001b[36mdecision_tree\u001b[39m\u001b[34m(X, y, test_size, random_state)\u001b[39m\n\u001b[32m 49\u001b[39m mse = mean_squared_error(y_test, y_pred)\n\u001b[32m 52\u001b[39m \u001b[38;5;66;03m#Visualization\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m54\u001b[39m train_sizes, train_scores, test_scores = learning_curve(best_dt, X, y, cv=\u001b[32m5\u001b[39m, scoring=\u001b[33m\"\u001b[39m\u001b[33mneg_root_mean_squared_error\u001b[39m\u001b[33m\"\u001b[39m, train_sizes=np.linspace(\u001b[32m0.1\u001b[39m,\u001b[32m1.0\u001b[39m,\u001b[32m20\u001b[39m))\n\u001b[32m 56\u001b[39m \u001b[38;5;66;03m# Average scores across folds\u001b[39;00m\n\u001b[32m 57\u001b[39m train_scores = -train_scores\n", + "\u001b[31mNameError\u001b[39m: name 'np' is not defined" + ] + } + ], "source": [ - "### code for that" + "### Decision Tree Regression\n", + "from scripts import DecisionTree \n", + "\n", + "results = DecisionTree.decision_tree(X_selected, y)\n", + "print(f\"Test R²: {results['r2']:.3f}, MAE: {results['mae']:.3f}, MSE: {results['mse']:.3f}\")\n" ] }, { @@ -4716,122 +8199,1762 @@ "name": "stdout", "output_type": "stream", "text": [ - "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n" + "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; 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total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.5s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; 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total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; 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total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; 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total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; 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total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.4s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.2s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.2s\n", + "Meilleurs paramètres trouvés : {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n", + "MSE : 16.467475138605856\n", + "R² : 0.8396492656081895\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:528: FitFailedWarning: \n", + "/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/model_selection/_validation.py:528: FitFailedWarning: \n", "540 fits failed out of a total of 1620.\n", "The score on these train-test partitions for these parameters will be set to nan.\n", "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", "\n", "Below are more details about the failures:\n", "--------------------------------------------------------------------------------\n", - "268 fits failed with the following error:\n", + "164 fits failed with the following error:\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n", + " File \"/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n", " estimator.fit(X_train, y_train, **fit_params)\n", - " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 1382, in wrapper\n", + " ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/base.py\", line 1382, in wrapper\n", " estimator._validate_params()\n", - " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 436, in _validate_params\n", + " ~~~~~~~~~~~~~~~~~~~~~~~~~~^^\n", + " File \"/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/base.py\", line 436, in _validate_params\n", " validate_parameter_constraints(\n", - " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n", + " ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n", + " self._parameter_constraints,\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " self.get_params(deep=False),\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " caller_name=self.__class__.__name__,\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " )\n", + " ^\n", + " File \"/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/utils/_param_validation.py\", line 98, in validate_parameter_constraints\n", " raise InvalidParameterError(\n", - "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n", + " ...<2 lines>...\n", + " )\n", + "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n", "\n", "--------------------------------------------------------------------------------\n", - "272 fits failed with the following error:\n", + "376 fits failed with the following error:\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n", + " File \"/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n", " estimator.fit(X_train, y_train, **fit_params)\n", - " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 1382, in wrapper\n", + " ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/base.py\", line 1382, in wrapper\n", " estimator._validate_params()\n", - " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 436, in _validate_params\n", + " ~~~~~~~~~~~~~~~~~~~~~~~~~~^^\n", + " File \"/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/base.py\", line 436, in _validate_params\n", " validate_parameter_constraints(\n", - " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n", + " ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n", + " self._parameter_constraints,\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " self.get_params(deep=False),\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " caller_name=self.__class__.__name__,\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " )\n", + " ^\n", + " File \"/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/utils/_param_validation.py\", line 98, in validate_parameter_constraints\n", " raise InvalidParameterError(\n", - "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n", + " ...<2 lines>...\n", + " )\n", + "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n", "\n", " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", - "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_search.py:1108: UserWarning: One or more of the test scores are non-finite: [ nan nan nan nan nan\n", + "/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/model_selection/_search.py:1108: UserWarning: One or more of the test scores are non-finite: [ nan nan nan nan nan\n", " nan nan nan nan nan\n", " nan nan nan nan nan\n", " nan nan nan nan nan\n", " nan nan nan nan nan\n", - " nan nan -15.43366587 -11.6164328 -11.27435849\n", - " -14.5820768 -11.23554016 -11.36335203 -15.76857583 -12.44221896\n", - " -12.12038863 -14.66678277 -11.81629708 -11.61178998 -15.03447951\n", - " -12.10550901 -11.79386245 -15.61337517 -12.82035289 -12.67222122\n", - " -17.47154562 -13.5015248 -13.25917603 -17.47154562 -13.5015248\n", - " -13.25917603 -14.9627059 -13.17550421 -13.18977974 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-13.63662545 -10.77072323 -10.52588871 -14.02678377\n", + " -11.42242344 -11.2228354 -13.27654848 -11.56161826 -11.29411378\n", + " -13.27654848 -11.56161826 -11.29411378 -14.80151842 -11.82639631\n", + " -11.78372091 nan nan nan nan\n", " nan nan nan nan nan\n", " nan nan nan nan nan\n", " nan nan nan nan nan\n", " nan nan nan nan nan\n", - " nan nan nan -15.91594522 -12.16781458\n", - " -12.06318706 -16.250399 -11.91238305 -12.01762806 -16.13582335\n", - " -12.50536945 -12.63605136 -15.5718352 -12.82225453 -12.11801577\n", - " -14.72627438 -12.28481043 -12.25241906 -16.73890135 -12.97095\n", - " -13.08337804 -16.24138596 -13.48690631 -13.3459846 -16.24138596\n", - " -13.48690631 -13.3459846 -16.35859837 -13.62628501 -13.44813366\n", - " -15.91594522 -12.16781458 -12.06318706 -16.250399 -11.91238305\n", - " -12.01762806 -16.13582335 -12.50536945 -12.63605136 -15.5718352\n", - " -12.82225453 -12.11801577 -14.72627438 -12.28481043 -12.25241906\n", - " -16.73890135 -12.97095 -13.08337804 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nan nan nan nan\n", " nan nan nan nan nan\n", " nan nan nan nan nan\n", - " nan nan nan nan -15.91293058\n", - " -11.5947586 -11.45373592 -14.44348788 -11.32279156 -11.47162727\n", - " -15.49815865 -12.46546805 -12.09982189 -14.27964486 -11.78431829\n", - " -11.5571342 -14.91128126 -12.11213898 -11.79477435 -15.61337517\n", - " -12.82248465 -12.66730308 -17.47154562 -13.5015248 -13.25917603\n", - " -17.47154562 -13.5015248 -13.25917603 -14.9627059 -13.17550421\n", - " -13.18977974 -15.91293058 -11.5947586 -11.45373592 -14.44348788\n", - " -11.32279156 -11.47162727 -15.49815865 -12.46546805 -12.09982189\n", - " -14.27964486 -11.78431829 -11.5571342 -14.91128126 -12.11213898\n", - " -11.79477435 -15.61337517 -12.82248465 -12.66730308 -17.47154562\n", - " -13.5015248 -13.25917603 -17.47154562 -13.5015248 -13.25917603\n", - " -14.9627059 -13.17550421 -13.18977974 nan nan\n", + " nan nan nan nan -13.89546845\n", + " -10.34825859 -10.25085297 -13.75597579 -10.63870527 -10.42240933\n", + " 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-11.61178998 -15.03447951 -12.10550901 -11.79386245\n", - " -15.61337517 -12.82035289 -12.67222122 -17.47154562 -13.5015248\n", - " -13.25917603 -17.47154562 -13.5015248 -13.25917603 -14.9627059\n", - " -13.17550421 -13.18977974 -15.43366587 -11.6164328 -11.27435849\n", - " -14.5820768 -11.23554016 -11.36335203 -15.76857583 -12.44221896\n", - " -12.12038863 -14.66678277 -11.81629708 -11.61178998 -15.03447951\n", - " -12.10550901 -11.79386245 -15.61337517 -12.82035289 -12.67222122\n", - " -17.47154562 -13.5015248 -13.25917603 -17.47154562 -13.5015248\n", - " -13.25917603 -14.9627059 -13.17550421 -13.18977974]\n", + " -13.69457016 -10.1510443 -10.08206995 -13.95498701 -10.79043974\n", + " -10.50185661 -13.89475875 -11.06122316 -10.90554598 -13.48481275\n", + " -10.61435071 -10.55525274 -13.63662545 -10.77072323 -10.52588871\n", + " -14.02678377 -11.42242344 -11.2228354 -13.27654848 -11.56161826\n", + " -11.29411378 -13.27654848 -11.56161826 -11.29411378 -14.80151842\n", + " -11.82639631 -11.78372091 -13.69457016 -10.1510443 -10.08206995\n", + " -13.95498701 -10.79043974 -10.50185661 -13.89475875 -11.06122316\n", + " -10.90554598 -13.48481275 -10.61435071 -10.55525274 -13.63662545\n", + " -10.77072323 -10.52588871 -14.02678377 -11.42242344 -11.2228354\n", + " -13.27654848 -11.56161826 -11.29411378 -13.27654848 -11.56161826\n", + " -11.29411378 -14.80151842 -11.82639631 -11.78372091]\n", " warnings.warn(\n" ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Meilleurs paramètres trouvés : {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 50}\n", - "MSE : 15.536399122952776\n", - "R² : 0.8255571826701679\n" - ] } ], "source": [ @@ -4841,17 +9964,17 @@ "y = work_df[\"nutriscore_score\"]\n", "\n", "from scripts.rf import random_forest_GS\n", - "best_rf, X_train, X_test, y_train, y_test, y_pred = random_forest_GS(X, y)" + "best_rf, X_train, X_test, y_train, y_test, y_pred = random_forest_GS(X_selected_df, y)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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tLQAISZJ01/v3bcyZM6fM18W/917++3Ep6wPYf5XndZSRkSGMjY1Fy5YtxebNm8Xt27cf/YCJ8j+Hc3NzhbW1tRg9enSp9YoL5N69e4UQQsTExAgA4n//+1+J9fbs2SMAiF27dpVYvnbtWgFAXLx4UbfM2dm5zP80rFq1qsS6P/30kwAgfvzxxxLrFRQUCAcHB/H000+Xes1cu3ZNACgxfv+1dOlSIUlSmXv1bWxsHvlfkIyMDDF37lzh7e0tLC0tS7y+/v1B9osvvhAASuxpLTZ79mxhYmJS4gOYEEV7/f+99734b1j//v1LbeOtt94SBgYGIiUlpdTj0LdvX9GjRw8hRNEH3vr16wtXV1exZs2aUq8hqttYeKlOGj58uDAxMXnkvwOF+P9yfOvWLSGEEG+88YZwcHAQd+7c0a3zzDPPiDZt2pR5fVdXV/HCCy/ofm/SpEmJN9wFCxYISZJKlYlt27YJACI+Pl63rPgN7L97gc6cOVNiz9ilS5ceWER+//13AUD8+uuvQggh9u/fX2aJLt7b9s033zzwsSksLBSmpqZiypQppS777bffBADx999/CyGE2LJly0P3qv/3vv5XcdkvPjyksLBQd3jEu+++K3799Vdx7NgxERUVJerXry969+79wG0JIcTly5dLfKCoyGMmRFFJ6dKlizA0NBQmJiZi6NChIikpSXf53bt3xcyZM0WjRo0EAOHq6iref//9EodPlMXf37/M51LxHsi4uDghhBDz588XzZs3F/369RMBAQG6Qu3k5FRmafiv4uL27z3cxbfx3z2/D/Ltt98KAOKPP/545LrleR0JUbT3vFevXsLY2FgYGhqK3r17i4iIiIduu7zP4b/++kv3Qfe/igtrcTkMDQ0VAMTly5dLrLdgwQJhaGgocnNzSyx/4403ShxSlJqaKgCILVu2lLqtuXPnCqVSqdtDPGfOHGFsbFxqm8UfQB72U3wITVn69OkjfHx8Si3Pzs4WCoVCLFiw4IHXTU1NFa6urqJJkybi888/F3v37hURERG6PbaffPKJbt0XX3xRNGzYsMzt9O7dW/Tp06fU8uKxWbdunRDi//+GFX+g+7fiwy8e9DN06FDduiqVSgwePFiYm5sLSZJE586ddR9iqG7jLA1UJ504cQLe3t4wMDB45LrFsyGcPHkS9vb2WL16Nb7++mtYW1vr1klLSyvzLPWTJ0/i2rVrujPjMzMzcf78eUybNk23TmxsLJo1awYrK6sS1z1w4ABMTEzQsmVL3bKYmBg0b94clpaWJdaNiYkBAN3JYcUnbDk5OZXKtG/fPpiYmKBbt2666yoUilJn5BfPmfnvE87+KzMzE/fv3y/zdr7//nsYGBjAz88PAHDlyhUAwOHDh2Fqalrm9v59X/8rOjq6xAlwYWFhOHDgALZv347hw4fr1ouLi0NaWtojT8Ypvn/FMzRU5DEDgKFDh2Lo0KHIzs7Gjh07MHXqVLz00kuIiIgAAJibm2PhwoVYuHAhzpw5g88//xwLFiyAk5MTXnvttQfmOn/+PJ599tlSy0+cOAETExPdyY2+vr6YO3cuzpw5g4iICEiShBs3biA1NfWhY1asT58+MDExKfE12idOnIAkSWjduvUjrw8UTelnaWlZrrmfy/M6AoCnn34aTz/9NO7fv4+9e/di6tSpGDBgAG7evPnAbZf3OfyoMXZ2dtadrBcTE4P69evDzc2t1Da9vLxKPYdjYmJKPO7FJ6w5OzuXWE+r1WLbtm3o2bOn7hsdY2Ji0KZNm1LbLH7NbN26FU2bNi3zvj9s+sSrV6+Wyg8AW7ZsgVarfejsJMuWLUNqaiquXLlS4vEqPmHv36+vmJgYdOzYscztxMTEYODAgaWWb968GQYGBropAYv/hpW1nStXrmDkyJGYMmVKmbfx72klfXx8sHPnTmg0Ghw8eBDTp0/HwIEDkZ6eXuq5RnULCy/VOVlZWTh//vwDJ+7/r+I36oSEBPz2229o1aoVxo0bV2Kdxo0bIzExscQ0UYWFhZg2bRocHR0REhIC4P/fgP/9ZpGamgoXF5cS27tw4QK2b9+ONm3alJgMPj4+vsyz52NiYmBlZaV7U2zcuDEAlJhftfj3r776CuPHj9f98T9x4gRatGgBCwuLUts0MzODp6fnAx8bGxsbKJVKnDx5ssTyyMhIbNu2DV5eXjA3NwcA2NnZAQCsrKweaz7OqKgotGjRQlf2L126BKDkG35hYSEmT54MoPTMC2Vtz9zcXFeyK/KY/ZuVlRXGjBmDb7/9ttRZ/sU8PDzw3nvvYc2aNSgoKHhorry8PNjY2JRaXlyKip8PHTp0wKBBg9CuXTtdcSkuDf8ufqmpqWUWvF9++QV5eXl47rnnStxGkyZNSn2gKsuNGzfwyy+/YNy4ceWaOqs8r6N/MzU1xdChQ7F79+4SM1+UpbzP4X+PcUBAgG69v/76Cz/99BM+//xz3awtMTExZT6HYmJiSkztBhQ97+Lj4/Hmm2/qlhUX3tOnT6N79+665aGhoUhOTkZoaGiJ/GXNLV38mjExMXms+XIdHByQmJiIwsJC3Yf727dvY8GCBQAe/hq5dOkS7OzsSjx3rl+/jo8//hjA/3+IuHfvHlJSUvDyyy+X2sb58+dx69YtnD59usTy1NRUfP7553j55Zd1f/tiYmJgYWFR5t8bOzs73L17t0KPgVKpRO/evTFixAjMmzcPgl8qW+ex8FKdc+LECQghYG5ujvDw8FKXu7i4lNgr4uzsDHt7e3z99ddITEzEwYMHS0zjBACvvvoqBgwYgFdeeQUvvvgisrOzsXz5cpw4cQIHDhzQvRFHR0dDqVSWKHwtW7bEDz/8gKNHj8LT0xPHjx/HBx98gLy8vFJ7rGxsbHDixAns27cPNjY2aNy4MRwdHXHixAm0bdtW92Ztb2+PoUOHYsmSJbC3t4e3tzdUKhU+/vhjdOzYUffNXcWPR1l76IrnznzYXnBDQ0MMGjQIW7ZsQfPmzdGtWzeoVCosWrQIkiSV2IM0ePBgzJw5E0OHDsW7776LFi1a4O7du7h8+TL27NmDLVu2lCos/83z75LSsWNHKBQKTJ06Fe+88w4yMjLw5Zdf6qZ7Ks8e3rZt2+rGsryP2XfffYdvvvkGQ4cOhYeHBzQaDX788UccPHgQmzdvBgC88MILcHBwwFNPPQVnZ2dcvnwZn3/+OVxdXREcHPzQXPb29rh3716JZfn5+Th16hReeeUV3TInJ6dSRbB4Dt3iMnLlyhU0b94cQUFB6N27N1xdXXHr1i38+eefCA0NRefOnTFnzpwSt9GxY8cyXxeWlpYlPmwtXrwYkiSVex7XR72ODh8+jHfffRcjRoxAy5YtYWBggAMHDmDjxo345JNPHrrt8j6H27Ztiw4dOmDWrFkAiqYLO3LkCBYtWoTAwEBMnToVQNG30iUkJOi+8KNY8bzN/31uJSYmIjc3t8Ty6OhoNGrUCPPmzYORkRHc3d2xb98+LFmyBPPnz0fXrl0BFO2FfdB/JLp164YWLVpgwoQJuHDhAnx8fKBWq3Ht2jXs378fH330EZo3b/7Ax2XYsGGYNGkSXnrpJbz88stIS0vDxx9/jMzMTLi7u6NevXoPvG6nTp2wZcsWTJ06FQMHDkRiYiI+//xzGBkZoUmTJroyXvxFJWXlL/6An5GRgcmTJ2Pw4MG4dOkSFixYgIYNG2Lp0qW6dU+cOAE/P79Sf1sBYPz48Zg7dy4mTpyIgQMHwtzcHDdv3kR0dDRcXFwwdepUXLp0Cf3790dISAi8vb1hbm6OY8eOYeHChZgwYUKZHyKpjpH7mAqi6vb5558/9HiwsqY/6tmzZ6ljxf7r+++/F23atBGmpqbC0dFRjBw5Upw7d67EOkOGDBF+fn4lll27dk307t1bmJmZCXt7exEcHKw7XnXNmjUl1o2KihLt27cXJiYmAoBuWipbW1sxbdq0EutmZWWJSZMmCTc3N2FiYiK8vLzEokWLSkyJdefOHSFJkli2bFmp+2NnZycmT578wPtbLD09XQQGBgobGxthYWEhnnnmGd1JOF9//XWJdc+cOSNGjhwp3NzchJGRkWjQoIHo2rXrI8+qflDO9evXi8aNGwsTExPRrl078f3334sJEyaI+vXrPzK3nZ2deOutt0osK89jduDAAdG/f3/h6uoqjI2NhaOjo3juuedKHD/6wQcfiI4dO4p69eoJExMT4eHhId56661ynUQzZMiQEic5CiFEdHR0mc+H/xo8eHCJ+56Wlibee+890blzZ+Ho6CiUSqWwsrISnTt3Fl999VWJM+qLb+NBPyNHjtStm5iYKJRKpfjwww8feX/+7WGvo+joaPHCCy/oxtPe3l489dRT4ueff37oNiv6HE5NTRUjR44Ujo6OwszMTLRt21asXr26xEmFxSdc/vf42OLZOv47w8PGjRsFAHH+/Hndsvr164upU6eKLVu2iCZNmggjIyPh5+cnNm/eXOK6P//8c4ljs/8rNTVVvP7668Ld3V0YGxsLe3t70aFDB/Huu+8+8hyEgoICMWfOHOHs7CyMjY1FmzZtRGhoqPDy8ioxc8yDrjtlyhRRr149YWlpKfr06SMiIyNF8+bNS8x68eWXXwpJkkodiy2EEDNmzBC2trbi4sWLomfPnsLExEQ4OTmJqVOnljpnwdbWttTrsZhWqxWhoaGibdu2wsrKSpibm4tmzZqJUaNG6R63c+fOiZCQEOHh4SHMzMyEjY2N6Nixo9iwYUOp6fSobpKE4H5+IqKaYuPGjZg4cSIyMjLKdWhBdSssLET37t2hUChw8ODBEofcEBHVVPymNSKiGmTYsGGwtLTEjh075I5Spg8//BCpqan48ccfWXaJqNZg4SUiqkEsLCywYcMGGBkZyR2llLt378LAwABhYWFo0KCB3HGIiMqNhzQQERERkV7jHl4iIiIi0mssvERERESk11h4iYiIiEiv8RTbMmi1Wly/fh2Wlpa6ifyJiIiIqOYQQiAnJwfOzs5lfmnJv7HwluH69etlfv84EREREdUsV65cgaur60PXYeEtQ/Fk71euXIGVlZXMafSbRqPBgQMH0Lt3byiVSrnjUDXgmNdNHPe6h2Ne91T3mGdnZ8PNza1cX9LDwluG4sMYrKysWHirmEajgZmZGaysrPgHsY7gmNdNHPe6h2Ne98g15uU5/JQnrRERERGRXmPhJSIiIiK9xsJLRERERHqNhZeIiIiI9BoLLxERERHpNRZeIiIiItJrLLxEREREpNdYeImIiIhIr7HwEhEREZFe4zetyaxQKxB5IRNpOXmob2kCf3c7GCge/Y0hRERERFQ+LLwy2ncyFfN/PY3UrDzdMidrE8wd4IW+3k4yJiMiIiLSHzykQSb7Tqbite9OlCi7AHAjKw+vfXcC+06mypSMiIiISL+w8MqgUCsw/9fTEGVcVrxs/q+nUagtaw0iIiIiqggWXhlEXsgstWf33wSA1Kw8RF7IrL5QRERERHqKhVcGaTkPLruPsx4RERERPRgLrwzqW5pU6npERERE9GAsvDLwd7eDk7UJHjb5mJN10RRlRERERPRkWHhlYKCQMHeAFwA8sPSO7tSY8/ESERERVQIWXpn09XbCqpFt4Whd8rAFE2XRkGyNuox76gI5ohERERHpFX7xhIz6ejuhl5djiW9aa9HAEv1X/I2Lt3Ix75dT+Gy4j9wxiYiIiGo17uGVmYFCQqem9TDI1wWdmtaDnYURlgb6QiEBP8Rcxe54fgEFERER0ZNg4a2BOjaphzeeaQYAmLUjHtfu3Jc5EREREVHtxcJbQ03u6QFfNxtk5xXgra0qfusaERER0WNi4a2hlAYKLAvyhbmRASIvZmLVobNyRyIiIiKqlVh4a7BG9czx4SBvAMDS388g9vJtmRMRERER1T4svDXc0LYuGOjjjEKtwJStKtzlVGVEREREFcLCW8NJkoSPhnjDxcYUlzNzMXfXKbkjEREREdUqLLy1gJWJEsuCiqYq++nEVfwSd13uSERERES1BgtvLdG+sR0m9fAAAMzZmYCrt3NlTkRERERUO7Dw1iKTezRD24Y2yMkrwFvbVCgo1ModiYiIiKjGY+GtRQwNFFgW5AcLY0NEXbyNlYfOyR2JiIiIqMZj4a1l3OzM8NHgoqnKlv1xBjGXOFUZERER0cOw8NZCg/1cMNi3aKqyqdtikZOnkTsSERERUY3FwltLfTjYG662priSeR8fcKoyIiIiogdi4a2liqYq84OBQsLO2GvYpbomdyQiIiKiGkn2whsaGopevXrB3t4ekiQhJSWl1DqHDh1Cnz59YGNjAwsLCwQEBECjefC/8b/77jtIklTqJzU1tSrvSrVr18gWk/+Zquy9nSdxJZNTlRERERH9l+yFNycnByEhIRg1ahSsrKzg4eFR4vLQ0FCMGjUKQUFBiIiIQHR0NKZOnQqlUvnAbUZGRqJPnz5ITU3V/dy4cQNOTk5VfXeq3RvPNEX7RrbIURdgytZYTlVGRERE9B+GcgeYNm0aAGDcuHHw8/ODJEm6yxITEzFjxgwcO3YMXl5euuWenp4P3WZkZCQGDhwIR0fHqgldgxgaKLA00BfPLfsbJy7fwYo/z+KtXs3ljkVERERUY8heeIvFxMSgR48eJZYtXrwY3bt3x2effYZ9+/bBwsICY8eOxaxZsx64HY1GA5VKhStXruCLL75Aw4YNMX36dIwcOfKB11Gr1VCr1brfs7Ozddt62KETNYWjpRLzB7bEtB8SsOLPM+jkboN2jWzljlUuxY9vbXicqXJwzOsmjnvdwzGve6p7zCtyO5IQQlRhlnJRq9WwtLTExo0bERISAgDQarWwtbWFWq3G7Nmz0b9/f4SFhWHmzJnYvn07hg8fXua2UlNT8eOPP8Lf3x+GhoZYtWoV1q1bh99//x09e/Ys8zrz5s3D/PnzSy3fvHkzzMzMKu+OVrHvzioQla6AnbHAO20KYVpjPs4QERERVa7c3FyEhIQgKysLVlZWD123RhTe6OhodOjQAUlJSWjRogUA4OzZs/Dw8MDMmTOxcOFC3bpeXl7o2bMnVqxYUa5ta7VaNGrUCMOHD8eSJUvKXKesPbxubm7IyMh45ANYk9xVF2Dg18dx5fZ99G/tiCXDW5c4RKQm0mg0CAsLQ69evR56XDbpD4553cRxr3s45nVPdY95dnY27O3ty1V4a8Q+wJiYGFhYWJQ4YS09PR0AMGzYsBLrKhSKCu11VSgUUCqVMDAweOA6xsbGMDY2LrVcqVTWqheprVKJ5cF+GPa/4/gt4QZ6tGyAoW1d5Y5VLrXtsaYnxzGvmzjudQ/HvO6prjGvyG3IPksDUFR4/fz8oFD8f5x69eoBAP69AzoxMRFJSUno06dPubd95MgRXLhwAYMGDaq8wDWYX0NbTO1Z9MHhg12ncPkWpyojIiKiuk22wlt8cplKpUJ4eDicnZ2hUqlw/vx5AECzZs3g7e2N999/H0lJSQgLC8OgQYMwYMAA3cltSUlJ8PT0RFRUFADgwIEDWLNmDRISEpCSkoI1a9Zg8ODBePXVV9G1a1e57mq1e/2ZZvBvbIe76gJM3hoLDacqIyIiojpMtsIbGRkJPz8/+Pn5ISEhAdu2bYOfn5/ueF2FQoEdO3ZAq9XC398fEyZMQGBgILZs2aLbRmxsLJKTk9GkSRMAQGZmJlasWIGAgAAEBARgw4YNWLp0KVauXCnLfZSLgULC0iBfWJkYQnXlDpb/cUbuSERERESyke0Y3i5duuBR58t5eHhg//79D7w8ODgYwcHBut+DgoIQFBRUaRlrMxcbU3wytDUmbY7F1wfPopuHA/zd7eSORURERFTtasQxvFQ1+rdxxrB2rtAK4K1tKmTd51yIREREVPew8Oq5eQNboVE9M1y7cx9zdiY8cq86ERERkb5h4dVzFsaGWBbkB0OFhN/iU/HTiWtyRyIiIiKqViy8dYCvmw3e6tUcAPDBrpO4mHFP5kRERERE1YeFt4549amm6Ohuh9z8QkzhVGVERERUh7Dw1hEGCglLA31hbapE3NUsfPl7ityRiIiIiKoFC28d4mxjioVDWwMAVh46h/Dzt2RORERERFT1WHjrmOdaOyGwvRvEP1OV3cnNlzsSERERUZVi4a2DPhjgBXd7c6Rm5WE2pyojIiIiPcfCWweZGxtiWZAvDBUS9iTcwA/RV+WORERERFRlWHjrqDauNni7TwsAwLxfT+ECpyojIiIiPcXCW4dN6NYEnZvW001Vll/AqcqIiIio4gq1AhEXMhGTISHiQiYKtTXrcEkW3jpMoZCwZIQvbMyUiL+ahSVhnKqMiIiIKmbfyVR0XfwnRq6PxqYzBhi5PhpdF/+JfSdT5Y6mw8Jbxzlam2DR0DYAgNWHz+HYuQyZExEREVFtse9kKl777gRSs/JKLL+RlYfXvjtRY0ovCy+hr7cjgv2Lpiqbti0Ot+9xqjIiIiJ6uEKtwPxfT6OsgxeKl83/9XSNOLyBhZcAAO/390ITB3PcyM7DrB2cqoyIiIgeLvJCZqk9u/8mAKRm5SHyQmb1hXoAFl4CAJgZGWJ5kB+UBhL2nbqBrVFX5I5ERERENVhazoPL7uOsV5VYeEnH28UaM/6ZquzDX0/jXPpdmRMRERFRTVXf0qRS16tKLLxUwriuTdC1mT3uazhVGRERET2YqdIAkvTgyyUATtYm8He3q7ZMD8LCSyUoFBK+GOEDWzMlTl7LxhcHkuWORERERDVMzKXbGLUuAsWn/Py39xb/PneAFwwUD2nF1YSFl0ppYGWCT4f5AABWHz6PI2c4VRkREREVOX7uFkati0COugD+je2wdIQPHK1LHrbgaG2CVSPboq+3k0wpSzKUOwDVTL28GuDFjg3xfcRlTNuuwr6p3WFnbiR3LCIiIpLR4ZR0jN8UDXWBFl2b2WPNS+1gZmSIgb4uOH42DQf+jkDvbh3RqVn9GrFntxj38NIDvfe8F5rVt0Bajhrv/hTPqcqIiIjqsN9P38S4b4rK7jMtHLB2dHuYGRXtOzVQSOjobod29gId3e1qVNkFWHjpIUyNDLAsyBdGBgqEnb6JzZGX5Y5EREREMtiTkIpXv4tBfqEWfVo1wOpR7WGiNJA7Vrmx8NJDtXK2xjt9i6YqW/DbaZxNy5E5EREREVWnnbFXMWnzCRRoBQb6OOOrkLYwMqxdFbJ2pSVZjO3ijm4e9sjTaPHmFhXUBYVyRyIiIqJqsDXyMqZtj4NWAMPbuWJpoC+UBrWvPta+xFTtFAoJXwz3gZ25ERJTs/HZPk5VRkREpO82Hb+ImTsSIAQwMqAhFr/QpsYdm1teLLxULvWtTPDZsDYAgLVHLuBwSrrMiYiIiKiqrDl8Dh/sOgUAeKWrOxYM8oailpZdgIWXKqBnywZ4qVMjAMD0H+Jw665a5kRERERUmYQQWP7HGXyyJwkA8MYzTfHe8y0hPewr1WoBFl6qkNnPtUTzBhZIz1HjnR85VRkREZG+EELgs/3JWBKWAgCY3qs5ZvTxrPVlF2DhpQoyURpgWZAfjAwV+CMpDd+FX5I7EhERET0hIQQW/JaIlYfOAQDmPNcSb/b0kDlV5WHhpQpr6WSFmX09AQAf7U5Eyk1OVUZERFRbabUC7/18EuuPXgAAfDioFcZ3byJzqsrFwkuP5eUujfFUcweoC7SYvCUWeRpOVUZERFTbFGoF3vkpHt9HXIYkAZ++0AYvdWosd6xKx8JLj0WSJHw+3Af2FkZIupGDTzlVGRERUa2iKdRi6jYVfoy5CgOFhKUjfDGig5vcsaoECy89NgdLY3w2zAcAsP7oBRxKTpM5EREREZWHuqAQkzafwK9x12GokPBVsB8G+7nIHavKsPDSE3nGsz7GdG4MAHj7h3hkcKoyIiKiGi1PU4hXv43B/lM3YWSgwOpR7dCvtZPcsaoUCy89sZn9PNGigSUy7qox44c4TlVGRERUQ+XmF+CVb6JwMDkdJkoF1o1pj54tG8gdq8qx8NITM1EaYHlw0VRlB5PTsek4pyojIiKqaXLyNBi9PhJHz96CuZEBNr7sj24eDnLHqhYsvFQpWjhaYs5zLQEAH+9JRNKNbJkTERERUbGsXA1GrotE1MXbsDQ2xKZXOiKgST25Y1UbFl6qNC91aoQenvWRX6DFlC0qTlVGRERUA2Tey0dwaDjirtyBjZkSm8cHoF0jW7ljVSsWXqo0kiTh02FtYG9hjOSbOVi0N0nuSERERHVaWk4egtYcx+nUbNhbGGHrhAC0drWWO1a1Y+GlSmVvYYzPh7cBAGw8dhEHkzhVGRERkRxSs+4jaHU4Um7eRQMrY2yd0AmejlZyx5IFCy9Vuqdb1MfYLu4AgBk/xiE9h1OVERERVacrmbkYsfo4zmfcg4uNKbZP7IRm9S3kjiUbFl6qEu/0bQFPR0tk3M3H2z/EQavlVGVERETV4ULGPYxYfRxXMu+jUT0zbJsYgEb1zOWOJSsWXqoSJkoDrAj2g7GhAn+lpGPjsYtyRyIiItJ7Z27mYMTq40jNykNTB3Nsn9gJrrZmcseSHQsvVRmPBpZ47/miqcoW7U1CYiqnKiMiIqoqp65nIXBNONJz1PB0tMS2iZ3QwMpE7lg1AgsvVamRAY3wbMv6yC/UYvKWWE5VRkREVAXirtxB8JpwZN7LR2sXa2wZHwB7C2O5Y9UYLLxUpSRJwuIX2sDB0hhn0u7i492JckciIiLSK9EXM/Hi2ghk5xWgbUMbfDeuI2zNjeSOVaOw8FKVq2dhjCUjfAAA34Zfwu+nb8qciIiISD8cO5uBUesicVddgI7udtj0SkdYmyrljlXjsPBStejm4YBxXYumKnvnp3ikZefJnIiIiKh2O5Schpc3RuG+phDdPOyx8WV/WBgbyh2rRmLhpWozo28LeDlZIfNePqZzqjIiIqLHduDUDYzfFA11gRbPtqyP0Jfaw9TIQO5YNRYLL1UbY0MDLA/2g4lSgb/PZGD90QtyRyIiIqp1fou/jte/PwFNocBzrR2x8sV2MFGy7D4MCy9Vq2b1LfB+fy8AwKf7knGaU5URERGV208xVzF5SywKtAJD/FywPMgPRoasc4/CR4iqXYh/Q/TyaoD8Qi3e2p6AfM5URkRE9EibIy4XHRIogKAObvh8uA8MDVjlyoOPElW74qnK6lsa43zGPfx8iU9DIiKih9lw9AJm70wAALzUqRE+GdIaBgpJ5lS1B5sGycLO3AhLA30hScDRmwr8npgmdyQiIqIaadWhc5j/62kAwITuTTB/YCsoWHYrhIWXZNOlmT1e6dIYADD751O4yanKiIiIdIQQWBqWgsX7kgAAk3t6YFY/T0gSy25FsfCSrN7q2Qyu5gK3czWYtl3FqcqIiIhQVHYX7UvCsj/OAABm9GmBab2as+w+JhZekpWRoQIveRTCVKnA0bO3sPbIebkjERERyUoIgfm/nsbqv4reE9/v74U3nmkmc6raTfbCGxoail69esHe3h6SJCElJaXUOocOHUKfPn1gY2MDCwsLBAQEQKPRlGv727dvh62tLfr161fZ0amSNDAF3nvOEwDw2f5knLyWJXMiIiIieWi1ArN3nsTGYxcBAB8N9sYr/3xTKT0+2QtvTk4OQkJCMGrUKFhZWcHDw6PE5aGhoRg1ahSCgoIQERGB6OhoTJ06FUrlo78nevfu3VCr1cjLy0PHjh2r6i5QJRjezgV9WzlCUygweUsscvML5I5ERERUrQoKtXj7hzhsibwMhQR8NqwNRgY0kjuWXpD9C5enTZsGABg3bhz8/PxKHJuSmJiIGTNm4NixY/Dy8tIt9/T0fOR2jx49iuPHjyM4OJiFtxaQJAmLXmgN1ZU7OJ9xDwt+O42FQ9vIHYuIiKhaaAq1mLpVhd0JqTBQSFga6IuBPs5yx9IbshfeYjExMejRo0eJZYsXL0b37t3x2WefYd++fbCwsMDYsWMxa9ash24rISEBy5Ytw5YtW7B161YAgL+//wPXV6vVUKvVut+zs4u+/Uuj0ZT70Al6PMWPr0ajgblSic9e8MZLG6OxJfIKuja1Q2+vBjInpMr27zGnuoPjXvdwzMtPXaDF1G1x+D0pHUoDCV+OaIPeXg617rGr7jGvyO1IQgjZT4tXq9WwtLTExo0bERISAgDQarWwtbWFWq3G7Nmz0b9/f4SFhWHmzJnYvn07hg8fXua2Ll68iFGjRmHXrl2ws7PDjBkz8PPPP+PMmTMPvP158+Zh/vz5pZZv3rwZZmZmlXMnqdx+uaTAH9cVMDMUeLdNIWyM5U5ERERUNfILgXXJCiRlKWAoCYxtoUUrW9mrWa2Qm5uLkJAQZGVlwcrK6qHr1ojCGx0djQ4dOiApKQktWrQAAJw9exYeHh6YOXMmFi5cqFvXy8sLPXv2xIoVK0ptJy0tDX379sWmTZvg7e0NAHj22Wfh6OiI77777oG3X9YeXjc3N2RkZDzyAaQno9FoEBYWhl69eumOy84v0CJobSQSrmUjwN0WG8e057fJ6JGyxpz0H8e97uGYP9o9dQEmfh+LiAu3YapU4H8v+qFz03pyx3ps1T3m2dnZsLe3L1fhrRGHNMTExMDCwqLECWvp6ekAgGHDhpVYV6FQPHCva2hoKFQqFXx9fXXLCgsLIUkSwsPDcfbs2TKvZ2xsDGPj0rsRlUolX6TV5N+PtVIJLA9ui+eX/43wC7ex4fgVvPZ0U5kTUmXj66tu4rjXPRzzsmXnafDKt7GIuXQbFsaGWD+mA/zd7eSOVSmqa8wrchuyz9IAFBVePz8/KBT/H6devaJPOP/eAZ2YmIikpCT06dOnzO28/PLLiI+Ph0qlgkqlwr59+wAAmzZtQlhYWBXeA6ps7vbmmDegFQDgiwPJiL96R95AREREleRObj5Gro1AzKXbsDIxxLev+OtN2a2pZCu8Go1GV0zDw8Ph7OwMlUqF8+eLJllu1qwZvL298f777yMpKQlhYWEYNGgQBgwYoDu5LSkpCZ6enoiKigIAODs7w9vbW/dTXJb79OkDd3fOYVfbDG/viudaO6JAKzBlqwr31JyqjIiIareMu2oErQlH/NUs2JopsXl8APwa2sodS+/JVngjIyPh5+cHPz8/JCQkYNu2bfDz89Mdr6tQKLBjxw5otVr4+/tjwoQJCAwMxJYtW3TbiI2NRXJyMpo0aVLmbZw+fRr16tWDg4NDtdwnqlySJGHhkDZwtjbBhYx7+PDX03JHIiIiemw3s/MQtCYcSTdyYG9hjG0TO8HbxVruWHWCbMfwdunSBY86X87DwwP79+9/4OXBwcEIDg5+4OVTpkzBlClTHjsjyc/aTIklgb4IDg3HtugreKqFA55r7SR3LCIiogq5duc+XgwNx8VbuXC0MsHm8R3RxMFC7lh1Ro04hpfoYQKa1MPr/5y0NvOneFy/c1/mREREROV3+VYuRvzvOC7eyoWrrSm2T+zEslvNWHipVpj6bHP4uNkgO68Ab21ToVAr+2x6REREj3Qu/S5GrD6Oa3fuw93eHNsndkLDepzjv7qx8FKtoDRQYFmgL8yNDBBxIRP/++uc3JGIiIgeKvlGDgJXh+NGdh486ltg24QAONuYyh2rTmLhpVqjsb055g8q+kKRpWEpUF25I28gIiKiBzh5LQtBa44j464aLZ2ssHVCAOpbmcgdq85i4aVa5YW2LujfxumfqcpicZdTlRERUQ0Te/k2gkPDcTtXAx9Xa2wZ3xH1LEp/wRVVHxZeqlUkScLHQ1rDxcYUl27lYt4vp+SOREREpBNx/hZGro1ATl4B2jeyxXfjOsLGzEjuWHUeCy/VOtamSiwN9IVCAn6MuYrf4q/LHYmIiAhHzmRg9IZI3MsvRKcm9fDNWH9YmvBrlWsCFl6qlfzd7TDpmWYAgFk7EnD1dq7MiYiIqC77M+kmxn4ThTyNFk81d8CGlzvA3Fi2rzug/2DhpVprck8P+DW0QU5eAaZti+NUZUREJIt9J29g4rcxyC/QopdXA6x5qR1MlAZyx6J/YeGlWsvQQIFlgX6wMDZE5MVMrDx4Vu5IRERUx+xSXcMbm09AUyjQv40TVr7YFsaGLLs1DQsv1WoN65nhw0GtAABf/nEGJy7fljkRERHVFdujr2DqP1+GNLStC5YF+UFpwGpVE3FUqNYb4ueCgT7OKNQKTN2qQk6eRu5IRESk574Nv4R3foyHEEBIx4b4fJgPDBSS3LHoAVh4qdaTJAkfDfGGq60pLmfmYu4uTlVGRERVZ+3f5/H+zycBAC93aYyPB3tDwbJbo7Hwkl6wMlHiy3+mKtsRew27VNfkjkRERHro64Nn8dHuRADAa083xQf9vSBJLLs1HQsv6Y32je3wZg8PAMB7O0/iSianKiMiosohhMAXB5Lx2f5kAMBbzzbHO31asOzWEiy8pFfe7NEM7RrZIkddgLe2qVBQqJU7EhER1XJCCHyyJxEr/iyaDWhmP09MedaDZbcWYeElvWJooMCXgb6wNDZE9KXb+PrgObkjERFRLabVCsz95RRC/74AAJg3wAuvPtVU5lRUUSy8pHfc7Mzw0RBvAMCyP1IQcylT5kRERFQbFWoFZu1IwKbjlyBJwMKhrTGmi7vcsegxsPCSXhrk64Ihfi7QCmDKVhWyOVUZERFVQEGhFtO2q7At+goUEvDFcB8E+zeUOxY9JhZe0lsfDmoFNztTXL19Hx/8M30MERHRo+QXaPHmlljsUl2HoULCiuC2GNrWVe5Y9ARYeElvWZoo8WWgHwwUEn5WXcfO2KtyRyIiohouT1OI176Lwd6TN2BkoMCqke3wfBsnuWPRE2LhJb3WrpEtpvQsmqrs/Z9P4fItTlVGRERlu59fiPGbovFHUhqMDRUIHd0evbwayB2LKgELL+m9N55phg6NbXFXXYCp22I5VRkREZVyV12A0Rsi8feZDJgZGWDDyx3wVHMHuWNRJWHhJb1noJCwNNAXliaGOHH5Dpb/M48iERERAGTd12DUughEXsiEpbEhNo31R+em9nLHokrEwkt1gqutGT4e0hoA8NWfZxB1kVOVERERcPtePl5cG47Yy3dgbarE9+M7on1jO7ljUSVj4aU6Y6CPM15o6wqtAKZuVSHrPqcqIyKqy9Jz1AgODcfJa9moZ26ELeMD0MbVRu5YVAVYeKlOmT+oFRrVM8O1O/fx3s8nIYSQOxIREcngRlYeAtccR9KNHNS3NMbWCQHwcraSOxZVERZeqlMsjA3xZaAvDBQSfo27jh0nrskdiYiIqtnV27kYsfo4zqffg7O1CbZN7ASPBpZyx6IqxMJLdY5fQ1u89WzRVGUf7DqJS7fuyZyIiIiqy8WMewhcHY7LmbloaGeGbRM7wd3eXO5YVMVYeKlOeu3pZvB3t8O9/EJM2aqChlOVERHpvbNpORix+jiu3bmPJvbm2D6xE9zszOSORdWgwoU3OTm5KnIQVSsDhYQvA31hZWII1ZU7WPb7GbkjERFRFUpMzUbg6nCk5ajRvIEFtk4MgKO1idyxqJpUuPB6eXkhLS2tKrIQVStnG1MsHNoGAPD1obOIOH9L5kRERFQVEq5mITg0HLfu5aOVsxW2TuiE+pYsu3VJuQtvVlYWADzwrPabN2/CwYHfSEK1y/NtnDC8nSuEAN7apkJWLqcqIyLSJzGXbiMkNBx3cjXwdbPB5vEBsDM3kjsWVbNyF147Ozt4enpCkiQsX74ce/bswbVr/3+G+71795CTk1MlIYmq0ryBrdC4nhmuZ+Vh9s8JnKqMiEhPHD93C6PWRSBHXQD/xnb4blxHWJsq5Y5FMih34T1x4gSmT58OIQT+/PNPjBw5Eg0bNoSDgwN69OiBvn37om3btlWZlahKmBsbYlmQHwwVEnbHp+KHmKtyRyIioid0OCUdYzZEIje/EF2b2WPj2A6wMDaUOxbJpNwj7+PjAx8fHxw7dgxLly6FjY0NLl68CJVKhYSEBGi1WrzyyitVmZWoyvi42WBa7+b4dF8y5v1yCh0a23GaGiKiWur30zfx+vcnkF+oRQ/P+lj5YluYKA3kjkUyqvBHnfXr10OSJABA48aN0bhxYwwePLiycxFVu4ndm+JwSjrCz2di6tZY/PhaZygNOHMfEVFtsjs+FVO2xqJAK9C3lSOWB/vByJB/y+u6Cj8DnnvuOXz11Ve63yMiIrBq1SqcP3++UoMRVTcDhYSlgb6wNlUi7moWloalyB2JiIgqYGfsVby55QQKtAIDfZzxVQjLLhWp8LMgKioKnTt3BgCkpqaiR48e+Pzzz9GuXTtERkZWekCi6uRkbYpFQ1sDAFb9dQ7Hz3GqMiKi2mBr5GVM2x4HrQCGt3PF0kBfGPK/dPSPCj8T7t+/r5t+bOvWrfDx8cG5c+cwe/ZsvPfee5UekKi69WvthKAObrqpyu7k5ssdiYiIHmLT8YuYuSMBQgAjAxpi8QttYKCQ5I5FNUiFC2/Tpk1x8uRJAMBPP/2EkSNHAgBeeOEFxMTEVG46Ipl8MMALTezNcSM7D7N2cKoyIqKaas3hc/hg1ykAwLiu7lgwyBsKll36jwoX3jfeeAMTJkxAcHAwoqKidCes3bt3D/n53BNG+sHMqGiqMqWBhL0nb2B79BW5IxER0b8IIbD8jzP4ZE8SAGDSM80w5/mWuhPrif6twoV34sSJWLRoEYyMjLBp0yY4OzsDAI4cOYKmTZtWekAiubR2tcb03i0AAPN+OY1z6XdlTkREREBR2f1sfzKW/HNy8du9m+PtPi1YdumBHuto7hdffBHffPMNAgMDdcuMjIzw+uuvV1owoppgQrcm6Ny0Hu5rCjF1qwr5BVq5IxER1WlCCCz4LRErD50DALz3fEtM6uEhcyqq6R7rK0eOHj2KgwcPwtDQEJMmTYKFhQW/dIL0kkIhYckIX/RddhgJ17LwRVgyZvVrKXcsIqI6SasVeH/XSXwfcRkAsGBQK4zq1FjeUFQrVHgP7+rVq9G7d29ERkbigw8+QFpaGgDgm2++QXR0dKUHJJKbo7UJFr/QBgCw5vB5HDubIXMiIqK6p1Ar8M5P8fg+4jIkCfj0hTYsu1RuFS68n376KTZu3IhffvkFxsbGuuWFhYWYP39+pYYjqin6tHJEsH/DoqnKtqtw+x5P0CQiqi6aQi2mblPhx5irMFBI+DLQFyM6uMkdi2qRChfe69evw9/fv9Ty9u3bcw8v6bX3+7dEUwdz3MxWY+aOeE5VRkRUDdQFhZi0+QR+jbsOpYGEr4L9MMjXRe5YVMtUuPB6e3sjNja21HJLS0tkZWVVSiiimujfU5XtP3UTWyI5VRkRUVXK0xTi1W9jsP/UTRgZKPC/ke3Qr7WT3LGoFqpw4Z07dy6mTZuGM2fOAIBuCpB9+/ahYcOGlZuOqIbxdrHGO308AQAf/nYKZ9M4VRkRUVXIzS/A2I1ROJicDhOlAuvGtEfPlg3kjkW1VIVnaejfvz/OnDmDtm3b4v79+1i8eDHu3buH7du3Y/HixVWRkahGeaWrOw6fScffZzIwZWssdrzeGcaGBnLHIiLSGzl5GozdGIWoi7dhbmSAdWM6IKBJPbljUS32WPPwvvXWW4iPj8cHH3yA9PR0aDQahIaGYurUqZUcj6jmUSgkfD7cB7ZmSpy6no0vDqTIHYmISG9k5Wowcl0koi7ehqWJIb4d15Fll55YhffwOjk54fTp03B3d8cHH3xQFZmIarwGVib4dJgPxm+KxprD59HNwx7dPBzkjkVEVKvduqvGqHWROJ2aDRszJb57pSO8XazljkV6oMJ7eG/evAmNRlNq+e3btzF8+PBKCUVUG/TyaoCRAUXHrU/fHodMTlVGRPTY0nLyELQmHKdTs2FvYYStEwJYdqnSlLvwtm3bFq+++iokSUJMTAzu3i15ss79+/fx888/V3Y+ohptznNeaFbfAmk5arzzI6cqIyJ6HKlZ9xG0Ohxn0u6igZUxtk7oBE9HK7ljkR4p9yENo0ePRlRUFIQQGDRoELRaLdzd3eHj44M2bdrg9OnTaNy4cRVGJap5TI0MsDzID4O/PorfE2/i+4jLGBnQSO5YRES1xpXMXISsDceVzPtwsTHF5vEd0aieudyxSM+Uu/BOmTIFAHD06FH8/fffyMjIgEqlgkqlwl9//QWtVotVq1ZVWVCimsrL2Qrv9vPEgt9OY8Fvp9HR3Q4eDSzljkVEVONdyLiHkNBwpGbloVE9M2weHwAXG1O5Y5EeqvBJaxcuXAAAuLq6wtfXt7LzENVKL3dujL9S0nE4JR2Tt6rw8xucqoyI6GHO3MxByNoIpOeo0dTBHJvHB6CBlYncsUhPPda0ZJUpNDQUvXr1gr29PSRJQkpK6SmeDh06hD59+sDGxgYWFhYICAgo88S5Yp988gm8vb1hZWUFCwsLdOnSBceOHavKu0F1XNFUZW1Qz9wIianZ+HRfstyRiIhqrFPXsxC4JhzpOWp4Olpi28ROLLtUpWQvvDk5OQgJCcGoUaNgZWUFDw+PEpeHhoZi1KhRCAoKQkREBKKjozF16lQolcoHbrN+/fpYuXIl4uLicPToUZiYmGDw4ME8oYiqVH1LE3w6rA0AYN2RCzicki5zIiKimifuyh0ErwlH5r18tHaxxtYJAbC3MJY7Fum5Ch/SUNmmTZsGABg3bhz8/Px0X1UMAImJiZgxYwaOHTsGLy8v3XJPT8+HbnPcuHElfg8KCsKxY8eg1WphYMB/M1PV6dmyAV7q1Aibjl/CtO1x2De1G/+QExH9I+piJl7eEIW76gK0bWiDjWP9YWXy4B1YRJWlQoW3sLAQP//8M3r37g1Ly8o9KScmJgY9evQosWzx4sXo3r07PvvsM+zbtw8WFhYYO3YsZs2aVa5tCiGgUqmwZMkSzJkz54FlV61WQ61W637Pzs4GAGg0moceOkFPrvjx1afHeUavZjh+LgNn0u5hxg8qrH6x5Ae5uk4fx5wejeNe9/x3zI+fv4WJ38XivkYL/8a2WD3SD6YGfE7ok+p+nVfkdiRRwf/zm5qa4tSpU2jSpEmFgz2IWq2GpaUlNm7ciJCQEACAVquFra0t1Go1Zs+ejf79+yMsLAwzZ87E9u3bH/olF+np6XB3d0d+fj4kScKqVaswduzYB64/b948zJ8/v9TyzZs3w8zM7MnvINU51+8BXyQYoEBIGOZeiG6OPJyGiOquxNsS1iUroBESPK21eKWFFkb8hys9odzcXISEhCArKwtWVg+ft7nChfepp57CBx98gJ49ez5RyH+Ljo5Ghw4dkJSUhBYtWgAAzp49Cw8PD8ycORMLFy7Urevl5YWePXtixYoVD9xefn4+Ll++jKysLKxZswZbtmxBQkICGjUqe37Usvbwurm5ISMj45EPID0ZjUaDsLAw9OrV66HHZddG3xy/hI/2JMPYUIGdrwbAo4GF3JFqBH0ec3owjnvdUzzmioa+mPbjKWgKBXq0cMDyIB8YG8p+ChFVgep+nWdnZ8Pe3r5chbfCx/BOnjwZs2fPxo8//gg3N7fHDvlvMTExsLCwKHHCWnp60Qk/w4YNK7GuQqF45F5XIyMjNGvWDACwdOlSrFmzBkePHn1g4TU2NoaxcenjLJVKJf8wVxN9fKxf6dYUR85l4lByOqb9mICf3+gCEyV3aRTTxzGnR+O41y0nMiR8H3EKBVqB51o74stAPxix7Oq96nqdV+Q2KvysGz58OKKiotCqVSuMHDkSa9euRUxMDPLz8yu6KZ2YmBj4+flBofj/OPXq1QOAEjMrJCYmIikpCX369Cn3to8cOQIAugJMVF0kScJnw3xgb2GEpBs5WLQ3Se5IRETVZkfsNWw6o0CBVmCInwuWB7Hsknwq/My7cOECdu7cibfffhu5ublYuHAh/P39YWFhgTZt2pR7OxqNRvdNbeHh4XB2doZKpcL58+cBFBVUb29vvP/++0hKSkJYWBgGDRqEAQMG6E5uS0pKgqenJ6KiogAU7c3dsmULkpKSkJSUhFWrViEkJARBQUHw9/ev6F0lemIOlsb4bLgPAGDjsYs4mJwmcyIioqq3OeIy3t1xCgISRrRzwRfDfWBowLJL8qnwIQ2NGjVCo0aNMGjQIN2ynJwcqFQqxMfHl3s7kZGR6Nq1q+73hIQEbNu2DePGjUNoaCgUCgV27NiBSZMmwd/fH/Xq1cPIkSMxZ84c3XViY2ORnJysO4EuOzsbK1euxJUrV2BmZoYmTZpgwYIFmDBhQkXvJlGleaZFfYzp3Bgbj13EjB/isHdKdzhYcqoyItJPG45ewPxfTwMAujlqsWCgFxQKzlRD8qpw4d25cyd8fHxKzNJgaWmJbt26oVu3buXeTpcuXR75RRAeHh7Yv3//Ay8PDg5GcHCw7ve5c+di7ty55c5AVF1m9vNE+PlbSLqRg3d+jMP6MR04VRkR6Z1Vh85h8b6iw7fGdW0M74KzLLtUI1T4/wuvv/46PDw8YGVlha5du+KNN97AmjVrEBERgdzc3KrISFTrmSgNsCzID8aGChxMTsc3xy7KHYmIqNIIIbA0LEVXdif39MA7vT3Az/VUU1R4D29qaipu3LiB2NhYqFQqREVFYdOmTcjNzYUkSSgoKKiKnES1XgtHS8x5viU+2HUKn+xNQkDTevB05LR3RFS7CSGwaF8SVv9VdA7OO31b4PWnm/ELJahGeayvFnZ0dES/fv3Qr18/AEBGRgZGjhyJESNGVGo4In0zKqAR/kpOxx9JaZi8JRa/TOrKqcqIqNYSQmD+r6ex8Z//Wr3f3wuvdHWXNxRRGSrllEl7e3usXbsWW7durYzNEektSZKweFgb2FsYI+XmXSzckyh3JCKix6LVCszemaArux8N9mbZpRqrwoX3+PHjuHfvXqnlrq6uSEhIqJRQRPrM3sIYX4womqrsm+OX8GfSTZkTERFVTEGhFm//EIctkVegkIDPh/tgZEDZX+5EVBNU+JCG4pkYmjRpAh8fH/j6+qJFixaIjIyEiYlJpQck0kdPNXfAK13dse7IBbz9Qzz2Te2G+pZ8/RBRzacp1GLqVhV2J6TCQCHhy0BfDPBxljsW0UNVuPDm5OQgLi4O8fHxUKlU2LNnD7788kvY29tj5cqVVZGRSC+907cFjp27hcTUbLz9Qzw2junA6XuIqEZTFxRi0uZYhJ2+CaWBhBXBbdHX21HuWESPVOHCa2pqioCAAAQEBFRFHqI6w9jQAMuDfNF/xREcTknHhmMXefwbEdVY9/MLMfG7GBxOSYeRoQKrR7bDM5715Y5FVC4VPoZ39OjR2LBhg+73S5cuYe/evcjKyqrUYER1gUcDS7zX3wsAsHhvEk5fz5Y5ERFRaffUBXh5YyQOp6TDVGmADWM6sOxSrVLhwrt//354enoCAG7fvo22bdti6NCh8PLyQnJycqUHJNJ3Izs2xLMtGyC/UIvJW2NxP79Q7khERDrZeRq8tD4S4eczYWFsiE2v+KNLM3u5YxFVSIULb1ZWFlxdXQEA27dvh7OzM7KyshASEoJZs2ZVekAifSdJEj4d1gb1LY1xNu0uPt5zWu5IREQAgDu5+Ri5NgIxl27DysQQ343riA6N7eSORVRhFS68bm5uuHDhAgBgx44dGD16NIyMjDB+/HgcPXq00gMS1QV25ka6qcq+C7+MsNOcqoyI5JVxV42gNeGIv5oFWzMlNo8PgK+bjdyxiB5LhQvvmDFjMGnSJMyaNQt//vknBg8eDAAoLCzE3bt3KzsfUZ3RzcMB47sVnbT27k/xSMvOkzkREdVVN7PzELQmHEk3cmBvYYxtEzvB28Va7lhEj63ChXfWrFkIDAzEsWPHsGjRIjRr1gwAEBUVhYYNG1Z6QKK65O0+LeDlZIXMe/mY/kMctFohdyQiqmOu3bmPwNXHcTbtLpysTbB9YgCaN7CUOxbRE6lw4ZUkCXPmzMFff/2F6dOn65bfvHkTISEhlRqOqK4xNjTA8mA/mCgV+PtMBtYduSB3JCKqQy7fysWI/x3HxVu5cLU1xfaJndDEwULuWERPrMLz8D7IjBkzKmtTRHVas/oW+KB/K8zemYBP9yehU9N6/FciEVW5c+l3ERIajpvZarjbm+P7cR3hbGMqdyyiSlHhPbxEVPWC/d3Qp1UDaAoFpnCqMiKqYsk3chC4uqjsetS3wLYJASy7pFdYeIlqIEmSsGhoGzSwMsa59HtYsJtTlRFR1Th5LQtBa44j464aXk5W2DohAPWtTOSORVSpWHiJaihbcyMsGeELSQI2R1zGvpM35I5ERHrmxOXbCA4Nx+1cDXxcrbFlfADqWRjLHYuo0pW78L7//vtQq9VVmYWI/qNLM3tM6N4EADBzRzxuZHGqMiKqHBHnb2HU2gjk5BWgQ2NbfDeuI6zNlHLHIqoS5S68n3zyCbKysnS/f/zxx5x3l6gaTO/VAq1drHEnV4PpP6g4VRkRPbEjZzIwekMk7uUXonPTevhmrD8sTVh2SX+Vu/AKUfJNdvHixUhLS9P9npaWBnd398pLRkQAACNDBb4M8oWp0gBHz95C6N/n5Y5ERLXYn0k3MfabKORptHi6hQPWj+kAM6NKm7SJqEZ67GN4/1uAhRC4fPnyEwciotKaOlhg7gAvAMDnB5KRcDXrEdcgIipt38lUTPw2BvkFWvTyaoDVo9rBRGkgdyyiKseT1ohqicAObujn7aibqiw3v0DuSERUi+xSXcMbm2OhKRTo38YJK19sC2NDll2qGypUeP/++29kZmZWVRYieghJkrBwaGs4WZvgfMY9fPgrpyojovLZHn0FU7epUKgVeKGtK5YF+UFpwH1eVHeU+9nu6uqK4cOHw8HBAY0aNUJeXh5WrVqF3bt34/r161WZkYj+YWP2/1OVbY26gr0JqXJHIqIa7tvwS3jnx3gIAYR0bIjPhrWBgUKSOxZRtSr3UeqXL19GZmYmYmJiEBMTgxMnTmDHjh344osvIEkSbGxsqjAmERXr1LQeXn2qKVYdOoeZOxLg29AGTtb8RiQiKm3t3+fx0e5EAMDLXRrjg/5ekCSWXap7KnRapp2dHXr16oVevXrplmVlZelKcGxsbKUHJKLSpvVqjmNnMxB3NQtvbVPh+3EB3GNDRCV89ecZfH4gBQDw2tNN8U6fFiy7VGeVu/CeOnUKxsbGaNasWYnl1tbW6NGjB3r06FHp4YiobEoDBZYF+eG55X8j/HwmVh8+h9efbvboKxKR3hNCYElYClb8eRZA0QfkN3s0Y9mlOq3cx/BOmzYNK1euLLFs165dCAwMxJtvvokLFy5UejgierDG9uaYN7AVAGDJgRTEXbkjbyAikp0QAp/sSdSV3Vn9PDG5pwfLLtV55S68cXFxeOGFF3S/JyYmYvjw4Th69Ci2bt0Kf39/nrxGVM2Gt3PF862dUKAtmqrsnppTlRHVVVqtwAe7TiH076IdUPMGeGHiU01lTkVUM5S78GZlZcHNzU33+6ZNm9CkSRNcunQJV69eha+vLxYtWlQlIYmobJIk4ZMhreFsbYKLt3Ix75dTckciIhkUagVm7ojHt+GXIEnAwqGtMaYLv/2UqFiFpiVLTf3/KZB+//13jBgxAgYGBjA2NsasWbNw4MCBKglJRA9mbabE0sCiqcp+iLmK3fGcqoyoLiko1GLadhW2R1+FQgKWjPBBsH9DuWMR1SjlLry9evXCkiVLAACXLl1CbGxsidkamjZtiitXrlR+QiJ6pI5N6uGNf05am7UjHtfu3Jc5ERFVh/wCLd7cEotdquswVEhYEdwWQ/xc5Y5FVOOUu/DOmTMHBw8eRJMmTdCpUye4ubmha9euustv3rwJCwuLKglJRI825VkP+LrZIDuvAG/9841KRKS/8jSFeO27GOw9eQNGBgqsGtkOz7dxkjsWUY1U7sLr4uKCqKgoDBkyBP369cOOHTtKnPX5559/onnz5lUSkogerWiqMl+YGxkg8kIm/vfXObkjEVEVuZ9fiPGbovFHUhqMDRUIHd0evbwayB2LqMaq0BdPNGrUCF988UWZl50+fRrDhg2rlFBE9Hga1TPHh4O8Mf2HOCwJS0HnpvXg19BW7lhEVInuqgswdmMUIi9kwszIAOtGd0CnpvXkjkVUo5V7D++8efPwyy+/4Nq1a2VevmnTJkyZMqXSghHR4xna1gUDfJxRqBWYslWFu5yqjEhvZN3XYNS6CEReyISlsSG+fcWfZZeoHMq9h/fDDz/UHcJgb2+Pdu3aoW3btmjbti3atWuHRo0aVVlIIio/SZLw0WBvnLh0G5czczF31yl8McJH7lhE9IRu38vHqPUROHktG9amSnz7ij/auNrIHYuoVij3Ht4OHTrAxcUF7733HubNmwcXFxfs2bMHwcHBaNKkCezt7dG7d++qzEpE5WRtqsSXQb5QSMBPJ67ilzh+KQxRbZaeo0bQmnCcvJaNeuZG2DI+gGWXqALKvYc3IiICGzduxOzZs+Hn54elS5eiefPm0Gg0iI+Px4kTJxAbG1uVWYmoAjo0tsOkHh5Y/scZzNmZgLYNbeBqayZ3LCKqoBtZeQhZG47z6fdQ39IYm8d3RLP6lnLHIqpVyr2HFwDGjBmDlJQUtGrVCu3bt8eMGTOgVqvRrl07jB8/HitXrqyqnET0GCb3aIa2DW2Q889UZQWFWrkjEVEFXL2dixGrj+N8+j04W5tg+8ROLLtEj6FChRcALCws8OmnnyImJgZJSUlo1qwZ1q9fXxXZiOgJGRoosCzIDxbGhoi6eBsrD3GqMqLa4mLGPQSuDsflzFw0tDPDtomd0NjeXO5YRLVShQsvAGg0Gty/fx9BQUFo2LAhxo8fj8zMzMrORkSVwM3ODAsGtwIALPvjDGIu3ZY5ERE9ytm0HIxYfRzX7txHEwdzbJ/YCW52PCSJ6HGV+xjejz/+GAkJCUhISEBKSgrMzc3Rpk0bdOzYERMnToS1tXVV5iSiJzDEzxWHktOxS3UdU7fFYs/kbrA0Ucodi4jKkJiajZFrI3DrXj5aNLDEd+M6wsHSWO5YRLVauQvv+++/j8aNG2PMmDEIDg6Gh4dHVeYiokq2YLA3Yi7dxpXM+/hg1yksDfSVOxIR/UfC1SyMWh+BO7katHK2wrevdISduZHcsYhqvXIf0tC1a1fcunUL8+bNg6+vLzp16oRJkyZh/fr1iIuLQ2FhYVXmJKInZGWixLIgXxgoJOyMvYZdqrK/RIaI5BFzKRMhoeG4k6uBX0MbbB4fwLJLVEnKXXgPHz6MrKwsJCcnY926dejWrRsSExPx9ttvw8/PDxYWFvD396/KrET0hNo1ssObPZoBAN7beRJXMnNlTkREAHD83C2MWheJHHUB/N3t8O0rHWFtysOOiCpLuQ9pKObh4QEPDw8EBQXpll24cAHR0dGch5eoFpj0TDMcOZOB6Eu3MXWbCtsmBMDQ4LHOXyWiSnA4JR3jN0VDXaBF12b2CH2pPUyNDOSORaRXKuVdzt3dHcOHD8cnn3xSGZsjoipkaKDA0kBfWBobIubSbaz486zckYjqrN9P38S4b4rKbg/P+lg7mmWXqCpwtw5RHeRmZ4aPhngDAFb8eQbRFzmtIFF12x2file/i0F+oRb9vB3xv5HtYKJk2SWqCiy8RHXUIF8XDPVzgVYAU7aqkJ2nkTsSUZ2xM/Yq3txyAgVagUG+zlgR7AcjQ74lE1UVvrqI6rD5g1qhoZ0Zrt25j/d/Pil3HKI6YWvkZUzbHgetAEa0d8WSEb48jp6oivEVRlSHWZoo8eU/U5XtUl3Hztirckci0mubjl/EzB0JEAIYGdAQi4a2gYFCkjsWkd5j4SWq49o2tMXUnkVfJPP+z6dw+RanKiOqCmsOn8MHu04BAMZ1dceCQd5QsOwSVQsWXiLC6880g39jO9xVF2DKtlhoCrVyRyLSG0IILP/jDD7ZkwQAeLNHM8x5viUkiWWXqLqw8BIRDBQSlgb5wtLEELGX72DFH2fkjkSkF4QQ+Gx/MpaEpQAA3u7dHNN7t2DZJapmLLxEBABwsTHFJ0NaAwC+OngWkRc4VRnRkxBCYMFviVh56BwA4L3nW2JSDw+ZUxHVTSy8RKQzwMcZw9q5QiuAt7apkHWfU5URPQ6tVmDOzyex/ugFAMCCQa0wrlsTmVMR1V2yF97Q0FD06tUL9vb2kCQJKSkppdY5dOgQ+vTpAxsbG1hYWCAgIAAazYPfiFesWIGnn34aDg4OsLKyQr9+/XDmDP9FS1Qe8wa2QqN6RVOVzdmZACGE3JGIapVCrcA7P8Vjc8RlSBLw6QttMKpTY7ljEdVpshfenJwchISEYNSoUbCysoKHR8l/94SGhmLUqFEICgpCREQEoqOjMXXqVCiVyjK3d//+fezZswfjx4/HoUOHcOjQIaSnp2PIkCHVcXeIaj0LY0MsC/KDoULCb/Gp+OnENbkjEdUamkItpm5T4ceYqzBQSPgy0BcjOrjJHYuozjOUO8C0adMAAOPGjYOfn1+JA/kTExMxY8YMHDt2DF5eXrrlnp6eD9yeqakp9u7dW2LZq6++ivHjxyMnJweWlpaVfA+I9I+vmw3e6tUcn+1PxtxdJ9G+kS0a25vLHYuoRlMXFGLylljsP3UTSgMJy4P80K+1k9yxiAg1oPAWi4mJQY8ePUosW7x4Mbp3747PPvsM+/btg4WFBcaOHYtZs2ZVaNuHDx+Gu7v7A8uuWq2GWq3W/Z6dnQ0A0Gg0Dz10gp5c8ePLx7nmeaVzQ/yVnIbIi7cxecsJbB3vD2UlfBsUx7xu0vdxz9MUYtKWOPx1JgNGhgp8FeSDZ1rY6+39LQ99H3MqrbrHvCK3I4kacICeWq2GpaUlNm7ciJCQEACAVquFra0t1Go1Zs+ejf79+yMsLAwzZ87E9u3bMXz48HJt++uvv8Zbb72FX375BX379i1znXnz5mH+/Pmllm/evBlmZmaPf8eIarnbamBxnAHuF0ro5aJF/4acn5fov9SFQGiSAmeyFVAqBMa30KKFjexvrUR6Lzc3FyEhIcjKyoKVldVD160RhTc6OhodOnRAUlISWrRoAQA4e/YsPDw8MHPmTCxcuFC3rpeXF3r27IkVK1Y8dJtarRZz5szB119/jS1btuD5559/4Lpl7eF1c3NDRkbGIx9AejIajQZhYWHo1avXA4/LJnntPXkDk7fFQ5KAb19uj47udk+0PY553aSv456TV4AJ351A9KU7MDcywJpRfvBv/GSvEX2hr2NOD1bdY56dnQ17e/tyFd4acUhDTEwMLCwsSpywlp6eDgAYNmxYiXUVCsUj97revXsXI0eORFxcHI4ePYrWrVs/dH1jY2MYGxuXWq5UKvkirSZ8rGuugX5uOHIuE9ujr2LGTyexb0p3WJs9+VhxzOsmfRr3rFwNXt50AnFX7sDSxBDfjPVH24a2cseqcfRpzKl8qmvMK3Ibss/SABQVXj8/PygU/x+nXr16AFBiSqTExEQkJSWhT58+D9zWpUuX0KVLF9y6dQuRkZGPLLtE9GhzB7SCu705UrPyMGtnPKcqozrv1l01gkPDEXflDmzNlNgyPoBll6gGk63wajQaqFQqqFQqhIeHw9nZGSqVCufPnwcANGvWDN7e3nj//feRlJSEsLAwDBo0CAMGDNCd3JaUlARPT09ERUUBAMLDw+Hv7w83Nzd8//33KCwsxI0bN3Dz5k257iaRXjA3NsSyIF8YKiTsSbiBH6Kvyh2JSDZpOXkIWhOO06nZsLcwwtYJneDtYi13LCJ6CNkKb2RkJPz8/ODn54eEhARs27YNfn5+uuN1FQoFduzYAa1WC39/f0yYMAGBgYHYsmWLbhuxsbFITk5GkyZF314zf/58pKWlYffu3WjUqBGcnJzg5OSErl27ynIfifRJG1cbTO9ddIz9vF9P4ULGPZkTEVW/1Kz7CFodjjNpd9HAyhhbJ3RCC0dOd0lU08l2DG+XLl0e+W9RDw8P7N+//4GXBwcHIzg4WPf7f+ffJaLKNbF7ExxOScfx87cwZWssfny1M4wMa8SRUURV7kpmLkLWhuNK5n242Jhi8/iOaFSP81MT1QZ8pyKiclMoJCwJ9IG1qRLxV7Ow9PfSXwVOpI/Op9/FiNXHcSXzPhrVM8P2Vzux7BLVIiy8RFQhTtamWPxC0cmg//vrHI6dy5A5EVHVOnMzB4FrwpGalYemDubYPrETXGxM5Y5FRBXAwktEFdbX2wnB/m4QApi2LQ637+XLHYmoSpy6noXANeFIz1HD09ES2yZ2QgMrE7ljEVEFsfAS0WN5v78XmjiY40Z2HmbtSOBUZaR3VFfuIHhNODLv5aONqzW2TgiAvUXpOduJqOZj4SWix2JmZIjlQX5QGkjYd+oGtkVdkTsSUaWJupiJkWsjkJ1XgHaNbPHduI6wMTOSOxYRPSYWXiJ6bN4u1nj7n6nK5v96GufS78qciOjJHTubgZfWReKuugABTeywaaw/rEz4TWFEtRkLLxE9kfHdmqBLs3q4rynElK2xyC/Qyh2J6LEdSk7DyxujcF9TiG4e9tgwxh/mxrLN4ElElYSFl4ieiEIhYckIX9iaKXHyWja+OJAsdySix7L/1A2M3xQNdYEWz7ZsgLWj28PUyEDuWERUCVh4ieiJNbAyweIX2gAAVh8+j6NnOVUZ1S6/xl3H69+fgKZQ4PnWTlg1si2MDVl2ifQFCy8RVYrerRwR0rEhAGDadhUyOVUZ1RI/xlzFlK2xKNQKDPVzwbIgXygN+PZIpE/4iiaiSvP+815o6mCOm9lqvPtTPKcqoxpvc8RlvP1DHLQCCOrghs+H+8CQZZdI7/BVTUSVxtTIAMuD/WBkoEDY6ZvYHHlZ7khED7T+yAXM3pkAABjTuTE+GdIaCoUkcyoiqgosvERUqVo5W+OdvkVTlS347TTOpuXInIiotJWHzuLD304DACY+1QRzB3ix7BLpMRZeIqp0Y7u4o5uHPfI0WkzeooK6oFDuSEQAACEEloal4NN9RbOJTOnpgZl9PSFJLLtE+oyFl4gqnUIh4YvhPrAzN8Lp1Gx8to9TlZH8hBBYtC8Jy/44AwB4p28LvNWrOcsuUR3AwktEVaK+lQk+/WeqsrVHLuBwSrrMiagu02oF5v96Gqv/Og8A+KC/F15/upnMqYiourDwElGVedarAUYFNAIATP8hDrfuqmVORHWRVisw5+cEbDx2EQDw8RBvjO3qLm8oIqpWLLxEVKXmPN8SHvUtkJ7Dqcqo+hUUavH2D3HYEnkFCgn4fLgPXuzYSO5YRFTNWHiJqEqZKP9/qrLfE9OwOeqq3JGojtAUajFlqwo7Yq/BQCFhWZAfhrVzlTsWEcmAhZeIqlxLJyvM7OcJAFi4NxmpuTIHIr2nLijEa9+dwO6EVCgNJKx8sS0G+DjLHYuIZMLCS0TV4uUujfFUcweoC7TYdMYAag2nKqOqcT+/EOM3xeD3xJswNlRgzUvt0aeVo9yxiEhGLLxEVC0kScLnw31gZ67E9VwJn4edkTsS6aF76gK8vDESh1PSYao0wIYxHfBMi/pyxyIimbHwElG1cbA0xqIh3gCAjccv41BymsyJSJ9k52nw0vpIhJ/PhIWxITa94o/OzezljkVENQALLxFVq2daOKCboxYA8PYP8cjgVGVUCe7k5mPk2gjEXLoNKxNDfDeuIzo0tpM7FhHVECy8RFTtBjbUonl9C2TcVWPGD3GcqoyeSMZdNYLWhCP+ahbszI2wZUIAfN1s5I5FRDUICy8RVTsjA2DpiNYwMlTgYHI6Nh2/JHckqqVuZuchaE04km7kwMHSGFsnBKCVs7XcsYiohmHhJSJZNG9gidn/TFX28Z5EJN/IkTkR1TbX7txH4OrjOJt2F07WJtg2IQDNG1jKHYuIaiAWXiKSzejOjfFMCwfkF2gxeUss8jhVGZXT5Vu5GPG/47h4KxdudqbYPrETmjhYyB2LiGooFl4iko0kSfhsuA/sLYyRfDMHi/YmyR2JaoFz6XcxfPUxXLtzH+725tg+sRPc7MzkjkVENRgLLxHJyt7CGJ8PbwMA2HjsIg4mcaoyerDkGzkIXB2Om9lqeNS3wLYJAXCyNpU7FhHVcCy8RCS7p1vUx8tdGgMAZvwYh/QcTlVGpZ28loWgNceRcVcNLycrbJ0QgPpWJnLHIqJagIWXiGqEd/t6wtPREhl38/H2D3HQajlVGf2/E5dvIzg0HLdzNfBxs8GW8QGoZ2EsdywiqiVYeImoRjBRGmB5sB+MDRX4KyUdG49dlDsS1RAR529h1NoI5OQVoENjW3z3ij+szZRyxyKiWoSFl4hqjOYNLPHe8y0BAIv2JiExNVvmRCS3I2cyMHpDJO7lF6Jz03r4Zqw/LE1YdomoYlh4iahGGRnQCM+2rI/8Qk5VVtf9mXQTY7+JQp5Gi6dbOGD9mA4wMzKUOxYR1UIsvERUo0iShMUvtIGDpTHOpN3FJ3sS5Y5EMth3MhUTv41BfoEWvb0aYPWodjBRGsgdi4hqKRZeIqpx6lkY44vhPgCATccv4ffTN2VORNVpl+oa3tgcC02hwAAfZ3z9YlsYG7LsEtHjY+Elohqpe3MHjOvqDgB456d4pGXnyZyIqsP26CuYuk2FQq3AC21d8WWgL5QGfKsioifDvyJEVGPN6NsCXk5WyLyXj+mcqkzvfXv8It75MR5CAC92bIjPhrWBgUKSOxYR6QEWXiKqsYwNDbA82BcmSgX+PpOB9UcvyB2Jqsjav8/j/V2nAABju7jjo8HeULDsElElYeElohqtWX1LvPe8FwDg033JOHU9S+ZEVNm++vMMPtpddHLi6083xfv9W0KSWHaJqPKw8BJRjfdix4bo5dVAN1XZ/XxOVaYPhBD44kAyPj+QAgCY1qs5ZvRpwbJLRJWOhZeIarziqcrqWxrjXPo9fLT7tNyR6AkJIfDJnkSs+PMsAGBWP09M7unBsktEVYKFl4hqBTtzIywZ4QsA+D7iMg6cuiFvIHpsWq3AB7tOIfTvomOy5w9shYlPNZU5FRHpMxZeIqo1unrYY0L3JgCAd3+Kx01OVVbrFGoFZu6Ix7fhlyBJwKKhrTG6c2O5YxGRnmPhJaJa5e3eLdDK2Qq3czWYtl3FqcpqkUIBzPgpAdujr0IhAUtG+CDIv6HcsYioDmDhJaJaxchQgeXBfjBVGuDo2VtYe+S83JGoHPILtPgmRYFf42/AUCFhRXBbDPFzlTsWEdURLLxEVOs0dbDABwOKpir7bH8yTl7jVGU1WZ6mEG9sUSEuUwGlgYT/jWyH59s4yR2LiOoQFl4iqpWCOrihT6sG0BQKTN4ai9z8ArkjURnu5xdi/KZoHErJgFISWP2iH571aiB3LCKqY1h4iahWkiQJi4a2gaOVCc6n38OC3xLljkT/cVddgNEbIvH3mQyYGRlgYkstunnYyx2LiOogFl4iqrVszY2wZIQPJAnYEnkZ+05yqrKaIuu+BqPWRSDyQiYsjQ2xYXQ7eFjzBEMikgcLLxHVap2b2WNi96I5XGfuiMeNLE5VJrfb9/Lx4tpwxF6+A2tTJb4f3xFtG9rIHYuI6jAWXiKq9ab1ao7WLta4w6nKZJeeo0bQmnCcvJaNeuZG2DohAG1cbeSORUR1HAsvEdV6RoYKLAvyhanSAMfO3cKavzlVmRxuZOUhcM1xJN/MQX1LY2ybGICWTlZyxyIiYuElIv3QxMEC8wYWTVX2+f5kxF+9I2+gOubq7VyMWH0c59PvwcXGFNsndkKz+pZyxyIiAsDCS0R6ZER7NzzX2hEFWoEpW1W4p+ZUZdXhYsY9jPjfcVzOzEVDOzNsmxiAxvbmcsciItJh4SUivSFJEhYOaQMnaxNcyLiHD389LXckvXc2LQcjVh/H9aw8NHEwx/aJneBqayZ3LCKiElh4iUivWJspsTTQF5IEbIu+gr0JqXJH0luJqdkIXB2OtBw1PB0tsW1CJzham8gdi4ioFNkLb2hoKHr16gV7e3tIkoSUlJRS6xw6dAh9+vSBjY0NLCwsEBAQAI1G88Bt7ty5E88//zycnZ0hSRIOHDhQlXeBiGqYgCb18NpTxVOVJeD6nfsyJ9I/8VfvIDg0HLfu5cPbxQpbxgfAwdJY7lhERGWSvfDm5OQgJCQEo0aNgpWVFTw8PEpcHhoailGjRiEoKAgRERGIjo7G1KlToVQqH7jN27dvY+DAgZg8eTIAoG3btlV6H4io5nmrV3P4uFoj674Gb21ToZBTlVWamEuZeDE0AndyNfBraIPvxwXA1txI7lhERA9kKHeAadOmAQDGjRsHPz8/SJKkuywxMREzZszAsWPH4OXlpVvu6en50G2OHTsWAPDRRx+hUaNGsLfnV1kS1TVKAwWWBfnh+eV/I+JCJv731zm88UwzuWPVesfP3cIr30QhN78Q/u52WD+mAyyMZX8rISJ6qBrzVyomJgY9evQosWzx4sXo3r07PvvsM+zbtw8WFhYYO3YsZs2aVe5ttmvX7pHrqdVqqNVq3e/Z2dkAAI1G89BDJ+jJFT++fJzrjuoccxdrI7z/vCdm7jyFpWEpCGhsgzau1lV+u/rq77MZeO17FdQFWnRpWg+rQnxhrBDlGku+1usejnndU91jXpHbkYQQsv+fT61Ww9LSEhs3bkRISAgAQKvVwtbWFmq1GrNnz0b//v0RFhaGmTNnYvv27Rg+fPgjt9uwYUO8+uqrmD179kPXmzdvHubPn19q+ebNm2FmxrONiWozIYBvzigQe0sBexOBGW0KYWIgd6ra52SmhPUpChQKCa1stXi5uRZK2Q+KI6K6LDc3FyEhIcjKyoKV1cO/5KZGFN7o6Gh06NABSUlJaNGiBQDg7Nmz8PDwwMyZM7Fw4ULdul5eXujZsydWrFjx0G1mZGTAwcEB+/btQ58+fR66bll7eN3c3JCRkfHIB5CejEajQVhYGHr16vXQ47JJf8gx5ln3NRjw9XGkZuVhqJ8zFg/1rpbb1Rd7T97AtB8SUKAV6ONVH0uGt4GRYcXaLl/rdQ/HvO6p7jHPzs6Gvb19uQpvjTikISYmBhYWFiVOWEtPTwcADBs2rMS6CoWiXHtdY2JiAJTvhDVjY2MYG5c+u1ipVPJFWk34WNc91Tnm9kollgX5IWjNceyIvY4eLRugfxvnarnt2m5n7FVM3x4PrQAG+Trji+E+MDR4/F27fK3XPRzzuqe6xrwit1Ej/iEVExMDPz8/KBT/H6devXoAgH/vgE5MTERSUtIj99gWb9PNzQ0ODg6VH5iIah1/dzvdSWuzdiTgGqcqe6StkZcxbXsctAIY0d4VS0b4PlHZJSKSi2x/uTQaDVQqFVQqFcLDw+Hs7AyVSoXz588DAJo1awZvb2+8//77SEpKQlhYGAYNGoQBAwboTm5LSkqCp6cnoqKidNuNi4uDSqXC4cOH0bhxY6hUKiQlJclyH4moZpnc0wO+bjbIySvAW1s5VdnDfHPsImbuSIAQwKiARlg0tA0MFNKjr0hEVAPJVngjIyPh5+cHPz8/JCQkYNu2bfDz89Mdr6tQKLBjxw5otVr4+/tjwoQJCAwMxJYtW3TbiI2NRXJyMpo0aQIAuHbtGnx9feHn54f9+/fj77//hp+fH958801Z7iMR1SxFU5X5wtzIAJEXM7Hy4Fm5I9VIq/86h7m/nAIAjO/mjg8HtYKCZZeIajHZjuHt0qULHnW+nIeHB/bv3//Ay4ODgxEcHKz73cXF5ZHbJKK6rVE9cywY7I1p2+Pw5R9n0MXDHm0b2sodq0YQQmDFn2exJKzoGy/f7NEM03o1LzE/OhFRbcSDsYiozhni54KBPs4o1ApM3apCTh7nCRVC4LP9ybqy+3bv5pjeuwXLLhHpBRZeIqpzJEnCR0O84WJjisuZubp/39dVQggs+C0RKw+dAwC893xLTOrh8YhrERHVHiy8RFQnWZkosSzIFwoJ2HHiGnaprskdSRZarcCcn09i/dELAIAFg70xrlsTmVMREVUuFl4iqrPaN7bT7cl8b+dJXMnMlTlR9SrUCrzzUzw2R1yGJAGfvtAGowIayR2LiKjSsfASUZ02uUcztG1ogxx1Ad7apkJBoVbuSNVCU6jF1G0q/BhzFQYKCV8G+mJEBze5YxERVQkWXiKq0wwNFFgW5AdLY0NEX7qNrw+ekztSlVMXFGLS5hP4Ne46lAYSvg7xwyBfF7ljERFVGRZeIqrz3OzMsGCwNwBg+Z9nEHMpU+ZEVSdPU4iJ38Zg/6mbMDJUYPWodujr7SR3LCKiKsXCS0QEYLCfCwb7Fk1VNmWrCtl6OFVZbn4Bxm6MwqHkdJgoFVg/ugN6eDaQOxYRUZVj4SUi+seHg73hZmeKq7fv44OfT8odp1Ll5Gkwen0kjp27BXMjA3zzsj+6etjLHYuIqFqw8BIR/cPKRIkvA/1goJDws+o6fo7Vj6nKsnI1GLkuElEXb8PSxBDfjuuIjk3qyR2LiKjasPASEf1Lu0a2mFw8VdnPtX+qslt31QgODUfclTuwNVNiy/gAfpUyEdU5LLxERP/xxjNN0b6RLe6qCzBla2ytnaosLScPQWvCcTo1G/YWxtg6oRO8XazljkVEVO1YeImI/sPQQIGlgb6wNDbEict3sPzPs3JHqrDrd+4jcHU4zqTdhaOVCbZNDEALR0u5YxERyYKFl4ioDG52Zvh4aGsAwFd/nkHUxdozVdmVzFyMWH0cFzLuwcXGFNsndkJTBwu5YxERyYaFl4joAQb6OGNoWxdoBTB1qwpZ92v+VGXn0+9ixOrjuHr7PhrXM8P2VzuhYT0zuWMREcmKhZeI6CE+HOSNhnZmuHbnPt77+SSEEHJHeqAzN3MQuCYcqVl5aFbfAtsmdoKLjancsYiIZMfCS0T0EBbGhlgW5AsDhYRf465jx4maOVXZqetZCFwTjvQcNTwdLbF1QgAaWJnIHYuIqEZg4SUiegS/hrZ469miqco+2HUSl27dkzlRSaordxC8JhyZ9/LRxtUaWycEwN7CWO5YREQ1BgsvEVE5vPZ0M/i72+FefiGmbFVBU0OmKou6mImRayOQnVeAdo1s8d24jrAxM5I7FhFRjcLCS0RUDgYKCUsDfWFlYgjVlTtY/scZuSPh2NkMvLQuEnfVBQhoYodNY/1hZaKUOxYRUY3DwktEVE4uNqb4pHiqsoNnEXH+lmxZDian4eWNUbivKUT35g7Y+LI/zI0NZctDRFSTsfASEVVA/zbOGNbOFUIAb21TISu3+qcq23/qBiZsioa6QItnWzZA6EvtYKI0qPYcRES1BQsvEVEFzRvYCo3rmeF6Vh5m/5xQrVOV/Rp3Ha9/fwKaQoHnWzth1ci2MDZk2SUiehgWXiKiCiqaqswPhgoJu+NT8WPM1Wq53R9jrmLK1lgUagWG+rlgWZAvlAb8M05E9Cj8S0lE9Bh83GzwVq/mAIC5v5zCxYyqnars+4hLePuHOGgFEOzvhs+H+8CQZZeIqFz415KI6DG9+lRTdHS3Q25+IaZsja2yqcrWH7mAOTtPAgDGdG6MT4a0hkIhVcltERHpIxZeIqLHVDxVmbWpEnFXs7A0LKXSb2PlobP48LfTAICJTzXB3AFekCSWXSKiimDhJSJ6As42plj0z1Rlq/46h+PnKmeqMiEEloal4NN9yQCAKT09MLOvJ8suEdFjYOElInpC/Vo7IbC9G4QApm1X4U5u/hNtTwiBRfuSsOyfL7d4t68n3urVnGWXiOgxsfASEVWCDwZ4wd3eHKlZeZi14/GnKtNqBeb/ehqr/zpftN3+Xnjt6aaVGZWIqM5h4SUiqgTmxoZYHuQHpYGEvSdvYHv0lQpvQ6sVmPNzAjYeuwgA+HiIN8Z2da/kpEREdQ8LLxFRJWntao3pvVsAAOb9chrn0++W+7oFhVq8/UMctkRegUICPh/ugxc7NqqqqEREdQoLLxFRJZrQrQk6N62H+5pCTNmqQn7Bo6cq0xRqMWWrCjtir8FAIWFZkB+GtXOthrRERHUDCy8RUSVSKCQsGeELGzMlEq5lYckjpipTFxTite9OYHdCKpQGEla+2BYDfJyrKS0RUd3AwktEVMkcrU2waGgbAMDqw+dw7GxGmevdzy/E+E0x+D3xJowNFVjzUnv0aeVYnVGJiOoEFl4ioirQ19sRwf4NIQTw1nYVMnLUOH7uFnapruH4uVvIvq/ByxsjcTglHaZKA2wY0wHPtKgvd2wiIr1kKHcAIiJ99X7/loi4cAvn0++hy+I/of7X8bxKAwmaQgELY0NsfLkD2je2kzEpEZF+4x5eIqIqYmZkiKD2bgBQouwCgKawaJ7eN55pxrJLRFTFWHiJiKpIoVZgwz9z6j7IpuMXUah9vC+pICKi8mHhJSKqIpEXMpGalffQdVKz8hB5IbOaEhER1U0svEREVSQt5+Flt6LrERHR42HhJSKqIvUtTSp1PSIiejwsvEREVcTf3Q5O1iaQHnC5BMDJ2gT+7jxpjYioKrHwEhFVEQOFhLkDvACgVOkt/n3uAC8YKB5UiYmIqDKw8BIRVaG+3k5YNbItHK1LHrbgaG2CVSPboq+3k0zJiIjqDn7xBBFRFevr7YReXo6IvJCJtJw81LcsOoyBe3aJiKoHCy8RUTUwUEjo1LSe3DGIiOokHtJARERERHqNhZeIiIiI9BoLLxERERHpNRZeIiIiItJrLLxEREREpNdYeImIiIhIr7HwEhEREZFeY+ElIiIiIr3GwktEREREeo2Fl4iIiIj0Gr9auAxCCABAdna2zEn0n0ajQW5uLrKzs6FUKuWOQ9WAY143cdzrHo553VPdY17c04p728Ow8JYhJycHAODm5iZzEiIiIiJ6mJycHFhbWz90HUmUpxbXMVqtFtevX4elpSUkSZI7jl7Lzs6Gm5sbrly5AisrK7njUDXgmNdNHPe6h2Ne91T3mAshkJOTA2dnZygUDz9Kl3t4y6BQKODq6ip3jDrFysqKfxDrGI553cRxr3s45nVPdY75o/bsFuNJa0RERESk11h4iYiIiEivsfCSrIyNjTF37lwYGxvLHYWqCce8buK41z0c87qnJo85T1ojIiIiIr3GPbxEREREpNdYeImIiIhIr7HwEhEREZFeY+ElIiIiIr3GwktVTqvV4sMPP0RAQABsbW1hZ2eHESNG4ObNm7p18vLyMGPGDDg5OcHW1hZBQUG4ffu2jKmpsq1cuRJKpRKvvfaabhnHXX+pVCq88MILsLe3h6mpKXx8fHD9+nUAHHd9c/HiRYwePRouLi4wNzdHp06dcPjwYd3lHO/aLzQ0FL169YK9vT0kSUJKSkqJy8szxrdv38a4ceNgb2+P+vXr4/XXX0deXl613QcWXqpy58+fR1xcHN555x2Eh4fjl19+QVRUFF555RUARYV40KBB2L17N3bs2IGDBw8iPj4e7777rszJqbKsW7cOlpaWKCgoQMeOHQFw3PXZ3r170adPH3Tv3h2HDh1CfHw8Zs6cCQcHB467nsnKykLnzp2RkZGBX375BSqVCi1btsTzzz+PO3fucLz1RE5ODkJCQjBq1ChYWVnBw8NDd1l5xjg3NxdPP/00kpOT8ccff2DHjh3YsWMHlixZUn13QhDJYM6cOcLOzk4IIcTq1auFpaWluHbtmu7yFStWCEdHR7niUSX64YcfxFdffSV+++03AUCcPn1aCMFx11fp6enCzs5O/Pnnn2VeznHXL7t37xYARFpamm5ZbGysACBOnjzJ8dYzr7zyinjqqadKLCvPGM+aNUs0bNhQ3L17V7ds+vTpIiAgoMozF+MeXpLF33//DR8fHwDAkiVLMHHiRDg7O+sut7OzQ1pamlzxqJL88ccf+OOPP/DGG29ApVLB2toanp6eADju+uqrr76Ch4cHdu3ahYYNG6Jx48aYPn06NBoNAI67vmnVqhVMTEywY8cOaLVa3Lx5E/PmzUO3bt3QsmVLjreeiYmJQbt27Uose9QY5+XlYeXKlXjnnXdgbm5e5jrVgYWXqt0777yDEydOYMmSJUhMTERycjJeeOGFEuukpaXB1tZWpoRUGaKjo7F48WJ8+eWXAIqO6ezQoQMkSeK467GffvoJMTEx0Gq12LFjB+bOnYtly5bhyy+/5LjroYYNG2LevHl47bXXYGxsDEdHR7i7u2Pfvn1ITk7meOsRtVqNU6dOlSi85XlNHzx4EFlZWbI/Dwyr7ZaozlOr1Rg/fjzCwsLwxx9/wNfXF1u2bIEkSfDz8yuxbnR0tG4PMNU+KSkpGDduHPbu3av7isnY2FgEBQUBKCq/HHf9o1arcfr0aYwYMQLLly8HALRv3x4//PADDh8+DFdXV467HhFCYMSIEbh69Sr27duH+vXrY926ddi+fTtmzJjB17meSUhIgEajKVF4yzPGKpUKTk5OcHR0fOA61YF7eKla3Lx5E8888wwSEhIQGRkJf39/AEVnbVpaWpb43m21Wo09e/ZgwIABcsWlJ/TZZ58hPj4ebm5uMDQ0hKGhIc6dO4eFCxeiZ8+eHHc9lZmZCa1Wi2HDhpVYrlAoYGZmxnHXMzt37sSePXuwd+9e9O7dG76+vlixYgUA4LvvvuN465mYmBhYWFiUOGGtPGN8+/ZtODg4lNhWamoqwsPDq/V5wMJLVS4uLg7+/v5wcnLCkSNH4ObmprvMwcEB9+7dQ35+vm7ZihUrIITAmDFjZEhLlWHOnDmIj4+HSqWCSqXCunXrAABhYWH45ptvOO56ytbWFgqFAkII3bKMjAwcOXIEffr04bjrmcTERDRo0AA2Nja6ZTk5Obhz5w4MDQ053nomJiYGfn5+UCj+vzqWZ4wdHByQmZlZYlsff/wx3N3d0b9//2rJDoCzNFDV2rVrlzA3NxevvPKKuH79ukhNTRWpqakiPT1dCCFEWlqaMDc3FzNnzhTnz58Xy5YtE0qlUmzbtk3m5FSZ1qxZI8zNzYVWqxVCcNz1Wb9+/US7du1EXFycOHr0qOjYsaPo0KGDyM/P57jrmb/++ktIkiQ++ugjce7cOXHs2DHxzDPPiPr164vU1FSOtx7Iz88XsbGxIjY2VrRu3VoEBgaK2NhYce7cOSFE+f6Wnzx5UigUCrF8+XJx7tw5MWfOHGFsbCz+/vvvar0vLLxUpTw9PQWAUj/PPvusbp09e/aIli1bClNTU+Hv7y/27t0rY2KqClOnThXt2rUrsYzjrp9SU1PFsGHDhK2trXB2dhavv/66uHPnju5yjrt+2bRpk2jVqpUwMTERbm5uYsyYMeLixYu6yznetduRI0fKfA8fN26cbp3yjPHGjRtFkyZNhJmZmXjmmWdEREREdd4NIYQQkhD/+t8TEREREZGe4TG8RERERKTXWHiJiIiISK+x8BIRERGRXmPhJSIiIiK9xsJLRERERHqNhZeIiIiI9BoLLxERERHpNRZeIqqVEhMTIUkSbt68KVuGV199FcHBwZW+3cDAQLz22muVvt3HlZ6eDg8PD0RFRckd5YEmTpyIkJAQuWNUmmPHjsHDwwNarVbuKER6gYWXiB5L9+7dIUkStmzZUmL5ypUrUb9+/Sq/fZVKhQYNGqBBgwZVflsPEhcXBx8fHwCAo6Mjjhw5UinbValUuu3KLT8/H0OHDsWYMWPQoUMHuePgzp07UCgUUKlUJZYvXLgQoaGhVX77gwcPxtSpU6v8djp37gwA2LdvX5XfFlFdwMJLRBUmhIBKpYKTkxN++umnEpedOHECbdu2rfIMKpUKvr6+FbqORqOptNsXQiAhIaHCGR5Go9EgNzcXZ8/+X3t3HhPV1f4B/DvCDDMywzKA0FGLJCwiKFEKIo0CZbGVglVqMSKh1dS6RW21Wi0ajEaruFQgRit1qUpaC6JVQmkimkJVLMsM6Fh2wQW1lc3KDs/vD8LNe9kE27fv+/N9Pgl/zHPPPfecMyfx4XDOtWxY9XZ1df3bVgJ37tyJp0+fYuPGjf+W+ofr119/hVwuh6urqyiuVqthbGz8jzzf09PzL9VBROjo6HhuOW9vb5w/f/4vPYsx1o0TXsbYsJWWluLp06eIjo5Geno6mpqahGt5eXlwd3cXPjc3N2PDhg0YO3YslEol/P398dtvvwnXr1y5AqlUivT0dLi7u0OhUOCNN95AXV0dkpOT4erqCmNjY8yZM0eUsGq1Wtjb22PlypVQq9XQaDTYt2+fqJ3jxo3Drl27EBERAZVKJazM3b17FxERETA3N4e5uTkWLFiAurq6Qft87do1uLu7Qy6Xw8fHB1euXMGzZ88GXImtrq7G/PnzMXbsWCgUCjg7O4t+OWhvb4dMJkNiYiLefvttKBQKfPnll9DpdACAR48eYcqUKTA2Noafnx+qqqqEe48fPw5bW1skJSXB2dkZUqkUT548AQCcOHECEyZMgFwuh7OzM1JTU4X7urq6sGPHDjg4OEAul8Pa2hqRkZED9rm6uhq7du3Cjh07MGJE9z8XZWVlkEgkSEtLg7+/P0aOHAknJyfk5OQMOn69x9LHxwcKhQKjR49GTEyM6PrZs2fh4eEBpVIJc3NzTJ8+HY8fP0ZMTAyCgoLQ3NwMqVQKiUSC9PR03LlzBxKJRBijF51Tly9fRmBgIKysrGBsbIzp06ejqKgIAIRnPHjwABEREZBIJAgPDwcw9DmelpYGd3d3yGQyFBQUoLKyEuHh4bCxsYFCoYCTkxO+++474b5XX30V+fn5Qx5XxtggiDHGhikpKYnkcjm1traSjY0NJScnExFRS0sLSaVS4XNbWxv5+vrS66+/TlevXqXi4mKaN28eOTk5UXt7OxER7d+/n4yMjGj27NmUm5tLWVlZZGJiQn5+fhQeHk6FhYWUkZFBMpmMzpw5I7TB2tqa1Go1JSQkUEVFBcXGxhIAunbtGhER1dXVEQDSaDR09OhRKi8vp7t371JpaSlZWVnR5s2b6fbt25Sbm0uenp60ePHiAft75coVMjIyot27d1NFRQUdOXKELCwsaNSoUaL2ZGVlCZ+vXr1KR44cocLCQiovL6fo6GgyMjKi2tpaIiIqKCggAOTo6EgpKSlUXl5Ojx49ooMHD5JMJqM33niDcnNzKS8vj1xdXWnmzJlC3atXryZjY2MKDQ2lvLw8KioqIiKiVatW0aRJk+jHH3+kiooKSkhIICMjIyorKyMiou3bt5OLiwtlZmbSnTt3KDs7mxITEwfs9yeffEKTJ08WxZKTk0kikZCfnx9lZmZSSUkJBQQEkK+v74D19L7f0tKSjh07RuXl5ZSenk4WFhZ08uRJIiL6+eefydTUlJKSkqiyspK0Wi3t2bOH2tvbqaGhgebNm0cLFy6kmpoaqqmpofb2dkpNTSUzMzPhGS86p06fPk2pqalUUlJChYWFFBISQp6enkTUPZeTk5PJyMiI7t+/TzU1NdTY2DisOT5jxgzKzs4mvV5PLS0t5OjoSEuXLqWbN29SaWkpJScn09WrV4X27N69mxwdHYc0royxwXHCyxgbtnXr1gmJwLJly2j+/PlERHTjxg0CQJWVlUREFB8fTzY2NtTQ0CDcW1FRQQBIr9cTEdH7779PDg4O1NzcLJTx9fUlDw8P6ujoEGLjx4+nuLg4IiKqqakhALR3715RuxwcHGjnzp1E1J2kAqDvv/9eVMbf35+2bNkiiiUnJ5OdnV2/fe3o6CBHR0eKjo4Wxb28vCgoKGiQURJrbW0lAJSfn09ERMePHyeJREI5OTmickuWLCEbGxt69uyZEDt9+jQpFArq6uoiou7xcXNzExIqIqKsrCwyNzenP/74Q1Sfq6srnThxgoiIpk+fTuvXrx9Se7u6umjMmDHCePbYvHkzmZmZ0ePHj4VYQkICubi4PLfOhoYGsrCwoEuXLoniK1eupA8++ECof+rUqUJfe3Nzc6P4+HhRLCYmhnx8fITPLzKn+pORkUEmJibC5/3795O7u7uozFDnuEajofr6eqFMeXm5qEx/Vq1aRTNmzBjwOmNs6HhLA2Ns2PLy8oR9unPnzkVaWhpaW1uRl5cHtVqNcePGAQBOnTqFqKgomJiYCPfKZDIAQGtrK4DurQkLFiyAXC4XylRXV2PRokUwMDAA0L3n8d69e7CzswMAFBQUQKFQYOnSpaJ2GRsbC3tZtVotNBoNwsLChOtVVVW4dOkSYmNjoVQqhZ+FCxfC0NCw375mZ2ejtLS0z0ElqVQ66MGy9PR0zJo1C3Z2dlCpVFCr1QCAMWPGCO3z8vLqsx9Uq9ViyZIlGDlypKhfnZ2dkEgkALoPyy1fvlzU5q+//hqNjY2wtbUV9U2v1wvlQkNDsWfPHgQFBeHQoUOora0dsP3V1dW4d+8eZs6cKYrrdDqEhITAyspKiFVUVMDe3n7AunqkpqbiyZMnCA0NFbXx8OHDQhsDAgJQVFQENzc3bNu2DWVlZcL9bW1t0Ov1fca99yG/F5lTHR0dOHjwILy9vaHRaKBUKjF79mzh+wK6513vZw91jkdFRcHU1FQoM2bMGLi7u8Pb2xsffvghMjIyQER9xtrBweG548oYez5OeBljw1ZQUCDs0/X19YVMJkNGRgby8/MxefJkodytW7f6HC7S6/WQyWRwcnJCR0cH9Ho9vLy8hOsNDQ2orKzE1KlThVh5eTn+/PNP4SCXVquFg4ODKClsampCcXGx8HydTgcfHx8hSeyJqdVqFBYWQqvVCj9FRUW4fPlyv33VarWwtbWFhYWFEGtra8PNmzcHPFh28uRJzJs3D35+fvj222+Rl5eH6OhoaDQaIVHU6XTw9fUV3dfV1dVvvfn5+UKsuroadXV1fe7V6XTYvHmzqF9arRbFxcWYM2cOAGDdunW4ffs2AgICEB8fD3t7e1RWVvbbh+rqagAQfnn51+dMmzZNFCsoKBjSITudToe33nqrTxv1ej2++OILAN1v/6iqqsKaNWuQlZWF8ePHC3ufb926hfb2dkyaNKlPvT3Pf9E59dFHH2H//v1YtmwZLl68CK1WizfffFPUr/7enjHUOd77+5LJZLh+/TrOnDkDmUyGd999F3PnzhWuP3r0CFlZWcJ3xxj7a/pf0mCMsQFUVFSgvr5eWOE1NDRESEgIUlJScPPmTQQEBAhlVSoVmpubRffv27cP77zzDhQKBQoLC9HW1iZKkrVaLQwNDeHi4iLEdDodLCwsRKujnZ2donoPHz4MKysr+Pv7C2V6vyNXKpXi6dOneOWVV4Z8on/EiBFoaWkRxRITE1FXVzdgknfs2DEsWbIEn376KQCgs7MT58+fF5XX6XR93rVbXFyMpqYmUd+am5vx1VdfCQe7tFotlEplnxVVqVSKtra25660Ojo6Yv369Vi9ejVMTU2h1+uFVc5/1bM6qVQqhVhDQwOqqqpE31dPm1atWjXoc3va2NDQ8Nw2WlpaYtGiRVi0aBH8/Pxw/fp1hIWFoaioCLa2tqKV0sbGRty5c0dIRPV6/bDnVGdnJ7755hukpKQgNDQUAPDgwQNkZmYiOjoaQHciffv27T7J9ovM8R6GhoYIDAxEYGAgPD09sW7dOuHa0aNHYW1tjaCgoEHHijE2NJzwMsaGJS8vDzKZTLSqFRYWhsjISDQ1NWH9+vVCPDg4GHFxcZgyZQpGjhwpvIXg+vXrALqTDo1GI3qXrlarxYQJE4Q/C/eU673S9vDhQxw6dAjBwcFIS0vDpk2bcPbsWchkMmFVrXeSMXXqVJiYmCAyMhJbtmyBUqlEWVkZ0tPTceDAgX776+3tjYcPH2L79u2IiIhAVlYWNm7cCLlcDicnp37vUavVyM7Ohl6vR1NTE3bs2IHCwkJ8/PHHALrfElFbW9tv4qhSqRAbGwtnZ2d0dnZi5cqVcHBwwOLFi4WxcHNzE96a0GPWrFnYu3cvXFxcMG3aNNTW1uKXX36BnZ0dQkJCsHv3blhbW8PDwwMGBgZITEyEubm58L7X3mxtbQFA9Gd/nU4HAwMD0SpnVVXVoMl/7zbGxsZi69atWLBgAdra2lBUVIT79+9j7dq1OHfuHEpKShAQEAC1Wo3MzEzk5uZi27ZtALpXwOvq6qDX66FWq2FlZSW0qSeZfZE5ZWBgADMzM1y4cAETJ05ESUkJYmJi0NjYKHxH1H3mBfn5+Rg/frywHWMoc3z06NGiLSBNTU1YvXo1wsPDYW9vj3v37iEhIUHYflNfX4/Y2FgcOHAAUqn0uePKGBuC/+gOYsbY/zufffYZTZkyRRRraWkhlUpFAKikpESI19XVUWRkJJmbm5OZmRm99957VFVVJVxfu3YtBQcHi+qKioqiqKgoUSw0NJTWrl1LRETPnj2jESNG0Llz52jGjBlkZGREEydOpMzMTKF8UVERAaDff/+9T/tzcnLI19eXTExMSKVS0eTJk2nfvn2D9nnXrl00atQosrKyopCQEFq2bBm99tprA5YvLi4mDw8PoW3nzp0jtVotHKC7cOECqVSqPgezNmzYQMHBwRQXF0eWlpZkampKy5cvFx1gCwsLoxUrVvR5ZltbG23atIlsbW1JJpORRqOhsLAwKi0tJSKirVu3kqOjI8nlcrK0tKTZs2cPemCqq6uLNBoNXbx4UYjFxcX1OZzW+w0Jz3Py5ElydXUlhUJBFhYW5OfnR+np6URElJKSQh4eHqRSqUipVJKXlxelpaUJ9zY0NFBgYCDJ5XKSSCRUX19P8fHx5OrqKpR5kTlFRPTDDz+Qra0tKRQKCggIoKSkpD5zKDY2liwtLQmAcNjtReb4w4cPKSQkhKytrUkmk5GdnR19/vnn1NLSQkTdh9V8fX0HPLjHGBs+CVGvXfKMMcYYgI0bN+Lu3bs4derUf7op/zN++uknLF26FDk5OaJVYcbYX8OH1hhjjPVrzZo1/zX/xfH/isrKSly8eJGTXcb+ZrzCyxhj7G9x4sQJrFixot9rEyZMwI0bN/7hFjHGWDdOeBljjP0tGhsb8fjx436vGRkZYezYsf9wixhjrBsnvIwxxhhj7KXGe3gZY4wxxthLjRNexhhjjDH2UuOElzHGGGOMvdQ44WWMMcYYYy81TngZY4wxxthLjRNexhhjjDH2UuOElzHGGGOMvdQ44WWMMcYYYy+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" ] @@ -4880,14 +10003,14 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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usccec3epTz75ZLvxxhvDj09p/YDfww8/7DqbOjbl9Xud19Q6pvnz59vdd99tZcuWta5du7qUIKX9KLBR0KLr41Fqzz333OMCEqX6qLOsTrX2EQs9p45Hx6kgRl/LKaec4j4rLUlBhTrWeo4DBw64gOqJJ56wnTt3ujv+ctVVV9l///3njueuu+5y5yb+gCo1LVu2zH1WTUWk6tWru/eP6jR03Aowtm3bZldccUXUtqIgxQsslE6laxkKhaxLly5WvHhxF3j069cvTc4FAGJFYAEAKaQ78Bqd0IhBYlSEG1kDoEBEhcMqjFbuvKhW4LXXXnMdZX2dGgoWLOhGKSJHDlLjmDRa8/jjj9u5557r2njPodma1ElXio8345MCriFDhrjA48033wx35tVWHeJY6Hh1DN9//70LLCKPR8ev0Qk/nY9GYTSqdP3117tjVAe/YsWKLrBo0qRJeOQlrVO31OmP5G1TMOEVbSfW1hvt8NqKXlsFSa+//robcRKNbNx///3222+/pcn5AEAsKN4GgOPouK9duzZBAa6f14HXXeW9e/farl273N338uXLuxmC0pI61pFBRWodk4qo//77bzdblLcP76N+/frhNqJOrlLFlA7kBRXe9VNwkRr8gZKCHh3Hv//+axdffLHrfCtVKiNo5ERy586d4GdevYvXJqm23javjYI1jU5Uq1YtHFR4NEIFABmJEQsASCGl0ijvXXfdNTuQ7s43bNjQ5cefdNL/3a9RmovqHzQTUWSBrx6TlryUpkipcUx//PGH++ylJEWjwENUjCwKXCKpDsJPHWalLvnlzZvXBSFJUX2Hai3mzJnjgphICjIygo5dDh06lOBnCoD8bZJq623z2uga6bWLdk3POOOMVD0HAEgpAgsASCGl0ihHXneOld+vNB0VS6sgWWkq6lhratECBQq4+gV1+NQxVL3DCy+8kORMQrFSRzwxXifUT4XNqXFMGu0QFalXqlQpaptoKT3J8UY2/GIpQn7wwQddvYbqRlTQrRoSBXd6bZTedezYMcsIXl2Ll+bk523z0py86xWtrZcC5S8AB4DMisACAI6DOrDK99eHOtuqX3jrrbfs66+/dh1E3UlXAbR/ViVvvYLIlBd17pN6Hj0mkjcaECvVFqTGMXmjIUpBqlOnTpLP6Y2CrF+/PtGRD8+pp54ab/rVWAIUrUehoEKvwQMPPBDvZwr2IiV1nVObUpXkl19+iTc7lajQXcdSpUqVcBCiwEHbI3nbzjnnHPdZqWu69tGuaUalfQGAhxoLAEjhSEHkAmvqJHorKauT7qVDeXf3PZrZyEsT8lNHMbGUHXXk1bn0cuxFbVO6aFpqHVPdunVdUbcKo6MFPDpO1TZ4nWFNw6tjVe2DR7UZ77//foK6AwUq/g+tPH0856TCaa03EclbByI90qM0Y1bVqlXtiy++iDcSoa+1rXbt2uFRDWnevLmbpWvevHnx3muaOlirhHv1K5pVSxMHaNapn376Kd5zvv3222l+XgCQFEYsACAFdNe/RYsWrp5CwYTuIGuthffee8/NfqTtyqHXCIZWidYiauoYar2Bb7/91q1VEJnGpClFlUqlWZZUe6BARftR516P1/SxmuVId+YV1KjTrHUjogUEiVHHNLWOSbNhDRw40BVgK31JnWgdl+6Ya2REC8NpVESdYK2toNmKtL6F7txrmwINjcQoPSsIpXWpSFtrcygw0SiB1t6YNm2aGy2JDHw0k5W8/PLLbmpXjdKcddZZbraoWH3yySfuOUTB0uHDh93sTOKt5eHR6tl63TQ1rGZtEgUKSs+644474u1X10fT5D700EOuCFujNVrbQtPMapvO1dOnTx9buHCh9e/f372WGu3QyI0XvKXnyAwA+BFYAEAKqC7hmmuucak2+lCgoTvP6nRrzQQvfUedV6X2jBs3zt1ZP//88906D88991y4Y+q59dZbXSd46tSproOuO/DqfKsTrw6w7nJrDQpN5aoOszqq2mdKZpdS8JBax6RRC00fqw916lVQrKBKz6FOsX9tCC3YpudS51tF1hrt8BbI69u3b+DXQ1PfKmD65ptvXKdfQY6OXbNiRU4HrFmUtNaDAg+tc6FgSgvkpSSwULCluho/FcSLajz8gYV3fRWc6UMdfk17q3U+IutTNGvWG2+84c5Fr7VqXhTQPfXUU9asWbN4bVUfo2upFbknT54cXiDv3nvvdat1Z/VV1gFkXXGhyDFkAACQ5Wh6X63boYDthhtuiPlxcUOOpOlxAYguNPDEu79PjQUAAFmMv+ZGdI9QkwdIckX1AJBWTrxQCQCAGKlGIloReiTV0qg+JLPQIogqAFcal9KmlAqmlckvv/zy8AxSAJDeCCwAANmWCthVYJ0c1Zecfvrpllk0btzYBROffvqpqxXRsek8UpICBQCpjRoLAEC2palnVZuQHBV+n6hF0dRYABkjdALWWJx4ZwQAQIw0mxU1CQCQOijeBgAAABAYgQUAAACAwEiFAgAgGxtVeKxb3DFXrlwZfSgAsjhGLAAAAAAERmABAAAAIDACCwAAAACBEVgAAAAACIzAAgAAAEBgBBYAAAAAAiOwAAAAABAYgQUAAACAwAgsAAAAAARGYAEAAAAgMAILAAAAAIHFhUKhUPDdAACArChuyJGMPgSksdDAnBl9CMgmGLEAAAAAEBiBBQAAAIDACCwAAAAABEZgAQAAACAwAgsAAAAAgRFYAAAAAAiMwAIAAABAYAQWAIATzi233GJt2rTJ6MMAgGyFFVMAANnC9OnTbc+ePXbttdce1+N37NhhU6ZMsd9//91+++0327Vrl7Vu3doGDRqU6GNmzJhhkyZNsvXr11uBAgWsYcOG1rdvXytatGiCtr/++quNGDHCfY6Li7PzzjvPta1cufJxHS8ApDdGLAAA2SawmDx58nE/ft26dTZu3Dhbu3atVa1aNdn2b7/9tgs6ChYsaAMGDLCrrrrKZs+ebb169bL9+/fHa7t06VI3yrJp0yb3c329YcMG69mzp61evfq4jxkA0hMjFgAAxOCcc86xOXPmuNEGjVY0bdo00bb6+WuvveYCEH3OkSOH267v77rrLhfg3HjjjeH2zz//vOXKlcvGjBljJUqUcNsuv/xy69ixo7300ks2fPjwdDhDAAiGwAIAkOEOHjxo48ePt1mzZtlff/3lOtklS5a0evXq2e233+7a6G7/zJkzbeXKlfbPP/9Y/vz5rUaNGta7d287++yzk9y/6i22bNnivq5Vq1Z4+8iRI+N9nxSlMukjFnPnzrUDBw5Y586dw0GFNGrUyEqXLu3Owwss/vzzT1u+fLm1bds2HFSIvr7sssvcSIvSsIoVKxb+2RdffGGvv/66S7FSoHPllVfa+eefb7fddps9+uij1JcAyBAEFgCADPfss8/axx9/bK1atbLrrrvOjh496jrcixYtCrd59913rUiRIta+fXvXyd64caN98MEHdtNNN9nEiROtXLlyie5fqUivvvqqG0nQiIGnQoUKaXI+y5Ytc59VJxGpevXqLoDat2+fC46Sa6vrorqOBg0ahAOsBx980MqUKeNSpRS4qJbjm2++SZNzAYBYEVgAADKc7vBrdGLw4MGJtnnllVcsX7588bYpEFExtgqk77vvvkQf26RJE9dGIyMtW7a0tKYRBilevHiCn2lbKBSy7du3W/ny5ZNtK9u2bXOfjxw54lKjNErx5ptvWuHChd32Dh062DXXXJOm5wQAyaF4GwCQ4VTgrKLopAqVvaBCnfK9e/e60Qd1sNU510xKmYnSoCR37twJfpYnT554bVLSViMXCkg0G5UXVIhGPlQcDgAZiRELAECGU3qSagO6dOniahBU96CpWVWTcNJJJ4U71aqJ+OGHHxLMqqTHZCZ58+Z1nw8dOhT+2qNRE38bf9tIkW01a5QomIoUbRsApCcCCwBAhlOqkmoJFixYYEuWLLHvv//ePvroI7vgggvc2g5///23m4JVxdOqqTjjjDNcZ1vrPbzwwgsJAo2M5hVaa3ShbNmy8X6mbTpuL83J3zaSt81f1A0AmRWBBQAgU1Bhtuof9KF0J9VUvPXWW/b111+7DraKnV988cUEszjt3r07ahpRJHXm00u1atVcYfkvv/ySILDQmhUaXVD6ktdW1LZdu3YJ2uq4q1Sp4r4//fTT3WfNBhUp2jYASE/UWAAAMpRmgNKK2H7qTHsrTitw8NKhFHD4qfOu0YxYqCP/77//JthHWmjcuLGrj9BMVjo/z7x581w6U4sWLcLbFHhofQtNIesftdDX2la7du3wqIbW0tDXmgVK5+JR0DVt2rQ0Py8ASAojFgCADKVOsTraqqdQMKGC7M2bN9t7773nCpS1XbUGGsF45JFHrFOnTlaoUCH7+eef7dtvv3XTrvo774k599xz3ZSszz33nJvaVcGKOu2nnHJKzMeqtSP8tQ+rVq0Kb6tZs6b7EJ1Dnz59bOjQoXbrrbda8+bNXaCgaXGVxqWZrCKnw9V6HDfffLNb+0KmTJlix44dszvuuCPcLmfOnO77hx56yLp37+7Wr9B0s1rrQiM+ClrSc2QGAPziQulx6wYAgEQcPnzYRo0a5eoq1DFWoKG78kp56tGjR3h9CtVeaAVqdeYVFGhBuH79+rlAQYvfqXPtUT1G5DbNrKS28+fPdzNKqdOekgXyJKm2WlOiV69e8bbp+TXNrdKUVB+itSh0zNGCGaVCaZVuzXCl4EDBT9++fcNpUH6ff/55eIE87UsBhhYJvPvuu+3pp592q3bHKm7IkZjbImsKDeQ+MtIHgQUAACcAjYZohGTcuHFuYb1YEVic+AgskF6osQAAIIuN8ESmfmmUZ+rUqS4dKtoIBwCkB0JYAEC2pQ65PpKiGgbVTGQWShfr37+/NWvWzM0SpZW7P/nkE7ddq4/nypUrow8RQDZFYAEAyLYmTJhgY8aMSbJNqVKl4tVqZLSTTz7ZFaLPnDnTdu7c6QKfihUrunqMlNRWAEBqo8YCAJBtbdy4MbyadWI0bWyNGjXsREWNxYmPGgukF95pAIBsS1PV6gMAEBzF2wAAAAACY8QCAIBsbFThsW69EIq+AQTFiAUAAACAwAgsAAAAAARGYAEAAAAgMAILAAAAAIERWAAAAAAIjMACAAAAQGAEFgAAAAACI7AAAAAAEBiBBQAAAIDACCwAAAAABEZgAQAAACAwAgsAAAAAgcWFQqFQ8N0AAICsKG7IkYw+BKSh0MCcGX0IyEYYsQAAAAAQGIEFAAAAgMAILAAAAAAERmABAAAAIDACCwAAAACBEVgAAAAACIzAAgDSUZs2beyWW25J9f1Onz7datWqZYsXL7asfi4AgKyJyY0BAJnOihUrbO7cuS54Of300y0zWLdunX344Yf2+++/u4+9e/daz549rVevXlHbHzt2zCZPnmzTpk2zLVu2WNGiRa1p06bWu3dvy5cvX4L28+fPt7Fjx9rKlSstd+7cVrt2bevfv7+VLl06Hc4OAIJjxAIAkOmocz1mzBjbvHmzZRZLly61t99+2/766y8755xzkm3/4osv2ksvvWRnnnmm3X333XbZZZfZO++8Y3feeacLOvy+/PJLt/3AgQN2++232/XXX28//vij3XTTTbZ9+/Y0PCsASD2MWABAGjt69KgdPnzY8ubNa9nRf//9ZwUKFLCsrlGjRi4AKFSokC1fvty6deuWaNs1a9bYlClT7JJLLrHnn38+vF2jL0OGDLHZs2dbixYt3LYjR464NiVLlrTXX3/d8ufP77bXq1fPBRijR4+2Bx98MB3OEACCIbAAkK2pwz9p0iSbNWuWrV+/3nLmzGnlypWz1q1bW+fOnV0b3TGeOHGiLVq0yKW0HDx40KWntGrVynX8cuTIEa/WYfDgwTZ8+HB3h1vfb9261R566CGX1uNRKs3QoUNt2bJllitXLmvYsKG7U33KKafEO75du3bZqFGjbN68efb333/bqaee6jq4Sr85+eSTE5xPKBSyCRMm2HvvvWfbtm2zUqVK2Y033ujOxzvfK664wp2j0m4ivfXWW/byyy+7zmzNmjXdNh2/jvV///uf+17bBwwYEPV6qs5Dz9WyZUt33Bp50N197S/W66jHabRClDbk0X4HDRrkvj506JDb12effWYbN250qUMXXHCBuy5VqlQJP0YjAxol+Pjjj93oR1xcnLuGNWrUsAceeMC93rEqUqRIzG31ftJrce2118bb3r59e3v11Vft008/DQcWP/zwg7s2OlcvqJDKlSvbhRde6IKQe++9N96x6vXV+1bX8bTTTrMuXbq4x+q9N3LkSPc6AEB6I7AAkG2pk923b1/Xsbv44otdh1sd1NWrV9tXX30VDixWrVrlvm/SpImVKVPG3WFWJ1sdxE2bNkW9mzxs2DDXTh1J3a0vX758+Gfq8Pfp08cuvfRSlx6jIEMd399++8117L2RDeXwKyj4888/rW3btq7DrNoDdSrVOX/zzTcTjAQooFGH/aqrrnLnorbqjOu41ZlWEKMOujrlqhk444wz4j1ex6Ggwwsq9uzZ4wq0lf6jfSqtZ8mSJa4Dr+eJRnfzdWe/Xbt24YAmJddR12XHjh32wQcfWI8ePaxChQpuux4jely/fv3sl19+cQFMp06d3LVSe6UOKSipWrWqa6vgSR1tBW5XX321nXTSSS7AUKCm4CQlgUVK6BrouapVqxZve548eaxSpUru5/62Ur169QT7Offcc91rraD3rLPOctvGjx/vrpneD7fddptLn1IwqRoOAMhIBBYAsi3d8VVQoc6rOmh+/hx4dbI/+ugjd7fbozvRDz/8sNuuTnaxYsXiPV6dPe0/WvqT7rDfdddd8e5mq8OufHzdXb/hhhvcNgUOGzZscHerO3bsGG6rjulzzz3nghAFKH7qLGu7AghR4HLllVfau+++6wILUbCjwELHrlESz08//eSCDXXaPdqXOuKPPPKIC25Ex/LCCy+4wuRo1q5d6wKcOnXqxNse63U8++yz7bzzznOBgvYRefddKUZ63V555RWrW7dueHuHDh1cMKjRFY2QiAIZBSa6tn7+c0wLGoHQiJKCu0glSpRwQZECW71OXg2Ftkdr6+1PgcXu3btd4FSxYkV74403XKAiCuIUOAFARqJ4G0C2pTSawoUL280335zgZ7rb7FFw4HWG1RlU504pSurUKgDx3332d3ITq6nQKIM/UBB9r+3qCHs0K5LuQisQ8NPIgbb72/r34wUVXsdUIxAa9fBo9ESdfKXj6O6/R517pSP5Rxl0DEodUrqSX/fu3S0xCnwig4rjvY7RzJw50420KMVKj/c+dC563p9//tkFdlKwYEE3QqSgKT3p+f2vg58XbHjH6H2O1j6y7XfffedGivT+8oIKUUCmETcAyEiMWADItjQaoDx2fwctGnVYlX6ijrg66Mqd9/v3338TPEad+cSoriCyE6kOpLYrJcijkQJ1niPTdbw6EKVQRdt3tNoA1UlEBieq+9AUp0pNUoH1559/7lKGFEh4dDxKK/LXkXgdWRUxR5PYuR/PdYzmjz/+cJ1rTd2aGAUaqj3QSNTAgQNd8Fi8eHFXs9CgQQM3kpNYxz81KIjauXNn1J9pVMlr4/+sYCu5tt4sWf7UOk+0bQCQnggsACAZSqNR+s3ll1/uah40WqDOvTr2SseJ7CBLRs0A5R9p8Ys8RtUxKODQKIUCizlz5tj+/ftdSk1QiZ378VzHxCgVSNOzJsarN1BKldaeUC2HFg9UCpVGqpRGpBmYUlKQnRIKYhQAKTCITIfSCIrSpLzARm297V49ib+tvw0AZGYEFgCyLd3hVU1BtM6fn+6wK3Xo6aefjrfdn16UEhoF8PLrPToGbfcXU2v0QUW7utPvH7XQ9xptCbJwms5X6U3q6Ct/XwGG0qb8NQveMeg8NWWuf9RCxdUq7E6JlFxHfx1GpLJly7rRAC0gl1gg5afZkjRCoQ+ZOnWqPfvss+6ck5oyNgiN8ixcuNDN+qXZqjwaadFMWV5xvNdWNItYZArZr7/+Gq/4X7N8id4XOn8/bQOAjESNBYBsS9N9Kv1Gd68j+e+eq/MaeTddd/dVnH08lHakzq2fvtd2jR54Gjdu7DrQuuPup++1XWskBKHaDQUMml5WnVrVVkSmPOkYNM3tJ598Em+7CstTKiXX0VuZOlp6lAIiHZMWq4tGP/OnREXypqONNfXqeDRr1swFR5HnpoJ01Ut4U82K0rOUWqbXdd++feHtCkA0wqKULy+wVODhzfbln5VLgZ5qTwAgIzFiASDbuuaaa+ybb75xgYUKh9VpU72FZjXS3d8RI0a4drrTPW3aNLv//vvtoosuch1XrU9xvGk0mjZVM/toETXVUGiaWU3zqtEKrUfgL5D+4osv3AxQmmZW9SD6rDvtuoMd9G670m40U5Q6pOoEe7M++ek5lDr05JNPuuPUzETq7GpWo2jraCQlJddR07QqENF0sQoAFGho9ETTr+p1UxGzpvTVVKy6c6+7+qoj0ffqeGstDFGRs6Zx1f6UTuRNY6vRInX+U0JT2mrWLtF+RKtjK6XKC8I0o5WXqqVCes3GpVW369ev71Kj9HiNVvgDCwUNqgPRdVEtiAI+BZkKSpTSpdmyPLrmPXv2dLNuaWpdFWwrUNE56T2h93FSoz0AkJYILABkW+pcaj0ATb2qBc0USKhTquJj/2J2mhpWHVfVIXz99dduhWR1/pTCcuutt6b4eZVy9Mwzz7hpUfW8Og51NO+4447wnXpvRiMFPd4CeQo+VFitaUXV2UyN1ax1HpoxSVO6eutE+GnWLHWcX3zxRZfKJOoY65gip7pNTkquowqvNcWtRkZ0rZT+pREVBRbqiOva6a69jskLIhQ4KIDwz2rVtWtXW7BggUv5UmCgBQi1D00xrNmrUkIBjtbE8FPdhj5E5+MFFqJFBLXStoIpFckrKNB0uFoILzKFS6MSCmr1euvc9D5UwNS/f/8E09Dq2HUdFaTo/atrpQUGNRqkwCK5yQgAIK3EhVJSLQcAOKGok6875U888US8u+jIejSypRESjTBFrquSlLgh//+UwzjxhAZyDxnphxoLAMjGVNuhO+maJQpZQ7QVz5WapToYpaqlJKgAgNREGAsA2cw///xj33//vUuBWrJkifXt2zfJWbFOZEqP8hafS4xS1dJqWtrjoRoX1ZcoGFSalNa2UOG3CuHTekVxAEgKgQUAZDMqTtfieFrgTvUaqkPIroYMGWIzZsxIso1qSkaPHm2ZhabbVT2MCra1ermCQtWp3HDDDVFXPAeA9EKNBQAgWwdZWscjKSpg1+xdJypqLE5s1FggPfFuAwBkW2eeeab7AAAER/E2AAAAgMAILAAAAAAERioUAADZ2KjCY92ie5r9CgCCYMQCAAAAQGAEFgAAAAACI7AAAAAAEBiBBQAAAIDACCwAAAAABEZgAQAAACAwAgsAAAAAgRFYAAAAAAiMwAIAAABAYAQWAAAAAAIjsAAAAAAQWFwoFAoF3w0AAMiK4oYcyehDyFZCA3Nm9CEAaYYRCwAAAACBEVgAAAAACIzAAgAAAEBgBBYAAAAAAiOwAAAAABAYgQUAAACAwAgsAAAAAARGYAEASBejRo2yWrVq2ebNm5PcBgDImlilBQCAGOzbt88mTpxov/32m61YscK2bdtmNWvWtNGjRyf6mPnz59vYsWNt5cqVljt3bqtdu7b179/fSpcunaDtunXr7JVXXrElS5bY4cOHrUqVKtarVy/3GADIChixAABkmJtuuskWLFhgpUqVssxu165dLohYvny5nX322ZYjR44k23/55Zd255132oEDB+z222+366+/3n788Ud3ztu3b4/XduPGjW770qVLrVu3bq69Apm+ffvad999l8ZnBgCpgxELAECGyZkzp/vICooVK2affPKJlSxZ0n3fsGHDRNseOXLEnn/+edf29ddft/z587vt9erVcwGGApQHH3ww3P7VV1+1PXv22IQJE6xy5cpuW6tWraxTp0727LPP2vvvv29xcXFpfo4AEAQjFgCQzR08eNDVOlx11VVWv359a9KkiXXu3NmGDRsWr92HH35o1113nWvTuHFju+222+ynn35KsL9jx47ZuHHjrG3btq4jrc7xzJkzoz53tBqLQYMGuW3RaLt+7tHjtE37mTNnjl177bXu+Nq1a2cff/yxa7N161a755577NJLL7VGjRrZww8/bP/991+Kr5NSmbygIjk//PCDG5XQcXhBhShouPDCC2327Nku+JD9+/fbvHnz3HYvqBA9To/fsGGDLVu2LMH+e/To4c61efPmNmTIEFuzZk34WgBARsgat4kAAGlGd8TVCdcdcgUOR48etT///NMWLVoUbvPyyy/bW2+9ZdWqVbNbb73Vpel88MEHrgbghRdesAYNGoTbvvTSSzZ58mRXf6CO/j///OOeI1pdQWpRLcO0adOsQ4cOVrhwYfvoo4/sscces1y5ctnw4cNdnYKOW2lMOlcFCQow0oqeR6pXr57gZ+eee667tuvXr7ezzjrLVq1aZYcOHUq0rbc/72sFc0qR0nl2797dChUq5IKqn3/+Oc3OBwBiQWABANnc3Llz3cjC4MGDo/5cRcVK0Tn//PNt5MiRrrMuupvesWNHFzTUrVvX1Ryo7TvvvOM68krv8eoQNFqgFKC08scff9jUqVPDtRrNmjVzgdIjjzzi6hW6du0abquUI6U0DRgwIN5oQmryaihKlCiR4GfeNrVRYBFrW8+LL77o0qLeeOMNK1OmjNum1+GWW25Jk3MBgFiRCgUA2VzBggVt7dq1tnr16qg///rrry0UCrmiYi+okOLFi1ubNm1sy5YtbpYkf1uNfPiLmzXDUZ06ddLsHJS+5S8AL1q0qJUvX95OOukkl4rlV6NGDZeGlJZT3KpgW/zXy6PREn+blLT9+++/3eiFUtG8oEJUp3LNNdekybkAQKwILAAgm7vrrrvcXfwuXbrYlVdeaY8//rgbxVCthHgdcN1dj+Rt27RpU7zPZ5xxRoK2FSpUSLNziJZmpRQhFVx7nXOPUohk9+7daXY8efPmdZ81bWwkpT3526SkrfdaKGiKFG0bAKQnUqEAIJvT3X7VHWjaV62h8P3337sahQsuuMBGjBiR7seT2OxHXrFzNBqZSMl20chKWtFojmiti8iAStv8bfxtI0W2BYDMjBELAIAVKVLEWrZsaQ899JALKpT2pDUXlNrkjQZo1qFISqESr433WbUW0eogYpHYiII3GpIVVK1a1X3WuhSRfv31VytQoEB4hKFixYpuVCWxtv79eeleKvyOFG0bAKQnAgsAyMY0A5TSoCJHDLxpT9W51xSt2qYCbv+owY4dO2z69Omus+u1V+6/2r799ttu357ff//djYTEoly5cu5zZHutep1VaOpYpWFpil7NoOXRCtyaKrZp06bh9TtUQK41MbRdP/focXq8rodm4xLtU0GGAj4tqufR66KZuAAgI5EKBQDZmDqvLVq0cMGDggMVPSuP/7333nMjB9quNBzN6KTpZnv27GmXX355eLpZfVZNhleordoKzVD07rvvWp8+fdxsUJpuVt9rtWqvyDspWpdBKVhPPvmkG/nQcfzvf/9zK19ntClTpoQDMXXmtUaGFsCTSpUqueslChoGDhxo999/v918883Wvn17t3bGpEmT3DXWNL1+mj5WU9Dqs6bo1YiGrq9mgxo6dGi89DDNcqU1RLRSt6bXVfG9ppv1gj4W0gOQUQgsACAbU1GwZhPS6IA+FCjorrg6yFqAzcvt79+/v5UtW9ZN6appZDWDke6iP/HEE64Ww08d6lNPPdV1jLXInh537733uoXeYgks1FHW4zStqhbay5cvnwtQFMBccskllpE0aqJZsDwKwjQFr7Ru3TocWIhGJfLkyeOmhVVwoHQnTcOraxk5tayukdq98sorNn78eFfIrZm0tH5I5GxaGg1RO63PoeujInUFewoQb7jhBvecAJAR4kJpWb0GAADSxRdffOECOI30aNQnVnFDEi+KR+oLDeSeLk5c1FgAAJCF6H7gwYMH421TGpTqWpSSphENAMgIhM0AgGxLC8/t3bs32XZKD8sstLaFFiZU6pNmllKBvWosVq1aZd27d89UxwogeyGwAABkW+qQDx48ONl2ixcvtsxCheH169d3M0NpZi5RgKE0KBXOA0BGocYCAJBtqWMebX2OSJEF1CcSaizSFzUWOJHx7gYAZFtKGyJ1CABSB8XbAAAAAAJjxAIAgGxsVOGxbs0SrU0CAEEwYgEAAAAgMAILAAAAAIERWAAAAAAIjMACAAAAQGAEFgAAAAACI7AAAAAAEBiBBQAAAIDACCwAAAAABEZgAQAAACAwAgsAAAAAgRFYAAAAAAiMwAIAAABAYHGhUCgUfDcAACArihtyJKMPIcsKDcyZ0YcAZCqMWAAAAAAIjMACAAAAQGAEFgAAAAACI7AAAAAAEBiBBQAAAIDACCwAAAAABEZgAQBAJlarVi0bNGhQRh8GACSLwAIAAABAYAQWAAAAAAIjsAAAAAAQGGvRAwCQQocPH7ZJkybZrFmzbP369ZYzZ04rV66ctW7d2jp37hxut3nzZnvttdfsu+++sz179liJEiWsWbNmdtNNN1nevHnj7XPNmjU2dOhQ+/HHHy137txWr149u+uuuxI9htmzZ9uUKVNs1apVdvToUatYsaJdf/311rRp0zQ9dwBIDIEFAAApDCr69u1rP/zwg1188cV2xRVXuEBg9erV9tVXX4UDiy1btlj37t1t79691qFDBxd46DHjxo2zn3/+2UaMGOECEtm0aZP17NnTDh06ZJ06dbKSJUvaN998Y/369Yt6DHrs2LFjXfDRu3dvO+mkk9xz33fffXbPPfe4fQBAeiOwAAAgBTRSoQChR48edtttt8X72bFjx8JfDx8+3Hbu3OlGIRo0aOC2dezY0YYNG2YTJkywGTNmWLt27cKBwr///msjR450s0CJgoO7777bVqxYEe85fv/9dxdURD5/ly5dbMCAAe55W7VqZQUKFEjT6wAAkaixAAAgBT777DMrXLiw3XzzzQl+ppEDL8CYN2+eVa5cORxUeG644QbXbu7cueG2Gp2oWrVqOKiQuLg469atW4LnmDlzpvuZgoddu3bF+2jUqJH9999/tnTp0jQ4cwBIGiMWAACkwIYNG1zAkCdPnkTbaKRi3759duaZZyb4WZEiRaxYsWIu/Un++ecf17Z8+fIJ2kZ7/B9//GGhUMilVyXm77//TsEZAUDqILAAACCL0YjFyy+/HB4hiXTWWWel+zEBAIEFAAApoJGFdevWuUJrFW1HU7RoUVfjsHbt2gQ/Uy3Fjh07rFKlSuG2+fPnd7NLRYr2+LJly9q3335rp512mlWoUCFVzgkAUgM1FgAApECLFi1ccPDGG28k+JlSlEQjCQ0bNnSF1woC/MaPH+/qKpo0aeK+z5Ejh6vDWL58uS1evDjevt56660Ez9GyZUv3WUXammY2EmlQADJKXMj7KwgAAGKabvbWW291603UrVvX6tSp4+otNLqgUQfN8ORNN6viaxVTqx5CIw1LliyxOXPmWM2aNeNNN7tx40br2rWrHTlyxE1Xq/UuVNCtWg0FJ1ofY9CgQeFjGD16tPtQypPWrShevLgbBfntt99swYIFtnDhwpjPJ27IkTS4StlDaCCJH4AfgQUAACl08OBBmzhxolsgT0GBUqK0TkWbNm3clLIeFWhrCll19LVAntanSGyBPK2D8dJLL7k1LvwL5Kl9ZGAh8+fPt3feeceNdOzfv99OOeUUF2hoZqikCrsjEVgcPwILID4CCwAAsjECi+NHYAHER40FAAAAgMAILAAAAAAERmABAAAAIDACCwAAAACBEVgAAAAACIzAAgAAAEBgBBYAAAAAAmMCZgAAsrFRhcdajx49LFeuXBl9KACyOEYsAAAAAARGYAEAAAAgMAILAAAAAIERWAAAAAAIjMACAAAAQGAEFgAAAAACI7AAAAAAEBiBBQAAAIDACCwAAAAABEZgAQAAACAwAgsAAAAAgcWFQqFQ8N0AAICsKG7IkYw+hCwjNDBnRh8CkKkxYgEAAAAgMAILAAAAAIERWAAAAAAIjMACAAAAQGAEFgAAAAACI7AAAAAAEBiBBQAAAIDACCwAAAAABMZKLwAAxGDfvn02ceJE++2332zFihW2bds2q1mzpo0ePTrRx8yfP9/Gjh1rK1eutNy5c1vt2rWtf//+Vrp06QRt161bZ6+88ootWbLEDh8+bFWqVLFevXq5xwBAVsCIBQAAMdi1a5cLIpYvX25nn3225ciRI8n2X375pd1555124MABu/322+3666+3H3/80W666Sbbvn17vLYbN25025cuXWrdunVz7RXI9O3b17777rs0PjMASB1xoVAolEr7AgAgTfz3339WoECBDD2GQ4cO2c6dO61kyZLu+4YNG9o555wTdcTiyJEj1qZNGxd8vPvuu5Y/f363XSMdCjCuvPJKe/DBB8Pt77vvPheITJgwwSpXruy2KbDo1KmTG+l4//33LS4uLk3OK27IkTTZ74koNJBEDyApjFgAAOJ1npW6ow5tvXr1rEmTJu6u+++//x6v3eLFi61WrVo2ffp0+/jjj137unXrWuvWre3NN9+Mum/d6R84cKBddtllru1VV11lb7zxhuuE+91yyy2uU667+Pfcc49deuml1rhx4/DPf/jhB+vRo4fVr1/fmjdvbkOGDLE1a9a44xk1apRro+PV98OHD496LBoR0D73798f87VRB98LKpKjY9SoRLt27cJBhShouPDCC2327Nnh89YxzJs3z233ggrR4/T4DRs22LJlyxLsP7lrAADpjdAbAOCoo9uvXz/75ZdfrGXLli5Y2Lt3r33wwQcuTWfMmDFWtWrVeI/RnfR//vnH2rZta4UKFbKZM2e6OgF1wFu0aBGv1uDuu++2smXLWteuXa1w4cIu7UedYNUfPPvss/H2q7v1qi8477zz7NZbb3XPIT/99JNLD9Lju3fv7p5zzpw59vPPP8d7vOoTNJrwySefWO/eveOlLak2YuHChe6Y8+XLlybXUkGUVK9ePcHPzj33XFu0aJGtX7/ezjrrLFu1apUL6BJr6+3P+zrWawAA6Y3AAgDgTJkyxd0JV2CgEQVPhw4drHPnzjZ06NAEaT9bt2619957zwoWLOi+V4qPRi20Ly+wOHjwoD3++OOuY/zaa69Zzpz/96/n6quvdrUKL730UngExLN79273cwUVfi+++KJLCdJIR5kyZdy2jh07ulGOSO3bt7ennnrK/ve//1mDBg3C2zXKcvToUXesacWroShRokSCn3nb1EaBRaxtj+caAEB6IhUKAOBotOGMM85wd/pVqOx9aCSjTp067o64CpH9lLLkBRWSN29ed+dd6TseFR///fffrq1GQPz7ViqP1yaSahH8tA/duVcKk9ehFgUq11xzTYLHK7BROtFHH30U3qayQqVuVaxYMTwCkBa865QrV66oKVX+Nilpm9JrAADpiRELAIDzxx9/uNGFpk2bJtpGwcBpp50W/j7atKlFihRxIw7+/cpjjz2W6H7VYfYrWrSoS/Hx27x5s/tcvnz5BI+Ptk1BheoPNEKhomvtUyMymzZtsgEDBlhaUoAlmjY2ktKe/G1S0jal1wAA0hOBBQAgTHfyVaydGHXO/ZKbclW8yQdVMF2pUqWobYoXLx7ve68jHZTSoVQjoloL1XZo9EKjAKohSUve+aieo0KFCvF+pm3+Nv62kSLbAkBmRmABAHBUWK07+1qQ7aSTUi9Ttly5cu6zCqWVUnW8SpUq5T6r6DlStG2iYnPNtKSAQjUVmtJVaUQaVUlLXpG7CtQjz/nXX391U+d6IwwK5hTsqG0ktfXv73iuAQCkF2osAABOq1atXErS22+/HVO6UqxUCH7KKafY+PHj46VIeVQ/oHUqklOsWDHXwf7666/dVLQe1YBMnjw5yVELpWM999xzLtVLU7imNU0dq+P98MMP3QxXHs2ApXQspZt5RexK2dKaGNqun3v0OD1egVm1atUCXQMASA+MWAAAHBX/qoh62LBhbjpUjVzozrpmftL3uqt+PGskaKRi8ODBbg0LzfSkaV41OrJnzx5bt26dffXVV/b888/HmxUqMUqnuu2229z0t5qtSoXjmmrVWxMi2iJyKuLWOak4XTUhF110kR0vzXal4xY9p67N66+/7r5XmlejRo3c1woadL7333+/3XzzzS64UfA0adIkl06mqXT9NH2srrE+X3vtte66K4VLs0FpNi7/eR3PNQCA9EBgAQAId4bVidX0sZ9++mk4iFB+v+6YaxrZ46VRCy2cpw918JVypXUYNLPRdddd56adjXUkQNPhauG7cePGuQLvyy+/3AUPN9xwg+XJkyfBY9TxVhvNBqWZqYJ0vCdOnGhbtmwJf69i6pEjR7qvdX28wEI0KqHj0bSwuq4KzBSs9e/fP8HUsgq01E7nppEdFXJrLY6XX345QSrV8VwDAEgPcSGvqg4AgCzqiy++sHvvvdeefPJJNxNUpGeeecaNACi4iHX17BPtGiQmbkj8lc+RuNBA7scCSaHGAgCQZehemOok/JQCpLoQzVClu/mRtHaGRknq1at3QgQVx3MNACA9EHoDALIMreugdCal/WhWJRWDq75g1apV1r17d1fc7Fm9erWtWLHCTTWrQugePXpELRxX4JEc/36z0jUAgPREYAEAyFJ1IFqtW7Mi7dixw21T51opQB07dkyQGjRmzBhXz6Cfn3feeQn2pw65CsuTs3jxYsuK1wAA0hM1FgCAbEsd8zVr1iTbLsj6G5kdNRaxo8YCSBq/IQCAbEtpQ6QOAUDqoHgbAAAAQGCMWAAAkI2NKjzWFbbnypUrow8FQBbHiAUAAACAwAgsAAAAAARGYAEAAAAgMAILAAAAAIERWAAAAAAIjMACAAAAQGAEFgAAAAACI7AAAAAAEBiBBQAAAIDACCwAAAAABEZgAQAAACAwAgsAAAAAgcWFQqFQ8N0AAICsKG7IEcvOQgNzZvQhACcMRiwAAAAABEZgAQAAACAwAgsAAAAAgRFYAAAAAAiMwAIAAABAYAQWAAAAAAIjsAAAZFqjRo2yWrVq2ebNmzP6UAAAyWDyZgBAqps+fbrt2bPHrr32WjtR7Nixw6ZMmWK///67/fbbb7Zr1y5r3bq1DRo0KNHHzJgxwyZNmmTr16+3AgUKWMOGDa1v375WtGjRBG1//fVXGzFihPscFxdn5513nmtbuXLlND4zAEgdjFgAANIksJg8eXLg/dx00022YMECK1WqlGW0devW2bhx42zt2rVWtWrVZNu//fbbLugoWLCgDRgwwK666iqbPXu29erVy/bv3x+v7dKlS+2WW26xTZs2uZ/r6w0bNljPnj1t9erVaXhWAJB6GLEAACTpv//+c3fbM0LOnDndR2Zwzjnn2Jw5c9xog0YrmjZtmmhb/fy1115zAYg+58iRw23X93fddZcLum688cZw++eff95y5cplY8aMsRIlSrhtl19+uXXs2NFeeuklGz58eDqcIQAEw4gFAGSD0QPVKXz33XeuZkHpO3Xr1rUuXbrYrFmz4rVt06aNu1uudB+l4TRu3Niuueaa8M+XLFlit956q9tev359u+666+zDDz9MsA+127Jli3te72Px4sXhNrob//DDD1vz5s3t4osvdo8ZNmxYgjv50WosvG0aQVCHu2XLlu58dJzz58+Pmo7UrVs3a9KkiTVo0MCuvPJKe+ihh2znzp0puo4KrqKlMEUzd+5cO3DggHXu3DkcVEijRo2sdOnSNnPmzPC2P//805YvX26XXXZZOKgQfa1t33//vUvD8vviiy/c+darV89atWplo0ePdq+vrotebwDICJnjNhAAIM298sorruPeoUMH9706oA8++KAdOnTIdew9f/31l/Xp08fdkb/00ktt3759bvu8efPs7rvvtlNPPdW6du1q+fPnd6k9TzzxhEvhue2221w7pf28+uqr7q697s57KlSo4D6rPqF3795WqFAhlx6kDvTKlSvtnXfesZ9//tl1kmMZpVCakdrpWA4fPuxGAQYOHGjTpk2z008/3bX55JNPXLsLLrjAPWeePHnc+Sm96p9//ok5UEipZcuWuc+qk4hUvXp1F9DpuuoaJtf2448/doGegiLRNdfrVqZMGZcqpcBFwdM333yTJucCALEisACAbEIdfXXelfMvCjA0aqFUG6Xd5M2b121XkKA7+u3atQs/9ujRo/bcc89Zvnz57M0337TixYu77Z06dXI1Adqm4KRcuXJuZEAFywcPHnSjCZEee+wxK1asmL311lvxUqwuuugiF7jobr4/0EnMySef7I5dhc6iu/Xdu3d3gYVGW7yRAz2H0pH8wYqCjLTkjTB418lP20KhkG3fvt3Kly+fbFvZtm2b+3zkyBF3zgqIdM0LFy4cfi39I0sAkBFIhQKAbEKdTy+oEH199dVX27///ms//PBDeHuRIkUSdOw1yrB161Zr27ZtvA6w6gKUZnTs2DH7+uuvkz0GFSKvWrXKWrRo4UYZFOx4HzVq1HCBy8KFC2M6HwVFXlAh1apVcyMASrPyn6NSkpQipc58etFzSu7cuRP8TKMm/jYpaauRCwUkSmfzggrReWv0BwAyEiMWAJBNnHHGGQm2eelJGqXwqAbAXxcgXo3DmWeemWAfZ511VoJ9JOaPP/4I10noIxqlKMVCqUCRFBTt3r07/H2PHj1cvYdSpPSzmjVrutoQjdCkZUG6N/qjNDPva49Gcvxt/G0jRbb1rrFGOiJF2wYA6YnAAgAQT2RHODV5owaqi1DBdTT+O/FJOemk6IPu/pEJpWZNnTrVFUAvWrTIBRmqCVFQoxmYogUnqUGpXqLRhbJly8b7mbZppMUb+fG3jeRt8xd1A0BmRWABANmEZlFKbARBoxRJ8X6uNRwiedv8+/CnKPmpo+8FBXXq1LH0oBQjFT57xc9Ki7rjjjvcOhP33ntvmjyn0rI++OAD++WXXxIEFlqzQqMLSl/y2ora+utavLa6llWqVHHfe0XpWnAvUrRtAJCeqLEAgGzivffes71794a/19fvv/++m53pwgsvTPKx6tiedtppbiYp/9SnKiaeMGGC6/xqClqPOs2q3Yisa9Aq0kqd0vNu3LgxwfNof/5UpqBUuxHtXCQ1nyeSroXqI959911X+O7RzFpKZ1KNiUeBh9a30BSy/lELfa1ttWvXDo9qaC0Nfa1ZoHR9PZphSkXrAJCRGLEAgGxCsyhp1iSvMFtBggqyNQNUculPqrm455573KxN2kf79u1d8KAF43RXXbUM3miEnHvuuW76U80kpWlUNUKhDvIpp5ziZoXSdLaaxUjF4KrbUHGyAo0vv/zSzegUy6xQsdAUuAqcNN1syZIlbc+ePe68FQhFm7EqOa+//nq82gcVonvbVL+hD9GsTTrHoUOHunU/tF6HAoWJEye6Wpdrr7023n41Ra9mqrr55pvd2hcyZcoUVxSv0RWPZrbS93rN9DpoTQ69Njon1ZAoaElstAgA0hqBBQBkE/369bOffvrJ1RyoQFqBgOoN/HfPk6LF3UaMGGFvvPGGG6XQrE7qJEdOTStaOE+dXN1x1+iEOsgjR450gYVGLZSGNG7cOHcHXz9XIXWpUqVcQKEAJDVnwlLwo7v5GqFQ51vPryBJ09OmlM7Bb8WKFe5DtKaEF1h4dSR6Pk29O2TIEHeOWhtEr4OXBuU5//zzXd2HpsXVh4IDBWTPPvusVapUKV5bvV4KMBTQ6DG6pgowzj77bBf4eTNJAUB6iwul5/x7AIB0p7vZgwcPdp3i4+lMI2vQaIhGSBSwaWG9WMUNOWLZWWgg91iB1EKNBQAAWYhGivx1G16NhUaiNELi1ZAAQHojTAcAZFvqkOsjKaphUM1EZqEUs/79+1uzZs3cLFEqpv/kk0/c9vvuu88tWggAGYHAAgCQbalWROtZJEW1H0ony0xF+CqOnzlzpu3cudMFPhUrVnRF71r4DwAyCjUWAIBsSzNRJbdiuIqha9SoYScqaiy4xwqkFn6bAADZllbeTqvVtwEgu6F4GwAAAEBgBBYAAAAAAiMVCgCAbGxU4bFu5XRmkwIQFCMWAAAAAAIjsAAAAAAQGIEFAAAAgMAILAAAAAAERmABAAAAIDACCwAAAACBEVgAAAAACIzAAgAAAEBgBBYAAAAAAiOwAAAAABAYgQUAAACAwOJCoVAo+G4AAEBWFDfkiJ1IQgNzZvQhANkWIxYAAAAAAiOwAAAAABAYgQUAAACAwAgsAAAAAARGYAEAAAAgMAILAAAAAIERWAAAAAAIjMACAJDmNm3aZAMGDLCmTZtarVq1bNCgQeHPftG2AQCyBlaRAQCkucGDB9uqVavsxhtvtFNPPdXKlCljM2bMsKxk3bp19uGHH9rvv//uPvbu3Ws9e/a0Xr16RW1/7Ngxmzx5sk2bNs22bNliRYsWdYFV7969LV++fAnaz58/38aOHWsrV6603LlzW+3ata1///5WunTpdDg7AAiOwAIAkKYOHTpkP/74o3Xq1Mmuv/768PYFCxZYjhw5LKtYunSpvf322y4oOuecc2zRokVJtn/xxRftnXfesUsuucS6du1qf/zxh/t+xYoVNmLECDvppP8/aeDLL7+0e++9184++2y7/fbbXdCioOSmm26yCRMmWPHixdPhDAEgGAILAECa+ueffywUClnhwoXjbc+TJ49lpKNHj9rhw4ctb968MbVv1KiRCwAKFSpky5cvt27duiXads2aNTZlyhQXVDz//PPh7aeffroNGTLEZs+ebS1atHDbjhw54tqULFnSXn/9dcufP7/bXq9ePReIjR492h588MHA5wsAaY0aCwBAmlG9ROvWrd3XY8aMcTUU+li8eHGS9RTfffed3XDDDVa/fn1r3ry564zv27cvQTvd2X/55ZetXbt2VrduXZdq9MADD9jGjRvjtZs+fbp7Pu1Xnfcrr7zSddznzJkT87kUKVLEBRWxmDVrlgumrr322njb27dv7wKZTz/9NLzthx9+sO3bt7tz8IIKqVy5sl144YUuCFHw4ffee+/ZVVdd5c5Z+1QQ452jri0AZARGLAAAaUad30qVKrm0IN2914dUqFAh0ceofuGLL75wHe1WrVq5jrJSiDQKMHz48HAKkYIK1Wxs3brV2rZta2eeeabt2LHDdboVlCiFqFSpUvH2PWzYMNdJV2e8QIECVr58+TQ5b41o6DirVauWYJRG10M/97eV6tWrJ9jPueee61Ku1q9fb2eddZbbNn78eHv11VetSpUqdtttt9mBAwfcuaqGAwAyEoEFACDNnHfeeVasWDEXWFSsWNFatmyZ7GNWr17tRiiaNGnivu/YsaP7XsGFRhg0giEjR450s02NGzfOddY9bdq0sS5dutioUaMSjIioEz5p0qSY05+Ol0YgTj75ZFeEHalEiRL2yy+/uDSsXLlyubbe9mhtvf0psNi9e7cb+dG1fOONN8LpZArCrr766jQ9JwBIDqlQAIBMRaMIXlDh0QiEzJ07131WmtHMmTPtggsucJ3vXbt2hT8045Lu9C9cuDDBvjt06JDmQYUXwChoiMYLNtTG/zla+8i2SuU6ePCgOw9/jYqCtyuuuCINzgQAYseIBQAgU4mWJqWOs+obNEIhO3fudHfvFTyoriIa/6xLnnLlyll6UPCiY0xsliyvjf+zRjCSa7t582b3OVoKV1qldQFArAgsAABZjkYs5KKLLrLu3bvH/Lj0GK0QTQ+r6WUVGESmQ23bts2lSXkjFN5UstoeGVRpm78NAGRmBBYAgExFHfJIKsres2dPeLE4FSprBOO///6zOnXqWGZTtWpVN5qybNkyl67lURqTFsCrWbNmvLbeOhmR5/Lrr7/GKzL3itFVzK0F9Py0DQAyEjUWAIBMRR1kr5bC8+abb7rPjRs3Dqc5aR0Iddw///zzRNfPyCjNmjWzuLg4Vyju98EHH7h6CW8NC9GUskr10qre/il1FYBoKlqleuXM+X/3ARV4aAREM18pSPEHXqo5AYCMxIgFACBT0YxHDz/8sJvpSDURmm5W08/qLr867B5Ntfrzzz/b/fff736u6VqVXrRlyxa3qrdWx05snYzjoeltNTOV15EXrSiudTG8oEcrZ3vnoNms3n33Xbv77rvdehzeyts6D39goaBh4MCB7jxuvvlmNxWuRmIUlGhkplevXuG2SqHq2bOnm3ZXq3KrYFuBigIWjWpo6loFNACQEQgsAACZitZnuPPOO23EiBE2bdo0lwrUqVMnF0j4C7ILFixoY8eOtYkTJ7ppaOfNm2c5cuRws0TVqFHDBSap6d9//3VT3Pop6PEWpNPK2V5gIQMGDHArbesc5s+f74KCzp07W+/evRMUlmtUQrM8aQrZoUOHulEJpTr1798/wTS0PXr0cNdEQYrWszjttNPcCt2qO1FgkdErmgPIvuJCXgUcAADIsp577jk3QvLZZ5+51KpYxQ2Jv6p3VhcayD1TIKNQYwEAQBbir63wKDXrk08+cYvopSSoAIDURFgPAMi2VDfhLT6XGNVtFClSxDILFXQPGzbMLr30UpcmpbUtVPi9f/9+69evX0YfHoBsjMACAJBtDRkyxGbMmJFkGxVbjx492jKLsmXLWpkyZVzBthYJVD2GpqzV6uSZcepdANkHNRYAgGxr7dq1tn379iTbFC5c2M0wdaKixgJAauG3DwCQbZ155pnuAwAQHMXbAAAAAAJjxAIAgGxsVOGxbm0MFakDQBCMWAAAAAAIjMACAAAAQGAEFgAAAAACI7AAAAAAEBiBBQAAAIDACCwAAAAABEZgAQAAACAwAgsAAAAAgRFYAAAAAAiMwAIAAABAYAQWAAAAAAIjsAAAAAAQWFwoFAoF3w0AAMiK4oYcsawsNDBnRh8CgP+HEQsAAAAAgRFYAAAAAAiMwAIAAABAYAQWAAAAAAIjsAAAAAAQGIEFAAAAgMAILAAAWcKmTZtswIAB1rRpU6tVq5YNGjQoow8JAODD5M8AgCxh8ODBtmrVKrvxxhvt1FNPtTJlyqTo8aNGjbLKlStbkyZNjuv5f/31V5s5c6b99ttv7jj2799vjz76qLVp0yZq+0OHDtnYsWPt008/te3bt1uJEiVc2xtuuMFy5kz473fGjBk2adIkW79+vRUoUMAaNmxoffv2taJFix7X8QJAeiOwAABkeuqk//jjj9apUye7/vrrj2sfY8aMsdatWx93YLFgwQKbOnWqnXHGGXb22WfbL7/8kmT7+++/377++mtr27atnXfeea79yJEjbePGjQlGW95++2176aWXrGbNmm5UZtu2bW7b0qVL7c0337R8+fId1zEDQHoisAAAZHr//POPhUIhK1y4cIYdQ4cOHaxbt26uk//5558nGVjMnz/fBRXXXXed3XnnnW5bu3btrFChQi5gaN++vZ1//vlu+65du+y1116zqlWrus85cuRw2/X9XXfdZZMnT3ajNACQ2VFjAQDI1HR3XyMN3qiD6iv0sXjxYjeCcNttt9kVV1xhF198sTVv3twefvhh27x5c/jx+lrtvXQj7/Hetlgp/SrWkYNZs2a5z9dcc0287d73SqnyzJ071w4cOGCdO3cOBxXSqFEjK126dLy2cvToUXv99dfdNalXr5516dLFZs+e7VK9dE7+cweA9MSIBQAgU7vqqqusUqVK9uKLL9oll1ziPqRChQr2+OOP27nnnus65UWKFLE1a9bYhx9+aIsWLbJ33nnHTj75ZFej8Nhjj9kjjzxiF1xwgRstSGvLli1zNRWnnXZavO36vnjx4rZ8+fJ4bUXpUpGqV6/ugpR9+/ZZ/vz53bbnnnvO3n//fRdEdO3a1Y14PPvss3b66aen+XkBQFIILAAAmZo63MWKFXOBRcWKFa1ly5bhnyl4iBxF0J3+W2+91T766CPr3r27+7keo8BCIwD+x6eVHTt2uMAnGgUWqqHwt/W2R2urFDAVf5cvX94FTgoq6tata8OGDbOTTvq/xAPNlHXttdem2fkAQCxIhQIAZFleUHHs2DHbu3evu3uv0Y2CBQu6WZwyilKbcufOHfVnefLkcT/3t5Vo7dXW3+abb75xn5X+5AUVooBLqWAAkJEYsQAAZFlKeVLdhdKJDh48GO9ne/bsybDjyps3r5vJKhodp37ubytq79/utfW38eonNHoRSdu+/fbbVDwLAEgZAgsAQJakYELrPGg9C31WjYHu8MfFxdkDDzzgRjEyilK3lL4Ujbemhb+tt71s2bIJ2up8oqVJAUBmQyoUACBL+uyzz9wMSS+//LKbbalx48YuHUg1GRk5WiHVqlVzdRRbt26Nt13fK1g455xz4rWVaNPXah0LjUR4hdtegbYW0YsUbRsApCcCCwBAluRNzariZj+tdh1ttEKd8927d6fLsWnaW9EaFH7e95oe16OASCMt7777rguUPPPmzbNNmzZZixYtwtu0GrdXtO4/x9WrV9vChQvT8IwAIHmkQgEAsiStoD1p0iS7/fbb3RSyuXLlsu+++851sjXNbCRNS/v999/b+PHj3bSvSjHyAoBYbNmyxT755BP39dq1a8Od/7/++st93apVKytVqpT7ukGDBi4I0GJ4KirXtLEafdBMVQoqatSoEd6vpsPt06ePDR061M1mpWPSqMbEiRPdKt/+2Z7OOussd64ffPCBa6troIJ1redRuXJl++2339x5AUBGiAtF3uoBACCTUdFy27ZtrWfPntarV694i8tpsbh169a5u/4XXXSRCzTUTp380aNHh9tu2LDBrfeg2aL+++8/t02L7MVKbXv37p3oz0eOHBlv0T0VXr/xxhtugTtNKau6ijZt2tgNN9xgOXMmvK83ffp0FygppalAgQIuOOnXr5+dcsop8dppVEP7VZCiFcmVKnXTTTe5mhMFI1osL/IxSYkbcsSystBA7pECmQWBBQAAJ4A777zTzZL19ddfx1vBOzkEFgBSCzUWAABkIf41MDyrVq1yU83Wrl07RUEFAKQmwnwAQLalYu7Dhw8n2UZrSGjBvcxixowZ9umnn1r9+vVdfYbSwFRzofQqf5oYAKQ3AgsAQLZ1991325IlS5Js07p1axs0aJBlFlWqVHG1JVOmTHGBkeoxVNtxyy23uJ8BQEahxgIAkG1pFqV///03yTZanO7MM8+0ExU1FgBSC7+NAIBsy79QHQAgGIq3AQAAAARGYAEAAAAgMFKhAADIxkYVHms9evRwK5cDQBCMWAAAAAAIjMACAAAAQGAEFgAAAAACI7AAAAAAEBiBBQAAAIDACCwAAAAABEZgAQAAACAwAgsAAAAAgRFYAAAAAAiMwAIAAABAYAQWAAAAAAKLC4VCoeC7AQAAWVHckCOW1YQG5szoQwAQBSMWAAAAAAIjsAAAAAAQGIEFAAAAgMAILAAAAAAERmABAAAAIDACCwAAAACBEVgAAAAACIzAAgCQKdxyyy3Wpk2bmNpOnz7datWqZYsXL05yW1LUTu31OABAcAQWAIAT1ooVK2zUqFG2efPmjD4UADjhsXQlAOCE0LJlS2vWrJnlypUrvG3lypU2ZswYu/DCC+3000+P175mzZq2YMECy5mTf4UAkBr4awoAOCHkyJHDfcTqpJNOsjx58qTpMQFAdkIqFAAgsP/++89GjBhh3bt3t8suu8zq1q1r7dq1s1deecUOHDgQr+2///5rTzzxhGvXoEEDV1vx22+/JbrvDz74wK6++urwPidNmmShUChBu8gaC6VADR482H3du3dv9zN9DBo0KGqNxR9//OG+f/HFF6MexwMPPGAXX3yx7dy5M7xtx44d9vTTT1urVq3cz1q0aGFPPvmk/fPPP/Eeu3v3bnvhhRfsyiuvtHr16rlz79q1q7311luWUt45/PLLL+7a6Rpqf48//rjt27cvxfsDgNTCiAUAILDt27fbRx99ZJdeeqnrXGvkYMmSJa7jrDqHV1991bU7cuSI9e3b15YvX+5Sl6pXr+7SlW699VYrUqRIgv0qiFBHv1KlSnbbbbe5IGXixIlWtGjRZI9Jx6KOvwKTHj16WIUKFdz2MmXKRG2vn1etWtVmzZplt99+e7zRj71799rXX3/tggLvubdu3er2e/jwYRcwaL9//vmnvf/++y5omTBhghUsWNC1ve+++9z1UIB09tln28GDB10g88MPP1i3bt1SfL11ze68805X7N68eXO3H11/jcI8+OCDKd4fAKQGAgsAQGClS5e2Tz75JF69QqdOney1116zN954w3799Vc799xz7eOPP3ZBRc+ePa1Xr17xOvUKIEqVKhXetmfPHjcKop+NHTvW8ubN67arM92hQ4dkj0kd+PPOO88FFnXq1HF3+pPTunVre+655+x///ufGwnwfP755y4Y0M89aqdA6e2337aSJUuGtzdt2tQFHNquc1RQsmjRInfM99xzj6WGVatW2bhx49w1FQUsGjXS9VXAkT9//lR5HgBICVKhAACBqWDaCyrU2Va6065du+yiiy5y2xRYyNy5c91IwHXXXRfv8ep0FyhQIN62hQsXuhGKjh07hoMKUSdeoyJpQXf/dS4Kkvw+/fRTN6LSsGFD972Chfnz51ujRo1cnYbO1ftQkbhGL7777jvXVj/PnTu3uwapNTuVRnq8oMJTu3ZtO3r0KDNgAcgwjFgAAFLF1KlTXRrQ2rVr7dixY/F+ptEH2bRpkxUrViycIuRRx1ujHl47r62cccYZCZ7rzDPPTJNzUPCgkYp58+a54EHHqY76jz/+6IIfb8apdevWuXNU+pE+otH5iB5z1113uRqLtm3bumPX6EmTJk3CgVdKefuOPHavngMAMgKBBQAgMNU9DB061BUwd+nSxQUP6lCr9kKFxpGBRmamQuyvvvrKpT+pWFyjFSoW1/ZIV1xxRbz0KD//jFMKShRIaJRD9RBffPGFvfvuu3b55Ze74u+USmr2q2iF7QCQHggsAACBqfOtFKCXX37ZFRB7vv322wR32pUi5I0GeA4dOuRGKAoXLhyvrTc6EHlnX6MisYiLi0vxuWjE4uSTT3bpUF5goVETf+qRUp20b6V9qX4jFgq2tD99KGXpkUcecYXimh2qWrVqKT5OAMhsqLEAAASmO+jqaPvvlqvTPX78+HjtGjdu7DrVKmz2e++991zxsZ867LrrrxQr/5S1f/31l+uQxyJfvnzus2o+YqVaEdVw/PTTT/bZZ5/Zhg0bEoxKKPCoX7++ffnll7Z06dIE+9B18Kal1bFHTrmr66Xi8pQeGwBkZoxYAAAC0zoKmlK2f//+dskll7ggQZ3/yFWtVWOgWZq0GrZGKDRrk6ajVdqRRgEUdHg0etGnTx+XYnXjjTe66WnVQZ82bZqVLVvWPS45GgnQCIpmlVIHXoGGRkIiC58jKZB45513XJqSHq+Up0iaQvbmm292M1wpTapy5cou5UvnpRoNHa9mhVq/fr1bb0LX5ayzzrJChQq5URgFUzqWCy64IEXXGgAyKwILAEBg119/vbtLr0JmFSmfeuqprn5AgYRmdfKo7mL48OE2bNgwty6E7vhr7QhtUwCxZcuWePtVmpCCAY1wqI1mhNI2pVE99thjyR7Xaaed5lKO3nzzTXvmmWfcKIqChuQCiypVqrggYM2aNS4Nyz+drH/fqi3RvnUuM2fOdEXoaqvZo3T+ou91HVRboVmxtO5F8eLFrX379m5BQf+MVwCQlcWFqPICACDbihtyxLKa0EDuiwKZETUWAAAAAAIj5AcAIIOp0NtfXxKNVtNmRW0AmRmBBQAAGaxbt24J6ksiqUhcxeAAkFkRWAAAkMEef/xxO3jwYIpX2waAzITAAgCADFajRo2MPgQACIzibQAAAACBMWIBAEA2NqrwWOvRo4dbYwQAgmDEAgAAAEBgBBYAAAAAAiOwAAAAABAYgQUAAACAwAgsAAAAAARGYAEAAAAgMAILAAAAAIERWAAAAAAIjMACAAAAQGAEFgAAAAACI7AAAAAAEBiBBQAAAIDA4kKhUCj4bgAAQFYUN+SIZRWhgTkz+hAAJIERCwAAAACBEVgAAAAACIzAAgAAAEBgBBYAAAAAAiOwAAAAABAYgQUAAACAwAgsAOAENWjQIKtVq5ZlZm3atLFbbrklow8DAJAKmBAaALKRuXPn2ooVK6xXr14ZfShZzrp16+zDDz+033//3X3s3bvXevbsmei1PHbsmE2ePNmmTZtmW7ZssaJFi1rTpk2td+/eli9fvgTt58+fb2PHjrWVK1da7ty5rXbt2ta/f38rXbp0OpwdAATHiAUAnKAeeughW7BgQYLAYsyYMZZZvP/++zZ8+HDLCpYuXWpvv/22/fXXX3bOOeck2/7FF1+0l156yc4880y7++677bLLLrN33nnH7rzzThd0+H355Zdu+4EDB+z222+366+/3n788Ue76aabbPv27Wl4VgCQehixAIATVM6cOd1HZqY781lFo0aNXABQqFAhW758uXXr1i3RtmvWrLEpU6bYJZdcYs8//3x4++mnn25Dhgyx2bNnW4sWLdy2I0eOuDYlS5a0119/3fLnz++216tXzwUYo0ePtgcffDAdzhAAgmHEAgCyEI1AqG5Cd76j6dGjh0u3UWc1ssZCtQwzZsxwX2u79zF9+vRwqs8zzzxjnTp1cp3o+vXrW9euXV36T6RRo0a5x65du9ZeeOEFa968uWvfp08ftx9RJ/y6665z21VLoZSgWGosvG3aj+7e61gaN25s99xzj+3YsSPma6XOu0YBWrVqZXXr1nUjBgMGDLBVq1bZ8ShSpIgLKmIxa9YsC4VCdu2118bb3r59e8ubN699+umn4W0//PCDG5Vo165dOKiQypUr24UXXujOQ6+n33vvvWdXXXWVOy/tU0GMXke9JosXLz6u8wOAoDL3rSwAQDwXX3yxnXrqqfbJJ59Yly5d4v1sw4YNLl1H26ONVNx4442us6sUm8ceeyy8/bzzznOf1SFdsmSJNWjQwN1ZV1rO559/bk888YTt3LnTBS2RFLyoXkA/27Vrl02cONH69evn6ghefvll69ChgxUuXNg++ugje+qpp1xaUI0aNZI9T3W0VbvQpEkTV2egYECByX///Rdz6tS7777rggF1vIsVK2YbN260Dz74wKUX6TjLlStnaUUjGieddJJVq1Yt3vY8efJYpUqV3M/9baV69eoJ9nPuuefaokWLbP369XbWWWe5bePHj7dXX33VqlSpYrfddpt7nSZMmOBqOAAgIxFYAEAWkiNHDmvZsqXrSGq0QB11j4INad26daJByWeffeYCC+0jku7sKxDw0x13BQnqzCotJzJgUZCjWoK4uDj3/cknn+xSfZ577jl3F/20005z25s1a+b2r85+LIHFn3/+aU8//bRdfvnl4W3qqE+dOtWNZJxxxhnJ7uOVV15JUCStY9A5TZo0ye677z5LKwqMdC2ipXqVKFHCfvnlFzt8+LDlypUrXEOh7dHaevtTYLF7925XI1OxYkV74403XKAiGu24+uqr0+x8ACAWpEIBQBajzrE/kBCNRMycOdN1PnUn+3j4O+EHDx50IxD//vuvC0g0UuClOPl17tw5HFSIFzQofckLKkR308uXL+8ChlgUL148XlAhXlpXrPvwzkfXRjM46Xy84/j1118tLWkUQUFDNF6woTb+z9HaR7b97rvv3GujANALKkQjMldccUUanAkAxI4RCwDIYnS3WsGDRh+UCqM7+Uph2rx5s0sbOl779u1zhcJz5sxxMx9FUpARqUyZMvG+V9qTKJUqkuoTtm7dGtOxRJtiVWlNorv2ojv+3tce1Sh4dQqaEnbkyJGuhmH//v3J7j81qY5C6WPRHDp0KNzG/1nnk1xbvcai4ChStG0AkJ4ILAAgi45aqGha+fd16tRxoxdemtTx0sxDWktBNQk1a9Z0HXkFLSoYV+pQ5BSpop9Hk9h2jR7EIrHH+/fx888/uzQtP29dCQUwKgAvUKCAq6lQ6pQ65xpd0XWLDDRSm0Zc/vjjDxcYRKZDbdu2zaVJeSMUauttr1ChQoK2/jYAkJkRWABAFqSpSocNG+YCivPPP9+++OILF2AoJSYp/rQlvz179rigQoHJAw88EO9n33//vWVGKoKOLOT2RiK++uorNwKj+o/I1cc1ypHW09xWrVrVFi5caMuWLbMLLrggvF1pTFoAT4Gbv62o8F6voZ9SthQceaMRpUqVcp9VzK0F9Py0DQAyEjUWAJAFqVZA6xyoA62UKNVAeLUXsdQdRKYQeSMEkSMKmt412nSzmYHSrtQR9394qVmJnY9mhfr777/T/NhUrK4gTiM9kc+veglvDQvRlLIKCHWdFQx5FIAojUvTB3tF8zpHBUWablZBiv91Uo0NAGQkRiwAIIvS7E/z5s1zqzsXLFjQTc2aHE1pqpmZtF6FppVVh1VTmupOv4q01TlVUbCmSd2yZYub4lU/iwxEMjutnaFZoR555BG3LofqO5Q69e2337rg4+jRoynepwrAvfVDvPU0NMOWFrUTrbVx9tlnh+tgOnbs6K61Vt3W8Sg1So/XaIU/sNBrMHDgQLv//vvt5ptvdqloChQVlCiAVGqXRylUSvfSSI1SvFSwrUBFAYtGNTR1bWKjUgCQ1ggsACCLatiwoauDUKdf0436ZwlKjBayW7FihVt0TelTqpt49NFHXfDw+OOPu874N99841KsypYta7feeqvr+A4ePNiyEgUPWkdDHfBx48a5EQyljGlhP02Fq6AppVS8rmJwP6394S1Ip5WzvcBCtBifitgVnCnNTEGBZtFSXUhkDYlGJfT6aQrZoUOHulEJpTqpGD9yGlqtGaL0KAUpWs9Cs29pKmCNziiwiOV9AABpIS4UayUdAADItBQwaYREqXHJ1dr4xQ2Jv6p3ZhYayP1QIDOjxgIAgCzEX1vhUWqWRpm0jklKggoASE2E/gCAbEt1E97ic4nRtLDeGhqZgQq6NSPYpZde6tKktLaFCr81hW6/fv0y+vAAZGMEFgCAbGvIkCE2Y8aMJNuo2FoLB2YWqn1RDYkKtr2pczVl7Q033JBguloASE/UWAAAsq21a9fa9u3bk53W9pxzzrETFTUWAFILv6EAgGzrzDPPdB8AgOAo3gYAAAAQGIEFAAAAgMBIhQIAIBsbVXisW3RPs18BQBCMWAAAAAAIjMACAAAAQGAEFgAAAAACI7AAAAAAEBiBBQAAAIDACCwAAAAABEZgAQAAACAwAgsAAAAAgRFYAAAAAAiMwAIAAABAYAQWAAAAAAKLC4VCoeC7AQAAWVHckCOWmYQG5szoQwBwnBixAAAAABAYgQUAAACAwAgsAAAAAARGYAEAAAAgMAILAAAAAIERWAAAAAAIjMACAAAAQPoHFps2bbIBAwZY06ZNrVatWjZo0CBLLZs3b3b7HDVqVKrtEwlNnz7dXefFixfH1P6WW26xNm3apPlx4fjpd0avqX6HTnS7du2yRx55xFq0aOHOWe/PrORE+33S3xG9Dvq7ktQ2AMCJL8Wr0AwePNhWrVplN954o5166qlWpkwZS0t79uyxSZMm2YUXXuj+UWVn+mf9ww8/2LXXXmuFChXK6MM5YTrklStXtiZNmlhmN3fuXFuxYoX16tXLMiv9ruq9mZYd55deesnmzJnj/gaVLl3aTjnllDR7LmQNCmD0v0J/G9PS+++/bz/++KP99ttv9ueff9qxY8eSvEGzbt06e+WVV2zJkiV2+PBhq1Klivv9rV27doK2e/futREjRthXX31lu3fvdv9bO3XqZFdffbXFxcWl6XkBQIYEFocOHXJ/VPXH7vrrr7fUVqpUKVuwYIHlyJEjvE3/LMaMGeO+zu6BhYIKXQt12ggsUoeuZ+vWrbNMYDFjxoyogcVNN91kN9xwg+XOndsy0uTJk93vcVoGFt99951dfPHF1rNnT8uKhg8fbqFQyE5kNWvWdH/Lc+bMmW6BxZYtW9I8sBg/frzr9OtmxIEDB+yvv/5KtO3GjRvd76X+n3Xr1s0KFixoH3zwgfXt29defvllq1OnTritgo5bb73V3Tjo3LmzVahQwb799lt75pln7O+//87UNxMAwC9Ff/X/+ecf9w+xcOHCybb977//rECBAinZvbsrkydPnhQ9BoC5Dlx6deIymjpaRYoUiant8fwdSmu5cuXK6ENI8+ty0kknnZB/yzXCedppp7nzu+OOO5IMLF599VV3Y2zChAkuEJFWrVq5G3PPPvusG/3wRiI+/PBDW758uQ0cONC6dOnitrVv397uvvtuGzdunLVt29YF7ABwwtRYqJZCd3a9u7waPfByaL26iNmzZ1vXrl2tfv369vzzzyeZTxytniJym4aY9Qc18jn9+9MdXN0N0h3nBg0a2JVXXmkPPfSQ7dy5M8nzuf/++91dT+VrRxu+1vO88MIL8bbr/HQHqlGjRu4cu3fvbp9//nmCxx89etRef/11d73q1avn/lHosYnlwe/YscOefvpp909Hx6Tc8SeffNIFcv7r743c6Jp418K7Vtu3b3cpIrpjd8kll7jn7dixo7vDpuOJRtv1eB1n3bp13XHOmjXLYrVhwwZ7+OGHrXnz5u649boMGzbM9u/fH6/d1q1bXQqd9zyXX365S2PRaxcL7z2kc3zggQfc+en6687f+vXrY6410D68fHzvvSY6Du96+kfF5s+f79pfdtll7vn0+ugffeRzJiXW90xyz6WfedfLf6xeDnu08/a2rV271r2X9Tpp33369HHvcfnyyy/tuuuuc9t1faZNmxb1HO688053THr9dIyqs1JKpJ+eS3eNlfbhP0b/MXmdJ+1D+7rqqqvsjTfesCNHjiR7Lb3z0c0N/2vmXQOv5uv7779317xhw4buuP0jPnrf6e+EfqavtS2x98nKlSvdXWS11XtWv186zoMHD9rQoUPtiiuucL9nGjn5448/LFbR/iam5D0uuqZ6fzRu3Nh93HXXXa7+zf8e9yR3XVLymuh66W+Mzlvvh9deey1qu8RqLPTavffee+H/EzoW3Y2PTCfy/y/45ptv3N94Pafew/ob439OnbPec3rv+d933j7XrFlj9957r3u9dH7ah55Tv3Mpdfrpp7ugIjn6Gzhv3jyXwusFFZI/f35r166d+9u5bNmy8PbPPvvM8ubN64IJP11rnat+B4/39QeA9BTzLU79s6lUqZK9+OKL7p+ePqRcuXLu89dff21Tpkxx+aD6SI27YRoO1h/MyOfUH2f55JNP3D/MCy64wHr37u3ukOkOkobg1SkvWrRoovvWP0XlaasjraFnP+3Xa+NR7uvYsWPdPzc9l/65KBf2vvvus3vuucfdhfI899xz7m6U/rnpH6iCF92h0j+lSOp09+jRww2FKyhSXq1yd/V4/WPU3S4Noev66y6jnlPX5OSTT3aPP/vss91ndfL0MwVY2of+Gf3vf/9zd830D+fBBx9M8NzK/dU/wA4dOrjv1QlQO6W8JZfKohxjXQelZOnYSpQo4Tpi77zzjv388882evRodwddx3Hbbbe5DpOeR+8X5RKvXr3apdV5wWpydJzqwFWvXt3tT+ek51IHV+87f/pcLPTeeOyxx1wRsN4/kf/QlXam63zWWWe510evgQJAdc70+pQvXz7Z54j1PRPLc6kTrE6ZrpmO23Peeeclexz6HcmXL5/bt96LEydOtH79+rljUkqGXheNQn700Uf21FNP2Zlnnmk1atQIP/7dd991IwS6RsWKFXMpHkrpUCdV+/L+Bui49Luq96aO13+tRR05dYbKli3rfi/0nEuXLnWdR7139DuSlEsvvdQ9NvI1818DdZIVLKnz5n9vTZ061e3/jDPOsJtvvtltU3CiDrU68noP+23bts29zxRQ6HmVfvX222+795kCNQUXChKVFqPfUb0P1WGOpdMZ9D2u11DtNHKjv7X6O6n3hV7PyKA+ueuSktdE7129b/V3TNdQx6O/GSnpoOu1099cBTH6G6O/ezNnznTnq7+b6iT76W+5rqvOUzdU9H9G11t/d7z3mK6P/s7puuj3yKProm0KpEX70GiDtunv16+//uqCzLSgv8f6O6rXMtK5554bfk30teo0fv/9d1d/ETnKU61aNTeqobae43n9ASDdhFJg06ZNoQsvvDA0cuTIBNsuuuii0Nq1axM8pmfPnqHWrVunaF/JbfMMHDgw1KhRo9Dhw4dDKXXkyJFQs2bNQtdff3287ceOHQu1atUq1Llz5/C23377zR3Dq6++mmA/d911lzuGvXv3uu9Xr17t2vbt2zd09OjRcLtVq1aFateu7X6mc/LceeedoaZNm4a2bt0ab7/Lli1z19R/3vo68vGe/fv3u2OP9NBDD7nn3b59e3jbxx9/7Paj89yzZ094u77WtksuucTtL6nXsEuXLqGrrroqfN6eL7/80u1bzyErV650348fPz50vPT80fbx5ptvuu3ffvttTNdI56B9+anto48+mqDtCy+84H72999/H9cxp+Q9E+tz6TjVLppo5+1tu+OOO+K9NyZPnuy26xi2bNkS3v7PP/+E6tatG7r//vvj7Xvfvn0Jnk+/6xdffHHo6aefTvYay4EDB9zv280335zg93XixInueBYtWhSKRWKvmbbrY+HChfG27969O9SgQYPQlVdemeD93rZt21DDhg1D//77b7xz0H7mzJkTbz/XXXddqFatWu53Ntr19L8PkxLt9ykl7/GhQ4e6bZ9++mm8tt72aO/xaNclJa+J/l62bNkydOmll4Z27tyZ4G+G/3de9LjIbd7fhvfffz/ec+m5u3btGmrTpk34unp/9+vXrx/vPa2fd+zY0R13ctdU5s6d6/Yze/bsUGq7/fbbE/19/Pzzz93Ppk6dmuBna9asife3QddT3993331R96X/Dz169Dju1z8W9vzhTPUBIOtKtXUsdOdHd07Sk+7sqoBOd8xSWgypu20aGtedIC8txLt7rFEE/1093VHTXSONYOhukf9DKS4aSdBdPtGwvSityH/3smLFii5dyE937nXs2ofuVPn3q7uCGnnQndJYaBjdy9fVXUDdSdV+NPSvO2L+O14e3anWNfToa90B+/fff911SIxGG3RHTilbei7/cetOt+6OL1y4MLxP77r6U7tSStfSyz32eDOrKK0gtXnHrbu8saTpRErJeybocyVHI3L+WWW80Qgdh+7g+kcWNDqiURI/vZ6i3zG9Z3UOXlvd9Y2F3se6w6q71N4+vA+lxHhtgtKoqr8o1tuv7uTq/RP5fte2ffv2JXhujcBpSm0/XTddg8SuZ9D3Yazvcf2N0ciRUnr8kppQI7HrEutrojv8Gg3WqIE3Wur/mxGLTz/91I1ka1TV/1x6bqVEKb0n8hqqrX+kV9ddI8E6br1uyfFebxVC63nSi/4vJVZP402w4LVJqq3X3mtzvK8/AKSXVKv29NIh0pNSO5Rbq3QGpWpoJhL9Q1T6gpeKpX8+kf+A1FZ/xBU8KJVDqU8aihd9raBDnWaP8qfVofBShqLRPzrx8smjpcpom/7BeRTQqNOvFBR9RKPpNGOhDqnqKfTPWx3DyEBLwUIkpYVE8oJDpWEkxssnV7pEYmuOeEGECg6VsqBj0zVVB0edJXXaNMzvUeqPn14ff4Fu8eLFE6QJeD9XEJXalKaktAvNyqKUsfPPPz+c4+2l9uiffWRnRR0ZBXkpec/E8lxBRE4J7U2+EC01TykmCqz9lKYxcuRIFxxGplrE+v703jP+NK7ErkdSv7PH83fIey8rxSuSty3y/Z7YtYl2zt719N6Hql2KrPHSe8If1EQT63tcf2P0uxOZdqVpdxObLS7adUnJa+Jdn2h/12K9oaS/dwqomzVrlmgb/d3wP0e095f/mnhpsYlRjYOCe6VsKdivWrWqC7D0PyLa+yG16PUW3XiJpBQpf5uk2nrtvTbH+/oDQJYLLPx/+PwSm387sYLilNA/S+VOKxd90aJFLsh44oknXGdXhc7qUCkf1yt69qiTpLteGkVQR1f/cFSkqbxp3TXWPx7dEYo8D+WjJ5ZDrfz446WRk8RqDWKdWUWFpcrD9gqj1SFVjYM6heqspub0lt6+lJOtEZFo/DOH6drqTqdGZ3766ScXROl1UUFm//79XRt/ICcKElWn4Ukqd91/bknN956S95zuyr711lsud1l3bfVZ9QN6b6l4VHn9qtFRUbrfo48+Gq5PifU9E8tzBZHY8ye23X89FWSoGFSBumoqFIx6o2MqCI81p9vb5+233+5+5xLrWEtSv7PH+3copZJ6vyV33bw7+376/U5uMdFY3+PHI9p1Sclrkhr0fPq7pL/RiYn8O5oa10S/o7qbr5s6+t3SzSTVPqkeI7K+LrV41021OpG8bV4b/a3U3/lobRVUaFRHfw8BICtI8/kp9UdTndtISd0R90tuYSANEysNyyvCU+dV0wCq0FIzgehulb8QVfz/RPUPX504FUrrrrnuqEV28lXYqH9KShtJ7u6cd6dTM7lE3imOnN1FP9f5abQhMk0hpddCIxX656PZpfwi01r8/ClgkXcxk7oT7d391D/9WI7bO1eleehDAZyKh9WZVnCiO22a298vlimNo/EepxEa/11nPade35Qs6KiRK/9MUUr/0vFqxhx1+BVURR631zFKyXsmlueSjFgkSwW7Gj3Q70hkx153jCPXzUjsGL33jNKqknvPJPc7m1Lea66i64suuijF7/eU0sKhke+L1OygaxTQW5zN3/HW3X5NbxqrlLwm3vWJNkNVrDNi6XdCqU4qaE5upCGlkvvd0E0kfehmhq6RCu9V8K3RwrT4vdJz6XfDS3f089IHNXoieg1VuK01LBRI+H+nNHOUAqhzzjkn1V9/AEgLqVZjkRgNa6uz7s/F1h9ErdAbCy+/O1oqT7SpYvUH2p86oE6F/mn6P/ydVt0pV6dOKVD6ULpC5MwkLVu2dJ/VWYh219tLFxDlCotmc9F5+usSvLoDj+5UK3VLoyTR/gHpH4o/pcL7ZxztWugfTOQdPN1NTuo6a7YVfyqPvtZsVBpOVwpBYjR9ojrQaqsZgiIpUPKuv/YZWTegu3NeGpZ3LpGvkf8faUp4aRSROfO6Dv7Xw39No6VSRXtveXfrvWPWqFbkcXsjXSl5z8TyXP7fhbRI/UqM13GJfG9pVij/OfiPMdr7U0GYAkhvgbFISivT34lYfmdTSo/XcWlEz3sO0dfapvdAZP1TEHp/Rx5/aqbdqDZGQXLk1NAa6UmJlLwm+n0sWbKkffzxx/Her97fjFgoYNTvoDr00UR7P8VKr6Hed5HvU51X5O+9/r4pUNL56YZDWtDx6H+B0gc1u5ZHQbrWrFBQ508FVdqjjidyumf93dL/J3/6WGq9/gCQJUcsNCWkhp41paHuVitP+osvvog5LUWdb93p0jze6nDoH6E6CfrjqroI/ZPQ1JP6p6e7Ncql1R0or2OXHO1P+ew6Jt0tUgpDtCn/lA6i1BzNK676AN2B1B93FTVqSkQvaFCHW+esjpdSgLxCRaVsqUOu9v47ZJp6VFM3avpA/eNVG/0j1IiO5kHXeXirrnrTFCq9RulTurOl59PdMU3fqH9KWp9Dd2X1T1rXIqmFxHRtdefOS91Re6W+aB2QpFJKdPzKy9Y0jtdcc427Zuo46R+jAg0FSpp/X/vVSJDW5NCUner06x+uroHSoXQ+0eo8gtC563mURqROhUYtNP2tAjd/0alHx6BUOnWuNLqgc9M/eaVrKDVBnULdIVQHRKlP6mj5pyFOTEreM7E+l+70aupX1WJohE6pbjr+1LzbHkmBr1LpNE2o7u7q903XU6Mx+n2M/D3WMeq11foGGqnR9dTvqn5nlZKieihv6lD9Xut3ViNnGhnR2jexpDullI5ZKXeaOlWrk3sjkppuVnd+Nd1scvUPmYl+Z7Xuga6n7mjrd0gpPr/88ot7j8d6Bz4lr4k6t1r7Qn9f9PyatlbbFGjob0xkXU40+h3Q3wS9hzWKrY63jlfvfR27/nYkVmuWHP0eqKhZU9YqdVABsWq5dJ3UOddU5Xq/6ndGKbOailtpoylNndPfZC9Q8EaDtWaR9z7zp1bpb6BSdPVZfwOUTqj/C5p6W+ug+F8n/c/Q31+ltGo9Dv3u6G+EXgOlIPpHX1Pr9QeALBlYqNMzZMgQN6e/8qT1T0idZf0TS6qw1e/xxx93qRi6+6vOqzpf6qzo8eqAqUOtTqT2rY655lpPSQdFHQ1vNqfEOo3qJGroWiMRkydPdqMBCkrUsdc/Zj8FC+pE6p+k0ljU0dU2/RNQp9IfuKgzq8DrzTffdAW8qvdQwKBAyVuYy6P0EKUQ6XzVGVWnTgGJAgvlC+sfl66H9qPH65+VjlkBTjTal2oeFPRoGF130bTfyHqHaHSdlW6mVWH1z1Z3LfX8em3UefBms9E6G/qnrjt3+meoY9Y5q/BeqT6pTZ0dvVf0ntPdaAWyuhutDr7+QUfS66IOp87DuzurwELvUf2j1yiWRo10bgqe1FZBXCxifc/E+lw6LqVLKMhWIKwAVDUdaRlYqDOmQFa/e7pG6rCpuFyBmzpx6gT56b2m30W9p9RB1R1kdT7VidUdcr3P9aH3uc5VIxF6Di3S563Jkha0WKRGk/z1G0qv0vtEwX9Wos6jOrPqnOraqiOpEUb9fVWqT0pWvE7Ja6LAQK+/nlu/T3ov62+nbuyo8xwLvV/1t1kdbAXzKlhW6phGmr0JNI6HjlU3Y/R7ob9F+t3Q9dB10e+M/r4rqNffB3XSlS7rX3soVrppErmwp55H9LfPH1goSFMqowJz71x1nvp9ikw9098p/Y/Uh0Yi9Duk10A35CKPMzVffwBIbXGaczbV94qodMdPd7DU8U/pgm4AkBSNjKrzr8X+NAqD7CXI6x83JPWnuQ4iNDDN73kCyKo1FtmRf85xj4pxlT6iO/kEFQBS+2+MRh0k1gkVkHXx+gPIrLgtkAY0VK5ZmpSfrukVla+soX/l93r1EgBwvDRFrFJvlFqjtB+NhCrdR/UFWS21KyNFW3MkmljXUUkvvP4AMisCizSgP/Zz5851Of7KlVXOvPKKlXPvzVoFAMdL9VeqyVFxr4r9VVOlmiXVXDEiGrtoa45EE+s6KumF1x9AZkWNBQAgW1KnXBNYJEfT7QaZ8jizo8YCQGrhtxcAkC15a44AAFIHxdsAAAAAAmPEAgCAbGxU4bFubaHMVKAOIGtixAIAAABAYAQWAAAAAAIjsAAAAAAQGIEFAAAAgMAILAAAAAAERmABAAAAIDACCwAAAACBEVgAAAAACIzAAgAAAEBgBBYAAAAAAiOwAAAAABAYgQUAAACAwOJCoVAo+G4AAEBWFDfkSIY8b2hgzgx5XgBphxELAAAAAIERWAAAAAAIjMACAAAAQGAEFgAAAAACI7AAAAAAEBiBBQAAAIDACCwAAAAABEZgAQDZ0OLFi61WrVrxPho2bGhdu3a1yZMn29GjR1276dOnh3++cOHCBPvZvHmz+9mzzz4bb3ubNm3c9ptuuinq8w8aNMj9fNeuXfG2r1y50h544AFr166d1atXzy677DLr0qWLPfnkk/b777/bicK7bvoYPnx41Da6hp06dUp0H++99557fOPGje3AgQNpeLQAEBtWpwGAbKx58+ZWv35901qp27dvtxkzZtgLL7xga9eutQcffDBe21dffdXq1KljcXFxMe//559/trlz51qTJk2SbfvNN9/YwIED7eSTT7ZWrVpZ2bJlbc+ePbZhwwZbsGCBlStXzqpUqWInGgVynTt3tmLFiqXocR999JGVKVPGNm7caJ9//rm1bt06zY4RAGJBYAEA2Zg66i1btgx/36FDB+vYsaN9+OGH1rt37/D2qlWr2vLly23WrFnWokWLmPZdqlQpdyd9xIgRbjQkR44cSbZX4JInTx576623rGTJkvF+duzYMdu9e7eltyNHjrjRGx1XWvCu66hRoxIEcknRyM5vv/1mgwcPtkmTJtnHH39MYAEgw5EKBQAIK1iwoFWvXt2NYGzatCm8XXfUS5QoYa+99podPnw4pn3ly5fPpUJp9EMpVcn5888/rXz58gmCCjnppJOsaNGiKTwbc6lCSrv67rvv7IYbbnCjMxqlGTJkiO3bty9eW3Xu1X7NmjX24osvuoBL6VhLly51P1fallK+NJpy8cUXu8/6PjKdKyWqVatml1xyiQsM1q1bl6LRivz589ull17qUqaWLFnirh8AZCQCCwBAmAIKpdaIUpI8umN/yy23uGDj/fffj3l/V199tZUuXdpGjx6dbB2A0noUhCh9KjWpNkMpVgqY7rjjDqtRo4a98847NmDAADcSEunhhx92wcR1113n2itFae/evXbjjTe6ugYFFXps3bp13fc333yz/ffff8d9fLfddpv7nFitRaRDhw7ZZ5995upPFLxpBClnzpwuOAGAjERgAQDZmDr7uuO+c+dOW7VqlSuSVpqNOuGqafDTnfEKFSrYG2+8EXNHOleuXNanTx/btm2b68wnRYGLOs0a5VDB9lNPPeXuzKvQOYjVq1fb448/7oIBpXlplEH7X7Rokc2ZMyfqqM2YMWNcYHHttdfaGWecYW+++aar9bjnnntc4KH9PPTQQ3b33Xe7kQalbx0v7b9t27b21VdfhUdHkqKaFaWFacTECwAbNGjg6mO8onsAyAgEFgCQjSn9p2nTpnb55ZfbNddc4+56N2rUyKUKRVKNhO6uKwiZMGFCzM+h1CPVcqhznlSdhI5DHXrdif/rr79s2rRpLiBQp/uuu+5yz3s8lF4VWTyutCivkx5JwYRGAPzUTqlY7du3j7f9qquuctsVFAShoCpv3rz28ssvJ9tWwdbpp59uF154YXib6itUfP+///0v0HEAQBAUbwNANqaOsjr0mulJaTUapShSpEii7dVBP//88+3tt992hd6x0L779u3rPsaOHWt33nlnom2VpqQPpWRphEDT4irdaN68eW6kQAXeKaVRlkhKbypUqFC8OhJP5EiNaNTknHPOSRBw6Hu1DzoVbvHixV1gN27cOHeuCu6i2bJlixtpufLKK8Mpa17wVKBAARd0aPQCADICIxYAkI2pU6wpZC+66CKX/pRUUOHp16+f7d+/340uxEp1CXqOqVOn2tatW2MKRtRZVo3G+PHjXZ2G1tHQSEZa08hBRujevbu7/qq1iFb7IRpR0s8++OADFxR6H0rNUnqapuw93pEdAAiKEQsAQIpoREGLsmlKWs1oFKv+/fvb9ddf72aWSslaGCocr1SpkhtdULpPtFmjkvLHH38k2LZjxw63RoYCllio3fr16930s/5RC32vkZVY95MU1XaovkQzUqleIpJGcbRd10KF5JH+/vtve/755+2TTz5xCx0CQHpjxAIAkGJKaxKtUREr1Vk0a9bMZs6c6QqqI3377beu8xxJd+B/+eUXV+OhRfNSSgFBZC2F6j1EAVIs1E7HoWDKT99re0oCrKRo5EH1E6p9USG7n6bMVSqUpsFV+lrkh6YE1mOZHQpARmHEAgBwXHULKhhWTn9KaIaoL7/8MmpNwr333munnHKKqxHQ/jUyoFGKTz/91N2N79mzZ0ypWpEqVqzo6jPatWvnUr9Ut/HFF19YzZo1XaATa5qSHvPcc8/ZihUrrHLlyu6zzl8pW926dbPUoFm0tDDhI4884r73n693rbV2RWL0s4kTJ7rZpZTaBgDpiRELAMBx6dWrV4pXpNZaFaqbiObRRx91nX0VJ6vO4Omnn3ad6bPOOstNEavnOx4aKdEsVxr1GDp0qP3444/WqVMne+mll9zCe7GmKWmaXc0CtWDBApdypM86F21X4XRqueKKK1y6k59m0/r666/duWhUIjFe0MGoBYCMEBeKNu4MAMAJQCtpa2RFq28jurghRzLkeUMDSZoATjSMWAAAAAAIjNsFAIAsRcXSya0wnT9/fveRnnRMsUz1qroJ1VIAwImGwAIAkKWoUFqzIyVFhd7HW5NxvLTGhlYJT87IkSNdihYAnGiosQAAZCk//fSTHTx4MMk2WldCheLpScekY0uOVvAuXLiwZRbUWABILQQWAABkYwQWAFILxdsAAAAAAiOwAAAAABAY45AAAGRjowqPtR49ejBTFYDAGLEAAAAAEBiBBQAAAIDACCwAAAAABEZgAQAAACAwAgsAAAAAgRFYAAAAAAiMwAIAAABAYAQWAAAAAAIjsAAAAAAQGIEFAAAAgMAILAAAAAAEFhcKhULBdwMAALKiuCFH0uV5QgNzpsvzAMg4jFgAAAAACIzAAgAAAEBgBBYAAAAAAiOwAAAAABAYgQUAAACAwAgsAAAAAARGYAEAAAAgMAILADiBDRo0yGrVqmWZWZs2beyWW27J6MMAAATEajUAkM3MnTvXVqxYYb169croQ8lSFi5caF9++aX9/vvvtnr1ajt06JCNHDky0cBt7969NmLECPvqq69s9+7dVqZMGevUqZNdffXVFhcXF6/tsWPHbPLkyTZt2jTbsmWLFS1a1Jo2bWq9e/e2fPnypdMZAkAwrLwNACewI0eO2NGjRy1PnjzxRjFmzJhhixcvtsxAHXR1tHPlymWZma7bZ599ZmeddZbpX+fKlSsTDSwOHz5sN910kwvgOnfubBUqVLBvv/3WBRk9e/ZMENQNGTLE3nnnHbvkkkusXr169scff9iUKVPsggsucMHJSSelXYIBK28DSC38lgPACSxnzpzuIzPLnTu3ZQW33nqrPfDAA+54J0yY4AKLxHz44Ye2fPlyGzhwoHXp0sVta9++vd199902btw4a9u2rZUqVcptX7NmjQsiFFQ8//zz4X2cfvrpLuCYPXu2tWjRIh3OEACCocYCALKYBQsWuLvkusMdTY8ePVwajUYrImssVMug0QrRdu9j+vTpbtu6devsmWeecSk7jRo1svr161vXrl1dRznSqFGj3GPXrl1rL7zwgjVv3ty179Onj9uPKHXouuuuc9tVS6FUn1hqLLxt2s/tt9/ujqVx48Z2zz332I4dO1J0vTQyo31cdtllbjTgyiuvtMcee8x27dqVov2UKFEi5iBIIxt58+Z1wYTftdde614XBQueWbNmuREQ/cxPj9U+Pv3003jbDxw4YC+++GL4et9www32/fffZ4l6GgAntsx9GwsAkMDFF19sp556qn3yySfhu+GeDRs22NKlS932aCMVN954o+vE/vjjj65z7TnvvPPCnfAlS5ZYgwYN3B1zdWI///xze+KJJ2znzp0uaImkDq3qAPQzddYnTpxo/fr1c/UBL7/8snXo0MEKFy5sH330kT311FN25plnWo0aNZI9z+3bt7uUoSZNmlj//v1t1apVLjD577//bPjw4TFdq/fff98FSgoKVNugUYKtW7faN998Y3/99ZedfPLJltpUL6E6jCpVqsRLQZNq1aq5tC+NZnj0tVKd9DM/PbZSpUrx2sq9997rgktdl4suusg2b97sRkL0egFARiKwAIAsJkeOHNayZUuXjqPRAnXUPQo2pHXr1okGJbqbrsBC+4jUqlUrFwj46U66goTx48fb9ddfnyBgUZCjO+heQbI660rhee6551yKz2mnnea2N2vWzO3/3XffjSmw+PPPP+3pp5+2yy+/PLxNHfCpU6e6kYwzzjgjyccrcNBxqN3YsWOtUKFC4Z9pVEUBQFr4999/7eDBgy6YiaQRD10fBU0efa1t0UZDtI9ffvnF1WyoBmX+/PkuqGjXrp099NBD4XYaqbjjjjvS5HwAIFakQgFAFqQOuj+QEI1EzJw50xUX62758fDPQKTOsUYg1FFWQKKRAi/FyU/Fyf5ZjrygQelLXlAhmumofPnyLmCIRfHixeMFFeKl+sSyD420qEOuYml/UOFJq4JojfJIYsXoCiC8Nl77pNr696mRFlF6mZ9GmFQgDgAZiRELAMiCKlas6IIHjT7cdtttrpOsFCalxSht6Hjt27fPRo8ebXPmzHF3/CMpyIikaVT9lPYk0VJz1MFXKlIsSpcunWBbkSJF3GdN3yoKHLyvPfnz53cfXvBRuXJlS0+qi/COLbFZsLw2XnulmSXW1r9Pvb56rcuWLZugrYI2zSYFABmFwAIAsvCohYqmFy1aZHXq1HGjF16a1PF68MEHXbqNCodr1qzpOvLqyCr9ZtKkSVHThxK785/Y9lhnOU9qRMHbx88//+zStPyiTeeanhRYqT5i27ZtUQMFjQLp2vpHZhQQ6GeR6VDah9KkMvtUvAAgBBYAkEVpCtJhw4a5gOL888+3L774wgUYxYoVS/JxkYuzefbs2eOCCgUmmlbVT7MOZUYqbo4s5PZGOsqVK+c+a1pY3c1PLwqINJqkNSwig4Vly5a5oOicc84Jb6tatapbfE8/07oV/lQ0Hbs/CFHxuYI7jcZEpj6tX78+zc8NAJJCjQUAZFGqWdD0qVp0TSlRqoHwai9iqaOITCHyRggiRxQ0vWu06WYzA40OKJjyf3ipWZpeVnf6x4wZ41bBjpSW68NqKljVRUROr6tRH40qqZDdo68V7Olnfh988IHbh38NC9WtePvxU0BIGhSAjMaIBQBkYZr9ad68efbSSy9ZwYIF3RSkyalevbqbmUnTsKroV7M8nXvuue5Ov4q0VQCuVB5Nf7plyxbXOdbPIgORzK5kyZI2YMAAe/bZZ930uwq6dMdf6UVff/21PfLIIymqv9B0t3qcaKYm0RoTP/30k/taz6HXQJRKprVB9LroGmp0QelkCgK1Ire//kT1Mh07dnSviaaN1doUChK0TolGK/yBhX5Wt25dF3Qopcqbblav0dlnn+2OEQAyCoEFAGRhDRs2dHUQ6vRrCtLIdRMSu5uuNB0t0qb0KaXWPProoy54ePzxx+2VV15xsw8pxUpFwlpxWsHH4MGDLavR1LkawXjrrbdcR10F1appqF27tgs8UkJrU4wcOTLeto8//jj8tVLIvMBCIyUjRoxwH1oAT6+PjkOBgxYfjKQASMGGAgSNPqiuQrNtqX7EX2uikQ1N4+vt99tvv3WBiabV1TS8WscEADJKXCgtx4IBAEC6UCCiVb21KGBKxA05YukhNJB7mcCJjhoLAACyEP8aGB6NcqxZs8bVmABARuH2AQAg29L6EUePHk2yjbcuRmbx+uuvu1S2Cy+80KVeaeYopWQpJa579+4ZfXgAsjECCwBAttWtWzdXXJ2UjF4XI5JWNtf6HRMmTHCzXSmguPTSS61Pnz4prhsBgNREjQUAINvSjE5aLyIpKmqPXF38REKNBYDUwm85ACDb0t1/AEDqoHgbAAAAQGCMWAAAkI2NKjzWevTo4dbeAIAgGLEAAAAAEBiBBQAAAIDACCwAAAAABEZgAQAAACAwAgsAAAAAgRFYAAAAAAiMwAIAAABAYAQWAAAAAAIjsAAAAAAQGIEFAAAAgMAILAAAAAAERmABAAAAILC4UCgUCr4bAACQFcUNOZLq+wwNzJnq+wSQ+TFiAQAAACAwAgsAAAAAgRFYAAAAAAiMwAIAAABAYAQWAAAAAAIjsAAAAAAQGIEFACDTW7FihfXp08cuueQSq1Wrlo0aNSqjDwkAEIGJpgHgBOyEz50719q0aWOnn366ZXVHjhyxe+65x33u3bu3FSpUyM4+++wU7WPPnj02adIku/DCC11gcjwWLlxoX375pf3++++2evVqO3TokI0cOTLR/e3du9dGjBhhX331le3evdvKlCljnTp1squvvtri4uLitT127JhNnjzZpk2bZlu2bLGiRYta06ZN3fnmy5fvuI4XANIbgQUAnGBWrlxpY8aMcZ3oEyGw2LRpk/u44447rHPnzse1DwUWuiZyvIHFZ5995j7OOussO+OMM9x1Tszhw4ft1ltvdUGejrlChQr27bff2jPPPGN///239erVK177F1980d555x03ItO1a1f7448/3Pd6vIKTk04iwQBA5kdgAQDZ2NGjR10nOG/evJZZqSMuRYoUydDjUKDwwAMPWO7cuW3ChAlJBhYffvihLV++3AYOHGhdunRx29q3b2933323jRs3ztq2bWulSpVy29esWWNTpkxxQcXzzz8f3oeCwiFDhtjs2bOtRYsW6XCGABAMgQUAnEBUe+DdmVcajad169ZuBGPw4ME2fPhwW7p0qU2fPt22bt1qDz30kEubUqrPRx995DrEO3bssFy5clm1atXsxhtvdI/1u+WWW1zKztixY+2ll16y//3vfy416IILLnCd5/Lly4fbHjx40MaPH2+zZs2yv/76y+23ZMmSVq9ePbv99tuTPB89z5IlS9zXOnZ9yMcff2ynnXaa66TruDds2ODSjU499VRr0KCBq8c4+eSTXdvFixeHr4WujXd91LHXNYhViRIlYm6rkQ0Fawom/K699lqXGqVgoXv37m6brksoFHI/89NjX331Vfv000/jBRYHDhxwoxh6nNKtlBamoEftZsyY4c4XADICgQUAnEAuvfRSFxR88MEH1qNHD5eCI8rvX79+vft62LBhrl5BHdcCBQqEgwB1stU5b9mypev4b9u2zQUa6rSqlkBBg9/+/futZ8+eVr16dbvttttcupLSdwYMGODuwOfIkcO1e/bZZ10g0KpVK7vuuuvcKMmff/5pixYtSvZ8FNScf/75LoDQ8XrHoBoEjbRo5EDn3LhxY9eRV1CkY/7pp59s4sSJLojRNbjrrrtcupFGBfQh+fPnt7SgegnVYVSpUsXy5MkT72cK1FRfoeP06GulOulnfnpspUqV4rWVe++91xYsWGBNmjSxiy66yDZv3uyCuRMh7Q1A1kZgAQAnEN29Pu+881xgUadOnXj1BF5goTveKmSOTH/SyEVkobAKjVVwrI59ZGCxa9cuu/7668N33r0O/8svv2zff/+91a1b121TIblGJ7zRhpS4+OKLLWfOnO75dV4Kejy6y++NDPip3RNPPOGe9/LLL3ejGOqEK7CoWLFivH2khX///deN0kQb4VAalUZStm/fHt6mr7VNP4ukffzyyy8uiFKQNH/+fBdUtGvXzr1eHr3OqkEBgIxENRgAZDMdOnSIWlPhDyr27dvnAgeNOpx77rm2bNmyBO11l92rH/DUrl3bfVZqkqdgwYK2du1aN5NSatKdf+88NAqiAm0ds3cMv/76q2UEBW6iQCAaBRBeG699Um39+/zmm2/cZ438+Cn9yxudAoCMwogFAGQz5cqVi7p948aNrv5CNQvqpPtFTo8qxYsXT5Dq4xVYK6XKozSkRx991AUhpUuXdnfXGzZsaI0aNQrPdqT2uivvV6xYsWTPZc6cOS7lSbMnKb0rcuQgI3jBTuT5eFSL4g/s9PXOnTsTbevfp9KedM3Kli2boK1S2jSbFABkFAILAMhmoo1WaIRC9RKqm7jmmmtcypDqLxRQqPA6Wj1EUlOgKk3JozQk1VgohUeF2EqTUh2EUqtUhKy79aoR8Iq0PckVIWtNifvvv9/VJmj2JdWF6A6/ahz69esX7xjSU+HChV3ApRqVaIGCRlVq1qwZL0BTQKCfRaZDaR9Kk0psRAMAMhMCCwA4wUQbXUiOOvvK9X/kkUfcVKh+r732WuBj0kiGahv0oQ7/K6+8Ym+99ZZ9/fXXbiG4O++8M8UjDJoFSR14zYTlD5bWrVuXKtfkeCngUuG2RlEigwWllOn8zznnnPC2qlWrulEi/cxfx6I6DU1p6w9CNJOVAicVv0emPnk1NACQUaixAIATjFcrkZKOujeDU+RdfnV4g9QqeLUPkZ38ypUrx0uZUkdbxeb+j+R4IybqaHt0/G+88UaqXJMgmjdv7uoitJK2n4rmda2bNWsW3qavdU30Mz8V4Gsf/qlmlT7m7cdPRd2kQQHIaIxYAMAJRqlB6nRrjQl1pNWpVm1DUmrUqOFmTxo6dKhbn0KzEeluuUYFlBZ1vIXXSrFSx1gdYgUTmjVKdQLvvfeeSxnyOsrH47LLLnPpUFqjQlPZqsZCIyD+wmiP0olUl6D1IzT17imnnOKuS0qef9WqVW7/opmaRNdHU9uKakhUqC6aGlfT92qND11PjS4oFUxrWNx0003xpobV9e3YsaO9++67LiWsfv364ZW3NVrhDyz0M822paBDKVXedLMKYDQjmI4RADIKgQUAnGC0cJxSmt5880175plnXIfbWyAvMYUKFXKLsWmqWK1BoZEGpfNozQvVQxxvYKEUJdVsKNVKHwo0VJStDr3W2VB9QZBRAe1Pd+91nDoH7bdv374u6Ij0+OOPuylnVaCu4ENpRSkJLLQ2hdbz8FPtiEdpXl5goZoI1Y94C9lpZEYBjQIHTd8bSWt/KNhQgKDRBwVCnTt3dkGTv5ZFIxvPPfdceL/ffvutC0y0QvfUqVPjzcYFAOktLpRR1W0AACDVKBBREPn++++n6HFxQ+LPppUaQgO5bwlkR9RYAACQhURL9dIox5o1a2KqTQGAtMItBQBAtqX1I5T2lZT8+fO7j8zi9ddfdzNOKbVNqVeqhVFKlmbe8q+CDgDpjcACAJBtdevWzRVXJ0Xre/Tq1csyCxXa//zzzzZhwgTbu3evCyguvfRS69Onj1vLAwAyCjUWAIBsSzM6ab2IpGhGLRVen6iosQCQWvjNBwBkW7r7DwBIHRRvAwAAAAiMwAIAAABAYKRCAQCQjY0qPNYtVqhF/QAgCEYsAAAAAARGYAEAAAAgMAILAAAAAIERWAAAAAAIjMACAAAAQGAEFgAAAAACI7AAAAAAEBiBBQAAAIDACCwAAAAABEZgAQAAACAwAgsAAAAAgcWFQqFQ8N0AAICsKG7IkVTbV2hgzlTbF4CshxELAAAAAIERWAAAAAAIjMACAAAAQGAEFgAAAAACI7AAAAAAEBiBBQAAAIDACCwAAAAABEZgAQDIMIsXL7ZatWrZ9OnTM/pQAAABsZINAAAx2LFjh02ZMsV+//13++2332zXrl3WunVrGzRoUKKPmTFjhk2aNMnWr19vBQoUsIYNG1rfvn2taNGiCdr++uuvNmLECPc5Li7OzjvvPNe2cuXKaXxmAJA6GLEAAGSYmjVr2oIFC6xly5aW2a1bt87GjRtna9eutapVqybb/u2333ZBR8GCBW3AgAF21VVX2ezZs61Xr162f//+eG2XLl1qt9xyi23atMn9XF9v2LDBevbsaatXr07DswKA1MOIBQAgw5x00kmWJ0+eJNuEQiHXEc+fP79lpHPOOcfmzJnjRhs0WtG0adNE2+rnr732mgtA9DlHjhxuu76/6667bPLkyXbjjTeG2z///POWK1cuGzNmjJUoUcJtu/zyy61jx4720ksv2fDhw9PhDAEgGEYsAACZpsbC//27777rOtb16tWzCRMmuJ8fOXLExo8fH95+2WWX2cCBAxPc1d+8ebPbz6hRo+ybb76xbt26ufbNmze3YcOGuf2klFKZoqUwRTN37lw7cOCAde7cORxUSKNGjax06dI2c+bM8LY///zTli9f7s7FCypEX2vb999/79Kw/L744gu75ppr3Dm1atXKRo8ebd999x31KgAyFCMWAIBMR3f0d+/ebe3atbNTTz3VSpYs6bY//PDDbtSgTp06dvXVV9vff/9tU6dOtR49eri7/VWqVIm3H6VZvffee65t27Zt7euvv3ZBSqFCheKNGKS2ZcuWuc+qk4hUvXp1mzVrlu3bt8+NwiTX9uOPP3Z1HQ0aNHDblE714IMPWpkyZVyqlAIX1XIogAKAjERgAQDIdLZu3eoCglNOOSW8beHChS6oUIrQU0895QqcRd9ff/31NmTIEHv99dfj7Uf1EBr5OP300933CjA0iqAi7LQMLLwRhuLFiyf4mbYpvWv79u1Wvnz5ZNvKtm3b3GeNtCg1SiMnb775phUuXNht79ChgxvBAICMRCoUACDTUXqPP6jw0otEAYEXVEilSpXcbEs//fST7dy5M95jmjRpEg4qRI9TupBGOjRikFaUBiW5c+dO8DOvpsRrk5K2GrlQQKLZqLygQjTyoeJwAMhIBBYAgEynXLlyCbapbkLF3hUqVEjwszPPPNN91qxKfqpniFSkSBH3WalWaSVv3rzu86FDhxL87ODBg/HapKStd34a6YgUbRsApCcCCwBApuN1pINSIJIYpSOllWLFirnPGl2IpG0aOfHSnJJrK/6ibgDIrAgsAABZgkYfjh07Zn/88UeCn3nboo1QZIRq1aq5z7/88kuCn2nNCo0ueNPnJtdWQYhXlO6ldWnBvUjRtgFAeiKwAABkCY0bN3aftUidf7RBU83OmzfPatSoEfN0sOlxrKqPUOH40aNHw9t1nEpnatGiRXhb2bJl3foWmkLWP2qhr7Wtdu3a4VENraWhrzUL1L///htuq3qRadOmpdv5AUA0zAoFAMgSLr74YjcDlKZb3bNnj5t+1ZtuVoXPWs8irXmzTnm1D6tWrQpv0yri+hAFOH369LGhQ4farbfe6tbPUKAwceJEO+OMM+zaa6+Nt1+tzN27d2+7+eab3axVopmrNEJzxx13hNvlzJnTff/QQw9Z9+7d7corr3TTzWrtCtWOKGjxF7YDQHoisAAAZBmPP/64Va5c2d2xV6c9X758rjOvTnzFihXT/PlHjhwZ7/sVK1a4D9GaEl5gIV27dnWd/UmTJrmpcLXAnlbr7tevX4JVxM8//3y3mJ9W6daHggOta/Hss8+6Wa/8NNqhAEMBjR6j2bMUYJx99tl29913J7uSOQCklbhQWlavAQCAdKHREAVbShXTwnqxihuS8lXIExMayP1KIDujxgIAgCzk8OHD8eo2vBoLpYRphCRy9XEASC/cWgAAZFvqkCe3UJ5qGDJLUbiojqJ///7WrFkzN0uUVu7+5JNP3Pb77rvPcuXKldGHCCCbIrAAAGRbEyZMsDFjxiTZplSpUq44OrM4+eST7dxzz7WZM2e6lcYV+Ki+pG/fvq64HQAyCjUWAIBsa+PGjQlW646kYmhNZXuiosYCQGrhLwAAINsqU6aM+wAABEfxNgAAAIDAGLEAACAbG1V4rPXo0YOibwCBMWIBAAAAIDACCwAAAACBEVgAAAAACIzAAgAAAEBgBBYAAAAAAiOwAAAAABAYgQUAAACAwAgsAAAAAARGYAEAAAAgMAILAAAAAIERWAAAAAAIjMACAAAAQGBxoVAoFHw3AAAgK4obcuS4HxsamDNVjwVA1saIBQAAAIDACCwAAAAABEZgAQAAACAwAgsAAAAAgRFYAAAAAAiMwAIAAABAYAQWAACcABYvXmy1atWy6dOnZ/ShAMimmIAaQLbsgPXu3Tvetnz58ln58uWtVatW1qlTJ8uRI4froA0ePNj9/NVXX7WLL7443mM2b95sbdu2tY4dO9q9994b3t6mTRvbsmWLnX/++fbGG28keP5BgwbZjBkz7PPPP7eTTz45vH3lypU2fvx4W758uW3bts0dU/Hixa169ep29dVXW5UqVexE4F03vzx58ljp0qWtadOm1q1bN8ubN2+GHR8A4PgQWADItpo3b27169c3rRO6fft219l/4YUXbO3atfbggw/Ga6vAok6dOhYXFxfz/n/++WebO3euNWnSJNm233zzjQ0cONAFGgpuypYta3v27LENGzbYggULrFy5cidMYOHR9dS5ys6dO23OnDk2evRo++WXX9z1BgBkLQQWALItddRbtmwZ/r5Dhw5u9OHDDz+MN6JRtWpVN4owa9Ysa9GiRUz7LlWqlB04cMBGjBhhDRs2dCMgSVFHWnft33rrLStZsmS8nx07dsx2795t6e3IkSN29OhRd1xpQcGS//p37tzZjVYsXLjQli1bZtWqVYv6uP/++88KFCiQJscEADh+BBYA8P8ULFjQpR19+eWXtmnTpngd3uHDh9trr71ml112meXKlSvZfSmN6brrrrMhQ4a4lKp27dr9f+3dCbxMdfz/8a9d9j0hW5askSW7CKWkKEJK9qgokZR+UUpJichWIlTIkiKVUrJWSlRUhCT7Tnbzf7y+/9+5vzNzZ+bOvee619z7fj4e47pnzpw528z9fs738/mesPPv3LnTXHPNNbGCCqRNm9bkzp073ttDvn2LFi1M8+bN7br/+eefdhubNm1qevfubbJkyRIz78SJE83kyZPNrFmzzEcffWTTtA4cOGADI5Zz5MgRO8/y5cvNwYMHTd68eU2DBg1Mz549/dK5vEifPr2pWbOmTQljfxBY9OjRw6aVsf5jxoyxaWzHjh2zP8E2sV4//fSTOXXqlE2nYps7duwYK5hje9555x2zYsUKm2rGvihdurQNZtxpbvQSsS++++47G9CRjkaKFuvCcXXs2bPHvvf3339v9wnLo6epdevWdh2coPCDDz4wCxcutClg9Hix76pUqWKeeuopu80OgtcpU6bYbfnvv/9scEqPTqdOnfzmAz1h9O5s377dnhu8X9WqVRPlOIiIJJQCCxGR/0VK1D///GP/T2N5x44d9v9csadROWzYMDN37lzTrl27iJZHXcT7779vG4D0dISrGyhSpIhNwSJ9itqMxLJ582bz5Zdf2sCGRioNchq6W7dutcESQYvbM888Y7eXoIhGcL58+cyJEydMly5dbGOf2gh6en7//Xfz4Ycf2kb1tGnTEq0HgUY93MEKjWwCmMqVK9uA6NChQzENcY4LjW56mmiwk1L2xhtv2ICD4+WgUd+1a1f7WnpJ6IUiENm4caMNIJzAYtOmTba3Knv27DZAKFCggA102GccG44l70dvzkMPPWRT6OjpoveF/bRlyxYbGDiBBYHChAkTbK8V5wP7m3UhQDt79mxMwECwM2DAABuYEBTlyJHDrhuBC+//8ssvx2zLsmXLzBNPPGEKFSpkunXrFlMPxDJERJKTAgsRSbVIVeJKPAEFV7O5Wk8jjl4LGoo0JN0F2TNnzrTF2Pw/koY0PRu9evUygwcPtg3TBx54IOS8NJAHDRpkG7+lSpWyjWiu2NeoUcM2IBOKhi69Jk6dBw1wfmd9qGmgzsSNq+70UrivkBOA0OCnQJ3XO8qUKWNGjBhh07fYzviiYc3+d2osPv30U9vgZnuvv/76mPnoNaBRTlDhxnacO3fO9kLQ8+D0LrEflyxZYoMgekDw0ksv2SCAoKN27dp+y6FXwfHcc8/ZYIptch9jlkPDn3Xk+G/bts0Gno888ojtUQiFIKBEiRJm1KhRftN5nePMmTPm+eefNxUrVrQ9M86+Z5vZLl7rjPhEahrbTeBBQOcEYMwbacArInKpaLhZEUm1uBpMigupQe3bt7fpKqT30HALxFVhrlDTAJ4+fXrE70HDnSv8NALD1UmwHqTfkGq1d+9eM2/ePNvYpHHcr18/+74JwUhXgcXjToBDOk2gDh06BE27Id2mVatWftO5os90Gs8JQcoV282DgIWr+wQU1JtkzJjRb9777rvP73d6Hijy5ng5QQXoZaF3Bc56sd9Xr15t6tSpEyuogNNrQxBGTwe9SwQsBD3Og9Ql0qCo/3ACMKxbty6mByUY5iPtav369SHnWbt2rU2lImCh18P9vgwu4Mzj9KhwfnBeuHt1eB+CCxGR5KQeCxFJtWgo06ilMUqjkV6KnDlzhpyfBjppSvRckP4SCZb98MMP2wcN58ceeyzkvDReedCDQg8BV6lJN+IqPilKCRkpiavlgbgiT6qPu47EwT4IROpOuXLlYgUc/M78pFslRMOGDe3QvuwjAgnSgEhnCkTwwvoGrhNKliwZdJsJFpztI4WLfVq2bNmw60MvhBNw8gjGCSKofyCAYXhgAhF6b+hd4nxyF50TjDLaFylL1GpUq1bN1KtXz69Wx3lfektCIfCAs00EjMG2W0QkOSmwEJFUi0YxQ57GByksNBLpXQiXAuNG/j6pNHPmzLE9I3GhoU3DkQe5+jS+uVLOlepgxd2JKSnvH0H9QiT7P6nWieAD1DgE69kAKUgOUrPoOaC2gR4JemDozaIYvE+fPnYeUtoYZYweEwJFejhI0yKl7q233rKBrPO+ffv2tQFKMAQlIiKXOwUWIiLxQI8CV9ppLDZq1Cji19HQJJ2HHPr43AuDQmoam1yppkYgvoGFczXcjXoS7pHBCEqRYD7qCShYdvda8Ds9K5EuJzE5dScUvAdipCTqJpz1oieEfU7BeThObw29HZEGnBTdU9vAg1oJAk/qMwhO8uTJY+dh9C16KHiAAJNibAIRghDnfek1i+t9nW1yBhaI61iLiCQl1ViIiMQTaU2gyDlS1Fk0a9bMFv+Syx9o1apVMVeu3aitoJaAGg8ayPFFAzSwloJ6DxAgRYL5WA+CKTd+Z3p8AqzEQqOd3gDSxNz7k31IMTec9aJXgPoK9rFTq+Dm7HdSpRjyl5G/nNHB3AiknDoZaiH4PTAILF68uP0/Q+LCKU53c2506MxD7wjbQ1pVsDocBhng3h0gJY3gknog97JZH9ZbRCQ5qcdCRCSeyGUnRYkrzvHByEncIyNYTQIjLtG4JP+e5dMzQC/F4sWLbX599+7dw9Z/hMIIU9RnMNwsV8ZJx2H4WYqkCXQiQcoXr2EEKK760wDnJ9tPuhZX3ZMDtQuMpsW+cYabJS2JtCPqHpwRocDwrNRE0HPEsaOBToOdG/FRL8F0ejWoc+A4kbJGmhM1HMxHoMGxI6ikyJr9+MILL5jGjRvbfUCvBIXV7BNGd3ICDGpxGGWMugvSmegtmj9/vq2vcPY/PRVDhw6120MBNu/r3Hmd3heK0F955RU7KhQBJnU6jHzFceG4Mo1Ag/ODe2uIiCQXBRYiIgnAfRXIlSf9JVKkzdBwZKjXQM8++6xZuXKlvS8EwQT3bqChyNVtRoVy0mjii9fTEKV3hZGmGEKVmg2KigPvYREKIw5RE+DcII9GLI14toX9kFx3weZeFBTEs14UuTs3yCMdiVQkN6ZT/0BdA/t50aJFtl6CEaXco10RNFGcT68H20ovANtH8EFAQYE2eB09Ik7NBMPAFixY0HTu3Nnvvfk/78dQxvQqEDwSeDCfu56CXgt6knjQq0VPEOvHOcM9RdwjX1EgzrFjW7ivBst0bpDn9KaJiCSHNL5gfe8iIhL1nDtvDxkyJLlXRS5jaUb6p3TFh6+/rk+KyP9RjYWIiIiIiHimSw0iIlGEFBnSbsIh359HUmKdIrmJH+ldzv0bREQkZVFgISISRSiU3r17d9h5KGam9iEpOXeDjsuECRNsipaIiKQ8qrEQEYki3IgtroJxCpUp+k1KrBPrFhdGY3LfZE6Sn2osRCSxKLAQERFJxRRYiEhiUfG2iIiIiIh4psBCREREREQ8Ux+miIhIKjYxxxR7wz6N1iUiXqnHQkREREREPFNgISIiIiIinimwEBERERERzxRYiIiIiIiIZwosRERERETEMwUWIiIiIiLimQILERERERHxTIGFiIiIiIh4psBCREREREQ8U2AhIiIiIiKeKbAQERERERHP0vh8Pp/3xYiIiEg0SjPyfLxf4+uf/pKsi4hEN/VYiIiIiIiIZwosRERERETEMwUWIiIiIiLimQILERERERHxTIGFiIiIiIh4psBCREREREQ8U2AhIiIiIiKeKbAQERG5TEycONFUr17d/Pvvv8m9KiIi8aY73IiIXEI//PCDefDBB/2mXXHFFaZYsWLmtttuM23btjXp0qUzH3/8sRk6dKh9fuzYsaZWrVp+r6Gh2bJlS9OmTRszcODAmOm333672b17t7nuuuvM22+/Hev9hwwZYj755BOzdOlSkytXrpjpf/zxh5k6dar57bffzL59++w65c+f31SqVMncdddd5tprrzUpxYEDB8yMGTPMqlWrzJ49e0yaNGlMnjx57DY2bdrUNG7cOLlXUUQkRVBgISKSBG6++WZTt25d4/P5zP79+21j/9VXXzV//fWXefrpp/3mJbC44YYbbAM4Uj///LP5+uuvzY033hjnvN9++63p37+/DTQIbq6++mpz/Phx8/fff5uVK1eaokWLppjAgqCrU6dO5uTJk+aWW24xd999t52+c+dOs27dOhvQKbAQEUkcCixERJIADfVbb7015ncauPQ+LFiwwK9Ho3z58rYX4bPPPrMN4UhcddVV5vTp0+bNN9809evXtz0g4RC4ZMqUybz77rvmyiuv9Hvu4sWL5ujRoyapnT9/3ly4cMGuV2KaPn26OXTokBk5cmTQoIveDBERSRyqsRARSQbZsmWzaUf0YOzatStm+j333GMKFChgxo8fb86dOxfRskhj6tq1q+394Ap8XLhaTypWYFCBtGnTmty5c8dza4ytCyDtau3ateaBBx6wvTP00tCg/++//4LWEWzdutW89tprNuCqU6eO2bhxo33+yJEj5uWXX7a9KaSE8ZPfmR5fbCtq1qwZ9Pl8+fL5/U5qWY8ePcz27dtN3759TYMGDUzDhg3NE088ESsIoedp1KhRpkOHDqZRo0Z2GwgWSTEjSArE8Zw2bZqdn/3Dcu+77z4za9assNvAsl588UVTo0YN+3oHvV7333+/DZjq1atn7rjjDjN48GBz+PDheO0jEZHEoh4LEZFkQEDxzz//2P+TkrRjxw77f67Y07AdNmyYmTt3rmnXrl1Ey6Mu4v333zeTJk2yPR2ZM2cOOW+RIkVsEEL6FLUZiWXz5s3myy+/NHfeeacNBqgv+eCDD2wAMW7cOBu0uD3zzDN2e++9916b9kUj/8SJE6ZLly42IKCmhJ6e33//3Xz44Yfm+++/tw3rrFmzRrxObCvmz59vG/SRpJcRMPTs2dM22Pv06WP+/PNPM2/ePJtOxXY4mL5s2TI7H+9Dr8vq1attjxDBojvFjaDi4YcftulXBEvNmzc3GTNmNFu2bLHLIKAMhp4olkOKGjU4Tq/XokWLbCBXtWpV2+PFfty7d6+djx6ahASHIiJeKbAQEUkCNBC54k5AwZVvrlJTQE2vBTUNNPLdV81nzpxpi7H5fyQN6QwZMphevXrZK9Y05uk1CIXAZdCgQbaXo1SpUqZy5cqmQoUK9op4oUKFEryNNJLdKUdcved31ueLL76wPRiBvTakb6VP/39/imi4U+tBgTqvd5QpU8aMGDHCpm+xnZEiaFm8eLHtWXjvvfdsQ5x0M36WK1cu6GsIaoYPH24Lux0ERXPmzLE9GcWLF7fTrr/+evPRRx/5BSsELwRMTCc4cXpEeG+Cis6dO5uHHnooVvpZMKSkPfbYY3a/vv76634F/dTTcF7Qs+Xef4EDBYiIJCWlQomIJAHSf5o0aWIbq+3btzcLFy60aTY0vANRI0Hjk5QWagQiRcOdK/xc1Q9XJ8F6TJ482dx00032KjdX459//nnbQ9CvX78Ep9KQXhVYx+AEODSEA9EIdzeKnfm42t6qVSu/6a1bt7bTubofH/Qk0JPjBClLliyx6VekINEbtGnTplivYXQsd1ABUrfcqVWgV8gJKuiRYJ8TPNauXdsGC9TKOHjfHDlymG7dusV6v8CeHKfonMCPng96oQJHCSMoI1hdsWKFDVZFRC4H6rEQEUkCNJRp0NMQpSaCXoqcOXOGnJ8GOmlK9Fw4IxnFhWWTbsNjypQp9mp3KFWqVLEPGqX0EJC2RLrR8uXL7RV30nniq0SJErGmccU+e/bsfnUkDvZBIIbVpSchMODgd+Yn3Sq+6IWhB4QHvUXr16+3qUSMjvXoo4+a2bNn+x2LwoULx1qG87w7YCP1iXoKekQIOAIb+MeOHYv5P/u4bNmyERenE+CxfHp7GLUrED0fP/74ox3di3Wj94S6DQKi+KSKiYgkJvVYiIgkARrFDCFLETHpT+GCCscjjzxiTp06ZXsXIsWVbd6DtB3u2RBJMEJPAzUaNJJpVK9Zs8b2ZFxq4epALhUCHQI8UqOoRTl48KCtS4irB8HhDh5YxoQJE2zA8Oyzz5rRo0fbVC6OW+C88UXv05kzZ2w6XLBUKc4njjEpUi1atLDHmrocglCndkdEJKkpsBARuUzRo8DIQQxJyxXvSFFwTGoO+ffxwdV0ahmcAub42rZtW6xp9BBwj4xgvQDBMB+F7Fytd+N39kGky4lExYoV7U9uEJgQ9FTQU0A9Bo17egwIHoP1GBC8UZ9x9uzZiJZNChnHkZGfCFqCjTJF8TejQdEzRcocQQbHjV4uEZHkoMBCROQyRloTKHKOFHUWzZo1M59++qkt/A3EHaiDXU2ntmLDhg22xiNY+k1cCAgCaymc4VEJkCLBfKwHwZQbvzOdYV3jgxQvahEC0QtAKhRKlixpEoKejcD9SA8ThdqB6B0hNSrY3dFD9WwwlCwpURxHivLdwVawoXedmxomx31IRESgGgsRkcsYdQtcDWeUofhg5KSvvvoqaE0CtQZ58uSxV7tZPvUL1EBwBZ7UoO7du0eUqhWIEaaoz2C4WVJ1aNQz/CxX9Ql0IsFdsnkNI0AxzCxpRvxk+7nqT2M7PmbMmGFH3OLGgTS8KXpmG9k3FG5TlM1+SAiK3yl8Z4Qt0s9YLvcRCbbvKNgnkCGwoKibng16iBj2l4AsVOBIgTsjfrE/CCzoHeF4UdxP7QqjW3E/EnqFeG9S29w3YhQRSUoKLERELnMMW8qoQuTcx2c0JOomKP4NRGoNdQXcF4JgghvY0Rim4c0VchrMCcHrScuhkUyDm5Sgtm3b2kZwuLoFNxr+NL4ZRYtCckbPyps3r90W9kN8C5MZWWnp0qXmp59+srUjXM2neJ6AisJt1i/SdQvEvmJ9GEr3m2++sQ18ivQZzrZ3795+8xIcUBBPoMNd1dlHpDIRgDGkcDiMaEUwwU3yuFEfNwukloL3ZT+zTRw/gjCed0awEhFJaml8GqdOREQ8ojFLzwo3bZPokmakfz1LJHz9dV1SRGJTjYWIiIiIiHimSw4iIhIUxdLBRiNyy5Ili30kJdYpkpv4kR5ECpKIiCQNBRYiIhIUhdLcATocCr2pfUhK3GODu4THhXtMqN5ARCTpqMZCRESC4g7VcRWMc18JCsWTEuvEusWFO3jnyJEjSdYpmqnGQkQSiwILERGRVEyBhYgkFhVvi4iIiIiIZ7rkICIikopNzDHFdO7cWYXuIuKZeixERERERMQzBRYiIiIiIuKZAgsREREREfFMgYWIiIiIiHimwEJERERERDxTYCEiIiIiIp4psBAREREREc8UWIiIiIiIiGcKLERERERExDMFFiIiIiIi4pkCCxERERER8UyBhYiIiIiIeJbG5/P5vC9GREREolGakecjntfXP/0lXRcRiW7qsRAREREREc8UWIiIiIiIiGcKLERERERExDMFFiIiIiIi4pkCCxERERER8UyBhYiIiIiIeKbAQkTkMjdx4kRTvXp18++//yb6slnukCFDTGrCfmS72a8iIpJ4NCC1iIhcUj/88INZt26d6dChg8mePbuJZmvWrDFfffWV2bx5s9myZYs5e/asmTBhgg1Ugjlx4oR58803zbJly8zRo0dNkSJFTNu2bc1dd91l0qRJ4zfvxYsXzfvvv2/mzZtndu/ebXLnzm2aNGliHnzwQXPFFVck0RaKiCSceixEROSSIqiYPHmyOX78uIl2S5YsMQsXLjQXLlwwxYsXDzvvuXPnTO/evc3cuXNN06ZNzYABA0yxYsXMSy+9ZCZNmhRr/tdee82MGjXKlCxZ0s570003mQ8++MA89thjNugQEbncqcdCRESiFg18GvCZM2dOkvcjUHjqqadMxowZzfTp080ff/wRct4FCxaY3377zfTv39+0a9fOTmvVqpUNGt555x3TsmVLc9VVV9npW7duNbNmzTKNGjUyr7zySswyChUqZEaOHGk+//xzc8sttyTBFoqIJJwCCxGRZESj+L333jOfffaZ2bFjh0mfPr0pWrSoadGihbnnnnv85iXtZty4cWbRokXm8OHD9or5Qw89ZOrVq+c33/nz582MGTPsfLt27bJpNFWrVrUpNaVKlYpovdauXWveffdd8+uvv9r3ZZ3uvvtu+3D7+eefzdtvv21+//132yORM2dOU7p0adO9e3dTqVIlW7/xySef2HlpSDt4vmfPnjHpQlOmTLEpRnv37jVZs2Y1NWvWtI14UoccH3/8sRk6dKjdBxs3brS/79mzxwwePNjcfvvt5tSpU3ZdvvjiC7Nv3z6TI0cOc8MNN5hevXrFNOC9KlCgQLx6Nwh4CCbcSAkjNYpgoVOnTnYax9/n89nn3Hjt2LFjzeLFi/0Ci9OnT9sUK17H/mOfs7+Yj/1N+pmISFJTYCEikoxBxcMPP2xThWrVqmWaN29ur4STu0/DMzCwoJFO4NGxY0f7WvLxuRpOTj5Xth3PPPOMbVzTqCaX/+DBg2bOnDmmc+fONiXp2muvDbteLG/48OE2MOjSpYsNTAg0SOEhUOnbt6+db/v27TawyZs3r70inydPHnPo0CGzfv16eyWf17du3dqcPHnSbk+/fv1Mrly57GtpCINGMe9BgEDgQRrQgQMHzIcffmgeeOAB2ysQGBSMHj3aBk80uglCSC/id/YlgQ4pROyjv//+26YhOUHSlVdeaZIKqUvUYbCvM2XK5PdchQoVbH0FvRkO/p82bVr7nBuvLVOmjN+8GDhwoFm5cqW58cYbbRBGQTo9Ie7zQEQkqSmwEBFJJvRUEFTQ4KeB7hYsp55GOTn4TtEvBcNc8SYQoFHtFBcTVJDT/+KLL8bMy+/33XefTat56623Qq4TjXrmadasmXnhhRdiprdp08ZOnzlzpg1W6EngvbhyznwVK1YMurzKlSvbXhICCxrBgQ1fCp8JVkgNogHtoAeCYIWRmwJHreI92Xfu9Kf58+fboIJtdAIfEFw9+uij9qr/888/b5LKsWPHzJkzZ4L2cBA8ciz3798fM43/M43nArGMDRs22GAyQ4YMZsWKFTaouPPOO21vjYPzgW0VEUkuKt4WEUkmpMqQrtOtW7dYz3H1OhANbfdIQlzdzpIli70y7/j666/tT3oB3PPSaK9fv77tTSCNKpSlS5fa1Kc77rjDHDlyxO/B6wl4vvvuOztvtmzZ7M9vvvnGNqLji9SfTz/91KZp0Xh2vxe9JAQrBC+BSMcKrKkgcGGfEaS5kSbGti9fvjxJC6AJfkAgEAwBhDOPM3+4ed3L/Pbbb+3Pe++9N9a2lihRIpG2QEQk/tRjISKSTAgIypYtGytVJhR3vYGDmgaGMXWQEkMDO1gDkzQjAg96CBjKNBjSm0C+fiikO4FeDXL66W2gB4HUJ1K6br755ohqGghwWHeCB4ZVDSZYgEW9RyC2O3/+/DZQC3TNNdfY1CwCFtK1koIT+NDLEAzBmzs44v+hAj7mdS/TOcZXX311rHlJC9u2bVuibIOISHwpsBARiRLBGtnOlf/E4iyLIul8+fIFnadw4cIxV9IpIP7ll19scPDjjz/a1CXqOIYNG2ZHOIrkvagRcIqYI5FUI0B5QYBDwEgRebBAgSDn+uuvj5lGUERAwHOB6VAsgzSpUD0aIiKXCwUWIiLJhKvL9BAEa0wmFI1+Un5opDoF0g7nSrYTGATjXAWnIUt9QiRIWXJqLCjCJkVn/PjxMYFF4I3gHPSacMM8irsjfa9Q2KbVq1fbkakCb8L3119/2SJvp3A8qYJACrcZLSvw+DLSFkFVuXLlYqaVL1/eBmc8R2qYgxQzelvcQQi9QRzjnTt3xuqZYmQxEZHkohoLEZFkwvChFPkyRGpi9UI0bNjQ/iQ9yb0MRpqizqBKlSoh06CcIm8awfQ8uGsAHIzi5KTmcNU9ECMvsXx3ehZ1IGBbAxvf7AMa09R2hEu7iguF4TS2p06d6jedImca9w0aNAjZ43OpkBLGPqS43o20sXTp0tlUMgf/JwDjOTeK0lmGe6hZtsVZjhtF3UqDEpHkpB4LEZFk0r59e1uIS2DBcKJctSd9hivsXHkmzSi+qHEgOOAeCVy9p6DXGW6WgIHhacMhMHjyySdtKhMjQd166632Cjn5/wQn1GiwLEZ3Yr25ys570GNAIMP20Atz//33xyzT6c0YM2ZMzJC61D0wWhSjYTGa06BBg8yXX35p6zRI+dm9e7cNCriqHzgqVDCMIsX9G6ZNm2ZrELjCzxV9hq1lONzAUbcS6s8//7TF6mCkJlBnQlG8U2DvFLUzHC732mAkL7aH3gW2iULzrl27+o2Qxb5gf8+ePdsOG1u3bl0bJHDnbbbFHVjwXO3atW3QQXDnDDdLAEMvFesoIpIcFFiIiCQTGtAMg8rN7LjRGYEEjW6Kk2koJxTDqlIUTkP79ddftyMs0TjlRnGR3CCP+0mwDqwXjVUCFNKISN1iGTTUnd4Rhqelt4GeBYIiUqkYApVRpRz0kjzyyCN2WQQs3C2bG+SxLjTCuTke78UwufSqcDWfUaJ4HUOqRoL7e7AvnRvk0XgnJYp7WlCIXrBgQZMYuDcFQ+S6LVy4MOb/BGJOYMHx5Zg6N7KjF4cCfAKHtm3bxlr2448/boMN9hO9D+xz7mXCjQ3dvS30bIwYMSJmuatWrbL7kuGACfrco4SJiCSlNL7ErPoTERGRZEMgws0CuTFgpNKMPB/xvL7+uh4pIqGpxkJERCTKBKt/oZdj69atngvhRUQSSpceREQkVaN+hPSscChAd4rQLwfcPZ2i9GrVqtnUK0aOIiWL+5rEZ+heEZHEpMBCRERSNQrNKa4Oh5qQnj17mssF9ScUvU+fPt2O1EVA0bhxY1sDQwG+iEhyUI2FiIikaozoxP0iwmHUq2B3Pk8JVGMhIolF3xAiIpKqcfVfRES8U/G2iIiIiIh4psBCREREREQ8UyqUiIhIKjYxxxTTuXNne0M/EREv1GMhIiIiIiKeKbAQERERERHPFFiIiIiIiIhnCixERERERMQzBRYiIiIiIuKZAgsREREREfFMgYWIiIiIiHimwEJERERERDxTYCEiIiIiIp4psBAREREREc8UWIiIiIiIiGcKLERERERExDMFFiIiIiIi4pkCCxERERER8UyBhYiIiIiIeKbAQkREREREPFNgISIiIiIinimwEBERERERzxRYiIiIiIiIZwosRERERETEMwUWIiIiIiLimQILERERERHxTIGFiIiIiIh4psBCREREREQ8U2AhIiIiIiKepfe+CBEREYlGPp/PnDp1yhw7dsxkyJAhuVdHRC5j2bNnN2nSpAk7Txof3yoiIiKS6hw4cMDkz58/uVdDRKLA0aNHTY4cOcLOox4LERGRVCpTpkymSpUqZtGiRSZbtmwmpTlx4oS57bbbtH1RStt3+fVYxEWBhYiISCpFWkO6dOnsVchoaNjEV9q0abV9UUzbF31UvC0iIiIiIp4psBAREREREc8UWIiIiKRSGTNmNN27d7c/UyJtX3TT9kUfjQolIiIiIiKeqcdCREREREQ8U2AhIiIiIiKeabhZERGRFGj79u1mxIgRZsOGDSZr1qzm1ltvNb17947zDttkSE+bNs3MmTPHHDlyxJQpU8b069fPVKpUyaSE7bv99tvN7t27Y01fuXKlva/H5WLnzp1m+vTp5pdffjFbt241xYoVM7Nnz47zddFy/BK6fdFw/JYuXWoWL15sNm/ebO9qX7RoUXPPPfeYli1bhr1zdbQcu3AUWIiIiKQwNGYefPBB26B55ZVXzL59+8yoUaPM6dOnzcCBA8O+lobNxIkTzcMPP2xKly5tGzn8f+bMmaZIkSIm2rcPN910k+nYsaPftMutgJbGNo3lChUqmIsXL9pHJKLh+HnZvmg4fjNnzjRXXXWVefTRR03u3LnN2rVrzQsvvGD27t1revToEfXHLiyKt0VERCTlmDJliq9evXq+I0eOxEybO3eur2bNmr59+/aFfN3p06d9DRo08I0dOzZm2tmzZ30tWrTwDR8+3Bft2we25aWXXvJd7i5cuBDz/2effdbXpk2bOF8TLccvodsXLcfv8OHDsaYNGzbMHhv3dkfrsQtHNRYiIiIpzKpVq0zNmjVNzpw5Y6Y1bdrUXhVes2ZNyNeRVnTy5EnTpEmTmGmkFjVq1MheXY727Yu2uzLHV7Qcv4RuX7TIlStXrGlly5a1x+bUqVNRf+zCSblHVUREJJWi/qB48eJ+07Jnz27y5ctnnwv3OgS+tkSJEmbPnj021Siat8+xZMkSU7t2bVO/fn3Tp08fs2XLFpMSRMvx8yoaj9/69etNgQIFbD1QSj52qrEQERFJYahBoKEdiGk8F+515KoHFsHyOgpLjx8/bjJnzmyidfvQoEEDU7FiRVOwYEGza9cuM2XKFNO1a9foymOP8uPnRTQev/Xr15vPP//c1lyk9GOnHgsRERFJNQYMGGCaN29uqlatalq0aGEmTZpkp8+YMSO5V01S4PHbu3evGTRokKlevbpp166dSekUWIiIiKQwOXLkMCdOnIg1nauePBfudWfPnjVnzpyJ9TqGyQzWSxBN2xcM6VNVqlQxmzZtMtEuWo5fYrqcj9/x48dtqha1QAyNHK6uJKUcOwUWIiIiKQx52oG1BjTEDxw4ECuHO/B12LFjh990lkXqyeWSipHQ7UvpouX4pQanT5+2qU+cl2PGjDHZsmVLFcdOgYWIiEgKU6dOHfPdd9/Zq53um3ZxxbRWrVohX1e5cmVbXMq8jvPnz5tly5aZunXrmmjfvmD2799vc+DLly9vol20HL/EdDkev/Pnz9v0J4KCN954wxZtp5Zjp+JtERGRFOauu+4ys2bNMo8//rjp0qWLvYHc6NGjTevWrU3+/Plj5uvVq5e9i/GCBQvs7xSOdu7c2eatc2OvUqVK2Zt0HT16NNYNyaJx+xhNaMWKFbahxnz//POPmTp1qkmXLt1ltX3OFW/WFWwDQ5E6jc5q1arZ4xOtxy+h2xctx+/ll1823377re2xYLs2btzoN+wsRdrRfOzCUWAhIiKSwpCvPX78eHtXahrfXAm98847Te/evf3mu3Dhgn24derUyY5CQzHs4cOHTZkyZexV18tpxJ2Ebl/hwoXtFe5XX33V9naQt16jRg3Ts2dP+9zl5NChQ+bJJ5/0m+b8PmHCBFsMHK3HL6HbFy3Hb83/3kvl9ddfj/XcwoULTaFChaL62IWThrvkJfdKiIiIiIhIdFONhYiIiIiIeKbAQkREREREPFNgISIiIiIinimwEBERERERzxRYiIiIiIiIZwosRERERETEMwUWIiIiIiLimQILERERERHxTIGFiIgk2L59+0zOnDnN5MmT/aY/8MADpnjx4sm2XinBkCFDTJo0acz27duT5P2mTp0a6/1OnTpl7xI8dOjQRDs3xPsx+vrrr5N7VSTKvx+mXqJzSYGFiIgk2ODBg03+/PlN586dI5p/z549pn///qZixYome/bsJkeOHKZ06dKmXbt2Zt68eX7z3njjjSZbtmxx/mH94Ycfgj5/+PBhc8UVV9h5pk+fHnI5BEDM4zwyZsxop3Xr1s3s3LnTpGbsvyeffNK88sorZvfu3Zf03JDUbf369fYznVSBtFwaCixERCRB/vnnHzNlyhTzyCOPmPTp08c5/44dO8x1111nxo0bZ2rVqmVeeuklM3z4cNOiRQuzefNm88477yTq+s2cOdOcOXPGlChRwq5nOEWKFLHBB4/Ro0ebG264wb6GnwcOHDCpWdeuXW3A9dprr12yc0Mic99999lepAYNGpiUGFjQM6bAIrrp0y4iIgkyceJE2+Bs3759RPOPHDnSpscsWLDA3HHHHUF7MxLT22+/bRo1amTf69FHHzV//fWXKVmyZNB5Sdnp2LFjzO+9evUyBQoUMGPHjrUBz4ABA0xqlTVrVtO6dWubOjFs2DCTKVOmRD83ktuFCxdsEJolSxZzOUuXLp19iFyu1GMhIpJEnJzWL7/80jz33HOmWLFiNtWEq+Jr1qyx83zzzTemXr16tjF31VVXmeeffz7oskj/adWqlcmXL59t6JUtW9a88MIL5vz5837zfffdd7beoUyZMrbRRPpR3bp1zfz582Mtk/lYv6NHj8Y0rDNnzmznX7t2baz558yZY6pXr27ni8Sff/5pf950001Bny9YsKBJLD/++KO9AtqpUyfToUMHe9U8rl6LQDfffLP9uWXLlpDzfPrpp3afjRkzJujztWvXtulA586di/fxCMY5RsEwnecDzZo1y55TvBfvyfn24Ycfmvho3ry57blZtmxZRPOHOjcuXrxoz1OuuHO8STsrWrSoPd8OHjwYM9+RI0fsuUdAE8ygQYPs9nKMHZy3AwcONKVKlbKfCfY7gQ0BZbDP4dKlS+3n65prrrHvNXv2bPv8559/bu655x4bhPL5zJUrl2nWrJn9bAYzd+5c2xPHMtgWrrqzbN6D93IjeHnxxRdNhQoV7Pws+/bbbzc//fRTgvPiE+t7hfQ/0g/57DRu3NimIebJk8d+hrgg4Hb8+HGb6sZ7ON9B7HfS5v77779Yy/b5fLbWhvlZLo9KlSqZ//mf/7HPkwLlpMxxMcBJSwx2PgfasGGD/S7Mmzev3afly5c3I0aMsMGil++3cOmXv/32m71Ywb7kM8V32u+//27nIaXz+uuvt8eAfTpp0qSgy3rrrbdi5uPCBufYihUrYs3HZ4aeXXpeWV/SSOmNDYWURbaPc5HPFzVSPXr0iHUM4yvS/czBFhGRJPDOO+/4+NqtXr26r2rVqr5XX33VN3z4cF++fPl82bNn982fP9+XJ08e35NPPul78803fTfeeKOdf/r06X7L+eSTT3wZM2b0lS9f3vfiiy/6JkyY4OvUqZMvbdq0vrvvvttvXpZ1ww03+J5++mnfpEmT7Ptde+21drkzZ870m5dlMJ35W7Ro4XvjjTd8Q4YM8eXIkcOXN29e37Fjx2Lm3bNnj523T58+QbeVZRUrVsxvWu/eve1rXnvtNd/Fixfj3F8NGzb0Zc2a1bd///6gjwEDBtjlff/997Fey3tly5bNd+LECft7q1atfEWKFPFduHAh1rysZ4UKFWJNZz1Z/lNPPRVyHc+fP+8rWLCgr1q1arGe++OPP2Lto/gcj2effdZO37ZtW6xjFAzTed6N92H6Lbfc4hs1apRv9OjRMefV2LFjg56f7vdzbN++3T43cOBAX1zCnRunTp3y5cyZ09elSxffyJEjfePHj7f/z5Ahg69ixYq+M2fOxMzbpk0be54fPHjQbxkcw6uvvtpXuXLlmGlHjhyxnweOOe87ceJEe+4WKFDAfr5Y/8DtvO666+xrOAbjxo3zrVq1yj7fvn17X5MmTez+nzx5sl0O5066dOl8y5cv91uXDz74wJcmTRpfqVKl7GdxxIgR9lzifOA9eC/H2bNn7b5nm7p27Wo/47x3yZIlfVdccUXQ8ziQs+7Lli1L9O8VPgfXXHONL1euXHb92Cf8ZPvYTydPnoyZd9OmTb4rr7zSfs5ef/11ey5xvJi3WbNmsdb73nvvjfluYT+xHhyncuXK2ed//vlnX48ePWI+b6wbD+eYhMI+y5Iliz2neN2YMWN8TZs2tcvp0KFDgr/fQnE+k+xr9iPv98wzz9h1YP+9++679vtg6NChdvlVqlSx83/77bd+y3niiSfs9Jo1a9rvGeYvXLiwL3369L5Fixb5zdu3b187b4MGDeznl88028uxDvy87tixw1eoUCF77Pms8h3De3EecI7yOQl3LiXGflZgISKSRJwvcv4guBtQH330kZ3OHxV344J5+CNVq1Ytv4YZf9Dr16/vO3fuXNCGsPsPhdOwdqOBUKZMmZg/6oF/eHv16uU3ffbs2XY6AYzjq6++stP4QxdpYLF161b7R5zX0TDkDxKN3R9++CFkYMG8cT0CG2TsIxpH7kb2ggUL7LyLFy+O9T6sJ417J2D566+/fFOmTLF/RDkmGzdu9IXTv39/u+xff/3Vb/rgwYPt9HXr1iXoeHgNLHhfpg0aNCjWvHfccYdtbLgbU+ECC7AvaJDFJdy5QUD533//xZr+1ltv2dfMmjXLL4BmGg1ct6VLl9rpNKAdNFIzZ87sW79+vd+8BBRsp3u/ONvJPnc3lsMdI4IlGp/NmzePmcbnj0YcwcuhQ4diph8/ftxXokSJWIGF8/lcsmSJ37KPHj1qPw+c714CCy/fK87ngPn5TLo5602w4l4GgVIg55xfu3ZtzDSOKdM6duwYK7B3/x6fhq6jTp06NuAjMHGfYwQ5LItzJSHfb6E4n0k+B+6LI5zrTM+ePbvv77//jpm+b98+X6ZMmXzt2rWLmbZ582YbgNWtW9fveO3atct+53AcuGDhnrdx48Yx05zPNtMDP68tW7b05c+f37dz506/9eb4s59Y/4Ts7/jsZ6VCiYgkMbqp6aJ21K9f3/4kTYD0EQfz1KxZMyaFCF988YXZu3evTRsgXYT0FOdx6623xqRyOEh9cJCiQLoJP0l12LRpkzl27Fis9Xvsscf8fmdeuNdj//799iepEpEiteTnn382Dz30kP39vffes+/FNleuXNmsW7cu1mvocmebgz0oZA2GVAT2DSkcDvYNqTGh0qEoHud5Hqxnly5dbIrHRx99ZFMPwnHe5913342ZRjt/xowZ9rWkO3g5HglFugRpG6yf+zzh0bJlS5vOsnr16oiXx7GOJJ0i3LnB+pD6AVIonHPYOcfcKSmkol155ZV++xX8TmrbvffeG7Ov2VbSqwoXLuy3nexvBgpwfybcn8NgNRXuY3TixAl7jKhr4PPpXj/O13///dem2OTOnTtmOmk+Dz74YKzlcj5ce+21plq1an7rePbsWdO0aVObBkNhdnJ8rzgYpa13795+0/id6e50PZaRIUMG+3/SLxmBjW1p0qSJnebeT07aDjVWadP6NzsDf48PzsVVq1bZc5nvD/c59vTTT9v/B0sxjOT7LS59+vTxS0l09nXLli3N1VdfHTOd7xPSVN3L5juFc/aJJ57wO16kLPG9ziAXTmqcM2+/fv38amv4TuGccSPF65NPPrHrwPem+xwjJYtUtWCfg8TezyreFhFJYoEFxE6jhBzaQDznzj2n8QkavqEQeLj/KJALzR+oYI1CGnY0GsKtHzm1cK+H80f1/18ojxx/4CiI5kEuMI0pRmL6+OOP7ehQv/76q1+DlD+mTmMlULB8ZKdomz/ojPTkro8gh5ncf/7QEjQErpdzvwUnL5k/xJFwggcaUOTP01havny5Hd2GHGS3hByPhOJc4fjQmI3kXIkLywpV3+EW17lBLcOrr75qG09O7YmDBqrDCR4YjeqPP/6wdSknT560gSPHkqDDCWQ4N2k0cdyDCdaAZXnBbN261TaYPvvsM3s8gm0btm3bZn/ScAwUbBrHg8Ah1DqCc9PdME2q7xX3MtyNXVA/wfTAWpU333zTTJgwwX5mqQMIdRxpVFOL4ByvxOLsf+pVApUrV84e88B1jvT7LbH39Y4dOyJab2ca600w6Kx/sM8wNQ7uQIEaD44D3388IlnvS7GfFViIiCSxUKO6RDLai9NY474CVapUCToPjWJnXhpgNGj69u1r/1BRJMj7MNIRPQaBDYJw6+FuKDqNo0OHDpmEorHRpk0b+6AByfosXrzYb3SmhPwRpMCYdQ3VcOTKMYWXgVepQwUwkbj//vvtMr/66iu7HK6qsx/d25LQ4+EWqmEfWLTvvB/zU2Ae6pgGayyEQmMxXKM4knODoIDCaK6YM6wvjWiurtJ7ccstt8TafvYrgQX7kxGpeD29CO7eKOe8ZL9TvB2pYL0VLJueDwIYjicFxhS903iigJbjm1CsJ8sLN2xvJPv3UnyvxBfb8Pjjj9vzmav3fOcQkOzatcv24MR1HienSL7fErqMdImw7IRy3oPvHPfnw83pLbyUFFiIiEQRbiYXaUOYUTxIPWLklcA7JzMiiRdOgzQ+6QPhkK5Cw5qGiRc00J0RaBhxJxC9BaRDBQYWXjHyFEPS0gBmlBlGXSJVgeApMY+H05tDo93dsxPsyiznypIlS+zoMFxZ9ILeF4KXuNLC4jo36J0ikCD4czfsSUULhtGWeBAMMpIR+5fjSlqGuzHONNLIvASHYGQl0ps4RwJv7Me54+bcWd4ZDcgt2DSOB70rpN54SQG6lDiPSM1y91owkhXT3VfNOY5sP0Gre1s43wIR4NNDR+9YuF6LSHrD3JzeAXpMAnE+Edwk5Ar9pVbyf9eJ9WZEMjdGm3LP4/xke0LN66CHlX3I8fP6OfCyny/PM1tERIIi75xhErm5XLArwqRakDvvvnoWeLXsl19+iXh401BozNGAdIazjARDZAbLIecPE6lQTvd+QrEcht7kqjB3zb777rtjPRh+dOPGjeb77783iYn9wZCsXFEnJYpGbuBVw8Q4Hk4vDMOZupFaFMipQXnqqadiDwkZzzQo5zg3bNjQ07nBPqDx476izf6gNyIU9iOpJASe9BjQ40Fw4qBhS48XQ/mGGkY30qE2Qx0jUk4ChySlx4nAkXPOnfpDrwcpQoHofeFeLaF6LOJzPC4VzltSnNz4nel33nlnrOPo3k8EnnwvBXJqYagpCOzJcL+e2pT49ILyPVinTh373cFnyL1MepfA8KiXm5YtW9p9R6+zOxWQ1FAujDBccNWqVf3m5Zxxf4YZEjjwO4CULmrJ+A4K9tljvzj1T/ER3/2sHgsRkShCTwVXbfkjTx43tRZcqSIXnKtH/FGhkcp49FylpoFHnj8FwsxPrjo3L6PxHaxYOj5IYeIqMn8Q3VfmQ6F4c+XKlXbcfmoSSAOiocV9AFgXxq+/7bbbErw+NP527txp7xQdyl133WXHoicHuUaNGiYx0QBeuHChTRFh29wNMSTG8SAwIlBgXHqON70WXCUOdndwto9t5UHaHMeLlBWOF+9F2hlXNyPBvNSlcIy8nBsEdxxvrtrT0KZhxQ0Tg937wN0wpVFKETEN02BpHtwbg3Orbdu29kEPGFfdCUhYdwqmA+8pEQz3euD+GhxDemmo0+FeGVyh5xgRlLprQDinWT9SuzjvmMb70MgjLc99FZ70NwYdoGeLAIl9QD3N33//bXtKnJ6c5MRVcXrTaECyzzhP6L2ht4KUJ/dx5F4iBNPca4TAg8DPKegOPBcIBvneoheLxjJ1B5z71LE4jVXOV4JEjiWBGt91XC2n+DwU0ukIdimeZlAIjh0FzCyXXsRQ98xJTmXLlrXnAN8DpN2xb7gYxP0uCEq5MOEEuOx3touaNM4Xvr8IkvmdnrzA+5+MHz/ensMsl88XAQqfGXqc6DViGt8H8RWv/RznGFMiIpIowg3vF+weBOGGF2UIVMaGZ7hL7gHAkJe1a9f2Pffcc37j/jPcJve2YFxzxsqvUaOGb968eYlyjwSGR2QoS+5HEMlws6tXr/b169fPjgHP+vJahldk2EuGDj19+nTQ+1iE4myDM5Qm28nvGzZs8IXDMKO8rzPsaaj7WMQXQ0dyvwDWoVu3bkHnic/xCDYNa9asscM/MowlQ6B2797dd/jw4ZDnEMO2cm+B3Llz23socE8G7mvBPSTcQg03y/CrHAeG1Y1UuHODsfUZWpf1Z9hT1p9zNtT6g+E9eb506dIh35OhYzn/uR8GQ89yTwuGEeZYsM8iHWaTITVvvvlmO2Qxy+A85P4VoT4fDFdaqVIlu28ZNpZ7I3BMA4fPdYaoZWhSPgPcF4AH9xdg6OXPPvss7D4Nte6J9b3C54BtZSjTRo0a2XVjHzBMLMPtujH0Kfej4L4XbHfRokXtfWV+++03u1z3sKbOsLLc64IhcTnv2a/sM/aV29SpU+25wXdauPPBjSGGGT7ZOb855i+//LLf8Kyhtjmu/RQo1GeS34NtN9ingd+FzueA+1zwOWCYWu6dEnifFGffDRs2zO5jto/vqhkzZoRcF4bM5rPKZ4Vl813HZ4Ihmd1DYsd3eN9I93Ma/vEef4mISGrEsJr0FJBT7r5aSQEnqU9c9ZXowJV26gq40u7UDzhXKxklyRndx+u5kRqQmta/f387nC+9J9GAY87DfVdvkfhSjYWIiCTYc889Z4dpJDdYUh5qYsibJ3UjPkFFajk3SCULrF8hnWXcuHE2Hcp9DxOR1EA1FiIikmAU9nFjJkmZGJ6SOomESA3nBrnr1Bm0a9fO1gOwr6ZNm2Z7fch3D7wnhEhKp8BCREREJAEYAYtUJwpuKaqleJsib3p5KCIXSW1UYyEiIiIiIp6pxkJERERERDxTYCEiIiIiIp4psBAREREREc8UWIiIiIiIiGcKLERERERExDMFFiIiIiIi4pkCCxERERER8UyBhYiIiIiIeKbAQkREREREjFf/D8U9YiKkGdVXAAAAAElFTkSuQmCC", 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", 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" ] }, "metadata": {}, @@ -4920,11 +10043,18 @@ "shap.summary_plot(shap_values.values, X_train, feature_names=X_train.columns, plot_type=\"bar\")\n", "\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "MachineLearning", + "display_name": "demo_desu", "language": "python", "name": "python3" }, @@ -4938,7 +10068,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.16" + "version": "3.13.5" } }, "nbformat": 4, From 4c7e486dc1d85d547d04a65c46f20b9bfcb0acc5 Mon Sep 17 00:00:00 2001 From: giapre <151830625+giapre@users.noreply.github.com> Date: Wed, 10 Sep 2025 18:11:22 +0200 Subject: [PATCH 6/6] Delete notebooks/project_starter.ipynb --- notebooks/project_starter.ipynb | 10076 ------------------------------ 1 file changed, 10076 deletions(-) delete mode 100644 notebooks/project_starter.ipynb diff --git a/notebooks/project_starter.ipynb b/notebooks/project_starter.ipynb deleted file mode 100644 index fad169c..0000000 --- a/notebooks/project_starter.ipynb +++ /dev/null @@ -1,10076 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Projet du cours de Machine Learning : analyse du dataset d'OpenFoodFact" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 1. Chargement des données " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### A partir du format csv" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "\n", - "path = '/home/daniela/Documents/PROJECTS/DESU_Data_Science/N_Rocher/OFF_Project/OFFProject/scripts'\n", - "os.chdir(path)\n", - "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '..'))) \n", - "sys.path.append(\"../../\")\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "from scripts import Data_filter_Jess as dfj\n", - "from scripts import encoding_func \n", - "from scripts import Imputing\n", - "from scripts import Scaling\n", - "from encoding_func import *\n", - "from Scaling import *\n", - "from Imputing import *\n" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_9642/3882293860.py:4: DtypeWarning: Columns (12,13,14,15,16,17,24,25,26,27,28,32,33,34,36,37,52,73) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df_test = pd.read_csv(path, sep='\\t', encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True, skiprows=range(1, 10001), nrows=5000) # skip rows 1–10000, keep header (row 0)\n" - ] - }, - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "code", - "rawType": "int64", - "type": "integer" - }, - { - "name": "url", - "rawType": "object", - "type": "string" - }, - { - "name": "creator", - "rawType": "object", - "type": "string" - }, - { - "name": "created_t", - "rawType": "int64", - "type": "integer" - }, - { - "name": "created_datetime", - "rawType": "object", - "type": "string" - }, - { - "name": "last_modified_t", - "rawType": "int64", - "type": "integer" - }, - { - "name": "last_modified_datetime", - "rawType": "object", - "type": "string" - }, - { - "name": "last_modified_by", - "rawType": "object", - "type": "unknown" - }, - { - "name": "last_updated_t", - "rawType": "int64", - "type": "integer" - }, - { - "name": "last_updated_datetime", - "rawType": "object", - "type": "string" - }, - { - "name": "product_name", - "rawType": "object", - "type": "string" - }, - { - "name": "abbreviated_product_name", - "rawType": "float64", - "type": "float" - }, - { - "name": "generic_name", - "rawType": "object", - "type": "unknown" - }, - { - "name": "quantity", - "rawType": "object", - "type": "unknown" - }, - { - "name": "packaging", - "rawType": "object", - "type": "unknown" - }, - { - "name": "packaging_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "packaging_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "packaging_text", - "rawType": "object", - "type": "unknown" - }, - { - "name": "brands", - "rawType": "object", - "type": "unknown" - }, - { - "name": "brands_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "brands_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "categories", - "rawType": "object", - "type": "unknown" - }, - { - "name": "categories_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "categories_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "origins", - "rawType": "object", - "type": "unknown" - }, - { - "name": "origins_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "origins_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "manufacturing_places", - "rawType": "object", - "type": "unknown" - }, - { - "name": "manufacturing_places_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "labels", - "rawType": "object", - "type": "unknown" - }, - { - "name": "labels_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "labels_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "emb_codes", - "rawType": "object", - "type": "unknown" - }, - { - "name": "emb_codes_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "first_packaging_code_geo", - "rawType": "object", - "type": "unknown" - }, - { - "name": "cities", - "rawType": "float64", - "type": "float" - }, - { - "name": "cities_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "purchase_places", - "rawType": "object", - "type": "unknown" - }, - { - "name": "stores", - "rawType": "object", - "type": "unknown" - }, - { - "name": "countries", - "rawType": "object", - "type": "string" - }, - { - "name": "countries_tags", - "rawType": "object", - "type": "string" - }, - { - "name": "countries_en", - "rawType": "object", - "type": "string" - }, - { - "name": "ingredients_text", - "rawType": "object", - "type": "unknown" - }, - { - "name": "ingredients_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "ingredients_analysis_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "allergens", - "rawType": "object", - "type": "unknown" - }, - { - "name": "allergens_en", - "rawType": "float64", - "type": "float" - }, - { - "name": "traces", - "rawType": "object", - "type": "unknown" - }, - { - "name": "traces_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "traces_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "serving_size", - "rawType": "object", - "type": "unknown" - }, - { - "name": "serving_quantity", - "rawType": "float64", - "type": "float" - }, - { - "name": "no_nutrition_data", - "rawType": "object", - "type": "unknown" - }, - { - "name": "additives_n", - "rawType": "float64", - "type": "float" - }, - { - "name": "additives", - "rawType": "float64", - "type": "float" - }, - { - "name": "additives_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "additives_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "nutriscore_score", - "rawType": "float64", - "type": "float" - }, - { - "name": "nutriscore_grade", - "rawType": "object", - "type": "string" - }, - { - "name": "nova_group", - "rawType": "float64", - "type": "float" - }, - { - "name": "pnns_groups_1", - "rawType": "object", - "type": "string" - }, - { - "name": "pnns_groups_2", - "rawType": "object", - "type": "string" - }, - { - "name": "food_groups", - "rawType": "object", - "type": "unknown" - }, - { - "name": "food_groups_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "food_groups_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "states", - "rawType": "object", - "type": "string" - }, - { - "name": "states_tags", - "rawType": "object", - "type": "string" - }, - { - "name": "states_en", - "rawType": "object", - "type": "string" - }, - { - "name": "brand_owner", - "rawType": "object", - "type": "unknown" - }, - { - "name": "environmental_score_score", - "rawType": "float64", - "type": "float" - }, - { - "name": "environmental_score_grade", - "rawType": "object", - "type": "unknown" - }, - { - "name": "nutrient_levels_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "product_quantity", - "rawType": "float64", - "type": "float" - }, - { - "name": "owner", - "rawType": "object", - "type": "unknown" - }, - { - "name": "data_quality_errors_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "unique_scans_n", - "rawType": "float64", - "type": "float" - }, - { - "name": "popularity_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "completeness", - "rawType": "float64", - "type": "float" - }, - { - "name": "last_image_t", - "rawType": "float64", - "type": "float" - }, - { - "name": "last_image_datetime", - "rawType": "object", - "type": "unknown" - }, - { - "name": "main_category", - "rawType": "object", - "type": "unknown" - }, - { - "name": "main_category_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "image_url", - "rawType": "object", - "type": "unknown" - }, - { - "name": "image_small_url", - "rawType": "object", - "type": "unknown" - }, - { - "name": "image_ingredients_url", - "rawType": "object", - "type": "unknown" - }, - { - "name": "image_ingredients_small_url", - "rawType": "object", - "type": "unknown" - }, - { - "name": "image_nutrition_url", - "rawType": "object", - "type": "unknown" - }, - { - "name": "image_nutrition_small_url", - "rawType": "object", - "type": "unknown" - }, - { - "name": "energy-kj_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "energy-kcal_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "energy_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "energy-from-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "saturated-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "butyric-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "caproic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "caprylic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "capric-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "lauric-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "myristic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "palmitic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "stearic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "arachidic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "behenic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "lignoceric-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "cerotic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "montanic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "melissic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "unsaturated-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "monounsaturated-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "omega-9-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "polyunsaturated-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "omega-3-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "omega-6-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "alpha-linolenic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "eicosapentaenoic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "docosahexaenoic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "linoleic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "arachidonic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "gamma-linolenic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "dihomo-gamma-linolenic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "oleic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "elaidic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "gondoic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "mead-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "erucic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "nervonic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "trans-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "cholesterol_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "carbohydrates_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "sugars_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "added-sugars_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "sucrose_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "glucose_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fructose_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "galactose_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "lactose_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "maltose_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "maltodextrins_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "psicose_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "starch_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "polyols_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "erythritol_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "isomalt_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "maltitol_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "sorbitol_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fiber_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "soluble-fiber_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "insoluble-fiber_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "proteins_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "casein_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "serum-proteins_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "nucleotides_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "salt_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "added-salt_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "sodium_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "alcohol_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-a_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "beta-carotene_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-d_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-e_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-k_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-c_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-b1_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-b2_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-pp_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-b6_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-b9_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "folates_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-b12_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "biotin_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "pantothenic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "silica_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "bicarbonate_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "potassium_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "chloride_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "calcium_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "phosphorus_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "iron_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "magnesium_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "zinc_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "copper_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "manganese_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fluoride_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "selenium_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "chromium_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "molybdenum_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "iodine_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "caffeine_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "taurine_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "methylsulfonylmethane_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "ph_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fruits-vegetables-nuts_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fruits-vegetables-nuts-dried_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fruits-vegetables-nuts-estimate_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "collagen-meat-protein-ratio_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "cocoa_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "chlorophyl_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "carbon-footprint_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "carbon-footprint-from-meat-or-fish_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "nutrition-score-fr_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "nutrition-score-uk_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "glycemic-index_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "water-hardness_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "choline_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "phylloquinone_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "beta-glucan_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "inositol_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "carnitine_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "sulphate_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "nitrate_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "acidity_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "carbohydrates-total_100g", - "rawType": "float64", - "type": "float" - } - ], - "ref": "292ffd4f-df53-4e5e-9e5c-d8ad2d81fda6", - "rows": [ - [ - "0", - "12409", - "http://world-en.openfoodfacts.org/product/00012409/bagnat-thon-crous", - "kiliweb", - "1573212227", - "2019-11-08T11:23:47Z", - "1581586728", - "2020-02-13T09:38:48Z", - "neuni", - "1743316740", - "2025-03-30T06:39:00Z", - "Bagnat thon", - null, - null, - "296,5g", - null, - null, - null, - null, - "Crous", - "xx:crous", - "crous", - "Sandwichs, Sandwichs au poisson, Sandwichs au thon", - "en:sandwiches,en:fish-sandwiches,en:tuna-sandwiches", - "Sandwiches,Fish sandwiches,Tuna sandwiches", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "en:fr", - "en:france", - "France", - "PAIN BAGNAT 40,5% : Farine de BLE tendre et BLE dur, eau, huiles végétales [olive, colza], levure, sel, émulsifiants : E472e, agent de traitement de la farine E300, levure désactivée. Garniture 59,5% : THON au naturel 28,3% (THON, eau, sel), tomate 17%, salade batavia 17%, OEUF dur 15%, MAYONNAISE 14,2% (huile de colza 68,5%, eau, vinaigre, jaune d'OEUF frais 5%, MOUTARDE de Dijon [eau, graine de MOUTARDÉ, vinaigre sel antioxydant : DISULFITE de potassium, acidifiantacide citrique], sei/ sucre, amidon modifié, épaississant: gomme xanthane, acidifiant : E330, antioxydant : E385, colorants : lutéine et extrait de paprika, arômes), tapenade noire 8,5% (olives noires 80%, huile d'olive vierge extra 0%, câpres, pâte d'ANCHOIS [ANCHOIS, huile d'olive] ail, basile jus concentré de citron, herbes de Provence, poivre). (% exprimés sur le garniture)", - "fr:pain-bagnat,en:water,en:vegetable-oil,en:oil-and-fat,en:vegetable-oil-and-fat,en:yeast,en:salt,en:emulsifier,en:flour-treatment-agent,en:deactivated-yeast,en:filling,en:tomato,en:vegetable,en:fruit-vegetable,fr:salade-batavia,en:hard-boiled-egg,en:egg,en:boiled-egg,en:mayonnaise,en:sauce,fr:tapenade-noire,en:soft-wheat-flour,en:cereal,en:flour,en:wheat,en:cereal-flour,en:wheat-flour,en:durum-wheat,en:olive-oil,en:colza-oil,en:rapeseed-oil,en:e472e,en:e300,en:tuna-in-brine,en:fish,en:tuna,en:canned-tuna,en:vinegar,fr:jaune-d-oeuf-frais,en:egg-yolk,en:dijon-mustard,en:mustard,fr:sei,en:sugar,en:added-sugar,en:disaccharide,en:modified-starch,en:starch,en:thickener,en:acid,en:antioxidant,en:colour,en:flavouring,en:black-olive,en:olive,en:extra-virgin-olive-oil,en:virgin-olive-oil,en:capers,en:plant,en:anchovy-paste,en:oily-fish,en:anchovy,en:garlic,en:root-vegetable,en:onion-family-vegetable,fr:basile-jus-concentre-de-citron,en:herbes-de-provence,en:herb,en:pepper,en:seed,en:mustard-seed,en:condiment,en:spice,fr:vinaigre-sel-antioxydant,fr:acidifiantacide-citrique,en:e415,en:e330,en:e385,en:e161b,en:e160c,en:e224", - "en:may-contain-palm-oil,en:non-vegan,en:non-vegetarian", - null, - null, - null, - null, - null, - "296,5", - null, - "off", - "9.0", - null, - "en:e14xx,en:e160c,en:e161b,en:e224,en:e300,en:e330,en:e385,en:e415,en:e472e", - "E14XX - Modified Starch,E160c - Paprika extract,E161b - Lutein,E224 - Potassium metabisulphite,E300 - Ascorbic acid,E330 - Citric acid,E385 - Calcium disodium ethylenediaminetetraacetate,E415 - Xanthan gum,E472e - Mono- and diacetyltartaric acid esters of mono- and diglycerides of fatty acids", - "0.0", - "a", - "4.0", - "Composite foods", - "Sandwiches", - "en:sandwiches", - "en:composite-foods,en:sandwiches", - "Composite foods,Sandwiches", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - "45.0", - "c", - "en:fat-in-moderate-quantity,en:saturated-fat-in-low-quantity,en:sugars-in-low-quantity,en:salt-in-moderate-quantity", - "296.5", - null, - null, - "1.0", - "bottom-25-percent-scans-2020,bottom-20-percent-scans-2020,top-85-percent-scans-2020,top-90-percent-scans-2020,top-country-fr-scans-2020,bottom-25-percent-scans-2021,bottom-20-percent-scans-2021,top-85-percent-scans-2021,top-90-percent-scans-2021,top-country-fr-scans-2021,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-10000-re-scans-2024,top-50000-re-scans-2024,top-100000-re-scans-2024,top-country-re-scans-2024", - "0.6875", - "1581586582.0", - "2020-02-13T09:36:22Z", - "en:tuna-sandwiches", - "Tuna sandwiches", - "https://images.openfoodfacts.org/images/products/000/000/001/2409/front_fr.3.400.jpg", - "https://images.openfoodfacts.org/images/products/000/000/001/2409/front_fr.3.200.jpg", - "https://images.openfoodfacts.org/images/products/000/000/001/2409/ingredients_fr.7.400.jpg", - "https://images.openfoodfacts.org/images/products/000/000/001/2409/ingredients_fr.7.200.jpg", - "https://images.openfoodfacts.org/images/products/000/000/001/2409/nutrition_fr.5.400.jpg", - "https://images.openfoodfacts.org/images/products/000/000/001/2409/nutrition_fr.5.200.jpg", - null, - "213.0", - "891.0", - null, - "10.2", - "1.1", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "20.9", - "0.9", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "1.7", - null, - null, - "8.6", - null, - null, - null, - "1.1", - null, - "0.44", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "180.375", - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "1", - "1241000224", - "http://world-en.openfoodfacts.org/product/0001241000224/threptin", - "smoothie-app", - "1723262866", - "2024-08-10T04:07:46Z", - "1723478958", - "2024-08-12T16:09:18Z", - "krishanti", - "1738830508", - "2025-02-06T08:28:28Z", - "Threptin", - null, - null, - "275 g", - null, - null, - null, - null, - "Threptin", - "threptin", - "Threptin", - "Snacks,Sweet snacks,Biscuits and cakes,Biscuits and crackers,Biscuits", - "en:snacks,en:sweet-snacks,en:biscuits-and-cakes,en:biscuits-and-crackers,en:biscuits", - "Snacks,Sweet snacks,Biscuits and cakes,Biscuits and crackers,Biscuits", - null, - null, - null, - null, - null, - "No gluten", - "en:no-gluten", - "No gluten", - null, - null, - null, - null, - null, - null, - null, - "India", - "en:india", - "India", - "Casein, Sucrose, Precooked Rice Flour, Edible Vegetable Fat, Bengal Gram, Raising Agent [500 (ii)], Natural Colour (150 c), Emulsifier, Vitamins, Acidity Regulator (525) and Antioxidant (304).", - "en:casein,en:protein,en:animal-protein,en:milk-proteins,en:sucrose,en:added-sugar,en:disaccharide,en:sugar,en:rice-flour,en:flour,en:rice,en:vegetable-fat,en:oil-and-fat,en:vegetable-oil-and-fat,en:bengal-gram,en:raising-agent,en:natural-colours,en:colour,en:emulsifier,en:vitamins,en:acidity-regulator,en:antioxidant,en:500,en:150-c,en:525,en:304,en:ii", - "en:may-contain-palm-oil,en:non-vegan,en:vegetarian-status-unknown", - null, - null, - null, - null, - null, - null, - null, - null, - "4.0", - null, - "en:e150,en:e304,en:e500,en:e525", - "E150 - Caramel,E304 - Fatty acid esters of ascorbic acid,E500 - Sodium carbonates,E525 - Potassium hydroxide", - null, - "unknown", - "4.0", - "Sugary snacks", - "Biscuits and cakes", - "en:biscuits-and-cakes", - "en:sugary-snacks,en:biscuits-and-cakes", - "Sugary snacks,Biscuits and cakes", - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded", - null, - null, - null, - "en:fat-in-moderate-quantity,en:saturated-fat-in-high-quantity,en:sugars-in-high-quantity", - "275.0", - null, - null, - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-10000-in-scans-2024,top-50000-in-scans-2024,top-100000-in-scans-2024,top-country-in-scans-2024", - "0.6875", - "1723262876.0", - "2024-08-10T04:07:56Z", - "en:biscuits", - "Biscuits", - "https://images.openfoodfacts.org/images/products/000/124/100/0224/front_en.3.400.jpg", - "https://images.openfoodfacts.org/images/products/000/124/100/0224/front_en.3.200.jpg", - "https://images.openfoodfacts.org/images/products/000/124/100/0224/ingredients_en.11.400.jpg", - "https://images.openfoodfacts.org/images/products/000/124/100/0224/ingredients_en.11.200.jpg", - "https://images.openfoodfacts.org/images/products/000/124/100/0224/nutrition_en.13.400.jpg", - "https://images.openfoodfacts.org/images/products/000/124/100/0224/nutrition_en.13.200.jpg", - null, - "438.0", - "1833.0", - null, - "14.0", - "7.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "48.0", - "30.0", - "30.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "30.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "2", - "1241380141", - "http://world-en.openfoodfacts.org/product/0001241380141/formula-1", - "zoneblockscommunity", - "1471281610", - "2016-08-15T17:20:10Z", - "1728042357", - "2024-10-04T11:45:57Z", - "fix-code-bot", - "1737547478", - "2025-01-22T12:04:38Z", - "formula 1", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "Italy", - "en:italy", - "Italy", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "unknown", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-to-be-completed, en:packaging-to-be-completed, en:quantity-to-be-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-to-be-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands to be completed,Packaging to be completed,Quantity to be completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - null, - null, - null, - "1.0", - "top-country-fr-scans-2019", - "0.2625", - "1559645420.0", - "2019-06-04T10:50:20Z", - null, - null, - "https://images.openfoodfacts.org/images/products/000/124/138/0141/front_en.3.400.jpg", - "https://images.openfoodfacts.org/images/products/000/124/138/0141/front_en.3.200.jpg", - null, - null, - "https://images.openfoodfacts.org/images/products/000/124/138/0141/nutrition_fr.5.400.jpg", - "https://images.openfoodfacts.org/images/products/000/124/138/0141/nutrition_fr.5.200.jpg", - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "3", - "12416", - "http://world-en.openfoodfacts.org/product/00012416/le-bollygood-canderel", - "kiliweb", - "1576148496", - "2019-12-12T11:01:36Z", - "1729149601", - "2024-10-17T07:20:01Z", - "llegris", - "1743314725", - "2025-03-30T06:05:25Z", - "Le bollygood", - null, - null, - null, - null, - null, - null, - null, - "Canderel", - "xx:canderel", - "canderel", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "France", - "en:france", - "France", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "unknown", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-to-be-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity to be completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - null, - null, - null, - "1.0", - "bottom-25-percent-scans-2020,top-80-percent-scans-2020,top-85-percent-scans-2020,top-90-percent-scans-2020,top-100000-fr-scans-2020,top-country-fr-scans-2020,top-75-percent-scans-2021,top-80-percent-scans-2021,top-85-percent-scans-2021,top-90-percent-scans-2021,top-100000-fr-scans-2021,top-country-fr-scans-2021,bottom-25-percent-scans-2022,bottom-20-percent-scans-2022,top-85-percent-scans-2022,top-90-percent-scans-2022,top-country-fr-scans-2022,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-ma-scans-2024,top-100000-ma-scans-2024,top-country-ma-scans-2024", - "0.375", - "1675082379.0", - "2023-01-30T12:39:39Z", - null, - null, - "https://images.openfoodfacts.org/images/products/000/000/001/2416/front_fr.16.400.jpg", - "https://images.openfoodfacts.org/images/products/000/000/001/2416/front_fr.16.200.jpg", - null, - null, - "https://images.openfoodfacts.org/images/products/000/000/001/2416/nutrition_fr.18.400.jpg", - "https://images.openfoodfacts.org/images/products/000/000/001/2416/nutrition_fr.18.200.jpg", - null, - "193.0", - "808.0", - null, - "5.2", - "1.8", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "26.0", - "1.2", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "1.7", - null, - null, - "9.5", - null, - null, - null, - "1.3", - null, - "0.52", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "4", - "12421", - "http://world-en.openfoodfacts.org/product/00012421/sirop-pur-sucre-banane-kiwi-super-u", - "openfoodfacts-contributors", - "1633542300", - "2021-10-06T17:45:00Z", - "1633542303", - "2021-10-06T17:45:03Z", - null, - "1743281810", - "2025-03-29T20:56:50Z", - "sirop pur sucre banane kiwi", - null, - null, - "70 cl", - null, - null, - null, - null, - "super u", - "xx:super-u", - "super-u", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - 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"To be completed,Nutrition facts to be completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be uploaded", - null, - null, - "unknown", - null, - "700.0", - null, - null, - null, - null, - "0.3", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - 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codeurlcreatorcreated_tcreated_datetimelast_modified_tlast_modified_datetimelast_modified_bylast_updated_tlast_updated_datetime...water-hardness_100gcholine_100gphylloquinone_100gbeta-glucan_100ginositol_100gcarnitine_100gsulphate_100gnitrate_100gacidity_100gcarbohydrates-total_100g
012409http://world-en.openfoodfacts.org/product/0001...kiliweb15732122272019-11-08T11:23:47Z15815867282020-02-13T09:38:48Zneuni17433167402025-03-30T06:39:00Z...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
11241000224http://world-en.openfoodfacts.org/product/0001...smoothie-app17232628662024-08-10T04:07:46Z17234789582024-08-12T16:09:18Zkrishanti17388305082025-02-06T08:28:28Z...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
21241380141http://world-en.openfoodfacts.org/product/0001...zoneblockscommunity14712816102016-08-15T17:20:10Z17280423572024-10-04T11:45:57Zfix-code-bot17375474782025-01-22T12:04:38Z...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
312416http://world-en.openfoodfacts.org/product/0001...kiliweb15761484962019-12-12T11:01:36Z17291496012024-10-17T07:20:01Zllegris17433147252025-03-30T06:05:25Z...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
412421http://world-en.openfoodfacts.org/product/0001...openfoodfacts-contributors16335423002021-10-06T17:45:00Z16335423032021-10-06T17:45:03ZNaN17432818102025-03-29T20:56:50Z...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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" - ], - "text/plain": [ - " code url \\\n", - "0 12409 http://world-en.openfoodfacts.org/product/0001... \n", - "1 1241000224 http://world-en.openfoodfacts.org/product/0001... \n", - "2 1241380141 http://world-en.openfoodfacts.org/product/0001... \n", - "3 12416 http://world-en.openfoodfacts.org/product/0001... \n", - "4 12421 http://world-en.openfoodfacts.org/product/0001... \n", - "\n", - " creator created_t created_datetime \\\n", - "0 kiliweb 1573212227 2019-11-08T11:23:47Z \n", - "1 smoothie-app 1723262866 2024-08-10T04:07:46Z \n", - "2 zoneblockscommunity 1471281610 2016-08-15T17:20:10Z \n", - "3 kiliweb 1576148496 2019-12-12T11:01:36Z \n", - "4 openfoodfacts-contributors 1633542300 2021-10-06T17:45:00Z \n", - "\n", - " last_modified_t last_modified_datetime last_modified_by last_updated_t \\\n", - "0 1581586728 2020-02-13T09:38:48Z neuni 1743316740 \n", - "1 1723478958 2024-08-12T16:09:18Z krishanti 1738830508 \n", - "2 1728042357 2024-10-04T11:45:57Z fix-code-bot 1737547478 \n", - "3 1729149601 2024-10-17T07:20:01Z llegris 1743314725 \n", - "4 1633542303 2021-10-06T17:45:03Z NaN 1743281810 \n", - "\n", - " last_updated_datetime ... water-hardness_100g choline_100g \\\n", - "0 2025-03-30T06:39:00Z ... NaN NaN \n", - "1 2025-02-06T08:28:28Z ... NaN NaN \n", - "2 2025-01-22T12:04:38Z ... NaN NaN \n", - "3 2025-03-30T06:05:25Z ... NaN NaN \n", - "4 2025-03-29T20:56:50Z ... NaN NaN \n", - "\n", - " phylloquinone_100g beta-glucan_100g inositol_100g carnitine_100g \\\n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - "\n", - " sulphate_100g nitrate_100g acidity_100g carbohydrates-total_100g \n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - "\n", - "[5 rows x 214 columns]" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# select rows from 10001 till 20000\n", - "path = \"https://static.openfoodfacts.org/data/en.openfoodfacts.org.products.csv.gz\"\n", - "#df_test = pd.read_csv(path, skiprows=10000, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n", - "df_test = pd.read_csv(path, sep='\\t', encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True, skiprows=range(1, 10001), nrows=5000) # skip rows 1–10000, keep header (row 0)\n", - "df_test.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_9642/1388454853.py:2: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n" - ] - } - ], - "source": [ - "path = \"https://static.openfoodfacts.org/data/en.openfoodfacts.org.products.csv.gz\"\n", - "df = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "code", - "rawType": "int64", - "type": "integer" - }, - { - "name": "url", - "rawType": "object", - "type": "string" - }, - { - "name": "creator", - "rawType": "object", - "type": "string" - }, - { - "name": "created_t", - "rawType": "int64", - "type": "integer" - }, - { - "name": "created_datetime", - "rawType": "object", - "type": "string" - }, - { - "name": "last_modified_t", - "rawType": "int64", - "type": "integer" - }, - { - "name": "last_modified_datetime", - "rawType": "object", - "type": "string" - }, - { - "name": "last_modified_by", - "rawType": "object", - "type": "unknown" - }, - { - "name": "last_updated_t", - "rawType": "int64", - "type": "integer" - }, - { - "name": "last_updated_datetime", - "rawType": "object", - "type": "string" - }, - { - "name": "product_name", - "rawType": "object", - "type": "string" - }, - { - "name": "abbreviated_product_name", - "rawType": "object", - "type": "unknown" - }, - { - "name": "generic_name", - "rawType": "object", - "type": "unknown" - }, - { - "name": "quantity", - "rawType": "object", - "type": "unknown" - }, - { - "name": "packaging", - "rawType": "object", - "type": "unknown" - }, - { - "name": "packaging_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "packaging_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "packaging_text", - "rawType": "object", - "type": "unknown" - }, - { - "name": "brands", - "rawType": "object", - "type": "unknown" - }, - { - "name": "brands_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "brands_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "categories", - "rawType": "object", - "type": "unknown" - }, - { - "name": "categories_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "categories_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "origins", - "rawType": "object", - "type": "unknown" - }, - { - "name": "origins_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "origins_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "manufacturing_places", - "rawType": "object", - "type": "unknown" - }, - { - "name": "manufacturing_places_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "labels", - "rawType": "object", - "type": "unknown" - }, - { - "name": "labels_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "labels_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "emb_codes", - "rawType": "object", - "type": "unknown" - }, - { - "name": "emb_codes_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "first_packaging_code_geo", - "rawType": "object", - "type": "unknown" - }, - { - "name": "cities", - "rawType": "float64", - "type": "float" - }, - { - "name": "cities_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "purchase_places", - "rawType": "object", - "type": "unknown" - }, - { - "name": "stores", - "rawType": "object", - "type": "unknown" - }, - { - "name": "countries", - "rawType": "object", - "type": "string" - }, - { - "name": "countries_tags", - "rawType": "object", - "type": "string" - }, - { - "name": "countries_en", - "rawType": "object", - "type": "string" - }, - { - "name": "ingredients_text", - "rawType": "object", - "type": "unknown" - }, - { - "name": "ingredients_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "ingredients_analysis_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "allergens", - "rawType": "object", - "type": "unknown" - }, - { - "name": "allergens_en", - "rawType": "float64", - "type": "float" - }, - { - "name": "traces", - "rawType": "object", - "type": "unknown" - }, - { - "name": "traces_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "traces_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "serving_size", - "rawType": "object", - "type": "unknown" - }, - { - "name": "serving_quantity", - "rawType": "float64", - "type": "float" - }, - { - "name": "no_nutrition_data", - "rawType": "object", - "type": "unknown" - }, - { - "name": "additives_n", - "rawType": "float64", - "type": "float" - }, - { - "name": "additives", - "rawType": "float64", - "type": "float" - }, - { - "name": "additives_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "additives_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "nutriscore_score", - "rawType": "float64", - "type": "float" - }, - { - "name": "nutriscore_grade", - "rawType": "object", - "type": "string" - }, - { - "name": "nova_group", - "rawType": "float64", - "type": "float" - }, - { - "name": "pnns_groups_1", - "rawType": "object", - "type": "string" - }, - { - "name": "pnns_groups_2", - "rawType": "object", - "type": "string" - }, - { - "name": "food_groups", - "rawType": "object", - "type": "unknown" - }, - { - "name": "food_groups_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "food_groups_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "states", - "rawType": "object", - "type": "string" - }, - { - "name": "states_tags", - "rawType": "object", - "type": "string" - }, - { - "name": "states_en", - "rawType": "object", - "type": "string" - }, - { - "name": "brand_owner", - "rawType": "object", - "type": "unknown" - }, - { - "name": "environmental_score_score", - "rawType": "float64", - "type": "float" - }, - { - "name": "environmental_score_grade", - "rawType": "object", - "type": "string" - }, - { - "name": "nutrient_levels_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "product_quantity", - "rawType": "float64", - "type": "float" - }, - { - "name": "owner", - "rawType": "object", - "type": "unknown" - }, - { - "name": "data_quality_errors_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "unique_scans_n", - "rawType": "float64", - "type": "float" - }, - { - "name": "popularity_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "completeness", - "rawType": "float64", - "type": "float" - }, - { - "name": "last_image_t", - "rawType": "float64", - "type": "float" - }, - { - "name": "last_image_datetime", - "rawType": "object", - "type": "string" - }, - { - "name": "main_category", - "rawType": "object", - "type": "unknown" - }, - { - "name": "main_category_en", - "rawType": "object", - "type": "unknown" - }, - { - "name": "image_url", - "rawType": "object", - "type": "string" - }, - { - "name": "image_small_url", - "rawType": "object", - "type": "string" - }, - { - "name": "image_ingredients_url", - "rawType": "object", - "type": "unknown" - }, - { - "name": "image_ingredients_small_url", - "rawType": "object", - "type": "unknown" - }, - { - "name": "image_nutrition_url", - "rawType": "object", - "type": "unknown" - }, - { - "name": "image_nutrition_small_url", - "rawType": "object", - "type": "unknown" - }, - { - "name": "energy-kj_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "energy-kcal_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "energy_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "energy-from-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "saturated-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "butyric-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "caproic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "caprylic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "capric-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "lauric-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "myristic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "palmitic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "stearic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "arachidic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "behenic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "lignoceric-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "cerotic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "montanic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "melissic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "unsaturated-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "monounsaturated-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "omega-9-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "polyunsaturated-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "omega-3-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "omega-6-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "alpha-linolenic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "eicosapentaenoic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "docosahexaenoic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "linoleic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "arachidonic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "gamma-linolenic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "dihomo-gamma-linolenic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "oleic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "elaidic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "gondoic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "mead-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "erucic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "nervonic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "trans-fat_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "cholesterol_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "carbohydrates_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "sugars_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "added-sugars_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "sucrose_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "glucose_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fructose_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "galactose_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "lactose_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "maltose_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "maltodextrins_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "psicose_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "starch_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "polyols_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "erythritol_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "isomalt_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "maltitol_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "sorbitol_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fiber_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "soluble-fiber_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "insoluble-fiber_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "proteins_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "casein_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "serum-proteins_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "nucleotides_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "salt_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "added-salt_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "sodium_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "alcohol_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-a_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "beta-carotene_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-d_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-e_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-k_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-c_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-b1_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-b2_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-pp_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-b6_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-b9_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "folates_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "vitamin-b12_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "biotin_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "pantothenic-acid_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "silica_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "bicarbonate_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "potassium_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "chloride_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "calcium_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "phosphorus_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "iron_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "magnesium_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "zinc_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "copper_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "manganese_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fluoride_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "selenium_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "chromium_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "molybdenum_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "iodine_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "caffeine_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "taurine_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "methylsulfonylmethane_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "ph_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fruits-vegetables-nuts_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fruits-vegetables-nuts-dried_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fruits-vegetables-nuts-estimate_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "collagen-meat-protein-ratio_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "cocoa_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "chlorophyl_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "carbon-footprint_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "carbon-footprint-from-meat-or-fish_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "nutrition-score-fr_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "nutrition-score-uk_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "glycemic-index_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "water-hardness_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "choline_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "phylloquinone_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "beta-glucan_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "inositol_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "carnitine_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "sulphate_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "nitrate_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "acidity_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "carbohydrates-total_100g", - "rawType": "float64", - "type": "float" - } - ], - "ref": "afe378a7-d223-441b-964e-a98c48a29936", - "rows": [ - [ - "0", - "54", - "http://world-en.openfoodfacts.org/product/000000000054/limonade-artisanale-a-la-rose", - "kiliweb", - "1582569031", - "2020-02-24T18:30:31Z", - "1733085204", - "2024-12-01T20:33:24Z", - null, - "1740205422", - "2025-02-22T06:23:42Z", - "Limonade artisanale a la rose", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "en:fr", - "en:france", - "France", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "unknown", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-to-be-completed, en:packaging-to-be-completed, en:quantity-to-be-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-to-be-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts to be completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands to be completed,Packaging to be completed,Quantity to be completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - null, - null, - null, - null, - null, - "0.1625", - "1733085204.0", - "2024-12-01T20:33:24Z", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/front_en.6.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.6.200.jpg", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null - ], - [ - "1", - "63", - "http://world-en.openfoodfacts.org/product/000000000063/m-amp-m-white-fitpiggy", - "kiliweb", - "1673620307", - "2023-01-13T14:31:47Z", - "1750061386", - "2025-06-16T08:09:46Z", - "bodysupport", - "1750061386", - "2025-06-16T08:09:46Z", - "M&M white", - null, - null, - "80 gram", - null, - null, - null, - null, - "Fitpiggy", - "xx:fitpiggy", - "fitpiggy", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "en:fr", - "en:france", - "France", - "Weizenmehl, Rapsöl, Speisesalz, 1,7% Meersalz, Fefe, Gerstenmaizextrakt, Säureregulator: Natrium - hydroxid; Backtriebnittel: Ammoniumcarbonate. Das Produkt kann Spuren von Sesam enthalten. DURCHSCHNITTLICHE NÄHRWERTE - pro %RM* 100g pro 100g Brennwert kJ/kcal 1630/385 19% Fett 3,7g 5% davon: - gesättigte Fettsäuren 0,6g 3% Kohlenhydrate 74,0g 28% davon: - Zucker 2,4g 3% Ballaststoffe 3,9g 12,0g 24% Eiweiß 3,9g 65% Salz *Referenzmenge für einen durchschnittlichen Erwachsenen (8400 kJ/2000 kcal) AP EDEKA KUNDEN UND ERNÄHRUNGSSERVICE 2509 (", - "en:weizenmehl,en:rapsol,en:speisesalz,en:meersalz,en:fefe,en:gerstenmaizextrakt,en:saureregulator,en:hydroxid,en:backtriebnittel,en:pro-rm-100g-pro-100g-brennwert-kj,en:kcal-1630,en:385-19-fett-3-7-5-davon,en:gesattigte-fettsauren-0-6-3-kohlenhydrate-74-28-davon,en:zucker-2-4-3-ballaststoffe-3-9-12-24-eiweiss-3-9-65-salz-referenzmenge-fur-einen-durchschnittlichen-erwachsenen,en:ap-edeka-kunden-und-ernahrungsservice-2509,en:sodium,en:minerals,en:ammoniumcarbonate,en:das-produkt-kann-spuren-von-sesam-enthalten,en:durchschnittliche-nahrwerte,en:8400-kj,en:2000-kcal", - "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", - null, - null, - null, - null, - null, - "80 gram", - "80.0", - null, - "0.0", - null, - null, - null, - null, - "unknown", - null, - "unknown", - "unknown", - null, - null, - null, - "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded", - "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded", - "To be completed,Nutrition facts completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded", - null, - null, - "unknown", - null, - "80.0", - null, - null, - "1.0", - "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-us-scans-2024,top-100000-us-scans-2024,top-country-us-scans-2024", - "0.6625", - "1746257766.0", - "2025-05-03T07:36:06Z", - null, - null, - "https://images.openfoodfacts.org/images/products/invalid/front_en.12.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/front_en.12.200.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.8.400.jpg", - "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.8.200.jpg", - null, - null, - null, - "359.0", - "1502.0", - null, - "19.0", - "17.3", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "26.0", - "1.0", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "17.0", - null, - null, - null, - "1.2", - null, - "0.48", - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - null, - "0.0", - null, - null, - 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" - ], - "text/plain": [ - " code url creator \\\n", - "0 54 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", - "1 63 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", - "2 114 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", - "3 105 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", - "4 2 http://world-en.openfoodfacts.org/product/0000... kiliweb \n", - "\n", - " created_t created_datetime last_modified_t last_modified_datetime \\\n", - "0 1582569031 2020-02-24T18:30:31Z 1733085204 2024-12-01T20:33:24Z \n", - "1 1673620307 2023-01-13T14:31:47Z 1750061386 2025-06-16T08:09:46Z \n", - "2 1580066482 2020-01-26T19:21:22Z 1751035658 2025-06-27T14:47:38Z \n", - "3 1572117743 2019-10-26T19:22:23Z 1738073570 2025-01-28T14:12:50Z \n", - "4 1722606455 2024-08-02T13:47:35Z 1749171851 2025-06-06T01:04:11Z \n", - "\n", - " last_modified_by last_updated_t last_updated_datetime ... \\\n", - "0 NaN 1740205422 2025-02-22T06:23:42Z ... \n", - "1 bodysupport 1750061386 2025-06-16T08:09:46Z ... \n", - "2 teolemon 1751035658 2025-06-27T14:47:38Z ... \n", - "3 NaN 1743653496 2025-04-03T04:11:36Z ... \n", - "4 altroconsumo 1749171851 2025-06-06T01:04:11Z ... \n", - "\n", - " water-hardness_100g choline_100g phylloquinone_100g beta-glucan_100g \\\n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - "\n", - " inositol_100g carnitine_100g sulphate_100g nitrate_100g acidity_100g \\\n", - "0 NaN NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN NaN \n", - "\n", - " carbohydrates-total_100g \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "\n", - "[5 rows x 214 columns]" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "object", - "type": "string" - }, - { - "name": "nutriscore_score", - "rawType": "float64", - "type": "float" - } - ], - "ref": "3879d587-b2c2-44ef-8d86-448adb4bd9fd", - "rows": [ - [ - "count", - "1207.0" - ], - [ - "mean", - "11.43661971830986" - ], - [ - "std", - "11.289823614835823" - ], - [ - "min", - "-16.0" - ], - [ - "25%", - "2.0" - ], - [ - "50%", - "12.0" - ], - [ - "75%", - "19.0" - ], - [ - "max", - "50.0" - ] - ], - "shape": { - "columns": 1, - "rows": 8 - } - }, - "text/plain": [ - "count 1207.000000\n", - "mean 11.436620\n", - "std 11.289824\n", - "min -16.000000\n", - "25% 2.000000\n", - "50% 12.000000\n", - "75% 19.000000\n", - "max 50.000000\n", - "Name: nutriscore_score, dtype: float64" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['nutriscore_score'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Data visualization\n", - "plt.figure(figsize=(10, 6))\n", - "sns.histplot(df['nutriscore_score'].dropna(), bins=30, kde=True)\n", - "plt.title('Distribution of Nutriscore Score')\n", - "plt.xlabel('Nutriscore Score')\n", - "plt.ylabel('Frequency')\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2. Pre-processing " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 2.1 Data curation " - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/daniela/Documents/PROJECTS/DESU_Data_Science/N_Rocher/OFF_Project/OFFProject/scripts/Data_filter_Jess.py:58: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " num_keep.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n" - ] - } - ], - "source": [ - "# aplication of the curation process to the training data set:\n", - "filtered_df = dfj.filter_nutriscore_data(df)\n", - "cat_df = dfj.categorical_filter(filtered_df, cat_keep= True)\n", - "num_df = dfj.numerical_filter(filtered_df, num_drop= True)\n", - "final_df = dfj.final_df(cat_df, num_df)\n", - "final_df.head()\n", - "target = df['nutriscore_score']\n", - "final_df = final_df.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 2.2 Enconding " - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['unknown', 'Beverages', nan, 'Fruits and vegetables',\n", - " 'Composite foods', 'Sugary snacks', 'Cereals and potatoes',\n", - " 'Salty snacks', 'Fat and sauces', 'Milk and dairy products',\n", - " 'Fish Meat Eggs', 'Alcoholic beverages'], dtype=object)" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['pnns_groups_1'].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " pnns_groups_1 PNNS_pro\n", - "6 unknown NA\n", - "9 unknown NA\n", - "11 Fruits and vegetables Plant_based\n", - "12 unknown NA\n", - "14 Composite foods Processed\n", - "... ... ...\n", - "9934 Composite foods Processed\n", - "9943 Sugary snacks Snacks\n", - "9949 Composite foods Processed\n", - "9963 Cereals and potatoes Plant_based\n", - "9986 Sugary snacks Snacks\n", - "\n", - "[1207 rows x 2 columns]\n" - ] - } - ], - "source": [ - "pnns_mapping = {\"unknown\" : 'NA', \n", - " 'Beverages' : 'Drinks',\n", - " 'nan' : 'NA',\n", - " 'Fruits and vegetables' : 'Plant_based',\n", - " 'Composite foods' : 'Processed',\n", - " 'Sugary snacks' : 'Snacks',\n", - " 'Salty snacks' : 'Snacks',\n", - " 'Cereals and potatoes' : 'Plant_based',\n", - " 'Fat and sauces' : 'Processed', \n", - " 'Milk and dairy products' : 'Animal_based',\n", - " 'Fish Meat Eggs' : 'Animal_based',\n", - " 'Alcoholic beverages' : 'Drinks'\n", - "}\n", - "\n", - "final_df['PNNS_pro'] = final_df['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n", - "print(final_df[['pnns_groups_1', 'PNNS_pro']])" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [], - "source": [ - "filtered_df = one_hot_encode_column(final_df, 'PNNS_pro')\n", - "filtered_df.head()\n", - "filtered_df = filtered_df.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "code", - "rawType": "float64", - "type": "float" - }, - { - "name": "additives_n", - "rawType": "float64", - "type": "float" - }, - { - "name": "nutriscore_score", - "rawType": "float64", - "type": "float" - }, - { - "name": "energy_100g", - "rawType": "float64", - "type": "float" - }, - { - "name": "fat_100g", - "rawType": "float64", - 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codeadditives_nnutriscore_scoreenergy_100gfat_100gsaturated-fat_100gtrans-fat_100gcholesterol_100gcarbohydrates_100gsugars_100g...vitamin-c_100gcalcium_100giron_100gfruits-vegetables-nuts-estimate-from-ingredients_100gPNNS_pro_Animal_basedPNNS_pro_DrinksPNNS_pro_NAPNNS_pro_Plant_basedPNNS_pro_ProcessedPNNS_pro_Snacks
6-0.135331-0.15384615.00.610567-0.1828840.3435180.00000-0.311282-0.425248-0.330891...-0.100514-0.0684940.000000-0.7993900.00.0-0.50.0-0.3333335.0
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" - ], - "text/plain": [ - " code additives_n nutriscore_score energy_100g fat_100g \\\n", - "6 -0.135331 -0.153846 15.0 0.610567 -0.182884 \n", - "9 -0.135331 0.538462 4.0 -0.327865 -0.217652 \n", - "11 -0.135331 -0.153846 4.0 -1.942693 -0.565340 \n", - "12 -0.135331 0.230769 6.0 -0.338517 -0.530571 \n", - "14 -0.135331 0.076923 -11.0 -1.634853 -0.582724 \n", - "\n", - " saturated-fat_100g trans-fat_100g cholesterol_100g carbohydrates_100g \\\n", - "6 0.343518 0.00000 -0.311282 -0.425248 \n", - "9 -0.584024 0.00000 0.977335 -0.125342 \n", - "11 -0.693147 7.79661 1.144866 -0.725154 \n", - "12 -0.747708 6.79661 1.846459 -0.582699 \n", - "14 -0.795722 7.79661 0.946442 -0.700162 \n", - "\n", - " sugars_100g ... vitamin-c_100g calcium_100g iron_100g \\\n", - "6 -0.330891 ... -0.100514 -0.068494 0.000000 \n", - "9 -0.820513 ... -0.100514 -0.103322 -0.190859 \n", - "11 -0.819292 ... -0.100514 -0.157204 0.653246 \n", - "12 -0.776557 ... 0.099131 -0.051773 -0.157285 \n", - "14 -0.865690 ... 0.379107 -0.157204 -0.428287 \n", - "\n", - " fruits-vegetables-nuts-estimate-from-ingredients_100g \\\n", - "6 -0.799390 \n", - "9 -0.391697 \n", - "11 -0.799390 \n", - "12 -0.799390 \n", - "14 -0.399689 \n", - "\n", - " PNNS_pro_Animal_based PNNS_pro_Drinks PNNS_pro_NA PNNS_pro_Plant_based \\\n", - "6 0.0 0.0 -0.5 0.0 \n", - "9 0.0 0.0 -0.5 0.0 \n", - "11 0.0 0.0 -0.5 1.0 \n", - "12 0.0 0.0 2.0 0.0 \n", - "14 0.0 0.0 2.0 0.0 \n", - "\n", - " PNNS_pro_Processed PNNS_pro_Snacks \n", - "6 -0.333333 5.0 \n", - "9 1.333333 0.0 \n", - "11 -0.333333 0.0 \n", - "12 -0.333333 0.0 \n", - "14 -0.333333 0.0 \n", - "\n", - "[5 rows x 25 columns]" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "work_df = Scaling.scaler_numeric(imputed_df, target_col='nutriscore_score') \n", - "work_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 2.5 Feature Selection --> Using the Lasso Estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [], - "source": [ - "X_train = work_df.drop(\"nutriscore_score\", axis=1) \n", - "y_train = work_df[\"nutriscore_score\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "n=23, CV score=-0.2037, Features: ['code', 'additives_n', 'energy_100g', 'fat_100g', 'saturated-fat_100g', 'trans-fat_100g', 'cholesterol_100g', 'carbohydrates_100g', 'sugars_100g', 'proteins_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'vitamin-c_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", - "n=22, CV score=0.2849, Features: ['code', 'additives_n', 'energy_100g', 'saturated-fat_100g', 'trans-fat_100g', 'cholesterol_100g', 'carbohydrates_100g', 'sugars_100g', 'proteins_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'vitamin-c_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", - "n=21, CV score=0.3910, Features: ['code', 'additives_n', 'energy_100g', 'saturated-fat_100g', 'trans-fat_100g', 'cholesterol_100g', 'carbohydrates_100g', 'sugars_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'vitamin-c_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", - "n=20, CV score=0.4338, Features: ['code', 'additives_n', 'energy_100g', 'saturated-fat_100g', 'trans-fat_100g', 'cholesterol_100g', 'sugars_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'vitamin-c_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", - "n=19, CV score=0.4687, Features: ['code', 'additives_n', 'energy_100g', 'saturated-fat_100g', 'trans-fat_100g', 'cholesterol_100g', 'sugars_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", - "n=18, CV score=0.5000, Features: ['additives_n', 'energy_100g', 'saturated-fat_100g', 'trans-fat_100g', 'cholesterol_100g', 'sugars_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", - "n=17, CV score=0.5047, Features: ['additives_n', 'energy_100g', 'saturated-fat_100g', 'cholesterol_100g', 'sugars_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", - "n=16, CV score=0.5065, Features: ['additives_n', 'saturated-fat_100g', 'cholesterol_100g', 'sugars_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_NA', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", - "n=15, CV score=0.5074, Features: ['additives_n', 'saturated-fat_100g', 'cholesterol_100g', 'sugars_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Animal_based', 'PNNS_pro_Drinks', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", - "n=14, CV score=0.5083, Features: ['additives_n', 'saturated-fat_100g', 'cholesterol_100g', 'sugars_100g', 'salt_100g', 'sodium_100g', 'vitamin-a_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Drinks', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", - "n=13, CV score=0.5083, Features: ['additives_n', 'saturated-fat_100g', 'cholesterol_100g', 'sugars_100g', 'salt_100g', 'vitamin-a_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Drinks', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", - "n=12, CV score=0.5076, Features: ['additives_n', 'saturated-fat_100g', 'sugars_100g', 'salt_100g', 'vitamin-a_100g', 'calcium_100g', 'iron_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Drinks', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", - "n=11, CV score=0.5067, Features: ['additives_n', 'saturated-fat_100g', 'sugars_100g', 'salt_100g', 'vitamin-a_100g', 'calcium_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Drinks', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", - "n=10, CV score=0.5229, Features: ['additives_n', 'saturated-fat_100g', 'sugars_100g', 'vitamin-a_100g', 'calcium_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Drinks', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", - "n=9, CV score=0.5177, Features: ['additives_n', 'saturated-fat_100g', 'sugars_100g', 'vitamin-a_100g', 'calcium_100g', 'fruits-vegetables-nuts-estimate-from-ingredients_100g', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", - "n=8, CV score=0.5122, Features: ['additives_n', 'saturated-fat_100g', 'sugars_100g', 'vitamin-a_100g', 'calcium_100g', 'PNNS_pro_Plant_based', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", - "n=7, CV score=0.5010, Features: ['additives_n', 'saturated-fat_100g', 'sugars_100g', 'vitamin-a_100g', 'calcium_100g', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n", - "n=6, CV score=0.4927, Features: ['additives_n', 'saturated-fat_100g', 'sugars_100g', 'vitamin-a_100g', 'PNNS_pro_Processed', 'PNNS_pro_Snacks']\n" - ] - }, - { - "data": { - "image/png": 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" - ], - "text/plain": [ - " additives_n saturated-fat_100g sugars_100g vitamin-a_100g calcium_100g \\\n", - "0 -0.153846 0.343518 -0.330891 0.000000 -0.068494 \n", - "1 0.538462 -0.584024 -0.820513 0.000000 -0.103322 \n", - "2 -0.153846 -0.693147 -0.819292 0.000000 -0.157204 \n", - "3 0.230769 -0.747708 -0.776557 -0.986486 -0.051773 \n", - "4 0.076923 -0.795722 -0.865690 0.000000 -0.157204 \n", - "\n", - " fruits-vegetables-nuts-estimate-from-ingredients_100g PNNS_pro_Drinks \\\n", - "0 -0.799390 0.0 \n", - "1 -0.391697 0.0 \n", - "2 -0.799390 0.0 \n", - "3 -0.799390 0.0 \n", - "4 -0.399689 0.0 \n", - "\n", - " PNNS_pro_Plant_based PNNS_pro_Processed PNNS_pro_Snacks \n", - "0 0.0 -0.333333 5.0 \n", - "1 0.0 1.333333 0.0 \n", - "2 1.0 -0.333333 0.0 \n", - "3 0.0 -0.333333 0.0 \n", - "4 0.0 -0.333333 0.0 " - ] - }, - "execution_count": 138, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.feature_selection import SequentialFeatureSelector\n", - "from sklearn.linear_model import Lasso\n", - "# Application of the feature selector to the training data set:\n", - "n_selected_features = 10 # specify the number of features you want to select\n", - "# use the n best features\n", - "estimator = Lasso(alpha=0.01, random_state=42, max_iter=1000)\n", - "sfs = SequentialFeatureSelector(\n", - " estimator,\n", - " n_features_to_select=n_selected_features,\n", - " direction=\"backward\", # use backward elimination\n", - " cv=5, # cross-validation to evaluate performance\n", - " n_jobs=-1\n", - " )\n", - "sfs.fit(X_train, y_train)\n", - "\n", - "selected_features = sfs.get_support() # Transform the data to select the features\n", - "X_train_selected = sfs.transform(X_train)\n", - "\n", - "# merge selected features with their names\n", - "X_train_selected_df = pd.DataFrame(X_train_selected, columns=X_train.columns[selected_features])\n", - "X_train_selected_df.head()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Apply all pre-processing steps to the test data set" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/daniela/Documents/PROJECTS/DESU_Data_Science/N_Rocher/OFF_Project/OFFProject/scripts/Data_filter_Jess.py:58: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " num_keep.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - ": shape of df with only numeric features=(3680, 26)\n", - ": shape of df with only numeric features=(3680, 25)\n" - ] - }, - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - 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"final_test_df = dfj.final_df(cat_test_df, num_test_df)\n", - "final_test_df = final_test_df.drop(columns=['nutrition-score-fr_100g'])\n", - "# encodage\n", - "final_test_df['PNNS_pro'] = final_test_df['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n", - "filtered_test_df = encoding_func.one_hot_encode_column(final_test_df, 'PNNS_pro')\n", - "filtered_test_df = filtered_test_df.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])\n", - "# imputation \n", - "imputed_test_df = Imputing.knn_impute_numeric(filtered_test_df, n_neighbors=5)\n", - "# scaling\n", - "work_test_df = Scaling.scaler_numeric(imputed_test_df, target_col='nutriscore_score')\n", - "work_test_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - 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" - ], - "text/plain": [ - " additives_n saturated-fat_100g sugars_100g vitamin-a_100g calcium_100g \\\n", - "0 3.083333 -0.272774 -0.432384 -0.000001 -0.912360 \n", - "1 0.583333 1.749871 0.806050 -0.000001 -0.850658 \n", - "2 -0.666667 0.564076 12.964413 3.199525 -0.998936 \n", - "3 -0.250000 4.848173 3.322064 -0.000001 -0.912360 \n", - "4 1.416667 3.818837 0.902135 -0.000001 -0.912360 \n", - "\n", - " fruits-vegetables-nuts-estimate-from-ingredients_100g PNNS_pro_Drinks \\\n", - "0 23.950075 0.0 \n", - "1 -0.629354 0.0 \n", - "2 -0.629354 1.0 \n", - "3 2.777358 0.0 \n", - "4 0.747962 0.0 \n", - "\n", - " PNNS_pro_Plant_based PNNS_pro_Processed PNNS_pro_Snacks \n", - "0 0.0 4.0 0.0 \n", - "1 0.0 -1.0 0.0 \n", - "2 0.0 -1.0 0.0 \n", - "3 0.0 -1.0 2.5 \n", - "4 1.0 -1.0 0.0 " - ] - }, - "execution_count": 149, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "# Apply the feature selection process to the test dataset\n", - "# make sure to drop the same columns as in training if any were dropped\n", - "work_test_df = work_test_df.reindex(columns=work_df.columns, fill_value=0)\n", - "\n", - "y_test_df = work_test_df['nutriscore_score']\n", - "X_work_test_df = work_test_df.drop(\"nutriscore_score\", axis=1)\n", - "X_test_selected = sfs.transform(X_work_test_df)\n", - "\n", - "X_test_selected_df = pd.DataFrame(X_test_selected, columns=X_work_test_df.columns[sfs.get_support()])\n", - "X_test_selected_df.head()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. Predicting the nutriscore" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 3.1 : Regression Model - Lasso" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(3680, 10)\n", - "(2296, 10)\n" - ] - } - ], - "source": [ - "print(X_test_selected_df.shape)\n", - "print(X_train_selected_df.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [], - "source": [ - "def linear_reg_lasso(X_train, y_train, X_test, y_test):\n", - " from sklearn.linear_model import Lasso\n", - " from sklearn.model_selection import GridSearchCV\n", - " from sklearn.metrics import mean_squared_error, r2_score\n", - " import numpy as np\n", - "\n", - " # defining the model \n", - " lasso = Lasso(random_state=42)\n", - "\n", - " param_grid = {\n", - " 'alpha': [0.01, 0.1, 1.0, 10.0],\n", - " 'max_iter': [1000, 5000, 10000],\n", - " 'tol': [1e-4, 1e-3, 1e-2]\n", - " }\n", - "\n", - " grid_search = GridSearchCV(lasso, param_grid, cv=5, scoring='neg_mean_squared_error')\n", - " grid_search.fit(X_train, y_train)\n", - "\n", - " best_model = grid_search.best_estimator_\n", - " y_pred = best_model.predict(X_test)\n", - "\n", - " print(\"Best parameters:\", grid_search.best_params_)\n", - " print(\"CV RMSE:\", np.sqrt(-grid_search.best_score_))\n", - " print(\"Test RMSE:\",(mean_squared_error(y_test, y_pred)))\n", - " print(\"Test R²:\", r2_score(y_test, y_pred))\n", - "\n", - "\n", - " # Get coefficients\n", - " coefs = best_model.coef_\n", - " intercept = best_model.intercept_\n", - "\n", - " coef_df = pd.DataFrame({\n", - " \"Feature\": X_train.columns,\n", - " \"Coefficient\": coefs\n", - " }).sort_values(by=\"Coefficient\", ascending=False)\n", - "\n", - " print(\"Intercept:\", intercept)\n", - " print(coef_df)\n", - "\n", - " return best_model, y_pred\n", - "\n", - "\n", - "def plot_learning_curve(best_model, X_train, y_train, y_test, y_pred):\n", - " from sklearn.model_selection import learning_curve\n", - " from sklearn.metrics import mean_squared_error\n", - " import numpy as np\n", - " import matplotlib.pyplot as plt\n", - " \n", - " train_sizes, train_scores, test_scores = learning_curve(\n", - " best_model, X_train, y_train, cv=5, n_jobs=-1,shuffle=True,\n", - " random_state=42, scoring='neg_mean_squared_error', train_sizes=np.linspace(0.1, 1.0, 10)\n", - " )\n", - "\n", - " train_score_mean = -train_scores.mean(axis=1)\n", - " val_score_mean = -test_scores.mean(axis=1)\n", - "\n", - " # RMSE (for interpretability)\n", - " train_rmse_mean = np.sqrt(train_score_mean)\n", - " val_rmse_mean = np.sqrt(val_score_mean)\n", - "\n", - " # ----- Plot RMSE learning curve -----\n", - " plt.figure(figsize=(8, 5))\n", - " plt.plot(train_sizes, train_rmse_mean, 'o-', label='Training RMSE')\n", - " plt.plot(train_sizes, val_rmse_mean, 'o-', label='Validation RMSE')\n", - " plt.xlabel('Training set size')\n", - " plt.ylabel('RMSE')\n", - " plt.title('Learning Curve (RMSE) - Lasso')\n", - " plt.grid(True)\n", - " plt.legend()\n", - " plt.show()\n", - "\n", - " # Loss function\n", - " loss = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(loss)\n", - " print(\"loss (RMSE):\", rmse)\n", - "\n", - " return train_sizes, train_rmse_mean, val_rmse_mean\n", - "\n", - "\n", - "def plot_learning_curve(best_model, X_train, y_train, y_test, y_pred):\n", - " from sklearn.model_selection import learning_curve\n", - " from sklearn.metrics import mean_squared_error\n", - " import numpy as np\n", - " import matplotlib.pyplot as plt\n", - " \n", - " train_sizes, train_scores, test_scores = learning_curve(\n", - " best_model, X_train, y_train, cv=5, n_jobs=-1,shuffle=True,\n", - " random_state=42, scoring='neg_mean_squared_error', train_sizes=np.linspace(0.1, 1.0, 10)\n", - " )\n", - "\n", - " train_score_mean = -train_scores.mean(axis=1)\n", - " val_score_mean = -test_scores.mean(axis=1)\n", - "\n", - " # RMSE (for interpretability)\n", - " train_rmse_mean = np.sqrt(train_score_mean)\n", - " val_rmse_mean = np.sqrt(val_score_mean)\n", - "\n", - " # ----- Plot RMSE learning curve -----\n", - " plt.figure(figsize=(8, 5))\n", - " plt.plot(train_sizes, train_rmse_mean, 'o-', label='Training RMSE')\n", - " plt.plot(train_sizes, val_rmse_mean, 'o-', label='Validation RMSE')\n", - " plt.xlabel('Training set size')\n", - " plt.ylabel('RMSE')\n", - " plt.title('Learning Curve (RMSE) - Lasso')\n", - " plt.grid(True)\n", - " plt.legend()\n", - " plt.show()\n", - "\n", - " # Loss function\n", - " loss = mean_squared_error(y_test, y_pred)\n", - " rmse = np.sqrt(loss)\n", - " print(\"loss (RMSE):\", rmse)\n", - "\n", - " return train_sizes, train_rmse_mean, val_rmse_mean\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best parameters: {'alpha': 0.01, 'max_iter': 1000, 'tol': 0.0001}\n", - "CV RMSE: 7.423040951562572\n", - "Test RMSE: 3254.303640585438\n", - "Test R²: -51.49091602408616\n", - "Intercept: 1.947423862727594\n", - " Feature Coefficient\n", - "8 PNNS_pro_Processed 5.705126\n", - "6 PNNS_pro_Drinks 3.919226\n", - "1 saturated-fat_100g 3.252060\n", - "0 additives_n 2.519466\n", - "9 PNNS_pro_Snacks 1.821796\n", - "4 calcium_100g 0.907920\n", - "2 sugars_100g 0.779826\n", - "5 fruits-vegetables-nuts-estimate-from-ingredien... -2.853652\n", - "7 PNNS_pro_Plant_based -3.554252\n", - "3 vitamin-a_100g -4.226487\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss (RMSE): 57.04650419250454\n" - ] - } - ], - "source": [ - "\n", - "##### Using the function\n", - "best_reg_Lasso, y_pred_Lasso = linear_reg_lasso(X_train_selected_df, y_train, X_test_selected_df, y_test_df)\n", - "\n", - "train_sizes, train_rmse, test_rmse = plot_learning_curve(best_reg_Lasso, X_train_selected_df, y_train, y_test_df, y_pred_Lasso)" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#%pip install shap\n", - "# SHAP \n", - "import shap\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# with the best model\n", - "model = best_reg_Lasso\n", - "# Use the selected features DataFrame for SHAP\n", - "explainer = shap.LinearExplainer(model, X_train_selected_df)\n", - "\n", - "# Compute SHAP values\n", - "shap_values = explainer(X_train_selected_df)\n", - "# Summary plots\n", - "shap.summary_plot(shap_values.values, X_train_selected_df, feature_names=X_train_selected_df.columns, plot_type=\"bar\")\n", - "shap.summary_plot(shap_values.values, X_train_selected_df, feature_names=X_train_selected_df.columns)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Conclusion: it seams that the model is not working" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 3.2 : Regression Model - SVM" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 3.3 : Decision tree" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'np' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[81]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m### Decision Tree Regression\u001b[39;00m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscripts\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DecisionTree \n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m results = DecisionTree.decision_tree(X_selected, y)\n\u001b[32m 5\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTest R²: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresults[\u001b[33m'\u001b[39m\u001b[33mr2\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.3f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m, MAE: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresults[\u001b[33m'\u001b[39m\u001b[33mmae\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.3f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m, MSE: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresults[\u001b[33m'\u001b[39m\u001b[33mmse\u001b[39m\u001b[33m'\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.3f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/PROJECTS/DESU_Data_Science/N_Rocher/OFF_Project/OFFProject/scripts/DecisionTree.py:54\u001b[39m, in \u001b[36mdecision_tree\u001b[39m\u001b[34m(X, y, test_size, random_state)\u001b[39m\n\u001b[32m 49\u001b[39m mse = mean_squared_error(y_test, y_pred)\n\u001b[32m 52\u001b[39m \u001b[38;5;66;03m#Visualization\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m54\u001b[39m train_sizes, train_scores, test_scores = learning_curve(best_dt, X, y, cv=\u001b[32m5\u001b[39m, scoring=\u001b[33m\"\u001b[39m\u001b[33mneg_root_mean_squared_error\u001b[39m\u001b[33m\"\u001b[39m, train_sizes=np.linspace(\u001b[32m0.1\u001b[39m,\u001b[32m1.0\u001b[39m,\u001b[32m20\u001b[39m))\n\u001b[32m 56\u001b[39m \u001b[38;5;66;03m# Average scores across folds\u001b[39;00m\n\u001b[32m 57\u001b[39m train_scores = -train_scores\n", - "\u001b[31mNameError\u001b[39m: name 'np' is not defined" - ] - } - ], - "source": [ - "### Decision Tree Regression\n", - "from scripts import DecisionTree \n", - "\n", - "results = DecisionTree.decision_tree(X_selected, y)\n", - "print(f\"Test R²: {results['r2']:.3f}, MAE: {results['mae']:.3f}, MSE: {results['mse']:.3f}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 3.4 Random forest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; 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total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.5s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=5; total time= 0.0s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.4s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time= 0.1s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time= 0.3s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time= 0.2s\n", - "Meilleurs paramètres trouvés : {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n", - "MSE : 16.467475138605856\n", - "R² : 0.8396492656081895\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/model_selection/_validation.py:528: FitFailedWarning: \n", - "540 fits failed out of a total of 1620.\n", - "The score on these train-test partitions for these parameters will be set to nan.\n", - "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", - "\n", - "Below are more details about the failures:\n", - "--------------------------------------------------------------------------------\n", - "164 fits failed with the following error:\n", - "Traceback (most recent call last):\n", - " File \"/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n", - " estimator.fit(X_train, y_train, **fit_params)\n", - " ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/base.py\", line 1382, in wrapper\n", - " estimator._validate_params()\n", - " ~~~~~~~~~~~~~~~~~~~~~~~~~~^^\n", - " File \"/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/base.py\", line 436, in _validate_params\n", - " validate_parameter_constraints(\n", - " ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n", - " self._parameter_constraints,\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " self.get_params(deep=False),\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " caller_name=self.__class__.__name__,\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " )\n", - " ^\n", - " File \"/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/utils/_param_validation.py\", line 98, in validate_parameter_constraints\n", - " raise InvalidParameterError(\n", - " ...<2 lines>...\n", - " )\n", - "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n", - "\n", - "--------------------------------------------------------------------------------\n", - "376 fits failed with the following error:\n", - "Traceback (most recent call last):\n", - " File \"/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n", - " estimator.fit(X_train, y_train, **fit_params)\n", - " ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/base.py\", line 1382, in wrapper\n", - " estimator._validate_params()\n", - " ~~~~~~~~~~~~~~~~~~~~~~~~~~^^\n", - " File \"/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/base.py\", line 436, in _validate_params\n", - " validate_parameter_constraints(\n", - " ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n", - " self._parameter_constraints,\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " self.get_params(deep=False),\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " caller_name=self.__class__.__name__,\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " )\n", - " ^\n", - " File \"/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/utils/_param_validation.py\", line 98, in validate_parameter_constraints\n", - " raise InvalidParameterError(\n", - " ...<2 lines>...\n", - " )\n", - "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n", - "\n", - " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", - "/home/daniela/anaconda3/envs/demo_desu/lib/python3.13/site-packages/sklearn/model_selection/_search.py:1108: UserWarning: One or more of the test scores are non-finite: [ nan nan nan nan nan\n", - " nan nan nan nan nan\n", - " nan nan nan nan nan\n", - " nan nan nan nan nan\n", - " nan nan nan nan nan\n", - " nan nan -13.69457016 -10.1510443 -10.08206995\n", - " -13.95498701 -10.79043974 -10.50185661 -13.89475875 -11.06122316\n", - " -10.90554598 -13.48481275 -10.61435071 -10.55525274 -13.63662545\n", - " -10.77072323 -10.52588871 -14.02678377 -11.42242344 -11.2228354\n", - " -13.27654848 -11.56161826 -11.29411378 -13.27654848 -11.56161826\n", - " -11.29411378 -14.80151842 -11.82639631 -11.78372091 -13.69457016\n", - " -10.1510443 -10.08206995 -13.95498701 -10.79043974 -10.50185661\n", - " -13.89475875 -11.06122316 -10.90554598 -13.48481275 -10.61435071\n", - " -10.55525274 -13.63662545 -10.77072323 -10.52588871 -14.02678377\n", - " -11.42242344 -11.2228354 -13.27654848 -11.56161826 -11.29411378\n", - " -13.27654848 -11.56161826 -11.29411378 -14.80151842 -11.82639631\n", - " -11.78372091 nan nan nan nan\n", - " nan nan nan nan nan\n", - " nan nan nan nan nan\n", - " nan nan nan nan nan\n", - " nan nan nan nan nan\n", - " nan nan nan -14.27083435 -10.97825769\n", - " -10.73629731 -14.01350569 -11.11713058 -10.92778791 -14.09089127\n", - " -11.54964448 -11.36892053 -13.4665218 -11.11185553 -10.86286951\n", - " -13.83839076 -11.19278293 -10.95000173 -13.73255289 -11.61510688\n", - " -11.40966486 -14.55066342 -12.07729755 -11.78588725 -14.55066342\n", - " -12.07729755 -11.78588725 -14.37274818 -11.96739957 -11.81966729\n", - " -14.27083435 -10.97825769 -10.73629731 -14.01350569 -11.11713058\n", - " -10.92778791 -14.09089127 -11.54964448 -11.36892053 -13.4665218\n", - " -11.11185553 -10.86286951 -13.83839076 -11.19278293 -10.95000173\n", - " -13.73255289 -11.61510688 -11.40966486 -14.55066342 -12.07729755\n", - " -11.78588725 -14.55066342 -12.07729755 -11.78588725 -14.37274818\n", - " -11.96739957 -11.81966729 nan nan nan\n", - " nan nan nan nan nan\n", - " nan nan nan nan nan\n", - " nan nan nan nan nan\n", - " nan nan nan nan nan\n", - " nan nan nan nan -13.89546845\n", - " -10.34825859 -10.25085297 -13.75597579 -10.63870527 -10.42240933\n", - " -13.89475875 -11.03650537 -10.89395529 -13.31542115 -10.61066027\n", - " -10.53626756 -13.63662545 -10.78049559 -10.5519798 -14.02678377\n", - " -11.40983128 -11.2260849 -13.27654848 -11.56161826 -11.29411378\n", - " -13.27654848 -11.56161826 -11.29411378 -14.80151842 -11.82639631\n", - " -11.78372091 -13.89546845 -10.34825859 -10.25085297 -13.75597579\n", - " -10.63870527 -10.42240933 -13.89475875 -11.03650537 -10.89395529\n", - " -13.31542115 -10.61066027 -10.53626756 -13.63662545 -10.78049559\n", - " -10.5519798 -14.02678377 -11.40983128 -11.2260849 -13.27654848\n", - " -11.56161826 -11.29411378 -13.27654848 -11.56161826 -11.29411378\n", - " -14.80151842 -11.82639631 -11.78372091 nan nan\n", - " nan nan nan nan nan\n", - " nan nan nan nan nan\n", - " nan nan nan nan nan\n", - " nan nan nan nan nan\n", - " nan nan nan nan nan\n", - " -13.69457016 -10.1510443 -10.08206995 -13.95498701 -10.79043974\n", - " -10.50185661 -13.89475875 -11.06122316 -10.90554598 -13.48481275\n", - " -10.61435071 -10.55525274 -13.63662545 -10.77072323 -10.52588871\n", - " -14.02678377 -11.42242344 -11.2228354 -13.27654848 -11.56161826\n", - " -11.29411378 -13.27654848 -11.56161826 -11.29411378 -14.80151842\n", - " -11.82639631 -11.78372091 -13.69457016 -10.1510443 -10.08206995\n", - " -13.95498701 -10.79043974 -10.50185661 -13.89475875 -11.06122316\n", - " -10.90554598 -13.48481275 -10.61435071 -10.55525274 -13.63662545\n", - " -10.77072323 -10.52588871 -14.02678377 -11.42242344 -11.2228354\n", - " -13.27654848 -11.56161826 -11.29411378 -13.27654848 -11.56161826\n", - " -11.29411378 -14.80151842 -11.82639631 -11.78372091]\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "### Random forest \n", - "\n", - "X = work_df.drop(\"nutriscore_score\", axis=1) \n", - "y = work_df[\"nutriscore_score\"]\n", - "\n", - "from scripts.rf import random_forest_GS\n", - "best_rf, X_train, X_test, y_train, y_test, y_pred = random_forest_GS(X_selected_df, y)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "from scripts.rf import plot_learning_curve, plot_mse\n", - "\n", - "plot_learning_curve(best_rf, X, y)\n", - "plot_mse(best_rf, X_train, X_test, y_train, y_test)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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E/STqimNPy+DEU1J48o5N1NYE0R063l7J1Fd46VXTiMM00dwGPdwW9XUWpgJ7qp18h5/q9Q1YmkZdahJNiQloOiSk26lJSaAgaGNAWRW96z0omw16JnLGdb355CsvFRUBerU0kbCzEXeajS0DsllZbyfdBZPtTaR5Wkjv62adM4Xv1nrJqmnguOOSmXhBLou/89PYECSzpgHPhgay+7k55if5uNMcUeekojrI0jU+MlJ0/D6LDSVBAgGFiUZSss7Rw52MGeyIOZftXy1F6bIq6rZ66DWhB+kDk1FK8d07FRSuamLw0WmMP70HVQX1VHxXQ8bgFPLGZ4bXb670svWTCpypDvqdnIPhiD+tbV2xh03vl2EGLOyJNlLzE1B+i0BjgLxjelD1TTVokH9GL+zJoc++4psqqtfXkzM2kx5HpkVtr6miha0Lymje1kRqXgK5YzNofL+Euk/LaazzkWQ30bLd0L8HmtNO3eoatEovmRMysY3Pwlvjx76zBUeCjcwL+rD2xi+oX16N06Hj6JNMMMEgIWDiSLRjJGh40fHUB/AFLEyfhSpuJGAYpIzL5MjbRuBbX4+rtxvr7bV4Fm7HdVQ2qX87iR0PfoPV4CPR5sdaspV6K5mAw4nmhMYGRYtfw52fSJ9rB7PpidU01/pJaW4hp9mD3jqnjLuvC0e6wtzUAD4/Rgo4A7XsbEihgUxStUYcNg38Jn4S0LBIdtaT2h98XhtmnZ+EQBW6t4mgmQzo2PDSdkHQwp3yLMAM/Z4NHatPT6zRg9FOHIx6ZCFqWyMafnS8KIJg2CArHapaIGgRrtnQNLQUG1qaAxo80OJDy0lGS3RBRjL8+TJMZYMb/4NWXomWlggj+hAs9aM8PoyBaSi3C3N9JVrfTOy3now261P44DvIToVZN8G41gmLF62BtdvghOHQMx0+WgnvfwtVjXDNj+DSSZ198eGf78J7y+GEI+DOC+MGQuLwIUHBD5jf7+fnP/85paWlnHfeefTv3x+fz8fmzZspLi4GoLCwECBu95mCgoKY5XPnzuX+++9n8ODBXHfdddjtdl5//XXWrFkDwNChQ8NpLcvin//8JxMnTuSyyy4jKSmJoqIi5s6dS1FREa+++iq21lrWoqIiLMti9erVLFiwgKlTp3L22WeTnJxMXV0dV155JcFgkHPPPZfevXvj8XgoLCxk+/bte31+CgoKOj12gNdffx3TNPnZz34W857b7QaguroagLq6Oj744AMmTJjAEUccEZVW13WcTmc4bZunnnqKZ555hrFjxzJt2jT8fj8vv/wyS5YsAaLP5eFubpHFxW+amG2FagAs8lKgzEP4McBLilun+9T1UAFdAQbUBTTq6lpX0zVwEiqwtwRjC/LQep/WIKjaWw9U637aZhAi9ACwpblp+NGiS+NKhe7tWuty3QBlQcAKBQbAUE9zzG4NBQm+IA1tDzXTNHDawLRC23EaZDcFKLM0KpNC3yHDa2HXgmzJSMRtKTI8XhymBar1Aq3pnFhZS77XB0Bh/3z6bysns74RDWh02NnUrzfJrUFSshnAsBSblUaKzx+Vv6CmUZ6ehmUYGKYCzcAgSMAT5OOCIIGkRBIDQXo2t9DX4w2fkka7jRKni+o3a/j8pbLwKaXFxLGxjgEtXmxma7cpj0mtp/20O7fUUG2F8taYlEBdSgpK11BKscPvJFgLWQTxJKbQ5DNJ9AcIljVz+3Me6lwuwIauXEyqb2JZchY1pS7SvD5GFW+n1LQoBbSldRjNWxlqWdgti00rYNV/N7GiXz7DKqvY1rr/jd808NUbZfz030eROzwFgM9WeLlnRi1tY7Y1S+EA/JqGvzWofPaDZs49zsVdV6bGfteAj25dzuaPyls3AMffNZLFC+tZ3eACYPGqaj6YXUGPb7eE1xl6QT4n3zea8uXVvHv9MoItoQxkDE7m/FnH40yODuhKv6rivRu+xGwLhpXCCJhoEQ1HDn8QQ8F3Was5/c2TWfnfjRS83L7PsTcNY+yvhwOw/fOdzL9hKaYvtD2XN0j/0jqM1u+3pcHq7GRSljWjW9XhZd5Ena2bPVhvbAufr/QqL8H7V6J0HdDxtigSVlZhC1g0AY022JmZhGnTQSkcviCuFpOG9ND5aVpZx46fLCZzZwupNJNC6Lvn2dqI/6312Fp/sAEUO0knAOi00Oyy0egI/Y6ain1sfqQQy3BBkovKpBSCtTby6+sIohEo9kBxW8uZHbPKokAbQaMeejJ5BTn0CWzDTei3q9Bp8GViLyglhZ0oAuHfg40mdNoKvx1H6Oj4ScRLGjbTi2vLDmxbSgn+72vAiY4P0FEkAArN9KEqvICJjhcNhYUdpVyo+gBafQ1622QBxd723ZzyR3RC3SIBqG+G4kpsrcvUxtD/bUCw0IVn/kaSqQilrW6E8bfDR3+EFxbBrE/bt2vTW4OTVh98B+98Ay+0V/aFTbgdvtkU+vf8lTDzY9j0ZGy6Q5BMPxqfBAU/YIsXL6aoqIjHHnuM448/Pm6agoICevbsGdN9pqKigpqamqiC6caNG3nwwQc56aSTeOCBB8IF+pNPPpkLL7wQiC7IKqWYO3duTPefrKwsnnjiCYqLixk4cCDQHpw0NjbyyiuvkJWVFU4/a9YsduzYwWuvvcaAAQP28mzEP3boPChYuHAhPXv2jDtAuKGhAYDExEQAPvnkE0zT5IwzzohJGwwGaW5uDqeFUGvLM888w6WXXsrvfve7cJ/1MWPGcN1116HrOoMHD/5+B3gIuWdxx4AgpKyB8Jz/YZrWXtC3Ebd5PZSO0A2ss940emsaRWvlndW+La11v5bCrwjNJWqpcIE/7jZtGqHEIT28Pnx2e1T+TKCx0RdqoXDZ2/cV3q9GWaILpfzhfZiaFuqCZNNpAUqdSaAUWnMAzbJwmWY4IADIqaqlR8Q4gUR/AEfQJGBvv5z3DJoUKwtvh+OwKUVuXT1lGenhPLXYDIqTErFax0Q0OB0EdJ2BDR6CGtgUpASC+AyDZptBRofTYlgKw4weR6EROr+aUhitBfKgoVOVkRber9duJxhRq6h0nZ0pKfSvqqYkJbk1IAixNI2lffOodYZq68dU1eAyrah1NZuB3dveXSrR52doZRUOK7rQFsTgk8c38pOnxgLwxJwGIidx0lu/M/4OwebbX3i5dHICg3tHF9bLvq5qDwgIrfv5I+tY26c/RFSalvrsuN0uEltCBbvCudsYeUV/vnx0fTggAKjZ0Mj614sZfW30Y+e+fXpDe0AAaKaKCgggNPuVETDxVnpZ/rfVbFxYEfX+iicLGX75ANyZTr76+5pwQACQ3tASDggAdAU5Nc34cEQtswUU3qT2FjilazQl2nAEIzKjafgcdgzLh2FaVKW1BgSt76XW+6jq0X49BLDsOk2JNno3eSMK30H0qB+jRgrNVJOKAz877Enhd/xOHatDl5vtqWnk19eho7DRsSudjqNDgb5Uy2Og2hFVXGwkk2TKopbtrmOPjgUYBEmkGTuJlKKFayYM2r8YGgoXYGLgCe/DwMRCYZEA2OlsBjEtfIGLXKZaxye1L7fhDQX/uLATEVhc/FAomIgU7BjkALMXw4xfgjOitezbze0BQZvNO+CtL+H8Y+LmVxz6pNPaD1hbwXXt2rWYZuxFRSlFUVFR3ELxunXrgOhC/tNPP41hGNx1113hgAAgLy+PXr164XQ66dOnT3i5YRjhgMDn81FXV0ddXR0pKaEaOr+/vRazLSj4f//v/0UFBJHHsXr16j04+t1bv349ED8oKC8vZ+fOnYwcOTLuulu2hGrf8vPzAVi1ahUAo0aNikm7devWqLQQGquQk5PDb37zm3BAAHDUUUfhdDrp3bt3VBekg62mpgafr71g6vF4aGxsL5j6/f6YlpDy8vaCUplnL3fcWUAArRV0HWr429jauhS1/r+tc35HfivUmtCW1mrdbhempnBaiqRAAK01qNEtRbUGStPA0/rdVhGBRisVGfSE89vhUqxpKJuBzTCiOiZolkXezujzrAPDtpdHLVOt/ylNjC54AbgCQRIiWhCabbZwQNCmxW7Dr2tRZ8xlmphxPo9dPfJHae1FE5/DER1AxWnh8be2sDTG6fbUEnHNSfHHmVEpTh9suxWngAPUlIYKQkopSqs6BDSq7asVm7/tlaG0FRUV4elp64ubYtJ5g7EFVABfh+OqL2miviT2x1Ff3BS1D4CG8uj9aHEeTKcisly/JXaAuRVQeFqPvb44er/2OIVBmxm7D6OLw1RMXQs/J8PXoTuULahQcUoflqFFFUq0OD9Eo7WAbNeiPzcV5/MyNR2laR0Ci8htWR3SG1gdvs16W3/B6L3F3V54OxGBlIUDCzvtgUBs9xotZq+g0fYb3TdzhRn4WtsNIni88RN3pFSoK1Gr8vJyWLU1ftrvtsR8d/fm/rH/aZ38dW8SFPyAnXrqqQwdOpQZM2YwZcoU7r33XhYvXhwOEIqLi2lqamL48OEx63YsMDc1NbFkyRImT55Menp63P0NHjwYI6Lmr6SkhD//+c+ceeaZTJo0iVNPPZVTTz2VBx54AIguJBcUFJCVlRV3WtALLriAXr168Ze//IVzzjmHv//973zzzTd7eVbaFRQU0KNHj7gzAm3bFmoa79evX8x7SilWr15NTk4OOTk54fS6rkcFRW1WrFgBEA4wiouLKSws5KyzzoqZ0lTTQt0qDrWuQxkZGVEtPklJSeEB1gAOh4PMzMyodXr27Bn+9wVDdnGx7fhWZEF6V0/kbVuvrYtPG524heyY/US2XBh6bCGw476DVnufGEKrOk2LdK+PNK+PRL+P1Qnu1m1b7XmMKU/EBgqRrx0BE7cviKYsXJrCZ+hsTgjVmutWqOZ9Z1oKRfl5bM/KxNQ17MHoktp2u4Fl18lviO3iBIRr7wEccSoMNBWqhdYjshnU9ahCfuTyjsFCuECgafhdjtbjCkSd03j7TWwNVnKaYvPd09MULgiXJsYGzLZAbGk1MpCIyBwDJ2a0Zk9j/LDosQJWawzZsdDtsMHoQaG0ubm54WC+18QsOj53LjPbhsMf3XULpUiKGAui23V6jsuk93HZMVnsfVxW1D4ABp7WKypNvAfTRdb09z0tD5s7ugDqynSSMSzUBar3pOj9NiTGTujQ7IoNzkxb7H7jDX63BS2MoImFhiMQ/VkH7Do2X4fCrlK4WoKYET9UK04RxYe9NblOUrC9oGn3mzG/2R7NTehKYaITjCmMK+qI/h6lqRpsEbXyGhZOvHQsyKu2MQOtqSL3amLHT1JU6uiCfZxgrtOCvxkRHMTT9UJsEBc2OvyuhvUCexc6jKS4oVf79b1nz55w6XHxu27+/LSY7+7e3D/EwSFBwQ9YSkoKL7zwAk888QRnnnkm33zzDbfeeivXXnstfr8/3H0mXlCwcuVKEhMTw4OBN2/eTCAQiFur7vV6KS0tjSrIrlmzhiuvvJKvv/6aiy++mAceeIB///vfTJ8+nX79+tG7d2+SkkIXzmAwyObNmxk3blzUhaRNr169eP3113nkkUeYNGkSCxcu5IYbbuC2227b63NTV1fHjh074h47hGoygHCrRqTly5dTVVXFj370o6j0SUlJ6HrsT2r+/PkYhhEe0Lyr875t2zb8fv8hFxR8X4+cYnDFCB2brX3sb3Yi/GKsjtuth+9tDhtkuLVQVx/TCo81CIu86UcWRMItAtquuxtF2t0sPm0tBkqFxhIEWrsfGRq4dWytefFpGhtcDuZlpFHfNpbAaWvvNmTTossBmobdiIgWNKC1QJNb10x+TRN5dc30afQSdBhg1/k8I5Wv0pIpSXCysXdP1g3oS1lWJhvz81gxeACrMlPZ7rSzw26w2u1gW4obFbDQzdgadQUEdQ0T0E2TZJ8fZ4egIsEfwIxoAfDrGi02g8xmL+m6HyyFqWk02WysS0+lxu3C1NvrVIOaRsAwCOo6danJ1CW6sQdN0usawp+hI2iS7mlCbw1Qkppb6FldA0qR09zC8Pq6cPCS2dzCyUVbOG3DVhL8AVb0SKckKaG1RUThsIJsTU+hIiU5NHRE02BoBiWDcmlqrZ1vS9urv5PJN7V3zbv9ylRGDAilcTogIyX0fUzCCtcwZ6bo3HttKunJsb/vlN4JTP7LaFzpoYAhfWASZz52NBedl4IrECrQ2ZXJlElOsnqFCkYJWU5OeXAMCT2cHPf7I+l9XKh11OYyGHPdIAaekRezn/HThnDEJX0xnDqaoaEMHVwGWmshPTHTgUOp0ExWF/Zl5K+Hc/KjR5OQEwook/MTOeWfEzAcoWOY9MfR9DymtUJEKZrcNgJHZqAl2LA0qElxUZ7pJmiPaN0xwLSBuzkYnknKGbBINy1cuhX+bHXTwm75MZWOiU5qnRc9ortXTbqLtDovdm/oe68HLZJ9Js6Aoj4xNVzrH8QgkJaE1VrwtuFDYQKKFuzkttST4fPgNAOk+VoYUFVNoteHPRgkt7GewVU7sdBowUE5mfiwEQo1TJy00E+V41JeUIosVcFR1ldkUoyLFtw0k0IDbjxo2MJ5CBXxbQRxYWHDwo6JixZSaSSDZtJREUGEHQ+hecbaauWjawo0Amj447RF2NBpRGFDte67/RupodxurI71GK1pIvegAL+WhCO9rVtTK5cdlv4NZt8MuWmhZb0zITM5apukJMA7dxEjwQXPTANna+BoN+CBK6OCh0OZam3j7PjX3cmYgh84wzCYMGECEyZM4Oabb+a+++7j7bffpqCgINwFpmPf9R07drBixQqOOuqocCHd6w1d0OIVej/44AOCwSBDhgwJL3vyySexLIvZs2dHjVcoLy+npKSEU045Jbxs06ZNnQYcbRwOByeeeCInnngit912G7fccguffvopVVVVezX3/666DkGoZgNCsyJ1NHv2bAzD4KKLLopKX1JSEpN27dq1fPfdd0yZMiV8HnZ1Lt955x3ghzXIGCDFqTH7PBuzzlVomkaFR5HpBruh8a/TFRVNCrsOPVv7Km+rt1hSamFaGv8rNFlfp3FGP410h8acQosdTdCsgc+AmJ4kilCBP7IWS6n2KpDwBB8Rt05TxdZ6BU20lkCogGK3ERVVNAfDhc+AzUY6MDAQYI2ho9t0lFOnpS2A8ZvYGrxYho7lsIHTRiDBEWpN0LTQ1IUBi6SGAIm+9sK5zVJk1LfQ3COZI/J0NlanYCtpIaNHdK/+xsQEHB4P2qBETF1jqMPihhxFEActczRUbXQ8pAGJnmbOvLEPX332KaU7+jFpoI3Bo5LZUBygrNGitgkyMOhhWvQb7CZ/kAsXFkeO64umaWz+po7tW1rY6dUZ5YXeA/qT5mlmyduVgMbkMzLwOR18vqoZW4YLf14i27Y14V6zg5x0GxPPyGHYIBctVS0Ufl1PRXoSDj2RoUkZuFIdZPdLILuXC69fsaOkhQQjgWBDCqbfInNQEl4TKpszyUpQuF0ariQ7lqnYsKyW5kovg47NILm1MKxUPpVbmvE2BuiRn0BCRnTLQG6mjSdv70Ftg0mCW8dpD7XWaZqGZVnUNCrSknRsu5hOdej5+Qw6qxe+ej8JPUL7PXFYKpOutKgsaSGrbwKGTUPd1I/mKh/udAd6a2uWO93JOTOOpaXWh81pYE+If1u2OQ1O/OMoTrhnJJqm0VLtw55kA0sRbDFxZTjxN4R+DI6UUEGt7yk9yZ+ci7fahzvLGVXpkpDt4pxZJ9BS4ws1pClwZoSClqDXpG5jAyl9k1B+C195Mxpgz3LhyHKi6zrBlmB4v20aviynZlE5mT/qBU4HRpqNYKUXzW7gGphM2QfbsRsauef1wfJa7JxXTLDGR48p+ST0T8ZX0Yw904WmgXdVJfbeydiyE7A8fixfEAydjOe/IuBTNPlcmG47qWUeDE2Rfv0oHANS8S7dRtPt77BzeTMBw4b9lL70mjaOhBw7juZ61MBcgu8WEpy3kty1JfQaXEvwyP44bb2wXX8FqroR1/TPCBZUoTmTsI0fRaBfJtbA3jiO7om67mlYWQIZ6Vi1XlRFI5bTjpmSjF7pQTf86JP6YHoNDEcQ+z+mga5h5PfAWrgO66EPYfvO0PVmSB7qwvFo/TKw7ngFvboO8tPRnHa0numoMYPRrjoB6prhq43oo/NDfcQSHGhH9EYDrLnfoH30LVp6ItrvLkDzeGHJ+tB4k4E5kJuOo09rl9yyanhjKQzIhXPGh5ZdOgkunAiV9ZCbDkETVheH/q1rof935ppT4OqToaIOclIPs5mHJACIR4KCH6ja2lrS0tKibgKGEXoIk6ZpZGdnhwu8zc3tTYper5d77rkn/HCvNm0tBt988w2XX355ePm2bduYPn06EF2QLS8vJz09ndTU9tk6WlpauPvuu7EsK6owvqsZkGpqasIF9DZtXW7cbndUE+Se2N0g4379+mG32/n666+55pprwsvff/99lixZwk9+8pOorkWDBg3iq6++YsWKFYwePRoInde//vWvJCYm8stf/jKcNvJcRk6HumbNGmbPng388IKCNm3fx9yk9u+lw9DokxJ9gc5P1flJaqjQdOWI6BvNPSdEb/PZVUF+Pd+i2Q9YFoYCU2mh2Yk0QgGC1wzV8Nv00P/N1sjApYfGFfjMUNDQFhhoGjgMbE6DvomKETkaZ/SHGd9ZfLslECqIAc0OB5auYQP6BEz6+JvI7qGReEQGA/MVuUnQL93OmN4J1HkVbluowWFpmWJVqckrqwM4ExxM7qcz+43amPPlCFhQ18JWZee7Xzg59fFU1PaGmHRHVtbwwE+PYEhG+3l85IFSKvz2mIHBAAnBIKeemMi2TTvombeDa665Ju7TuTsz6Jh0Bh3TsbCQxnEXRtdw/2hq5KtkIDd6lX5uBoyPl8MQl0Oj76DYrkJuoOPedUNj6KTYbWmaRvaA2LEVHaWntH/P2r6nuq7TI7WzNaIZdj0cEEQuyx3Yvm9N00jMcnVcFQgFB13Rljd3Znt6mzt0K28LBiLphkZCdvx9ArgzYvdrcxn0GNF+hp2ZcdK4beH9tkk5picpx/SMTtin/Rrd96L+7flK0Mn78cCopM7c9s/aPTanPW2SAz0pFMzZbj4RF6FvU9zjOaEv7qW/In5VUejaa/vlsfDLY9v3G5FCA+zHDaOzX4P23p1A7MiArnx6+mUT0S+L7SILwHnjo/dDRLG1TyaMyu+4RmibU4+GqUe3L0hLgh+3XySjrqx5mXDTObEbsRnQs/W3Y7fB2IGxaTqj65DX+W9YHF4kKPiBeuyxx1ixYgUnnXQS+fn5KKVYtmwZixcv5vLLLyc3Nzfcx/2OO+7gnHPOCU+r2dYXPLJgmpeXx8SJE1m8eDH/7//9P44++mjKysr43//+R2JiIg0NDQwa1D5bxvjx43nzzTe54447mDhxIjt37mTevHnh8QiRXWfagoJ4BeE77riDhoYGJk2aRF5eHj6fj0WLFvHtt99y22237dGDzdpmY4LQbEEQKphv2LABu93O1VdfHU6blpbGBRdcwOuvv84f/vAHxo4dy9q1a5k3bx7HHXdc1LMYAC6//HLmzJnDnXfeyRVXXAHAm2++SWVlJX//+9+j+tePGTOGAQMG8Nprr+H3+zniiCPYtGkT7733Xri7UsdASHTu2lE2ro0d3w3A0u0mN35k0uQ3uGG0xsgcjUyXxujc9lt+0FI0+SHVpeHxKxwGeIOhwdFDMkJPSW5zxUiLlL9Z0BIkYBhYHVsXNA2bXePhS5PoKM3V2t0COK2fxmn9dG6b1J6Pl1/v5AAVNNUFOOmJAAFNI7mpmcaIPvWGaZLe0MiqShUOCjyeIOtWNXd6gZcLvxCiO5OuQvHJveEHasKECdTX17NgwQJqa2tJSUmhf//+PPTQQ5x88skAnHbaaWzYsIF58+bxxBNPMHToUG699VbKyspYvnx5VHcggPvuu4+HHnqIZcuW8fnnnzNq1CgefvhhHnroIYYMGYIrYgrBm2++mWAwyKeffsrSpUsZPnw4d911F0uXLo15/kFhYSG9evWKW+s/efJklixZwjvvvENDQwMZGRkMHjyYp556ivHjx8ek35XnnnsuPEtQm5dffhmAIUOGRAUFALfccgu6rjN//nw+/vhj+vTpw6233sqll14a0/UnNzeXxx9/nH/9619Mnz6d5ORkjj76aB5++OGYaVQNw+Dxxx/nwQcfZP78+SxcuJBx48bx1FNPMW3aNMaMGbNHxyU6d2xvg+XX7LpJ26ZrpLZ+dZMcoRuFw4CUOPHm1nrCYxHizYwC7WOM97Xqeotja3YyYnMxG3rnUZecSILXx8DSCpymxYSe7Te5NaubUUqhK6hMTcGwLJJaWnAEYwf3CiGEEACaUrua3kOIXdu6dSsXX3wxN9xwA9ddd93Bzs5h7YsvvuCmm27iz3/+M2efffbBzo6IozmgSLw7NJXeFdt24HfYMSMDRKXoma3x7wdjZ6HanYG/roydiAnY3Dp7jxE0GV5ZybRVsU/GtoBfrWrvFrB2TTNP/Lk4akpOTSl61NVjtF7yb5t/DDNnzgTY4+5DQghxOPNpv4y73KkOj4ev7S8y+5Doksg5htt4PB7+8Ic/kJSUFH54mdi9toHGkaqqqvjb3/5Gr169OPXUUw9CrkRXOPX2Qraph54Y7A4EsZsmCYEA9YZGRua+b4DVbRq6UuTVx3/gQ8fGiYEDnVEBgT0QJLHFS+CwGggohBDiQJLuQ6JL5s+fzyuvvMLJJ59MZmYmpaWlvP322zQ0NPC3v/3toPaBr6mpwdpNn42kpKSo7k0H0/PPP8/y5cs57rjjSElJYcuWLcybNw+A6dOn79E4CXFgRXYb8xgGTkuREDGdZzDBudc9Ve0ujWCHRxD7gaweDv5+qsH/vaoItj5aoeM+atzR3xmbTcPh1PD7FIktXpLjzKIlhBDdlYwpiE+CAtElmZmZOJ1OXnrpJZqamkhLS2PcuHH87Gc/ixl7cKBdcskl1NfX7zLNzTffzFVXXXWAcrRr+fn5LF68mJkzZ+Lz+ejRowennXYaP/vZz+jdu/fBzp7YhcjZvN7JzSTTH+CYhibclsJmWaT4TfwOxy620LnXf5vO+ffVhJtvFXD2RBePXebiyxILy2aQ4vfHf4Bzx9d2nRPPzmTBnEoSJSAQQgjRBRIUiC457rjjOO644w52NuJ66KGHCARiH9IUqW/fvgcoN7t31llncdZZZx3sbIi9ENkiZSjFsQ1NOBSo1od12ZWivGHvRhqPzDFY80gPpn/mo6HB5P+d7cZtD4UIJ/bXmZivo30Xv3ar48PHAM6+PBtPSQNb5+9VdoQQ4gdMWgrikaBAHPbGjh17sLMguonIloJcXwBHx2kaNI2dnr2fuyHBrvG7H8V2c9M0jfnXunk6ewjqz1Uxt7Mke2wgomkaP75zMA/M3xHz3mk39Y9ZJoQQonuTgcZCCNFFdS3tBf5EM36LgM2/f+YkTXZq/PaiDLQ4Y4WveOvkTtc7/dboKXFzhyYwbmqvfZ09IYQ4bCi0uH/dnbQUCCFEFy0ptkJPPbYUtbb4dSpB2/69sVz/zdm8de0X7FhZiz3FzrlPTSA5J/apv23GnpvH2HPzqN/hJTHdgc0hdUFCiO5OAoB4JCgQQogu6p+ugaaBHpoGNAB0nN0/r9feDTTuKt3QmPr8pD1eLzXn0Jh9SwghxKFJqoyEEKKLRuQa/GysDTSNJqeDjj15/MBjv0w/GFkTQgjRRdJ9KD5pKRBCiD3w7EVOrhhtY0W5xYZFDnZs8WMAAV1jyskJJLnkAWFCCCEOPxIUCCHEHtA0jVMH2Th1EHBCLl6fScVOk/xedgxdapqEEEIcniQoEEKI78HlNOiXL60DQgghDm8SFAghhBBCiG5Dxg/EJwONhRBCCCGE6OakpUAIIYQQQnQj0lIQjwQFQgghhBCi25DuQ/FJ9yEhhBBdZinF0lKLskbrYGdFCCHEPiQtBUIIIbrk9YIgl76lwq/7JJkU/6rjM53FbpXVwP1vQJ8suPVcsMutWIgDSVoK4pOWAiFEt1bnVfhNtfuEIiogACjxwFVvBw5Sbg5TD/0Pel0HT3wAd8wCx6VQVX+wcyWEEBIUCCG6p4JqC+fDAdIfD+J8OMjIZ6VwuysN3vjnZ/a6A5yRw93ts2KXDbvpwOdDCCE6kKBACNEtHfGMid9sf72mEqa8JoFBZ6qbDnYOfsCqGw92DoQQQsYUCCG6p3gdhj7YAkFLYdOlv2lHtr2oQjKt0Fk25HwKIQ4hMqYgPgkKhBAiwtubFFMHyw2jo+AeTDYUtBQDnwxS4gm97psMm26wSXAghBCHMOk+JIQQEdZXy6DjePbkZtE/IiAAKG6EAU8H93mehBBi72id/HVvEhQIIUSEbyokKIhnT55KsN0Tu6ykYZ9l5bAW79sl3zghxKFAggIhhIjwZcXBzsGhaVdjCl4rkAeZfR9+3TjYWRCiW1Focf+6OwkKhBAiwqC0g52DQ9OugoK3N0pQ0FVWnIJHZWLyQciJEN2XBAXxSVAghBAR/nrC4V9rW9posbTMwlL7pmPK7DUB8p7q/P0+KXIz7So9Tmeh7CbpWyWEOPgkKBCHtM8//5zx48ezcOHCg50V0U1M6nVwC7hVzYoKz94X5k9+NUjvpyyOe9HCeNgk+bEg2+rN3a/YCW/Q4qp3d53mxrFyK/k+HJa0tAhxYMlA43hkSlJxSCsqKgJg6NCh4WXl5eXMmzePk08+mSFDhuzxNrdv3868efMoKCigsLCQ6upqzjjjDO6///5O11m1ahXPPvssq1atIhAIMGbMGH7zm9/Qv3//mLQ+n48XXniB999/n4qKCnr06MGFF17I1VdfjabJReeQpdHlEZ9NfsX6GshyK/JTNPTv+bku3h7k1FchEFE2HJsNT52uMThdJ9EOdkPDUoo6L9T7oCkAI7La9/vdDpOL3lJsqY/eticAff6jUL/bu7xdMGf3AcXOZuiZtHfb727kCiCEOFRJUCAOaYWFhSQmJtKrV6/wss8//5wZM2Zw/PHH79U2P/jgA+bMmcOQIUMYMGAA1dXVDBs2rNP077zzDn/5y18YNGgQv/jFL/B4PMyaNYtf/epXvPnmm7hcrnBaj8fDtGnT2LhxIxdddBH9+vVj0aJF/Pvf/8blcvHjH/94r/IsDhAdZq62mDoY0lw6lc2K9zaZ/GZRqCCe7AiNOVi+s30VA/jXKfDXL2Nn3UmxQ6IdqlpCF9sT8uHGMfDhVnh+HfiC0DMRShqJrqhS8O1OmDBbAR0K5RHBS6IBPx0BxQ3w3lYVelMDlKJj8TP5sQAtwdBbbf3aE7mMfnol/30ZLhka4JJhBgFLo0+KwmHobGuwWFyy+9NW3RK7PyGEOFTJjF/xSVAgDmmFhYUMHTo0qoZ9zZo1OByOvWolALj88su57rrrAJg+fTpff/01w4cPj5t2/fr13H///RxzzDE88sgj2Gyhn0xubi5/+tOfeOedd7j44ovD6f/85z9TWFjIE088wbhx4wCYOnUqF154ITNnzpSg4AAIWopaLxhaqEDutGlUNSt8puKVAotvKmB4ZpwCbGu59toPLa79EOJNwlnvjw4IIFRkn9ZJ77aGQOgPIKBgfknoL1KJh9jydORrFfGPtt9B6/+aTHhyZds6EStpWvRdTyk8gdidNJHAWrMvVMBXOzR+t8iM2L7Zvp3dtKK8uNakpMEi063x3Y7OE26osSipt3h2jcIXhKtH6NT7YEcznD3AYtZa+KocchOhX5qGTVOYCo7tpfP+JouX1kNzEIZlwMl9oLgu9AwEhw4XDtW4eKhOcwDe32xh6Iqvy0NBUL/U0P6TAn5yMhxMGWIjJxGqW6CHGzRNoyWgWFWpyHBBv1SN4npFaaOif5rGjmaYu95kVLbGyFyD3ETwm4oMt4ZSoVabZIdi8TYYlK7RNzV0rlsCCp8Jaa5dB0wK+MOnAc4frHF0XvttuaIySHOLRWa2DZ8JbpuGz4RUZ6jlKBhUeFsslENH1yDREdpPVbWX4ILVVJk2Us8cwe8Ww5sFFqYFTl1xbJ7Gf8+zkeYKrbeyOIC1qYH0LAf1OYnkJ2v0S4vuEmYpRXUz9EgIndPvyi3WlQbY0qhxbD+DEdk6y8tNlm5TjMo1OG+IxoodkORQ5Cbq9EgIned6r8JuhM5Zhkuedi3EoUSCArFPNTU18dprrzF//nzKysqwLIusrCzGjBnDH/7wBwDef/99FixYQGFhITU1NaSmpjJ69GhuvPHGqBaBpqYmtm/fzqRJk4BQgHDFFVeE3z/22GOB0I1m0aJFJCYmdimPCQkJ4X8XFBQA0d2TIj366KMYhsFdd90VDggAxo8fD8DGjRvDy7755hs++eQTLrvssnBAAKDrOmPGjOHtt9+mpqaGjIyM8HvFxcU8/fTTfPnllwQCAcaPH8+dd97JTTfdhN1u54UXXujSMYmQVwssfr3QorIl9NptgzQnlDd1TKnaR1QdqO7ce1v2aav579hFaU+qujrt3hRR2lcqNo+R+93FoOVn18Cza9RuMzVkhhmVZE6hFW4h+e0nEVkK7TBizegP6budob/IJF+UKX77ye66OjlC//sogNum0RKEQenw81E69y+1aPDT2tiiUBahYw9Y0bvXzFAUAti00KxM3g67PXuAxogs+Ne3ipYATBmgMescgwx3/M9BAfd/Afd/obDrAZZepbNgXh2fLPeyMjuFiiSiPsNMF1yb5cX/cQXvZWayKT0JmwEXDNGpW19BRYWXVT1Hg1JkP1RDZVIqStNA02hRGh+XQv/pQdA0BtY0cNm6reFxDQWZKbw6YgBDMjUW/sRGr2SN+Zssrn83wNY6cOgWfr8Fut6e+2WtkaPV+n2xAqGoXNdbvzcmfVJCT7b+fJtCKVAa9ErVmH6GjfOHyJgUcWDJTEPxSVAg9hm/38/Pf/5zSktLOe+88+jfvz8+n4/NmzdTXFwMgGVZ/POf/2TixIlcdtllJCUlUVRUxNy5cykqKuLVV18NF76LiopQSoUL7Glpadxzzz3ce++9HH300Zx77rkAOByOLgcEHRUUFNCrVy+Sk2OnBCwsLOTbb79l6tSp5OTkRL3X1mWouro6vOzll19G13WuuuqqmG253e5w+ragoKCggBtuuAGHw8FFF11EdnY2Cxcu5JZbbqGkpISzzjprr46pu9reqLjyPYtgRAGuJRj6i6utkKWr6NeHpO8REOzLLOzpfju2eER0jwq/r8VJ+33yFLeVJXbFtu/Fxlq441MrlLQt/onshtUxaFSAaYGhE1QQjBOHvLtZ8e7m9tfvbVbc9rHJzLN3cctt3XfAgsmzg5xU1EJRZhIVya6YpNVeeKjEyZFp6WzICF27/Ba8VqDIa7BR1rNvOO3OlPT4+9NDrUBTNpVGDXQeVt3AsMo61mvpXPOuyZypBpe8EaDBF3rfb2oRAUGroAoFAeHfVERLVeuykgZFSb1qX66gtAF+/JbJtl9p9Eg4lH9/QnQPEhSIfWbx4sUUFRXx2GOPddrfXynF3LlzcTqdUcuzsrJ44oknKC4uZuDAgUCoUA7ttfg5OTnhLkMnnnji9y40V1RUUFtbG1WrH2nBggUAnHHGGTHvNTY2AoSDkZaWFr744gtGjx5Nbm7ubtP7fD5+//vfk5qayrPPPktmZiYQ6mp02WWX4fP5Om29EPF9XKKiAoIuO6SDgVZdyeL+DhS+7/Y7Fv731TCEyMBgL7cX99A0wOzkoC1Cg0n2wIdbu34CPaaO19CpSnB0nkjTKE5NiFlclpIRJ3H89TXLokeLL+at7CYv64FPShTLtqtwQADEbzHq6qFpHaI4pfAGNRZvU1w49DD4HYofEPm+xSNtdmKfaWgIzbW9du1aTDN+M75hGOGAwOfzUVdXR11dHSkpKUCotaFNYWEhdrudAQMGhJe1dffZ1cDgrtrdtlatWoVhGIwYMSLmvS1btgDQu3dvANatW0cgEGDUqFFxt7VlyxacTifZ2dkA/O9//6O0tJTf/e534YAAQudn7NixAHs9ZmJ/qKmpwedrLxl4PJ5woAOhzy2y1QRCs0Tt6nVFRQUqooDxffcxILU7XeRV59159tfsevtye/sygNkn29rDjezFueib2FmTVbzNKeymRUJg192hkv2x23QGA13Ok9I0tiXHBhbFaaHKiz7JFlmOpuj8xQuiu3o+OvnO5jqaw/8+FK4lso+Dvw9xcEhLgdhnTj31VObMmcOMGTOYM2cOxx9/PJMnT2bSpEkYRqharaSkhJkzZ7J06VKqqqpitpGfnx/+d2FhIQMGDIjqy19YWIimafukwLy7oGD79u3k5OREzS7UZuXK0OjOtiBg27ZtAPTr1y8mbVNTExs2bGDkyJHhY5k/fz5ZWVmdtqjous7gwYP37ID2o8hxEABJSdHzTzocjqjgBqBnz567fN2xReX77uP43hoXDdaYs2EPC3jx+usf6jqrZY9Ztg+q45XaNwXveAOWv2/2OuZrL7d37iCd9zYrTCtifaXA0ENdhTrux9j1TgamhXrQbKgNvU6ww4OnOHe5TqSrj9ToaXPi2dJElduBacTW3w01vYzeVsncwb3w2ULX1zQnBHwmfqXCYwhC/fw7rKxp4QL6vCH5/HjdVjJbfAQ1jS/ys9maloyhwWOn2hmd7+RXRwf499cRzXAdfzMdz4dSoRMQ+ZrQUAx/ZGuernH1SI3j+rf/1g+Fa4ns4+DvY3+TMQXxSVAg9pmUlBReeOEFli9fzpIlS1i0aBHz5s3jyCOPZMaMGRQVFTFt2jRSUlK4+OKL6devH0lJSWiaxkMPPUQwGAxfLAKBAJs3b2bKlClR+ygoKCA/P3+vxxB03BZ0HhR4PJ5wS0AkpRQLFiwgPT09XKvv8YTmoow3NuHjjz/GNE1OOeWU8LLCwkImTJgQd7+bNm2iT58+4XEIouteP09nQbHis+2KgAVH52r0T4XF26GiyWLmGqhsBrsOvmDbAFtAay3EHA4BQlshva01QMGuo4SOfWv2oOTcVqjcDacOdhsk26HWC962CuwOq2pAn2Qo94T6zrsNyE0OzURj6BAIhmZ4shTYjdB2XTbolQSrK8GvorfltoUG+SqgdzKcN0ijqEaxqhIafKE0me5QugaPiQqEBhgfN8jJGQN0an1wTE+Nib1C08++UWhR5oG8RNheZ7GpAY7MMthSY/FZsWJQBlw4XKekUaOiCU7pC7oWmp0o0a74qhxO6K1zxZE63qDif0WKOp/i/EE6ecm7PuduA5LscO+JOjeMNYAsCjb5KK1XrAxorK+GnERIdMCoLJ3T+yWy7rtsLq9oZmNqEhlpNi4cqlHmSebFtUGWrm+h0dI5fZiDTQ0acwoVfpPW74+ibzIMydDITU7gw/zhOGp99M61MSjXxknJOj8dodMvLZTnf02xc9mRFt+UWTQF4dsyk7KKAHpjgCrdjjfZTpoLSutDMy6d0V/jmXNtPPKVYkutYkC6ztmDdQakafyvwGJnk8Jph6PzdE7uKx0WxIEnQUF8EhSIfcowDCZMmMCECRO4+eabue+++3j77bcpKCjg6aefxrIsZs+eTVpaWnid8vJySkpKogrNmzZtIhgMRvWrtyyLDRs2cOKJJ+6TvK5fv57c3NyovERKT0+npaUlZvnChQspLS3luuuuC9f8t9WEdExvmiYvvfQSSUlJ4TEQwWCQQCCA3nGwHqGWlNWrV3Pqqad+n0PrtjRN47R+Gqf1i14+JgdA528RXx3twYhuFopw//E/HQ+lHtjRFJq1aOXO6MJoPG4DWkx227fdoYdmu9lQE5qidJeDZCPz1vZe5MBXK/TmifmwqhLq/K1TunQczLub+U4ztXr6Z6eyrjp0Cib2DM2Wk+bU0HW48QOFn13z/tYevYcH43dhCfzW9r2noAxaisrm0LSle/YwQDsQ2+rXJitB45djIgcK7OGgAeDaiN6DLpvGT47oWv40oPl39pjlwwY6GQacEvNOyFFHJ3NUh2UpTvjLiXY4MXZ7AVNR1QI9k+LlaxfjF4Dj++gc36ftmmUDdt/ycd/k2GXXj9vz8yqEODAkKBD7RG1tLWlpaVE3acMw0DQNTdPIzs6mvLyc9PR0UlNTw2laWlq4++67sSwrqsa+4yBjCM3c4/V6o6Yt3VtVVVVUV1dz8sknd5pm8ODBLF68mPLy8nDzaFVVFY8++ii5ublceeWVUWkhNC3pmWeeGV7+3HPPsWHDBm699dbwcdtsNnJyclizZg1erzfcPcnr9fLnP/8Zy7JkkPF+1rSLUv4fJ8VeFiubLKq9MCxTJ2AqPimxqGhSbKjVOLaXxpT+GpXNUONVDMvU2Vxn4TSgV3LntaDegEVJo0Z2AhTUWJz0sooJPs4fAL+boDEyS2NrPQzN0Gj2W9y/LNQScsMYg+GZGuUexR8+M5m1VoW6Z8QpLH/xE8hI0BmaoeMNWBRUmSyZ9wIOzeSay6/Bbo8tRAL8Y5lFYV2nh7FH9sWc9DZdk6cn7yW7IedOCNE5CQrEPvHYY4+xYsUKTjrpJPLz81FKsWzZMhYvXszll19Obm4u48eP58033+SOO+5g4sSJ7Ny5k3nz5pGeHpoyL/IBYm1jByL71aenp5OQkMD8+fPJyckhISGBgQMHdrkAXV5ezrvvvhv+N0BdXR3PPPMMEBoTETkm4Oqrr2bRokXcdNNNXHTRRTQ3N/P6668TCAT497//HdUvcvDgwUyaNIm33noLu93OkCFDWLZsGQsXLuSCCy7g8ssvj8rLhRdeyPTp07nhhhs466yz8Hq9vPXWW+HBWRIU7F+uXVz5fEELpy26MJ+VqJPV2mPNbmic3j+2tjM7EbITQ4XeAWm77xLhsusMae1qOzHPwHcbfF0e5A+fQ14S/PV4ndyk9u2MCo1Rx2kzePhH0dvqmaTxzBQbT5yu+P0ik8e+jX6/dxIc27v9oF12nSN7mHyl7W5ef5h1LkyYtdtkYY9/s/ttCiGEOPRoSnU2hYUQXffuu+/y0UcfUVRURG1tLSkpKfTv35/LLrssXBvf1NTEP/7xDz799FMCgQDDhw/n6quvZunSpbz88st8/PHH4VmI/u///o/q6mrmzp0btZ/PPvuM6dOnU1JSgt/v56677mLq1KldyuObb77JX//6107ff/HFF2MK4wsWLOA///kP27ZtIz09nUmTJnHdddfFPLcAoL6+nn/84x989tlnBINBBg4cyKWXXhp36tRgMMh///tf3nnnHWpqahgwYACXXnopGzZs4NVXX2XhwoUxg7HEvuMLKlz/iD8bzNmDNd658PCuL2nyK3690GRZGfx8FPzm6NjjCQQCzJw5E4Brrum8pQBg9LMBVlZ2vj/1+/Z10x8PUOfdfbpuS7sw/nL15oHNhxDdWKV2d9zlWeq+A5yTQ4sEBUIcIgKBAFOnTiU/P58nn3zyYGfnB63Wq8h4PH5Q4HZqNN9yeAcFXbEnQQHA2+sDnDcv/nuRhX3joQBWJ8+LkKAACQqEOARIUBCfDPsX4iCInLMZQoOoH3roISoqKvjpT396kHLVfaS7OunbroWmjxSxRsc+ky8um0zqIYQ4xCm0uH/d3Q+/Okz84DU1NcWdJSiSzWbrdJahA62srIxrr72Wc845h169elFdXc0nn3xCYWEhV199Nccee+zBzuIPXklDJw2kCv5wjNwY4qlo6lq68bnwRen+zYsQQoh9T4ICcdibMWMGs2fP3mWaAQMG8Nprrx2gHO1a2+xCb7/9NvX19bjdboYPH87DDz/M5MmTD3b2uoUHv+qkfwtw83iZMjGej7Z2Ld1DJxtMmh072Hg3z/sSQogDSC5I8UhQIA57F1544W5r1xMSEg5Qbnavd+/ePP744wc7G91a25NmRdd1tVvQcb105l1ocl6HLvJfXCk3YSHEoUEG08YnQYE47PXp04c+ffoc7GyIw8jp/TQ+2nywc3F4OWsA/H5J19KeO9hO862Kv39pogO/m2jgksEGQghxSJOgQAjR7dw8VuN3H8cun5R34PNyuEje9QNvY7jtGn88Xm4xQohDjwwqjk9mHxJCdDt2Q+Nfp0QvS7TBp1dIIbYzrj0MCsQekPKJEOIQIHdAIUS3dON4O9cepXhhjcmRPTROyJcBxruSk2wHAjHLR2Ud+Lwc1gbmwKYd0cuev/ng5EWIbkpaCuKTlgIhRLeVYNe4YYxNAoIuumZE9Gsd+PKncu72yMYn4aoTwWGDFDe8dDNcddLBzpUQQkhLgRBCiK559mw7v59ocd9Sk7HZGjeNNzB0qXHbYy/cEvoTQhwkct2KR4ICIYQQXTY0U2fWOdLILIQQPzQSFAghhBBCiG5DxhTEJ9U9QgghhBBCdHPSUiCEEEIIIboNeaJxfBIUCCGEEEKIbkO6D8Un3YeEEEIIIYTo5qSlQAghDpCq9XW8c+0XNHgsdmYlk/aro5h2XioJdqm1EkKIA0euufFIS4EQQhwAylK8OXUR/lo/rkCQPmW1BO9fylmvBw921vaYt97PB9d/wSunfsj617Yc7OwIIYTYByQoEEKIA2DxH7+NWZbR7KPs62qWloWGvXmqfAR85oHO2h7x1fmYdfS7FH+6k7JGg3n/KuFfpy+mtsx7sLMmhBBdotDi/nV30n1ICCEOgI3vbIu7/OiN2/nj/DQy3t/EsJ21AIw8OoWL/j7qQGavy+Zc8AkA9T3SCDodADSa8OJta/jlC2Mx7FLXJIQQhyMJCoQQ4gAwg+CzGbx/1CAK8nqQU+/h7O82kN3YTNZrq+jd0IzP0KlKclO4rJavXtzKhCv6Hexsx2gqayFoM8IBQZuGnX62fFvPoGPSD1LOhBCia2RK0vgkKBBCiAPBhGdPHsOKfrkAbMlOZ12vLI5bs4kfldfydZ9s3ho5AJ/dRoI/QOHcDUy4Yv9myVeZgGbs+e1RU/HXsTmklUAIIQ5XcgUXQogDoCHBwcq+OR2WuWh0u2l02PDZDG5etILrl6wmp6GZV8cO4cFF+6effsVmD38751tqFg+m6pMh/OWCFfiauj7gWTctHM0d8mZZ2HVrH+dUCCH2PRlTEJ8EBUIIcQDoqLht1lmNTZi6zvGby8lq8jKoqp6ff7GWBH+QP34aRHVSK/99PDltLRCalE8HbEGL315biGl1bV8akFJdT2JdI44WL+56D8m1jXz93NZ9nlchhNjXJCiIT4ICIYQ4AJKaA4zfUha1LMPTzIDSStK8/qjldstiVGklKqDoYjm9y2a+50GLs02bN8DS71q6vB0NSGhsJrWqnsSGJjSlaK7x73Y9IYQQhyYZUyCEEAeCAVcvXknvmobQQOM6D2eu2oTXAhyxyQNBGOz1YejJ+ywLpZVB/vuOh9PivGc3LVYW+Dh+XMJut6OIfvSP0jSUoaNlJ+6rrAohxH4krQLxSFAghBAHQMAMtQCcuWoTZ67aFF7+Ve9Mhnmjn03QYLcxoLKW+qx9U8hu3NiA1Rxkk5WAUmABRsf8aeBp6Nq4go63U00pAprCzNh9QCGEEOLQJN2HxPeyaNEixo8fz8KFC7u8zsKFC/npT3/KSSedxPjx45kzZ85+zKEQB07AVDz9XZDrH6/k+ocq+OWbzby20o9SimZDjxlSoIA3Jx4ZtTwIJAWCjKxt4MxxcZoQ9kDlpkZe6TuHhePfZtEJ71M+5W2cpolPj7302yyLbE/TXu1HA/SgyZCxqd8rv0IIcSCoTv66O2kp2EfKy8uZN28eJ598MkOGDDnY2QHA4/Hw4osvMn78eMaNG7df9rFhwwYAhg4d2qX03377LXfccQfHHHMMv/nNb3A4HEyYMGGP9llUVMQnn3zCeeedR8+ePfc4z4WFhXzwwQcUFhZSWFhIfX0911xzDb/61a86XWfJkiXMmjWLgoICNE1j4sSJ3HrrrWRnZ8ekbWho4L///S8ff/wxVVVV5OXlcfXVV3PeeeftcV7FoSNoKe76zOTJFeA3FWf30/jj8Ro3fWTyeSkEA623FbuBRir9dtYxcOlW3uuTzc8Mg2Fjj+T/vl6DPWKQgAacsq4kqubdBgQ1jRYdRnrqgbS9yq9Sig/OXEhqXfssQe5mP5cvWM72wTkx6ZMCJhOOTdmrfVmAmZ/O+NMy4ufFDM1KpBlSDyWEEIcqCQr2kc8//5wZM2Zw/PHHH+yshK1cuZIZM2YwcODA/baPDRs2kJSURK9evbqU/vXXX8fpdPLQQw/hdrv3ap8ffPABL7zwApdeeulerf/aa6+xZMkShg4dSu/evamvr99lUPPf//6XJ598kvHjx/PrX/+a8vJyXnzxRbZv386sWbPQtPYiXUVFBddffz11dXVceumlZGdn884773DvvfeSmprKSSedtFd5FgeH31Qs2W7x5HeKeZsUftX2WWu8uUnxZoEJdiNUurcrMC1QCgVsyUln3NZyypKcBFNcbEq1o3+1Jmr7CvDqUJicyMrMNI6oa6TWYeez3CxOK63gP1tSeLIgSP80DZ8J43rqGHrX+sJWbm7CVRs7pWliiz9ub1oNxZHHxy/Ud4UnaKe2zEtm7/bftbIUW/o9jbmtMbSPLDcDtt+A7pBbjxDi4JGZhuKTK/M+smbNGhwOx35pJVBKEQgEcDj2rCvBmjWhAsiRRx65z/PUZsOGDQwZMiSqYLwrK1asYOTIkXsdEEDouPLy8khP37snp95666384Q9/AOCuu+5i7dq1DB8+PG7axYsX8+STTzJ16lTuuuuu8HKXy8XTTz/N0qVLOe644wCwLIvbb7+duro6Zs6cyYABAwA466yzOOuss5g5c6YEBYeYloDF7Yss3tus8DRb9ElWXDfaYM5m+HQb+Num3de02NG1mgb21ppv1TrdaGS3HAWfDu0DiXZ6NzRx+voSmtOScTZ7cfhCs/RoQHVGCgtH5tBS7+PrrEza3njrqIH4El1M/Z8JQQvcdlBB+jY3M6WxmrN+nEujYaNwq58JPXUcKQYPfxagTmn0y3Zwol2R7rDhDESPV2hOcMYcCoClacx9cjOnXNqbqnqLoAU5GQYpGXaCDX6sgMXm1Q1YxPY71QEtEGRncQtWwEJZodaB1ee/Q9/WgABAVbawKWc6g2tv3sNPas+YTQHMpiCO7NB1JlDvR5kKR4Zzn+3D2tGIluxES/h+Xby+Vx6a/KimAMZuBnj7P92K5jawG374ogBy01EnHImldAwVgJVbYcIgyGxtKZr3Fda0Z9CCAbQThoOpYGQfAidPQH2zAT3FjeqRju30oWhJXT+navlmaGhBO3n/3ZOEEHtPU/tjEuzDQFNTE6+99hrz58+nrKwMy7LIyspizJgx4QLj+++/z4IFCygsLKSmpobU1FRGjx7NjTfeGK4ZLyws5IorYh87qmkaixYtorGxkXPOOSdu95Tnn3+ef/3rX7zxxhv069cPgDfeeIMHHniAp556iuXLl/Pee+9RXl7OPffcwznnnMOSJUt4//33WbNmDVVVVSQmJjJ8+HCmTZsWru1ubGzk5JNPjnvcr776arjlYNWqVcyePZsVK1bQ1NREfn4+P/7xjzn//PNjCvnfffcdzzzzDKtWrcLpdHLuuedy7bXXcsopp3DZZZdx22237fJ8//nPf+btt9+OWf7ss88yatQoXnnlFT7//HM2bdpEXV0dGRkZHHvssUybNi1c+F+0aBG//e1vY7aRlpbGggULdrn/zlx00UXU1tby8ccfx7wXDAa5+OKL8fl8zJkzh4SE9kGU3377Lb/4xS/49a9/zdVXXw3AvHnzuPfee7nlllu48soro7Z1ww03sGbNGpYsWRK1fO3atcyYMYPvvvsOTdOYPHkyv/3tbznnnHM45phjePDBB/fquLq7co/ib19aLC1TeIOQkwg/Ga5zah+4YE6QVTtCXV7C2rr0KMCmga1DsVcpCFihwpFGe+uApUIlYtWhiN3eoECv+iauX7oOW8SlNqmmAafXB8CS/vl8OHYQ2VUNDN9RQ53DzidH9CWYGFHYUioUhLRu1+YLYho6DqUYXl5Pkt9EKUW5zaDMZoTiFZvBv2YtwGVZuPyhAcRBXWP1qH7g1EHT8NtstDgcKMBUCpc/gEspLMBn6NhNk8Hry8nZXkdzsgNPigufU8UdjBYEGrMzCDjtuFr8ZJfVkujxkuJvIb+xOnSagPU9sggYThLsGo5EG7ZhaXgTHbhz3Og2jfLPd6JMRZongNrYiFLg7JtI3qgkNqyop9EbOo82LNJ1RYNfw7LpJJjNDKzeSULAT73dTaU9mQR/EL+y0WRzoiuT9IAXTUHQrlGWlYLXsJNb7cHZYmJooc/Scjux0FEBE2XT8TdbaEqRbPORonxoQX9rLaMixfCQ/qux7JhXRvM2L+4ki4TT+hOoNrH1SUbpGt5vd2JVt2B4fTiSdJzH5uH3KJoXlKCCFnqCge62Y+uXgipvwNpWDwr0FAcpE1JIXfslWkMzanhvOOYIWLUda8tO/Nvq0bHw44YkFy7ViM/noCHYg0TqcNMMWDSTQZC2a5eJnXpS2AkovKTSQjoGQZzUY8dDCykkUh/+Crd9ay0MLAwUGjb8aK3vKMOGadppIRMTBzb8uLQmjKN6grcRragMy9JafxQ2giSg0HBodWgqGNqPpkFmMlZtE8p0oQwnqlcmRnUZenMTOO1w7Snw6DXgsIcytLMOHvgffLcFjh0Cv58Kqa3B0cJV8O/3QqP7TxgeSlPXBD85Aa6Of28E4NmF8PwnsLUSgiYcNxRunwqvLoHlm2HiEBiQDW9+CUkuuOUcmBS/Qkkc2jZpf4+7fKC6/QDn5NDSLVsK/H4/P//5zyktLeW8886jf//++Hw+Nm/eTHFxMRCq9f3nP//JxIkTueyyy0hKSqKoqIi5c+dSVFTEq6++is1mIy0tjXvuuYd7772Xo48+mnPPPRcAh8NBYmIiX3/9NQDDhg2Lycf69etxu9306dMnvKywsBCAf/zjH7jdbi655BI0TeOoo44C4Mknn6RPnz5MnTqVtLQ0iouLefPNN7nxxht54403SE1NRSnFvffey9///nd69uzJVVddFd5+3759AZgzZw4PPvggRx55JFdddRVut5vFixdz33334fV6+fGPfxxe54MPPuCPf/wjQ4YM4Ze//CUQ6ga0du1aLMvq0niCKVOmkJaWxqxZs7jkkksYOXIkAEOGDKG6upqZM2cyefJkjj32WJxOJ6tWrWLu3LmUlpbyxBNPANCvXz9uuukm/vnPf3LaaadxwgknAJCauneDG5ubm9m2bVun4y2WLFnC9u3bmTZtWlRAAIRbOqqrq8PLXnnlFRITE7n44otjtuV2u/F6vXg8HpKSkgD44osvuO2228jJyeHKK68kJSWFt99+m5tuugmPx9PlcRoiWsBUnPSKyYba1gUarKmGhSUWTs3CF+8hwZFVI4bWXghv47dCQUGbYDC8jqZBomli2nRanLbQ+hHrHlOyMyogAPAmuXF6fWiWxTlfbOCST9ZR0S+D+h6JWMCCcYOj89chSA86Q5fuQaV1JPnN1iQaeabFudvKcAaCLOqZRYtNI6WpvaVAVzBwYxmbj+yN1zBoimixc/v8OFvzqQNuM1QYLhmagyfdgWGFtmO0xD6LQAFN6Un43aFApiXRRfGgXPoXlaMBle4Ucloa0IBhVZWsys6n2Q/N/iBqaRVep0GwQyDm91v0CIaK3/7NHjZv9eDJdoULqyY6OyxIaQlgt3wc6SmlbSh3eqCFlICPZlwUuzOx0Elr8RKgtUAZgD5l9dTbXTgCFgqNoNLQLIWv0aItZAzqrWdDgzozAbfVgsIVzmOV6UL/51f4SSVAAoF6aH5ja8TMTqHcOvBjJ4CqgoatjZgRt16r3sSq92NWNAGhgEsHrHo/dR9VYZJIJjXw9Tb4urw1TQvu1mN10gSe0LbsgAMvFolYuPHhIkhkq6yBhot6epHGdlw0ECQVsBEgk0byyGBjdINYeE0Tg+hWJwDNDIbOH25AI4ANlEbSinUR67YxMfDhI52ASsFJTesXSKGqGlCkAjqaqdBKqjBxotME3gA88QGs2w6f3AuWBaf+GVaH7tcsWgNLi0LvLVoDp7emAXh3eXtmP1wRCg5uPifmOHhoLtz+QvSyN5bCvK+hNahmUXT3P976Gpb+Dcbtvy66QhxI3TIoWLx4MUVFRTz22GOdjgFQSjF37lyczuim0aysLJ544gmKi4sZOHAgOTk54S5DJ554ImeddVZU+oKCAoC43VPWr1/P4MGD0SO6HLQFBSNHjuSOO+6Ieg/gmWeeiel6M3ToUO6++25Wr17N8ccfT0pKCscffzz33HMP48ePj8nT8uXLefDBB7nqqqv49a9/HV5+8cUXc9111zFr1qxwULB582b+8pe/MGnSJB566CEMI3R5P+WUU5g6dSoQP+DpaMKECRQVFQEwderUqG5Wuq7z7rvvYrO1fx0vuugiHA4Hc+fOpampicTERPr168eWLVsAOOOMM5g8efJu97srhYWFWJbVaf7bWh/OOOOMmPcaGhoASEwM1UyVlJRQVFTE2Wefjcvlipte1/VwcFFdXc3dd9/N0KFDeeKJJ8LLzz77bM4//3yg64O3RbSPilVUQBDJ15UZN5UCE7Br7a8DVvy0hobSNDxtA2hNFdPKYFjt6+5MdPHOkf3YlppIbq0Hr2GwIzOZnBoP17/7LcmWosHlaH2K8W665ClFWksgZnGN281gbx3nbK+gR0sAPSIg0ZUi2RNqofDb7VHr2czYAh+EWica0pNJr64L7TZUSR59jKaFr0PgjKZR2yOZhBIfHruTnJbwFmMKnbagFRMUBB06fqeO0xc6f7oFetDCikyna1gapASb6Ti3k4FFi+HA1AycgQBWh/YNHzacHT5XhdZaA65hxukSaWDR8SvkIQk3LbS01sYHcWCnGYv28+ug/XMyYyaDjTyhWofX4CGLDLbRXrQ2w7X08VgRQYsV5xZvYsdBAAsDDZO2zmAWGiYGepyC/+4YBLDhDQcgGvHHrbT1wbPRhI/0Du846NgxTUNhYrQHI4vWwIYyKKttDwjaLFoD67fDUx+2BwTxPPFB/KBg+vvx0/t3cdEIBGHGRxIUHIZkTEF83XIqiLYC3dq1azHj3gjBMIxwQODz+airq6Ouro6UlFCfS7+/vbasreDfWWtAamoqeXl5Ucvr6+spLS2NKvgFg0E2bdpEXl4ev/3tb2MCAmivoQ4EAtTX11NXVxeuefb5fF3K06OPPkqvXr244oorwsfV9jd48GB27NgR3taTTz6JaZr87ne/CwcEADk5OfTr1w+HwxHu+rQ7hYWFOByOcF/7Ng6HIxwQeL3ecF7S0tLC4ym6clx7av369bvc1qpVq8jMzIw7iLotOMnPzw+nBRg1alTcbW3dupW8vLzwZ/r888/T2NjI3XffHdUKkZSUxBFHHAEcWkFBTU1N1PfL4/HQ2NjeV9zv90e1mkBoRq5dva6oqCCy9+K+2sf37hBp0jo/3W42pBFTg48Vu97y3lm01T0/f/RQNvZIxWe3UZydxo7M0IPJdqYnsnhCf0w91EqRUuOJ3m7QIqok3tqS0WLvWMCEhIjfi9rFWJ+YMQW7GMBsRVyL4qWyDJ3MnfUxy9ty7DR3E4114UNTxB6PAjQFAS228Bt6T0W86qiz4+38POjEFjZ1rPYWiF2k23sdw6jd0SP+FXt/CxXgQ9+Rjh3BtO9RVIre1u63oseEV/HFBED1zVRXVcVPbFm7/y5ZKu61JBjsWn7ibQ8O7Wvi4biP/U2mJI2vW7YUnHrqqcyZM4cZM2YwZ84cjj/+eCZPnsykSZPCBd+SkhJmzpzJ0qVLqYpzAWorDEKosKtpWtxBxgUFBZ0GCxBd8NuyZQs+n4/TTz897qDiqqoqnnvuORYvXkxZWVnM+5HdkDorPG/dujX83umnnx6zDQCn04nT6aS5uZklS5bwox/9KO7Un6ZpMnDgwHCBvrm5mebm5qg0aWlp4fcLCgqi0keei+eff56vv/6a+vrogkVKSgppaWlRx5WWlkZubm7cvO+JXQUYwWCQsrKyTrsWrVy5EmgPArZt2wYQN0DaunUr9fX14QHJSik++ugjRo4cyaBBg+JuPzMzkx49euzZAe1HGRnRs9K0BaJtHA4HmZmZUcs6fmc6vu74Ge6rfZxmKgakwuZ6YgYH23WIrVvvwGrtOhRUocpZjVCXIDOyUE6Xy2mbe6Qye+xghu6sozYhthUJ4Jw1WzhxU+g3nRYIctcXq7j3mJE0JbtDMxq1BLClu9A0DbO1u5CV5GRrj0SGlTeEi2NpXi89I5410JDopEd99G/S3xpIuP1+fDZbePyQ1+5As3ztXZ2UChfInBE3dKV3OBeAbimSa5qoyov4DJUivboRw7LIaokYbBzn+K04U5XaAhYOX3vhWukKy9Ci+robrd2LGmxuPIaTJLM9nwHsuK0ATjOA32bHHQgS+aEZmkWd20lSc3thUMNCtdbIG0phEdGNTKnWQnbkh69IwkMF7d/DBDyY2KPSBbDhaC0E2wl2CCJUh39Hn6EkKtFQKExCt2wjokUjloYv3MXJgRcTW7jFQCeIGw8BbDgx8dH+tGy9NSDw48ZBS1SOdvdVD+DCJLJV3YZCR4sJjkJbCuLGTkOHd/wo3EQGNYoOAVbfHjBuIJlj+sPwOaGWgTaThsGRfeDnp8HrSzsPDq4/Le61xDZtCtz1Umz6RCc0+WKXAxg6/N8pwKF9TTwc9yEOjm4ZFKSkpPDCCy+wfPlylixZwqJFi5g3bx5HHnkkM2bMoKioiGnTppGSksLFF19Mv379SEpKQtM0HnroIYLBYNSXuqCggPz8/HBXkjZVVVVUV1eHxxlEaitURgYFbV2Hxo8fH5O+tLSUa6+9FtM0mTp1KoMHDyY5ORld13n22WdZs2YN/fv3j8qTy+WKKaS2deG55ZZbGDy4Q7/lVm3dXzZt2kQgEIg7e5HH42Hr1q1Rx3b//ffz4YcfRqV79913ycnJwev1UlJSEjNX/6JFi/j9739P3759+elPf0qfPn1wu91omsadd94ZU2DvLMjaGwUFBSQmJkYFU22amkIFq7aWoUgtLS0sWbKEIUOG0Lt3byB0PjpL33ZOTjkldPOorq6msrKSH/3oR3HztXnz5kOqleBw4zA0Fv3Y4L6lFl+UKfwmZCXA5cN1fpSvccEck8LION8ANL21Np72UpAiFBjQ+tqhh163jZgNWKFO+pG11wbRr1sfH1zQI52CnE5my1KKiVsrwi+b7TZ0pThvwzZeHhQaA0SCnaBhwxEwSa5ppkXTsJoCNDh0tiY7yfEHcQYCjCvbES5SVTvsDG6OLczYW2cjslkWqc3NbE9JptluZ4fbhU/T6NPUTGZLCxktPjSlcHq8ZFbU4UsI3S4sXccWCIaCg9b8JzYF0JRG1rZK6nukYBoG7iYffredlKYanFZ7wdtjd4Qmc7Lp2FE48xNx9UogIScB3dCo+LIKpRSZSTaUz8QKWCSNSiMrXadkRT01ZmtrihXAneikRbOhJdpYrw0gpbKGBJ+PBqeLrKYWEv0BegQaqbElYtks7EGFQkfpsC03Ba/DhqppJq2hBQOF6pFA6vF5eFbUYTUHcKLw1QQwlEl6qgmkkNJQizJDAZM7Uyf11evg6jdoLg/gTghinDSY5hode59klK7jK6wNjREP+tD9ftyjsjEzkml8vQjL40dPteMcmI5zXC6Bb8vwf1sBpoWR6SZpSm/S1tRDcRoM7wWD+sDaMpQN/Mu2QCCIjyS0FCcOXwM+n51a8kihATfN6DZFsrYDM+BEoWPHj4mGk0oUYKeZIElYOFEo3NTiJRkLHQctKHQCJOLAg4YZM/gYQLldaF5wUYupnNgIYKcF68xj0GwWav5qlN9EJ0Ao7LJhowEds73TlM1AG5CDVuVB1VsouxOG9cQo3gx1rQPsjx0Ks28J/b4MAxb+Ce57o32g8V2tY7lOPQrevQv+9V6o68/xw2FF60Djy0+AX8SvDOPOiyA9CV76DOqbICcNTh8NFx0Lj85rH2jcPxv+1zrQ+DfnwjGHxnOJxJ6S7kPxdMugAELdgyZMmMCECRO4+eabue+++3j77bcpKCjg6aefxrIsZs+eHVVLXV5eTklJSbhwB6EByRs2bODEE0+M2cfmzZsBYmqDLctiwYIFGIYR9QyBtqAgXqH3ueeeo7q6mtdeey2q+01LSwsFBQUMGjQoqga+sLCQwYMHR3X5gfbC66BBgzjmmGN2eY5aWkK1RfEmqHr55ZcxTTOq8Hr55ZdHFfp1XScnJyecn47pAf75z3+Sk5PDCy+8ENUXf8WKFTQ0NESdi10FWXvK6/VSXFzMUUcdFXc61eTkZOx2e/gcRHrjjTdoaWnhsssuCy9rmyGpY/rm5mbefPNNevXqFR6/4vWGRrrG6x721VdfsWPHDqZMmbL3ByfIT9F4+ozYrjUABdeHlltKsbPJ4q2NiqdXKjZWayToFlccqbNgi2LVTkDX2rsJ2XVwRDY7WPRsaAFL0eiw0+yyYUVOUQqt3YkAHY4p2oZlM/h6YHR3NL01qddm8Mq4IazLzUBXimFl1eAwQn9OG5gWyjTJyXZyY3ojjUMSSMtwMrmvjtYU4LXlfgZNcbNuQRWegMbQsSm4PyuIOf62eEcD7JZF/7p6PHYb2Y027p85HC8GCS4Nt0tDB4JBxfZ1jTidGngDvHPZYjxJieTsqEPpGg6/iT2oqMxK4JL3JpOW58ZX1syOgMGyiz+kV3VteN8KSMxP4KJNP47JV1d0tTpAmRaaoYf/D2DWNGM1+rHlp6J16CqlrNCsUl2dWjlyHwCu7b/v8nptsp84rYspLwRix2G01cs7IvLhsBQpcbqB2QFV7UE1tGCrqoNaD2pEX3Svn8TemVjljWiJDli1BS1ooo4dgjn7G6xHPsQoq0JlZmGecCTa0QPRk0JBK04bnHoUWl4GOhGFCdMMFdo7qmuCuib0fqEHPkaevzZ71J+5ZwZM/0X896aMDf3tCU2DX54Z+uvoXz+Pfv3rs/ds20IcJrpdUFBbW0taWlrUxd8wDDRNQ9M0srOzKS8vJz09PWpWm5aWFu6+++6YganV1dV4vd64/c7bCogdC4rTp09ny5YtDB48OKqbUGFhIbm5uVGBSJvy8nLsdnu4ZhpCXVz+9Kc/0dTUFDOQubS0NG5w0dbt6cMPP2TixIkx71dXV4eb8dqOadmyZVFTbG7ZsoXZs2cD0S0du3oeQmdddSoqKhgxYkRUQFBTU8O9994LRA/QLi0tBYgZn7E3ioqKME2z01YHXdcZMGAAa9eupaWlJTyWY8uWLTzzzDMMHz6cc85pH6zW1ury9ddfR40rePTRR6murubhhx8OB225ubkYhsHy5ctRSoW/i3V1dTzwwAPAoTWe4IdK1zRykwyuHw3Xj459f6fHYsFGky8qFCt3wIoqhceivSXA0ClPdUc8myDUapDi8XLCymLspsmXw3tTnerGb7PTo6GZs1dtYsS2nWzJSqNfVT2ZDc0s7Z9Hs93G4kG9WNsz9NszNY21vbP42wkavZNguwcuHQoD0tqKgh1apHo4+GPf1mvJue1N9W91NlNwh8JvUiBIYiCI4bKRnRBdNLM7NPqPbr8WaoC7pYWdOWkke/wEHCZ+u44RDPDVw+s484ljcA9IoR+Qv2Iq6ye+jOOb0lBjy5GZ9F11TSeZ2nfaCpuRhU4jIwEjIyF++i4+EC7ePg62yHzs6ji0zCS0zCTonxV6HfGe3rf1O/OjkeH3bL88EX7ZXtkVP8SOI15AAJCWGPqLk28hDjQZaBxftwsKHnvsMVasWMFJJ51Efn4+SimWLVvG4sWLufzyy8nNzWX8+PG8+eab3HHHHUycOJGdO3cyb968cG1wZEE1PT2dhIQE5s+fT05ODgkJCQwcOJChQ4cybNgw7HY7TzzxBDU1NRiGwWeffRYOQiILfkopCgsLOfroo+Pme/z48SxbtoxbbrmFU089ldraWt5///1wYbVj4bZXr14sW7aMl19+OTzQefTo0YwbN45Ro0bx9ttvU1lZyXHHHYfD4WDHjh2sXLmSpKQkHnnkkfA2JkyYwLJly7j99tuZOHEipaWlvPHGGzgcDlpaWjrtgtRRQUEBhmHEtJqMGzeOpUuXcv/993PkkUdSUlLCO++8E+5PH3mu2wbqvvHGG+i6jsPhYMSIEXG7/8SzYcMGPv30U6C9VWb79u0888wzAFxwwQVR/fivueYa7rjjDn71q19x1llnUVlZyauvvkpaWlrUTEwAJ5xwAgMHDuSZZ57B6/WSm5vLwoUL+eqrr7jhhhuiZkqy2Wycd955/O9//+Omm27ipJNOoq6ujjfffDMcHElQcPBlJ+lcPlrn8tbXSike+Mrk6RWhLvVHZyvSnRqLS0w21YVqwfO31fLwrEUk+0IjF36yaB23Xn8qZdkp5NR5MJRi4sZSJm4sDe8noEFa0KIwO7Z70ZflcMcFXS6OxXBkOQhURk8huqvBdB9+5uHCM2K7wHWkK0VSowdNgaWBLRjqklTzbfT4K8PQGPH15fE2IYQQ4hDT7R5e9u677/LRRx9RVFREbW0tKSkp9O/fn8suuyz8wK+mpib+8Y9/8OmnnxIIBBg+fDhXX301S5cu5eWXX+bjjz+O6jv+2WefMX36dEpKSvD7/dx1113h6ToXLFjAE088wY4dO8jLy+P8889nzJgxXH311dx6661cfnnohrl9+3YuuOACbrjhBq677rqYfAeDQaZPn857771HU1MTAwcO5Mc//jG1tbU88sgjzJ49OyowWL16Nf/4xz/YuHEjXq836uFpzc3NvPTSS3z00UeUlZWh6zo9evRg9OjRXHDBBeFnCECoZeWhhx7iyy+/xO/3M2jQIK644gpmzZqFx+Nhzpw5XTrvl19+OcFgkNdeey1qeVVVFQ8++CBfffUVuq5z1FFHcd111zFz5kyWL1/OJ598EtWqM2/ePJ577jnKysoIBoP8+9//jtviEc/06dOZOXNmp+9/8sknJCcnRy17/fXXefHFF9mxYwdZWVn86Ec/4tprr407dqC8vJyHH36Yr7/+Gl3XGTZsGFdddRWTJk2KSev1enn88cf55JNPaGpqYujQofz0pz/l7bffZsWKFcyfP3+PujKIgy9oKXQNnv+okeLnN5MStBh6ST6nnd8Dh13jyRFzMeJMcLK4Ty69TIsvBuRxRFUtdW4nnwzuTWVyAreN03j45L0PCoLNQd7r+ypExAXvn3AkuQFfTD2ZAvyjs7n/wf7syowh/wNAN61w16c2yf2SuPjj2Cl8hRDiUFKoPRJ3+VB16wHOyaGl2wUFQhyqGhoamDJlCmeccQb33HPPwc6O2MeeGDEXW5yg4ONBvcj3m+Q0tz9Vrdlu46GTx/DCxU6mHvH9G3TrV9cQbAzgOqoH/3fzdoZvq4gbFAQy3Nz3cvxpddu0BQWapTCs6NvHhLtHceS1XWs9FEKIg0WCgvikU58QB1gwGIx5Pobf7+ePf/wjwWCQK6644iDlTOxPtk4aftJafGRFBAQACYEg44vKWFC02wlUuyR1ZAaZx+WQmGjQb2D8aVEBeji7XkekdA1T18KTaPY9vxdHXBN/il0hhDiUyHMK4ut2YwrED09jY2PUg1HicTqdMV2DDpYVK1Zw//33c+aZZ5Kdnc3OnTv54IMP2L59O7fffnvUjFTih68qOYGBvoaY5UdU1bGh6vsPqu/o7NOS+XhR/PdOuSp2woRdaQsMAGx9EqXLmxDisCADjeOToEAc9v7yl7/w8ccf7zLN8ccfz2OPPXZgMrQbCQkJ5Obm8vrrr9PY2EhycjIjR47k7rvv7vRhaeLwp2nxa6IG76xF06PHDSggz9NE/sB9f4k+bqybj3SwdXiulDPZYOQpXXhgnqVCU7V2sOrDao68qJnMPvFn+BFCCHFokzEF4rC3cePG3T4iPT09Pe4Tp4U4UJ4eOw+tueMTXmFNTjp9zNjLsF/TuO2TE3F21u/oe2is8vHoVSvACgUgDhf8/s0J6Mbu9zVj0JyIaVjbVfdIIWtYOv/37B7ODy+EEAfYOu2xuMuPULcc0HwcaqSlQBz2Bg0aFDPVqRCHmha7HTfRs/4ooCnJjapvjmnMtim1XwICgOQeTu6cN5YZ02eh6YrrfvnTLgUEu6QUlVuaqSv3ktaz83ELQgghDk0y0FgIIQ6AhObYaUA1IK+qgZ2J0YVoBZw8rd9+z5MtIYjhMnefsCt0A5tTx50idU1CiEObDDSOT4ICIYQ4AAx7/MttwKYx+q4RaBHDCgYdn8HRl/c7MBnbQ65sR8wyBeCwc+wVvXEmSlAghBCHI7l6CyHEATD4/HwKXi6OWZ49PI0rTkyCTydjmer7d+PZzy5+53RmT3wPlAItNCWplp3IFf8cSf7I1IOdPSGE2C2ZfSg+aSkQQogD4MQ/jolZpoBb/tO+/FAPCADcGU6uXHoWPSf2ICHXyTG/P5KfLzldAgIhhDjMSUuBEEIcID9ZeDpvXf4pzTt9uLOcnDf7BPQ4M/kc6tyZTs6ZdeLBzoYQQuwVaSmIT4ICIYQ4QJJ7JXDlp1MOdjaEEEKIGBIUCCGEEEKIbkNmGopPggIhhBBCCNFtSPeh+A6/zqxCCCGEEEKIfUpaCoQQQgghRLchLQXxSUuBEEIIIYQQ3ZwEBUIIIfbYhhqL014JcNorATbUWAc7O0II0WWqk7/uToICIYQ4AGauMjnn9QCvF5gHOyvf2+w1AYbMMFlQDAuKYcgMk1lrAgc7W9/fjI/AeQloF8LQX0FTy8HOkRBCHDAypkAIIfaz9EcD1PlD/353s0X+xxYl0+wHN1Pfw1Xvxi776btw1YgDn5d9ZsEK+MWT7a+LyiHzavC+dtCyJITYP2RMQXzSUiCEEPvRjO/aA4I22xph2fYfQM36D8k5f41d5guC13fg8yKEEAeBBAVCCLEf3fZx/OU/jVPbLg4iXzD+8oKSA5sPIcQBoHXy171JUCCEEPtRYydlzfKGA5uPA+Ho5wKMejbA4pLDf9xEmIyhFuIHR6HF/evuJCgQQoj9qLPbjMcCpX5Y8118swNWV8JJL1v8afEPpHuULgUFIUT3IEGBEEIcJJ+XHuwc7LnmQNcCmT8v3c8ZEUKIvSRTksYnQYEQQuxHu7rR+MzD7zb0z+Vd708TOAyPL9YP4RiEEGL3JCgQQoiD5Ni8g52DPbesrOuF5H98LR3yhRCHHhlTEJ8EBUIIcZAcjt2HRmV1/cY5b+MPoZZdbpNCiO5BHl4mhBAHgw7JjgOzq1fXW/zmY5PyptDr/GSN/52/59v569IAf/mi6+n7pOz5Pg45Slo7hPih+SFUV+wPUgUi9srll1/OZZddFrXs9NNP5ze/+c1BypEQhxkF9bt5LlZ1i+L8N4PkPRnk2veC1Hr3/Fb2/maTH79tUt4MaBpoGts8MP5FMFXXa/0tpbhr8Z7t+/9NNPZshUOSdCkQQnQP0lIg9lgwGGTLli2cdtpp4WUVFRXU1NQwbNiwg5izrnv55ZdZt24dhYWFFBcXA/Dpp5/icrnipm9oaOC///0vH3/8MVVVVeTl5XH11Vdz3nnnxU2/ZMkSZs2aRUFBAZqmMXHiRG699Vays7P32zGJg6u2xWLMCxbFjaHXQ9Lg7am7KFAqWFNlcUZ/nZIGhdumqG6BB7602FgHWW6Yu6k9+cx1MHOdSaodJvcGTYe8RMhOhESHxnF5Gi5DUeZRJDl0JuZpuGwa58+1QsFAHP+v4UJuSloY9z1LKfSI9X79YScPXNgFDcVhX6g+zLMvhIhlyQ87LgkKxB7bunUrgUCAoUOHhpfl5OTw+eefY7Md+l+pHTt28PjjjzNgwACGDx/Oli1b6N+/f6cBQUVFBddffz11dXVceumlZGdn884773DvvfeSmprKSSedFJX+v//9L08++STjx4/n17/+NeXl5bz44ots376dWbNmoXVSQBOHD29QsbTUYu5GxdE5cEo/jbynomvxi+pg6EwVKlTGq+DX4Lefwm8XxSlst60T+VVp3Ua9H97a3HGFyAn1FO1P3NpVoVyjjlTu9ZzPv56AgBUk1aVo8UONLzJV65b2or19xiqLf54aapBu9Fl8UwEjsjSyEvb8N1DnVSTYwWHI70cIIfaHQ78EJw45GzZsAIgKCjRNw+l0Hqws7ZGMjAw+++wz7HY7xcXFvPvuuwwfPjxuWsuyuP3226mrq2PmzJkMGDAAgLPOOouzzjqLmTNnRgUFixcv5sknn2Tq1Kncdddd4eUul4unn36apUuXctxxx+3fAxSdWlNl8eg3iuagYmAqLN8RKjYPzYCtdVDWBDtboN4bWu4Lhv4/IBUSbbCmGnxxu5jvosTc9vArpUJldY3oB2JFBg2R5d04AUHXRKyo4qysRexQAZpBrT/00uPRYkr/36fv7b++VTz1bYCYx5i1xko9XHDXsXD/MqhtDpLq92I3LRrdboI2O31SoV8yLCxpDXNaM5Noh9FZsKwCTAU9E+H+4+Gao+z85fMg079VeE2wFKQ4YHAafF0BgdbYSSnIcMPwTBicrnHFERrHAfY4x3Db5zqnpZic0V+PCujf3mDy6NcWRbXQww3PnKkxPs+GpRQfblZsb4QzB2jkp0QHMesrTW5cYGFa8PipOl5TY06hhUPX+PEROiNaB3JHbueM/vBJseKN9SZ5JeXUV/pYn5fLKSMSOadngL+/52VDo0ZlgpN+WQbnDYAX10NTEH45Gk7M0/jqs3pcFR4y8128oaWytkbDbQOvpbGprvWLaSkGZ2hkJmhsrgebDiflK/KTdao9FsUbW2h02DjpSAfJDo23NiqcOvxitMYVI20YEd/reUUmc9abjM3RuGG8DadNw+NXvFWk0DQ4b7BGkkMCPHFwyExD8UlQ8APS1NTEa6+9xvz58ykrK8OyLLKyshgzZgx/+MMfAAgEArz88su8//77lJSU4HQ6OeaYY/jNb34T07WlpaWFmTNn8sEHH1BdXc3gwYP57W9/GzcouPPOO1myZAmffvopuh6qGTz//PPJzMzk2WefjdrumjVr+NnPfsadd97JRRddBMCWLVu45JJLuPHGG8nOzuall15iy5Yt9OjRg1/+8peceeaZlJSU8OSTT/LVV1+hlGLy5Mn8/ve/3+NgxG5vv/UXFBQAdNrt6Z133mHdunXccsst4YAAIDExkeHDh7NmzZrwsmAwyCOPPEJWVlbM2Ipx48YBoYAqMijw+/288MILvPfee1RUVNC7d2+mTZtGaWkpjz76KK+++ioDBw7co+MT8U3/zuLGhfEHjX64ddfrFtTugwxoGuidFLF3dX/6PqXyuC0UEa0He3pfDDcbdH2VuM811kKbqGyBWz5p3a5uo9qZ1L4PCzbUhv6i9q+gKQCfl7cvLvfAtR/AtR8EotMCjQEo9cRmYUdz6G/RNsWMVYrPew/iuO0bo9Io4I2NOo+8bnHuIMXcCw10TeOORSYPftn+XSr1wNGzFL89OsA3FRqLSkInyK7D6xcYnD8kdE18Y73JJW+1rzd6phW1t/uXWjwwWec3R+uc/orJp9vaT7RmWYyrqEc1G0ACPeo9zKi386jDBloiJITSra6E1TtVuMvYvV/AsVvLOGNrBX6gAchMSWTLiIEoTQNDQes1Gw02VFlssOuh9ZXi5dWQ7vVyZskOVvbNptJt58ulrdGVDmgan5cqHl7m58trHbjtGlNeCfDBllDeX1hr8dfPvLx3pZNz3zApb/0s8pJgyVU2+qdJ4UwceDLQOD4JCn4g/H4/P//5zyktLeW8886jf//++Hw+Nm/eHO4z7/F4mDZtGhs3buT888/n0ksvpbS0lFdffZV169bx0ksvkZiYCIQCguuvv55NmzZxwQUXMGjQINauXcuNN95IXl4evXr1Ijk5Obz/wsJCBg8eHA4IGhsbKS0tZdKkSTF5Xb9+PRAdVBQWFgLw8ccf4/P5mDJlCjabjeeff54//vGPGIbBQw89xBlnnMG0adNYuHAh8+bNo2/fvlx99dV7fd52FxS88sorJCYmcvHFF8e853a78Xq9eDwekpKSWLJkCdu3b2fatGkkJCTEpAWorq4OLwsGg9x888188803nHnmmfzkJz9h27Zt3HXXXQwePBin00m/fv32+thEuya/4veLD4FZZLTYmvj9xtrFfjrr0rQr+7rs1lnryO6GIcTL++6OpwvHuzx/QExQoAFBIzRY+u2NivlbFMfkwSOdPH/h4a+J+nwDFtz6sRkOCn7x4e6/g3/8zCLdSVRAANC70UtOsz/82m1aDK9q5Ou89Nhz1qGLYsAefavv19BE/3oPm9OTQoNTItez6e3rt2Z3UkUN21ISqHR3qICxACO079WVGi+tsRiepYUDgrZt7gzo/N87Qco97fkq88Dflpr8Z4oUQ4Q4VMiv8Qdi8eLFFBUV8dhjj3H88cfHTXPXXXexadMmnnrqKUaNGhVe3rdvX/785z/z3nvvcckllwDwt7/9jaKiIv7zn/+E006dOhW73c7rr7/OySefHF6/qamJbdu2ccwxx4SXtRW243XLWbduHYZhMGjQoPCytqAgIyODhx9+ODw2weVy8cADD3Dffffx7P9n7z7Do6jeBozfsy29QUISAgEChN5DUUGq0lVAURGk2BAVFPUV7A39WxEUBWmKWABFaQooghRBICBNsiG0UBIgvW+2zPthyZJlN5RQUnh+1xVlZ86cObMs2fOcOmeOo9W8b9++dO3alT179lz+m1VMXFwcGo3GKUApkpiYSHx8PH379nU73yArKwuNRuMIAP744w8Aevbs6TYt4Ai6AGbMmMG2bdv43//+R48ePRzHAwIC+OKLL2jSpAlabflYvSUtLQ0fHx9Hr0xOTg6qqjoCw8LCQrKzs6latarjmqSkJMLDw0t8nZycTGhoqGNIxrW8R1KuvXW5XLhac2/Pz0O9wDl39y9NYFA8/4tdeynPWNr3wd11V/I8wOEg94sAFBSbJ2VMU4muomC+jPjyUAbk5hdSkJdNduHF12g1WWF7suuDBJhc554Ems5+qC/yPuYaXL/qfQvN7i90c6hagYmj/l4XvokCxlSV7OxMwPe8cwrHslwvMaZWvN8lco/rc49rTYYPuSdLklYSRZXOffv2YbVaXc7/+++/bNq0iSFDhjgFBHBuaEtRj8KBAwf47bff6N+/v0vaoop/8Ur0gQMHUFXV6VhRb4C7oGD//v3UqlXLqaJtNBrRarW88sorTpOVfX3tXy4jR450Gkbj6emJVqu94kqz0WikZs2aLi37ALt37wZweQ+KHDlyhOrVqzt6R3bv3k3VqlWJiIhwSXv48GEAatasCdh7DL777jt69OjhFBAAtGvXDoDo6OhSPtXVV6VKFadhWr6+vk49RQaDweUXevEvCXevw8LCnMZoX8t7RAVCnYALPeF1ZlMvvcfgcr67iir75cGlPF5pKvHF51Rfal6XcJ86aafdHg8oyHf8uUctDVGBCi1KWETM3VvfJVLBx8v+2b2Uz2CYDwxp7JpTpqfrjIcMN8fcCc/Od3pdqNGQEFRCgFK8h+lsMU55eVAjp+DCN7Gp3BalYUCLoLOrTjmf61TD9ZLb6igV7neJ3OP63EOUDQkKKokePXrQoEEDZs6cSe/evXnzzTdZv369I0AoasW+6667XK5Vz1ZOioa4rF69GlVVeeCBB1zSFuVXPABwNwQnLi7O7fCXgoICDh8+7NIybzQaadWqlcsvhiNHjjier7jk5GQKCgqoVauW65txiY4fP05WVlaJQ4eOHTsG4HYIz5EjR8jMzKRZs2aAfSjQyZMnSxzus2vXLuBcgPHXX39hMpnc/n0Ucdd7IUpHoygs6Kelpl/xY9exAKpqnxFrVUuu1F7IpVT2zz9fUnqnB3dTkLN7GTj9nO9yyn9+2vOfv9ifPQsLCc9MRWMroSm+hOtKDBIu473uv3+72+MWrRZfPUy7TUOTs5OAFw/QcUt117Sv3aLhvS4afM9uStcmTGF2n3MNF+uHaPAu1miv14B/sXp9uA8svEtLl1pa3u18Lp9IPzgd6Mkp73OJ83Qa0sK8CFTsv5M1RYHm+T9WG7uqBpAQ5IdVUUj28eT7RrXJ0+vs743Vdi5ItdicP6MaBRTYFFaFKgWFtDqdgbb4343mbDqrygs3abgtSkOtAIVpt+vQK2ffeJvKPfXguwE67mukoFHs2d7fWOG59lIFEWVDRXH7c6OT4UOVhL+/P/PmzSM2NpaNGzeybt06li5dSpMmTZg5cyYJCQl4eHhQvbrrN9nBg/bF0OvXrw/YK/Q+Pj5uK7hFE2vPnw+g0+mcWvLj4uKIjo52WaJ07969WK1Wp+uTkpKcKtjF7d+/n6CgIGrUcG5mutDwpEt1sTxycuwz4vz9XVvUVq1aBUD37t0B+xCqktLm5+ezceNGoqOjHc9xoXsX/X1UlD0fKoq24QpHH9VyIgeqeduXtkzKUdFpVPItcDQLLFaVUB9Ym6iy4YS9vqSq9tVfavhpmL3Hxu9HIevs0G4N5xb/LFHRqkPnKxqPfTmKD5EpPgzJ7fh8xT6pWD0v7aWUt3gejuOXkcdZj7eA22rD8+sgMdu+atATraB7pMJH21QMWhjaRENOocraRJVdpw3kmqtSwxO6RcJLN2n4eq+N9/+BQhsMjoaGVRU0GgjxVvj3lI0zeZBrUagXCE+01nIqF+LTreQUqhzJVKjmbb82rxD61oWVh+F0nkq7cA1Ng+3PmGWCsIkZbp/h77tMBN+kw1N37uGjAhU2DrNX0GfstLI/VeW5dhpq+NsruU+20ZBRANX9nN+wMD8tuc9pWZ9ow2yD7rU1WGwqiZkqGgUiAxTH3hATbtIyNuZcPiaLypm8apCSS/aZfKLbBqPVKKiqyuEMlSOZNvRaDV46+19UiDfsPQOtQhWqeBo4kOZHmkmhSVUYdNqGMRXaRSgUmGHaDpXkLCutQhRe66LnQLqCxaZSN0iDAhRa9ShEUZBsAg8N3lUN+OhhywkbqHBzTQ0+xVYSeryNllEtNOxPUakTAAFe9vfl+7s0TMu3f76qeEkFTIjyRoKCSkSr1dKuXTvatWvHuHHjePvtt1m2bJljAy2dTud2jfylS5fi4eHhWBUnPz/fJQ3YK8lLliwhKCjIaaUio9FIVFSUY1Ufs9nM8ePHHcOSiiuqTLubZOyuZTwuLs5t5fhiE4QvxcXyCAoKAlzfj7y8PBYvXkxERIRj/oafnx96vd7te/fjjz+Sn5/vtAN0QYG9K75o6FFxy5cvR6PROM25EFeHoijUKNZbEO57rgk+slg81zgYnmjten3vKPctm3lmlbDPrWS7m7dwgZbqgfVgx2nIKYT0AnsjrXOBS77Wbd5nF51pUtW+ZOf8/RTrZVDcXKPSQXeAu2+qT4NgDQatQu0AG78egiUHYE8K+HtAg0CFpCyVXalclk966DBoFQa46fTqWtv59YOubQIAPNlGy5Ouv0oAGNTANaoK9oYmISV/tTUtaf9A1X14V8NXA7qS/yIea+VaBm+9gvcFRvbcGnnuc6TTKEQFuc+/eD4eOoUa/oC/L0SdG7OvKPbro4JcP5u1ig1XahZ6rpw96mjoUefcuU6RULw60NZl+sDZ8p03r+C2qJKjWg+dQssw1+eSYECUB7L6kHsSFFQC6enpBAYGOlX4tVotiqKgKArVqlUjKiqKbdu2YTQanSrfS5cuZd26dTzyyCOO8fsRERH8+++/7N6922k8/ZQpU8jKyqJDhw6OY2azmUOHDtG7d2/HMZPJhM1mc6kgb9y4kSVLlgDO4+WLgoLzW83PnDlDampqiUFBQECAy1jGy1EULJU0TKeo52Tbtm1O78PkyZNJTU11mhCt0WiIiopi37595OfnO4ZiHT58mFmzZtGoUSP69evnyKNo3sH27dudJm3/8MMP/PvvvxfcTE2UP956hYOPaPlwm5UlCfbRGEMa2lu6DR+6zvEBeyfBT3e5/grOMqn8dczGjlPwxmb13JeXCm3DYOXdClW8tCTnqnjp7PXVdBPU8HOtbM3oqXLHYitrEt0UQFUZ5fEn7b0SGdmmPnp9UQVPQ3QVeDrGOfm/p8y0+uoS35CinCpD/e96rRYlhBBlTIKCSuCTTz7h33//pXPnztSsWRNVVdmyZQvr169nyJAhhIWF8cADD7BkyRKeeeYZBg8ejL+/P//88w9r1qzhtttu45FHHnHkN2DAAFasWMFzzz3HkCFD8PHx4ffff3eM7y9eiT548CAWi8XpmK+vL3Xq1OH3338nKCiI8PBw9uzZw969ewkPD8dmsxEQcK4JKy4uDn9/f5cJuhdqyY+LiyvVmPsFCxaQnZ0N2Cdle3l5sWDBAgAiIyO5/fbbHWk7depE3bp1mTVrFgUFBYSFhbFmzRq2bt3K6NGj6dKli1PeI0eOZMKECTzxxBP06dOHM2fOsGDBAgIDA/nggw+cJkX379+fr7/+mjfeeIP4+HhCQkLYsmUL8fHxJa6GJMq3EG+F9zrreK+z83EFq/sh7yVUmP09FPrX09K/Hoxvq/Jnoo2MfJXOkVpqB5y7KMzn3J99DO7z8tYrLLhDS6uvrRzLdj5X3Rfa69xFC+61DNWjxYz7EMe99cdUutWq4JGBm948IUTFJvMH3JOgoBJo164dmZmZ/PHHH6Snp+Pv70+dOnX44IMPHK3Q1atXZ9q0aUybNo2ZM2c65gC89tpr9O/f3ym/li1bMmnSJGbPns2MGTMICgrilltuoX///rzxxhtOrfwlVdzfeecd/ve//7F48WICAgLo2rUr33zzDQMGDKB1a+dxGfHx8W4rwUUrGJ2fd2pqKikpKfTp0+ey3qeCggI+/vhjl9WZpk+fDsD999/vFBRoNBo++eQTPvzwQxYsWIBGo6Fhw4ZMmTLF7f4LPXr04IUXXuDbb7/lo48+IiQkhLvuuotRo0a5zDWoXr06U6dOZcqUKcybN4/AwEC6du3K4MGDGT16tNv5FaJiKmmlTJsKBRYbnrqSK51+BoU7613ZCltVvRQSHtGy/KDKr4dsnMyGu6PhgUbw9VeXl1fGM1pu+sbK3pRLS+9zaYvjlG8lDCsSQlRclSEoyMvLY8mSJRw6dIjAwECGDh3q1OBaGoqqSt+oEOXFp59+yrx581i6dOkVDY0S5YfuvZJb15+MUfi0e9m0zZjNZubOnQvYe7mK7/R9Mcp7562PX8K3iPpCBYoKlIHuj+94H1rJ/B4hKpO/lDluj3dWR13nkpROQkICPXr0IDEx0bGR6rZt22jdujXr16+nZcuWbhc+uRjpFxWiDBRNNC4uNjaWb7/9lttvv10CgkpEX9JvWQWOZl7XolxdFb+h7dJIu5kQlY5awk9F8fTTTxMQEEB8fDyZmZkUb9//6quvePXVV0uVrwwfEhWexWIhIyPjoukCAwNdlkgtK+PHj6dKlSo0adIERVHYt28fq1atonbt2jz//PNlXTxxFVlLGn2iwtDSr6grhBDiBvX3338ze/Zs6tWr5zIkumvXrkyePLlU+ZaPGpIQV+DIkSPcd999F003b948GjdufB1KdHENGzZk7dq1rF27FrCvRvTQQw8xbNgwt7sri4rLcoFzvS6wpGNFV3m6oStS+6EQ4lJU9DkFNpvNscrh+cLDw0lMvPRFJIqToEBUeBEREUybNu2i6a5k9+OrbezYsYwdO7asiyGug5KqlAr2lYYqrIvUlXeOqMDPVpxSeQM3IUTF1L59exYvXkyvXr1czimKUuJ+UxcjQYGo8Ly8vGjfvn1ZF0MIt0ra9bhSrOF/nt/u1pBvgTujNY6deSs8RXoKhKhsKvq/6ldeeYUuXbrQsGFDnnrqKadz//zzD5GRkaXKt/L08AohRDnUIMj98Wo+17cc10OvuloGNNBWnoAAwKMCraAkhLghdOzYkZdffpnnnnvOMTdx06ZNfPrpp7z33nsMGjSoVPlKUCCEENfQinvc/5p9r7P8+i1XalZ1f7xh6VrchBDll4ri9qcief3111m6dCm+vr6oqsq4ceMYN24cMTExTJgwoVR5yreSEEJcQ3WCtDzlvF8fd9WHYU0r7lh1bzcDT70q7uPY7fzI9VhMXahMvR5CiEqlX79+7Nixg6SkJLZu3UpCQgJr1qzB19e3VPnJnAIhhLjGpt6m5/2uKocyVOoHKei1FbuiefBRDTW+sGE9OzBXq8Chxyp4G1NVf0ibB6M+BeNJmDAQHuxa1qUSQlwDFa1X4HwpKSkEBwc7XoeGhhIaGnrF+UpQIIQQ14GnTqFxcMX+IioS5qfF8n9atp60T6FuV72CBwRFgnzh54llXQohxDVW0vYxFUW1atWoUaMGrVu3pnXr1rRp04bWrVtf8canEhQIIYQolUoTDAghRAXy2WefsWPHDmJjY/n111+xWCwoikK1atUcQcKbb7552fkqqip7uAshxI3GbDYzd+5cAEaOHIleL6vsCCFuDKu089we72l98DqX5MqZTCZ2797NqlWrmDlzJunp6SiKQmZm5mXnJc08QgghhBBCVEAeHh60bduWl19+mX///ZemTZvy+++/lyovCQqEEEIIIcQNQ1Xc/1R0QUFBvPPOO7zzzjulul6CAiGEEEIIISqBli1bsmHDhlJdKxONhRCivNl1BE6kQq9WoJG2GyGEuJpUTcXuFvDz86NVq1aOVYfatGlDo0aNUBSF9evXY7VaS5WvBAVCCFFepGRC6CiwFVv/YcWL0Cem7MokhBCiXBk9ejSxsbHMnTuXKVOmoCgKXl5ehIaGkpiYyKBBg0qVrwQFQghRXoQ/7BwQAPR9B9TFZVMeIYSohNQK3gH7wQcfOP4cHx/Ptm3b2LlzJ0lJSYwcOZKnn366VPlKUCCEEOWFpXRdvkIIIS6dWgF3ld+1axdNmzZFq9U6HY+OjiY6OpoHHnjgiu8hQYEQQgghhBDlWKtWrfDy8qJly5bExMTQtm1b2rZtS4MGDa7aPSQoEEKI8uBSewlUFeathZwCGN0Tzms1EkIIcWG2CjjR+OeffyY2Npbt27fz/fff8+mnn6IoCr6+vrRp04a2bds6goXatWuX6h6yo7EQQpQ1mw20d5d8vmhOQeJpqDXa+dyfr0HXFpd9S9nRWAhxo1rm963b4/2zr3wIzvVy9OhRtm/fzrZt29i+fTs7duwgIyMDRVGoWrUqp0+fvuw8padACCHKWvv/u7R0dR53PdbtDZmILIQQl6GiTzQGqFWrFrVq1XJaaejAgQNs27aN2NjYUuUpQYEQQpS17YcuLd35KxMVKTSDQVr6hRDiRla/fn3q16/PkCFDSnW9BAVCCFHRrd4F/WQvAyGEuBQVffMygCVLlvDXX3/h4+ND06ZNiYmJoW7duleUpwQFQghR0VWCLzghhBCXZtq0aYwdO5aaNWty7Ngxx/GAgADHpON33nnnsvOtBKOqhBDiBnfb5U80FkKIG5WquP+pKKZNm8azzz5LQkICqqry2WefMXbsWAoKCti5cydTp04tVb4SFFQAhw4dIiYmhvnz55d1UUq0ceNGYmJi+PPPP8u6KEKUf6lZkJN/9fLTX79O32/22YieZSFyuoWZuyzX7b5CCCHsjhw5Qq9evVAUeyTTrl07Jk+ezD///IO/v79MNK7M9u/fD0DDhg0dx3Jycvj222+JiYmhTZs2ZVU0h7i4OICruonGtXL8+HGWLl1KXFwcRqOR1NRUevbsyaRJk0q8Zvfu3cyZM4fdu3djNptp1aoVzzzzDHXq1HFJazKZmDdvHr/99hvJyckEBwczcOBAhg8f7vgHLG5QiWeg1mPnXvt5Qqb7pfHKo8FLLCyKPzfZ+dFV8NhqM3oNVPFW+Lgz3N9YvlaEEOVbRZ9T4OPjg0ajQavV4u3tTUZGBgDNmjXj8ccf59VXX2XBggWXna/0FFQAvXr1YtOmTU6V/127djFz5kzS09PLsGTnjBgxgk2bNhEREVHWRbmolStX8tNPP2E2m4mKigKcA67zLV++nEceeYQzZ87w6KOPMnz4cHbt2sUTTzxBQUGBU9qcnBweeeQR5s6dyy233MKzzz5LrVq1+Oyzz0r1D1RUMsUDAoDsAqj+0HW7vbWk1YsuILfQxtg/zCgfmO0BQVFgezYrVYVCKyRnqwxZrqJ8YEb3oZnAKWa+3mNh3Bozj6+2sCP5EjdnE0KIa8ymuP+pKOrWrcuJEycAiIyM5MCBA45zMTExrFmzplT5SpNOBaDVatGet2vp3r17AWjSpElZFMmFTqdDp6sYH6chQ4bw8MMPA/Zxedu2baNRo0Zu0+7fv59JkybRvn17Pv74Y8czhoWF8frrr7N8+XLuvvvcplNvvPEGRqORzz//3BHEDRgwgIEDBzJ37lzuu+++a/x04rozWyDPBAE+545l5sIbi2BLPAzvDDc3gGa13V+fnHHlZXjtB5LzIT/QjzpVdCTN3kDB4RRCcrPJ8fQi2cuPPyMb8WaXgTQ5fYypv82jUUoS3f0CSffyYs63c0isHkFVcx7LOtzKVk1V8pzq8Aqg2gOBi+13qYIVyDTBiN/OplVUpu9SQbW5plfASwONQ6BrDUg1KVhtKvFpUNMPOtaA7acUPHRgskJ0IKw4DCazSpsweKARBHkqbE1SiT0NXjqF+xvBtiToWEOhfpC97ctbf+4b/0imjZxCaBri3C6WW2gjIQNq+ikoCvgbQFvBWxSFEJVPz549WbduHQ888AA9e/bkyy+/ZMSIEXh6erJlyxZsNje/ay9BxajFVUJvvvkmS5cuZeXKlQQHBzudS0pKYtCgQfTs2ZPXXnuN22+/nYYNGzJ16lSys7Pp2rWrI23//v0df16wYAF169YlISGBn376idjYWE6dOgXYN7kYMWIE3bp1c7rXxIkT2bBhAz/++CMzZsxg48aNmEwm2rZty6uvvoq/vz+LFi3ip59+4vjx40RERPDcc8/Rrl07Rx75+fl07tyZgQMHMmHCBAByc3Pp0qUL99xzD926dWPu3Lns3bsXvV5Pt27deO655zAYDJf0Xm3cuJHffvuNvXv3kpKSgo+PD40aNWLMmDGlGq7k7e3t+PPFhj1NnjwZrVbLSy+95BT0xMTYl39MSEhwHNu+fTtr167l3nvvderV0Wg0tGrVimXLlpGWlkaVKlUc544ePcqMGTP4559/MJvNxMTEMHHiRMaOHYter2fevHmX/XziOvpkub3yn5ELXZvCN0+ByQx1n8DRlL7Z/hmjqt+V3eu/YyWfe/NHwlBRgbe6D+SL259k2dwP+K7ZTQzau42WSYm0TErkmX9WASpF1dy66ac5rAZTJymJx9avBqC28RAP3D8W9MX+faqqPS5Qzj2WWwrnehKKbqKeDSaUYscolo8K+VaIPaUQe0p1Cjq2JMGiA27yOmvnaZi1u3hmgKLySezZcqg2UGzoNQojmyq810mhzXwbhzLtSXUaG78OVLipukKrr20kZKoUL2R1X/ikq4Z7GkinuhCVSUUfPvTGG2+QnZ0NwLPPPsvXX39N7dq1qVWrFrGxsQwdOrRU+UpQUEbq168P2CuV5wcFn332GRqNhscff5wzZ86QlpbmqLSqqsqbb77J+++/T3h4OMOGDXNcV6tWLQB++uknjhw5Qvfu3QkNDSUtLY0lS5YwYcIEpk+fTuvWrR3XGI1G/Pz8eOSRR+jQoQOjR49m69atrFmzhqlTp5Kbm8vp06e54447yM/PZ968eUycOJEVK1bg6ekJQHx8PDabzaliHR8fj6qq7N+/n3Xr1nHHHXfQvXt31qxZw+LFi4mIiGD48OGX9F598cUXREZGMmDAAAIDAzl69CiLFy/mySef5McffyQgIKAUfwN2cXFxRERE4OfnWmEzGo3s2LGDAQMGEBoa6nSu6NlTU1Mdx77//ns0Go3T30kRLy8vR/qioCAuLo7Ro0djMBgYNGgQ1apVY82aNTz99NMkJibSp0+fUj+XuA427odnvjr3eu1eeGwGJCSdPXBeDTo1+8rud+/HF0mgUKDT80b3QYz+53f+aNCcgIJ8GqYkFUvhWiuvk5FCnYxzn+OBe7fx9IYV/K/bgLPJig8ZKiEiKMr2/Dkz6tmTiur+fPGEjvsUr+Cfd42iXLynovh9zv7fbIMvd6vEJquOgADAYoN+i1U6RqgkZBZ/ELuTOTBkhY2bqivU8KvYlQghROVSVG+pUaMGGzdu5K233iIxMZGnn36a119/vVR5SlBQRoqCgoMHD9KhQwfH8f/++4/Vq1fz8MMPU61aNTZu3Aica8n29/enY8eOvPrqq8TExLitOI4dO9ZRCS3Sr18/+vbty4YNGxxBQW5uLseOHUOj0bgMd+nbty9LlizhzjvvZPbs2Y4JsoWFhcyZM4fExESio6MBe+W5eBmLH8vPz2fhwoX4+vo6ytG1a1fH8KdLMWvWLJfnadCgAS+//DJ79uyhY8eOl5xXccnJyaSnp5c4UfuPP/4A7N105yuK0H187ENG8vPz+fvvv2nZsiVhYWEXTW8ymXjhhRcICAhgzpw5VK1aFbC/9/feey8mk6lCTNq+oa38t4RjNuyVy9J137p1PBX2Jl40mZfFTPPkROqmnuL7Vh15asNvxc4q5/2/5Mp1l0P/2YOC4gFB0aXuLrvoBHo3FzodKhqedH6aUlTEnRv7nexOcT1WaIO/T5Z8kcUGfxxVGdFUggIhKouKtPyoO7Nnz+ahh87NR2vcuDHff//9FecrfaJlpHhQUNwnn3xCcHAwDz74IHBueEvxibDujhVXVIG2Wq1kZ2eTkZHhGPNvMpkc6Q4cOICqqgwaNMhluIuXlxf+/v783//9n9OKOUWV++JzHIxGI1qt1mknvaKg4KWXXnJcAziG4BS1tF+Koucxm81kZmaSkZHhyLP481yui72Pu3fvRqvV0rRpU5dzhw8fBuwROtiDObPZTPPmzd3mdfjwYTw8PKhWrRoAP//8MydOnOD55593BARgf1+LgraioKs8SEtLc3qvc3JyHIEO2IPF4r0mYB8Gd6HXycnJqMUqgRXuHnWq4aJONazBflx4jM3lSzbnovp7XzSdVVE4HlCF3xq0JCw7g9gaUWfPlPwNWKhxbRvaG1rTfWK3AUEJGbukVa7223LZ/N2OWFTx0124YAGWNKfXFf6zK/eQe5Tze4gLe+yxx2jTpg0bNmy4qvlKT0EZCQgIoFq1ahw6dMhxbN26dezYsYNXX33VURE2Go34+vo6repzocqsyWRiwYIFrFy5koMHD2K1Oq/4ERkZ6ZJPjx49nNIUFhZy8uRJbr/9djw8PJzOHT58GK1W66gMF+VTp04dp7RGo5Hw8HCaNWvmdP2xY8cwmUyOoU4A6enpTuXU6XQEBgYCkJKSwldffcX69es5efKky/MWf57LdbGg4Pjx44SGhroNYHbt2gXgCAKKdhSsXbu2S9rc3FwOHDhAs2bNHEHR6tWrCQkJKbGXQ6PROALH8qD4PAjAKdADMBgMTsENQHh4+AVfn9+jUuHuMaQjTF8N288G9jot/G8o2tZ1IGoMFx+Af+nC6kTCW/fBuNkXTGfWaAjOzeb36BYM3bGer1vfSteDe7lz/w7cD+iHFG9fNEBYjn1cTXxwGB90uaNYUtVlOI7zEJ9SNrmpJb5wf++i1xdTQnGqecN3feG2Rc53uy1S4YHGGkastLm9+M56CgNaOg/xrPCfXbmH3KOc3+NaUyv48uCxsbE888wzdOnShbvvvpsPPvjgiupDRSQoKEP169dn586dqKqK1Wpl6tSpREdH069fP0cao9FIdHS0U2t9XFwcnp6eLhVQs9nMmDFj2Lt3L/3792fIkCEEBQWh0+nYuHEj33//vVMFuKiFv3Hjxk75HDhwAIvF4lKhL7omKirKEQBYLBYOHTrkNMTGbDZz6NAhpwnRxcsOOFb7KSgooFevXk5BQbt27fj88885ceIEo0aNwmq1MmDAAOrXr4+fnx8ajYY5c+awd+9et/sEXKqLBQU5OTlOwU8RVVX5448/CAoKcrTq5+TkALidm/Dnn39itVrp3r2745jRaHSarF3cwYMHiYyMdBkyJcoZLw/Y9Db8sg1OpkG/NlDv7JfjoWnQfiKcyQYfA9zeAj54EOo9Ufr7je1bYlBg02oo1GhICwzk1x8/YUOVWqR5+9HuSDyP3/UwW2us5eW1S/CymM9OR7ZPNraiIaAgj2016jK9bVdyPD347JbemPSeOFWdiyYac35wUAK16D9uAhGnen2xVY1KzMs5KPHQqBRaz13ib4DoINifBrlme5YGRUVFoYonDG4AzappuDtaIchT4cwYK8N+VTmWDY+3gjEt7V+D0VUUXt1oI8ME/aPAx6ChUVXoVadiVx6EEJVPixYt+PPPP/n555954YUXaNiwIc8++ywTJ050WkzlcklQUIbq16/Ppk2bSEpKYsOGDSQmJvLFF1+g0dhHdWVnZ3Py5Ek6d+7sdJ3RaKR+/fouy5SuWbOGXbt2MWHCBKdlMgG+/vprNBqN05AUo9FIrVq1XFrCS6osFxYWcujQIXr37u04dvDgQcxms9P494MHD2KxWNwu8+ku7/O34y4aYvPVV1+RmprKwoULHfsJgH38flxcHPXq1buiZVD3799PWFiYo1fifEFBQeTnu+46u2bNGk6cOMHDDz/suH9RS8j56a1WK9999x2+vr6O+R8WiwWz2ez4ey4uMTGRPXv2uPTeiHLKoIfBN7serxMGp+det2JoLAvxBKqffV0bwGJF1WoYo4JWcx/s7QALN6Pc2Q5a1MZ8OJlvfvsFi6eekSNH0kWvB+DDs3lkF1j5/F8bk7ZAdiFn6/jqJQQE5yr5eq2KqoKnDuoFwis3wcAGesxWFatN5Uy+QrCXipf++o1kreqt5de7XY/fVF3D74NlRK0QN4KKtCfBhQwYMIB+/foxZcoUJk2axNy5c3n33XfdLnhyKSQoKENFw0N2797NrFmz6NSpE23btnWcdzeBF+DEiRNuW7eLxvWd34Pw3XffsXXrVurUqeOIIIta891Noo2Li0Or1boMX0lISMBisbidUOzumLugwGg0EhIS4ugq9PT0pH379i7pip5Hr9c7tdZbLBZef/11cnNzS9xb4FKkpKSQmprqtjejSP369Vm/fj1JSUmO7tGUlBQmT55MWFiY05JfRe/V9u3b6dWrl+P4V199xYEDBxg/frxjlSSdTkdoaCh79+6loKDAEZQVFBTwxhtvuKzkJCqRR3vAl384H9vyP+gw4erfS6dFAbRFX35Na9t/itSuhsVTX+Llfp5aXuig5YWz6yDkFdqoMtWKyd2QnrOdAhoF3uqo8OJNF/5q0WsV9FqFmnq40HwHIYS4Fir6kqTF6fV6nnvuOUaMGMErr7zCQw89xLRp09iyZctl5yVBQRkqqkhOnjyZrKwsxo0b53S+pBb7iIgItmzZwvfff09AQADVq1enZcuWtG7dGkVRmDRpEoMHD0ZVVTZt2kRWVpZLPkWt+e6Ci7i4OGrXrn1JPQhGoxFFUVx6INyVu+hcSZNxzxcTE8OWLVt4+umn6dGjB+np6fz222+OYTUX2oXYnaSkJFasWOH4M0BGRgazZs0C7HMrigdUw4cPZ926dYwdO5ZBgwaRl5fHokWLMJvNfPbZZ07jIuvXr88tt9zCkiVL0Ov1REdHs2XLFtasWcNdd93FkCFDnMoycOBApk2bxujRo+nTpw8FBQUsWbLEMTlLgoJKasYYeKwnPDwNgv3hu/H2/1cA3gYNp59S6PWjlc0n7YGAXguf9lB4rKV8lQghRFkoLCxk//797NmzB39/f+rUqcO2bdtKlZf8Ji9DtWrVwsPDg9TUVAYPHuzSwm80GjEYDC7HX3/9dT766COmTZtGQUEBI0eOpGXLlrRo0YLXXnuN2bNn8+mnnxIWFkafPn1o164do0aNuqQVjCwWCwkJCdx+++0u5TUajWg0GpdegYiICKcKctGx88fXJyUlkZmZecmV+aFDh5KZmcmvv/7K7t27qVu3Lg899BDp6ens37//soOCzZs3M336dKdjO3fuZOfOnQB06tTJ6VzTpk159913+fLLL5kyZQpBQUF07NiRhx9+2GXfArBvSPfRRx+xcuVKli9fTt26dXnzzTfdLhs7bNgwCgsLWb58OVOmTCEqKooRI0Zw4MABFixY4HbFI1FJtK4LOy6250D55O+h8PcD8rUhhKjYKvqSpJMmTWLPnj3s2bOHAwcOYLVaUVUVLy8vmjRpwqhRo0qVr6Kql7KcgxDiWjObzQwYMICaNWvyxRdflHVxxPWkDLzweXXxhdMVnb8MZrOZuXPt8x5GjhyJXl/yUCIhhKhMvqu50O3xIccGX+eSlI5Go6F69eq0aNHC6Sc6OtrtfMVLJU0+QpQBk8nktISrzWbjgw8+IDk5mZdeeqkMSyaEEEJUbhV9SdLTp08THBx88YSXSYICUeHl5ua6XSWouOJ7H5S1kydPMmrUKPr160dERASpqamsXbsWo9HI8OHDuemmm8q6iEIIIYQop65FQAASFIhKYObMmcyfP/+CaaKioli40H134fVWtLrQsmXLyMzMxMvLi0aNGvHhhx/SpUuXsi6eKAsGBQplJKcQQlwPlWVJ0qtNggJR4Q0cOPCiretXspnH1VajRg2mTJlS1sUQ5cmOj6HpMxdP9+wd8NFS52NdGrtPK4QQQlwGCQpEhRcZGXlVtvcWosw0qQWfPwxjZl043YcjoHFNGDsLrDaYMBBeu/e6FFEIISqLij6n4FqRoEAIIcqDx/tcPCgAGNXd/iOEEEJcRRIUCCGEEEKIG0ZF36fgWpGgQAghhBBC3DBslWD40MmTJ/n66685dOgQgYGB/N///R8hISFXlKcEBUIIUV7cczMs+tv52MiuZVMWIYQQ5VJsbCzdunXDarUSGRmJ0Wjk/vvvJyQkhIULF3LzzTdTo0aNy8639NueCSGEuLoWPgdznoQQf6jmDytfhjlPlXWphBCiUlEV9z8VxbPPPkurVq04efIku3fvRlXPLWm9fv163n777VLlKz0FQghRnozsZv8RQggh3NixYwc//PAD/v7+WK1Wp3O33HILkyZNKlW+EhQIIYQQQogbRkVfktRgMGCxWNyeCwsL48SJE6XKV4YPCSGEEEIIUUHceuutfP31127Pmc1ml96DSyVBgRBCCCGEuGGoiuL2p6J44403WLp0KU899RQ5OTlO59auXUvt2rVLla8EBUIIIYQQQlQQzZo1Y/r06UyfPp06deqgKAoLFy7k2Wef5aOPPuLBBx8sVb4SFAghhBBCiBtGRV99COChhx5i+/btdOnSBU9PT95//30+++wzhg0bxrhx40qVp0w0FkKI8iyvAOqMhtNZ9tcPdYdZT5RtmYQQogJTNRUsAiimsLCQZs2a8eWXX9K5c2cWL16Mqqqkpqbi5+eHh4dHqfOWoEAIIcoz3yGgFns9ew1UC4B3hpZZkYQQQpQNg8HAqVOnnPYmUBSF4ODgK85bhg8JIUR5lV/gHBAUeXfxdS+KEEJUFhV9onG3bt3Ytm3bVc9XggIhhCiv1u0t6xIIIYQoZ1599VU+++wz9uzZc1XzlaBACCHKg1Pp8NBn8OEvUNQtnFtQpkUSQojKSNUobn8qinbt2pGenk779u15+umn+euvv8jLy7vifCUoEEKIsjZ+LoQ9BHP+hOfngWYQZOeB9gLXaAbCniPXq4RCCCHKiddff52ePXsSFhbG1KlT6datGwEBATRt2pQRI0Ywbdq0UuUrE42FEKKsTV7meqzmo/D5IyVfowLNx4Mq8wuEEOKyVKD5A+68+OKLjj+np6ezY8cOYmNjiY2NZdOmTcyfP58nnrj8VeokKBBCiPIoMw9CfC+ebt9RaFLr2pdHCCFEuRMUFET37t3p3r2741hWVlap8pLhQ0IIUV5Fhl48za9XfwUKIYSozCr6nIIff/yRQ4cOlXje39+/VPlKT4EQQpSlv+NKPpeVf9HL1QIbFeerTAghxJUaPHgwiqIQEBBAy5YtadOmDa1bt6Z169Y0aNCg1PlKUCCEEGVpzpqSz13C6kNqWp4EBUIIcRkq0p4E7uzbt88xhyA2NpYZM2aQk5ODoij4+PjQsmVL1q9ff9n5yvChCuLQoUPExMQwf/78si5KiTZu3EhMTAx//vlnWRdFiIpDe4Ffw7qL/4pWgryuYmGEEKLyUxWN25+KolGjRgwdOpTJkyezfv16MjMzWb9+PX379sVgMODn51eqfKWnoILYv38/AA0bNnQcy8nJ4dtvvyUmJoY2bdqUVdEc4uLswyCupOvqeoiNjWXt2rXExcURHx9PXl4eL774IgMHDizxml9//ZUFCxZw8OBBPD096dy5M8888wy+vq4TQZOTk5k1axabNm0iMzOT2rVr8/jjj9OpU6dr+ViiIopNgLkXCKLPZF80C8VQsVu8hBBCXBlFUejYsSMdO3ZkyJAh3HXXXaXKp+KERTe4Xr16sWnTJqfK/65du5g5cybp6ellWLJzRowYwaZNm4iIiCjrolzQ7NmzWbt2Lf7+/oSG2idyFg+2zjdp0iReffVVqlWrxvjx4+nRowdLly7ljTfecElrNBoZNmwYGzZsYNCgQYwdOxaTycSzzz6L0Wi8Zs8kKiBVhZj/A7O15DSRVS6ej6JevTJdwLrDFpbHmQHYm2Sh/eQsWn6QyYr/TCRnW4ieUYj2g0JCJpvYk2y5LmUSQojSqOgTjS/kxRdfZMqUKaW6VnoKKgitVotW67yT0d69ewFo0qRJWRTJhU6nQ6cr/x+pDz/8EG9vbwBGjRqFTqejXr16btP+8MMP/Pzzzzz++OM89NBDjuMmk4lly5Zx+PBh6tSpA0Bubi7PP/88BoOBr7/+muDgYAA6derEgAED+Oqrr3j33Xev8dOJCqP/pAueVgHl1KUsK6eBgkL4Yzf5tWugjwpG561DtdpQbfDHW3vJW3+QluH5BHRtgGlfGsv35GFNy+eUVwhWrYG/3pxPUKEJY3RN9uZ7EJhjwtNkwaJRWNm8DgnVqtLwTBq5eh13Rkdg89ADBtAp9FsCWC2g14CikGJWaPG5CW9zNrkeno5SeuhVzCjYtBo8dPBSew1962n4cLOV3afhyRiFx9ro7M8NrDlspVagQv0qWsxWldijZjQFFprW88TbIO1ZQogb1/bt22nevDkGg8HlXEREBLt27SpVvuW/BleJvfnmmyxdupSVK1c6KpBFkpKSGDRoED179uS1117j9ttvp2HDhkydOpXs7Gy6du3qSNu/f3/HnxcsWEDdunVJSEjgp59+IjY2llOnTgFQq1YtRowYQbdu3ZzuNXHiRDZs2MCPP/7IjBkz2LhxIyaTibZt2/Lqq6/i7+/PokWL+Omnnzh+/DgRERE899xztGvXzpFHfn4+nTt3ZuDAgUyYMAGwV5K7dOnCPffcQ7du3Zg7dy579+5Fr9fTrVs3nnvuObcf6JJs3LiRRYsWsW/fPnJzcwkJCSEmJobx48e7HcZTkqKAwGq1Eh8fT1RUlNtyZGVlMWPGDKKjoxk5cqTTuTZt2rBs2TISEhIcQcH8+fM5efIk77//vtPfZ0REBOHh4SQkJLjcY8uWLcydO5d9+/bh5eVF7969GT58OD179mTYsGGMGzfukp9LlCOqCk/Phi9/hwIzNhSOKfXIVKriHWEgsuopDP9evOdI7fvORScR2yZ8j23CAnTYsGh9+SegBVXzzASZCjjoFY5J542PLZ/0ggwyFxwHVU9qs3rsatiM2tm5VE/NwDungIjMJGqcOUEM3viRz2tdbmP2La2xaRSwqhwOCQCtYt/0x3K2d8KigqfW/mO1Qb4FVPskvly9hz2y0SqgqJhM2Hs1NDYsZnh1s4ZXN9vORj8Kj/8Jj682QVo+6LRoffQM+sdIywNJrG9Wm78bRJCj02GjAJ0C3qoVq1bBqtcR4qvBolFIygVsKh5alafbaJlvhFN5KsFeCksHKLQNv9AW0UKIG0VFn2jcrl079Ho9jRs3pnXr1o7VhyIiIvjyyy9lTkFFVL9+fQASEhJcgoLPPvsMjUbD448/zpkzZ0hLS3OM1VdVlTfffJP333+f8PBwhg0b5riuVi37JkY//fQTR44coXv37oSGhpKWlsaSJUuYMGEC06dPp3Xr1o5rjEYjfn5+PPLII3To0IHRo0ezdetW1qxZw9SpU8nNzeX06dPccccd5OfnM2/ePCZOnMiKFSvw9LS3BMbHx2Oz2ZzmE8THx6OqKvv372fdunXccccddO/enTVr1rB48WIiIiIYPnz4Jb1XH330Ed9//z2NGjVixIgReHl5cejQIX7//XcmTpxYincfjhw5QkFBQYlDh5YvX052djYvvPACGo1zy6SXl31yZ2pqKgAWi4Uff/yRyMhIl6ALwNPTkzNnzjgd++WXX5g0aRL169fn4YcfRq/Xs2jRIkcPUHmfmyEu4Ikv4YtVjpcaVHRqIelqMOnHIO2YhhiMFxy/eSlfWTa0KFjRYQPAz5pDp7TNrAroTZIe8rX2z2mexpdEHx98cgrRW1U8PXxomJkDQFpQAGvbtKBwg55o8zG8MDNk0P2sa1L73I10in2wqVYDlrMV+SKFZ4c/qYBeax8OpSr2YECn2HsQNAp4qPZgSVGwAlqrDatW47yzqF4LQV6Qksf4X7fRZ89RXh/Sib+a1oKCc8OsLCpkafXgrUNXaOGYSVP0RoMCJpvCe9tV+31RSM6DDt/aMI3XoKskQwSEEDeuZcuWERsby/bt21m5ciVz585FKfa79NVXXy1VvhIUlKGioODgwYN06NDBcfy///5j9erVPPzww1SrVo2NGzcC5yqJ/v7+dOzYkVdffZWYmBj69OnjkvfYsWMdFdci/fr1o2/fvmzYsMERFOTm5nLs2DE0Gg2ff/65Y87CgAED6Nu3L0uWLOHOO+9k9uzZjg9cYWEhc+bMITExkejoaADHePniFdmiY/n5+SxcuNDRmt+vXz+6du3qqPxezPz58/n+++8ZNWoUo0ePdqqgjx07Fr1ef0n5nK9oYnSjRo3cnv/jjz/w8PCgS5cuLueys+0TQH18fAB7V156ejp3332327yys7MdacEeCL733nt07tyZ//3vf45hV127dnVMeJagoAKb7brMaBjHSKApoJCPL1Yub1KXFS1W9BiwL1OqAtmE4kUGBvIc6bTYqFZ4hhMekc4ZKPYxsyadlswqPk6nrDotp0KCCD6ZRZCSyV+Nzru2iIcWDBrIt4LtbGSg1TgHCToNZ2MU+/9NNvCwV86LBwDWkirnBi1ehWZu35dIUpAvfzWvDWabazqrPchwafBT7D0TnDfVwobCqxutvHOrfO0JccOr4G0DN910E3379nW8PnHiBDt37iQpKYlGjRrRsWPHUuUrAzPLUPGgoLhPPvmE4OBgHnzwQeBc5bV4i7a7Y8UVBQRWq5Xs7GwyMjIcY/5NJpMj3YEDB1BVlUGDBjlNYtZoNHh5eeHv78///d//OUWgRZX74nMcjEYjWq2WunXrOh0DeOmll5yG9xRVgIt6GS4kLS2NGTNm0K5dO8aMGePSYu/h4XHRPEpyoffQbDYTFxdHdHS023IePnwYgJo1awKwe/duAJo3b+6SNicnhzNnzjjSAsyYMQOtVstLL73kNA+jevXqRERE4OHhQWRkCRWzMpCWlub0ucnJyXEERmAPFIt6TYokJSVd8HVycjKqeq7mVpnuUfx1ERtain8TuanmXpCKBh1mx2sF0JOPiuuQmAKNx/l1Yvs1qr2FXrG53l1ntmFGR7rBC1Xj7qvhbNkVxR4YFDm/cq8orl+47uZSl9R9rygoioKiQp6HzunW7mguY5612VqxP1dyD7nHjXIPcWEhISFs377d8ToiIoJ+/frxyCOPlDogAOkpKFMBAQFUq1bNaavqdevWsWPHDl599VVHxd5oNOLr6+u0qs+FKrQmk4kFCxawcuVKDh48iNXq/I1cvLJZlE+PHj2c0hQWFnLy5Eluv/12l4r34cOH0Wq11KhRwymfOnXqOKU1Go2Eh4fTrFkzp+uPHTuGyWRyDHUCSE9PdyqnTqcjMDCQtWvXkp+fz5AhQ1ye80rFxcWh1WodwVlxp06dorCwkNq1a7u9dteuXej1esf7f+zYMQC36Xfv3o2qqo73ITc3l40bN9K9e3eCgoLc5l+/fn2XieVlqUoV51Vwzp/DYTAYqFq1qtOx8PDwC74OCwurvPcY0Q1m/u50/AS1HX/2II/L7d8qHhAU8SKD85vEz+hCSNVVw4AJM+f+PSo2FZ3FvvtxRGIax2ufG7Lom5VP1ZRsvMknzRRAeFo2SVXOG5OqLxYIFFX8S9pH4VIq6meHErkotJKn17GuYQTd9h8nKimNQ2FB9vsVz9egwT/PhBUwGYp9lalnewnOj1VQebuTFg9dBf5cyT3kHjfIPa61ij6nQFVVbG4ad8De2Dtt2jQ++eSTy85XgoIyVr9+fXbu3ImqqlitVqZOnUp0dDT9+vVzpDEajURHRzu11sfFxeHp6elSCTWbzYwZM4a9e/fSv39/hgwZQlBQEDqdjo0bN/L99987BRJFLfyNGzd2yufAgQNYLBaXCn3RNVFRUY4AwGKxcOjQIXr27OlUjkOHDjlNiC5edjg3bKegoIBevXo5BQXt2rXj888/d/Q2NG3a9MJv5GVSVZX4+Hhq167tticgJ8c+3trdZJ0TJ06wb98+Onfu7Lj2QulXr14NQPfu3QH7RnRms9ltQFdQUMCJEye48847S/lkolz4/FH7+PjZa8Bktg/9UXT4abLwqaGnVngqmi0XzqKo/nvhry4VFchVvEjW1yBDF8QJfQ0y/TR0PH6UE/pwsnVeWPQaVEXBrNegN9uIij+FR34BZ6r6E5yWR/UTqfiq2ZykKhm+XvTbHc/CmMZk+nienQ+gdQ4AVLXY6/Nq4G56SdAq54YbaRQUm4pqUe3N/Npiw4osNsg2gY+B9++/hRPr/mPUqn/5plszjBFV7D0ONpVqufm0jDvNsap+HI2sSqSnjXwUzuQDVhWtBh5opGH5EUg3qQQYFBb01+BxCZvBCSFEebR06VLi4uJo06aNvTe1hMAmLS2NTz/9VIKCiqh+/fps2rSJpKQkNmzYQGJiIl988YVjmEx2djYnT56kc+fOTtcZjUa3rclr1qxh165dTJgwwWV8+9dff41Go3HMAyjKp1atWi4V45J6IgoLCzl06BC9e/d2HDt48CBms9lpDPzBgwexWCxux+u7y3vq1KlOaapVqwbYW9WBEj/8pXX06FFyc3NLHH5V1IKfn5/vcm7+/PmoqsrgwYMdx4paQgoKCpxaQZKTk/n9999p1aqV430vKLCPCT9/KBTAypUrsVgsTn9HogLSaWHao/YfQAvUPT/Nba/BH3tKzEIB2PA2dHr5gmmU94bh++wd1Nt9lPTT0KR5GF4hHpCdT61sE2dm7sOy7wxphQa0DUPQ+CqcmRtH46RjWM5YOBoYQEaEyuGwOpyuGUb+fxn8Vz0Uq16Dh9WCd66ZfKuBAl9PFFSCU3M44+d9riJvVZ3H8NvO/lmrsQ9QtdlQ8qxE+KqcMWvQZhUSoFp58Q4/qvkqvLTBik2FN25RuLeJgWyTAX8PlR/jVTR3t6J3XS0vGRRWHrRyNMvG/Y00+Ht6AZewh4MQQrhREfckOHz4MC+99JKjh2DAgAG0bduWNm3aOH6qVatGQkIC/v7+pbqHBAVlrGjoyu7du5k1axadOnWibdu2jvPuJvCCvbXaXYW2aGzf+T0I3333HVu3bqVOnTqOJTmLWvOLt/AXKWloTUJCAhaLxe2EYnfH3AUFRqORkJAQR3ehp6cn7du3d0kHOIYobdq0yWVCtdlsvmaTjENCQggMDCQ2NhZVVR1BSWxsLIsXL6Zz585OS7IWvU/btm1zBEwWi4V33nkHq9XK008/7UhbNAxs+/btTsOijh07xrRp0wCZZHxDWPUaaN1PTHcwXMLnW7WAVgutonAajBbkizbIl7C3bgWgRrFTjV5vj9lsZu7cuYDKyJEjnf4tvXneLaw2lRUbc9Cl5tOrf1XWHlOZsMpMgDe8111PoJdC1/mFHMtR8DTA4kE6etcrqezOCyAMbuL8NVTFG0DhvvP+afaqqwU38yeEEOJyVcThQ+PGjePRRx9l586ddOzYkTZt2nDy5ElWrlyJyWRCURSqVKlCZmamU8Pt5ZCgoIwVVSYnT55MVlaWy7r0JbXYR0REsGXLFr7//nsCAgKoXr06LVu2pHXr1iiKwqRJkxg8eDCqqrJp0yaysrJc8ilqzXcXXMTFxbkdWuOuPEajEUVRXHog3JW76Jy7CbnuDBgwgIULFzJp0iT27dtHvXr1yMrKYvfu3dSoUYNnnnnmkvIB2LFjBzt27ADslXuA/fv3M2vWLACGDh3qeF6NRsODDz7I1KlTGTduHF26dOHIkSP8+OOPREdHu+xmfMcddzBnzhzee+89Tp48ia+vLytWrMBoNPLyyy87bTBXvXp1OnTowPr163nxxRdp27YtJ0+e5Oeff8bHx4esrKwSN1MTlYhGA6tegV5vXWAM/iUMzr8OGxprNQp33OoH2IfHda8D20Y7f30kjvVyc6UQQoirxcvLi5tvvpmBAwfy5ptv0rhxY6xWK3v37iU2Npbdu3cTGBhY6j2OJCgoY7Vq1cLDw4PU1FQGDx7s0sJvNBoxGAwux19//XU++ugjpk2bRkFBASNHjqRly5a0aNGC1157jdmzZ/Ppp58SFhZGnz59aNeuHaNGjbqkFYwsFgsJCQncfvvtLuU1Go1oNBqXXoGIiAinYTNFx84fY5+UlERmZmaJw3bOFxYWxrx585g+fTp//fUXixcvJiAggMaNG7vt4biQn376iVWrVjkdW7FiBWCfC/Dwww87nRs6dCgFBQUsWbKE7du3Ex4ezqhRoxg2bJjL5Gtvb2+mTZvGxx9/zJw5c/D09KR58+bMnDnTbQD09ttv88EHH7BlyxY2bdpE8+bN+fDDD/nggw9KXPFIVEK3t4L/pkKjse7Pp2RePI/rEBQIIURlUhF7Cor78ccfHX/WarW0aNGCFi1aXHG+iupu7TwhxHV35MgR7r77bkaPHu0SoIhK7FAy1B3j/lzCp1DvqQtf/9798H/3XPZtzw0fwmX4kBBCVGaTb3LdSwbgmc3dr3NJyhfpKRDiOjOZTC49DTk5Obzyyiv4+vo6Ni8TN4jvNpR8bv+Ji19vkXYdIYS4HBW9p+DMmTM8+uij/PXXX/j4+NC0aVNiYmIcP8WXsL8cEhSICi8jIwOLxXLBNF5eXk47Cpel1atX88MPP9C1a1eqVq3KiRMnWLZsGVlZWbz77rsuazqLSk57gWUyzRf+XAMgLfxCCHFDef7559m+fTtPPvkkb7/9NmazmVWrVjkWRQkNDeXkyZOXna8EBaLCe+qpp9i/f/8F09xzzz288MIL16lEF1a1alU8PDz47rvvyM3NJTAwkDZt2jBixAhZivRGNKwzvPit+3OGS/gVfWfbi6cRQgjhUNF7Cn7//XfefvttHnzwQd5++21+/fVXtFotY8eO5eTJk459kS6XBAWiwnvhhRcc+xmUJDQ09DqV5uJuvvlmbr755rIuhigvagS7Px7kAx6X0AsQXePiaYQQQlQaaWlp1K9f37HfkdlspnXr1vz555/ccsstdOnSpVT5SlAgKryrvduxENfda4PhjYXOx47PgsNJJV+j18K+qSWfF0II4VZF7ykIDg4mKysLRVEICQkhNTUVsK9ENG7cOD7++GPuu+++y85X9nwXQoiy9vp9kPENPHsnzHkC1MXg7QHxFwgKChdB/fDrV0YhhKgkVEVx+1NRtGnThvj4eADq1avHxo0bHedCQkLYt29fqfKVngIhhCgPAnzgw+HOx6qUj8nxQgghyo8xY8Y4NmMdNWoU48ePJyYmhkaNGvH+++8TFhZWqnwlKBBCiPKqee2yLoEQQlQ6qqbi9Aq4c/vttzs2mH3wwQdZuHAhd999N4qioCgKs2fPLlW+EhQIIUR5FeTv/vj0R69vOYQQQpRLer2elStXsnr1ahITE2nfvj3NmzcvVV4SFAghRHmW+z3UexySMkABXh0Mj/Uq61IJIUSFVZHmD1yIqqqcPHmSwMBAevbsecX5SVAghBDlmbcHnJxT1qUQQghRTpjNZiZMmMAXX3yByWRCURRiY2Np0aLFFeUrqw8JIYQQQogbRkVffeiNN97giy++YPz48cyfPx+bzYbVagXg7rvvZu7cuaXKV4ICIYQQQgghKojvvvuOt956i7fffpvBgwc7nevUqRM//PBDqfKV4UNCCCGEEOKGUZF6BdxJSkqidevWbs9FRUXx33//lSpf6SkQQgghhBCigoiKimLPnj1uzwUGBpKSklKqfCUoEEIIIYQQN4yKPqdg1KhRvPPOOxw8eNDl3NGjR6latWqp8pWgQAghKim10MqZsb+T2OZrMr92bVXS5qsY0m1lUDIhhCg7FT0oGDduHOHh4bRr145JkyY5Ni1LT09n8uTJtGvXrlT5Kqqqqle5rEIIIcqYalM5pPsAiv2G19ULoNaBxwA4WHcW5kNZKAAaqLl9CN6twsqkrEIIcT1N6rHZ7fGX/rjpOpek9DIzM3nooYdYvHgxYB82lJOTg8FgYNOmTaVanlQmGgshRAWR/ZOR5KEroMCKJsybyB0Pog/3c5v2xB0/OgUEAJaETMynckh+9HcsRQEBgA2Otf6OBur4a1p+IYQoDypSr0BJAgIC+PHHH9mzZw+//vorx44do0qVKjzwwAM0aNCgVHlKUCCEEBVA4bFMku9e6nhtS87jSPXp1Fefd5vetOKw2+OpE9aRvfQw2vOOq4A1LR9tFa+rVGIhhBBXy7333stHH31EjRo1nI43a9aMZs2aXZV7yJwCIYSoAI62m+/2eNbyBLfHSxoXWnAsC1sJrWQ5C42lKZoQQlQoquL+pzxbtGgRJ0+edDpW0gpEpSVBgRBCVAC25Dy3x1OmxLo9XlJQoGigUOv+V79qkUnHQghREVitVlq2bMmOHTuuWp4SFAghRAVgLqF1/1/F87LysaBFb3Nf+fe9p3TjUAEsKXmcHr2KlBfWYss3lzofIYS41ir66kNFrvZaQRIUCCFEBXAkONDt8d8M1S4vo3wLOpv7LxJdqM9llsoua94eEkM+JWfGv2S9v5Uj3h9j+u9MqfISQghRNiQoEEKICuB4UIDLkCAV+Dfo8jap0RkUSmoPM+dbSlM0Uob/6nLsRMu5pcpLCCGutYraU3DmzLVtbJHVh4QQogJQFK3byvxNyUlAtNMxVVWx4drqowKaKiUPN8pPMaGveZW+FsyyBY4QonwqabGF8u6OO+7A39+fFi1a0KxZMxRF4cSJE7Ru3fqq5C9BgRBCXGdpa05w5O3d+LWpQt13YtAYzl8g1FlWpoWwzGyX4wrQRZ/jelxRsKLBghYPzCiADcjDA33Oud4A69kwQ3u2D0LRVswvSiGEqOxWrFjBjh072LFjB7Gxsaxfvx6Au+66Cx8fH1q2bEmbNm1o3bo1rVu3pkmTJpd9DwkKhBDiOort9huZa5NRgMx1yRz/6D9uzR+G1rPkX8dZmVayPT1cjqtAI12By/HMv09hRYMZA4Xo0WDDigZQsORaULEHCNazuxVoseKFCc8Aw9V5SCGEKMfUEgdRll+9e/emd+/ejtdpaWmOAKEoWJg6dSqqqtobhqzWy76HBAVCCHGdZORZSd6UgnexYyqwve1S2u8ZWOJ11WsYiExJczmuALtyPXnhiSNoFZX7bvHi3vvDSPv9JP/UDaf1wVRUFEflX8GGPlBLFnrHMQArWkzosSTloq8XcJWeVoYPCSHEtVKlShV69OhBjx49HMeysrKIjY1l586dpcpTggJxQ/rrr7/4+++/iYuLIyEhAZPJxGeffUaHDh3cprfZbCxcuJBffvmFY8eO4e/vT69evRgzZgx6vd4l/aFDh5g5cybbt28nLy+Phg0bMnbsWFq0aHGtH02UY49MOsmo844pQM7eTLfprRYba/5I49cfkrnHTR07y8uTBDWQtvH2nocdh3X8vew0w+orZPh44k0+VnQoqNhQMGBBVTTY3KwxYUWDJd1UyidTzz6JEEKUfxVhUnFp+Pv707VrV7p27Vqq6yUoEDekKVOmYLPZaNCgASkpKZw6dYqGDRu6TWuxWHj22WfZvHkzffv2ZfDgwWzfvp1vvvkGq9XK+PHjndJv2bKF559/nuDgYB588EEUReGbb77hySef5OeffyY4OPh6PKIoZ+5ZVEjqURMGs31M/+EQf/bUqkbUqQyMoYE8/8h+DKFBxLT2Y2uSgjW7kB4bEvArtBAMbG3aiJ7bd6KzWrEB6b4+bG9YF7RabIpCvl7HscAA8gx6psYVMCwxFT0qepz3DNi3JgN3i5gqqMTe/TthTzcn+onGF53ncElsNtBchUXuLFZIzYK8QtQAH9RT2ShRwSgergF5cWqhBcVg/5pT802oP21FqR6I0qQGnM6GZpFXXjYhhKgkFPVq73wgxAWYzWa0Wi2aq1FRuAJ5eXl4e9sHcfTq1QudTsfy5cvdpp08eTLffvstb731ltN4vtGjR7N7925WrVqFn58fAMnJydx///2Eh4cza9Ysxz127NjBo48+yvDhw3nqqaeu8dOJq0lVVU5uSyXrRD7Bravw3cYCDqSr3NXZj7oReuZvNWFKyKRLiIXfdYFo/jxKh43xWL0MFFgV4ix6vurSkuPBfoz8Zz9dth5mY71wvry9leMeepMFs15LQL6JwVv3E3UmA8Wgw1zFFxQFjcWK3mLGKz+Pm/5LwD/fhA3YVSuCl+7tyZEgX7odSkFfbP8B/9w83vrhFwDMWg1pfr7kenqingatTcWPAooWJ1WwYUWhEB2KAumBngRl5+FtKUCPioqCCvhrUglTD6NRraRpa5CmDUdXWIgv2VjwxIq+WJ+BjTrPhFOw0og1LgWbRo+lQ2PMGUBmPrY8C1qzGb1agNVsxaZq8LGexkvNAg89qsWG1aIhTa2BDxl4kUkBVbCiw4NsNFgx44cZA6CixYKOfEz44kUuOszY0FKAFzpy0WNBgwaFQnAESgqFHoGoAb6Y/MNRPfV41dGhzctFUzcEze3NUE9lYDuQhikklMIT+eiOncD7qBGNjwHu7QR+XnBbc6h54WBfNVkwr9gP+Wb0/Ruj+F/epnNCiKvn5b7udwF+e8XVWcWnopKegkosOTmZr7/+mk2bNpGSkkJISAjdunXj0UcfxcvLC4AffviBDz/8kDlz5rBz506WL1/OyZMnCQ0NZcyYMU5j1Yps2rSJBQsWsHfvXsxmM3Xq1GHUqFF06dLFKd3w4cPJzc3l/fffZ/r06Y6hNBs3bkSj0XD06FFmzJjBP//8g9lsJiYmhokTJzJ27Fj0ej3z5s3DarXSpUsXmjRpwvTp013KMnv2bL744gtmz559WUNziirrKSkppKSkuJS9SGJiIj/88AOdOnVyCggA2rRpw/bt2zl69ChNmzYF4IsvviAnJ4eXX37ZcQ+Ali1botVqOXDggMs9Vq1axbfffsvBgwcJCAjg7rvv5uabb2bo0KH83//9H4MHD77k5xJXl2pT+XXcdo6uP41VUfjk1jYcOruJ2LSfQWcpwGJVAT/eOWHjyT9jGbF1r+P6fJ2OU7UjSA72Q9VpCcrMAZuVud2aO93H7KFDZ7Yye84K6qScG0p0pEYIxnrheOYXoAAWnYFNTRty8754AvLyiU4+g81mo2qe2SkgACgw6LEqClpVRW+1EZqRRWI1Ayt7tcSqaKmSns0tu+OokpODGS1aQI8Vk6onML0Ab6yADhULOqx4kkukbT9a7JPXwqwJ5Ft9qEYaOfhhwt5qX9QprwLK5F/RoyeV+litOjw3nSCPABRs+JCLAhSgx4a9gpxBLRQO451vfw80QACnMJBPGvVQz35lmQjCl5NYULDghYKCFTDjix+n0WE5e70FH7JRUdCgBSwoTj0nKgZTBmmnQ7CdziaAoxj25tlPrQHLl2uwEkAu/uQQ6LgqB09COYCy+ZD9gE4L3z4Fg292+zmynckhp+Pn2OJT7O9RsA++ax9D2zTMbXohhCgLsnlZJbVv3z7uv/9+NmzYQN++fXn++edp164d3333Ha+++qojXXx8PGBvDf/nn3+46667GD16NLm5ubzyyiskJyc75fv5558zbtw4VFXl4Ycf5oknnkCr1fLcc8/x119/OdJZrVYSEhKwWCyMGjUKf39/nnjiCcaOHYtOpyMuLo7hw4ezfft2Bg0axNixY8nPz+fpp58mMTGRBg0aAKDVamnSpAn79+/HZrM5leX06dN89dVX9OrVq9Rj9ePi4gBKHDq0aNEirFYrI0aMcDlXFFilpqYCkJGRwcqVK2nXrh2NGzd2SqvRaPDw8HCkLTJ9+nReeuklPD09GTNmDPfccw8//PAD77//PoDjfRBl4+j60xxdfxqAfWFVHQFBEYv23K9Qn0IL9+/Y73Tey2LBGFkVs84+FOe/akEk1Ap0vC6uYVKqU0AAEHkiBc+8AqfR+jaNhiNhIfZ7mgoZs24HZkUhMcCLXWH+HAn0wqpA06PH0Z7XEVzjdApoNJg99VQpyMXPYkKDig4VLSo6bPhgQosNGwr2PgQNGqAqJxwBAdgr/9VIQoeFQty1ettLrcOMP2cwUEA+vgAYKDy7TKqCjeLvhYqBfKccvMimEB9HQFAkj2AUNBTfik1FSwH+JZYF3G/OZkOHnlwM5Dkd11KICuSel2chPuQXCxKwWGH8PPtwKTdMkzc4AgIANSWXgldXu00rhLj2KurmZdeaBAWVUEZGBs888wwNGjTgxx9/5LHHHmPAgAG89NJLDB8+nLVr13L8+HEAjEYjAB06dODzzz/ngQceYNiwYTz11FOYzWZH0ADw66+/MmfOHCZMmMCnn37KkCFDuO+++5gxYwYRERHMnz/fkfbIkSOYTCaSk5P56KOPePnllxk0aBBDhgzBZDLxwgsvEBAQwPfff8+YMWO4++67+eyzzzCZTJhMJqfKcLNmzcjNzeXIkSNOz/npp5+iquoVDce5WFCwZs0awsPD3QYdWVlZAPj4+ACwdu1arFYrPXv2dElrsVjIy8tzpAX73INZs2YxePBgZsyYwQMPPMDIkSN5//332b17NxqNhvr165f62a6mtLQ0TKZzk1BzcnLIzj63bn5hYaFLwJOUlHTB18nJyRQfvVge75F+9NweAJlulgSl2JeIX4EJD4vrEnCehWcroio0Tk4lPCuXwDznZUS1VhvVM1z3G9CoKlo3Fc2iYKRAq6XQ05NAUyH7Qv05HujNvrAAYiOC8M/JdS3u2f+HpaTTdcc+fApMbicdG7BQtHqQcvb/Ogpdy+cIElzLqBZbfUiHCR0W1GKrINnTOH8JK1gvcp/i+Wsc+RVnc3NMcTyN67MWpde4CRgUx5O4XmfhvPkMJ9Ig2/73ev7nyrL/lMv1pr0nnV5XxH8fcg+5x7W6hygbEhRUQnPnziUzM5Onn36agoICMjIyHD9RUVEAHDt2DIvFwqFDh6hTpw6PPvqoUx46nb1VzsPDXhEym81MnTqV1q1b06NHD6c88/LyqFu3LomJiY7ri4KNoUOH0qZNG6e8f/75Z06cOMHzzz9P1apVHce1Wq1jV77o6HM7tDZvbh9qsW/fPsexXbt2sXLlSkaMGEFoaGip36v9++0tu+6CgqSkJE6fPk2zZs3cXnv48GEAatasCcDu3budyltcUUBTlBbsQU1oaCjPPPMMSrHKZYsWLfDw8KBGjRpOQ5DKUpUqVRyfBQBfX1/HPAoAg8Hg9HcJEB4efsHXYWFhTs9dHu9Ro925ceJNklNcK+jFhuwkBfqRcF5PggoEZZ8LAPRWG1pV5dENOwnOtrdK++eZCMwp4J+61cn2cN4n4GjVAFY1qc35apxJI8/DwLxeXThYszpdE1O4Z98x/Ez2oTFnfD3QWFz3LygSdfJcJVXBdVqZwrkvBxsaVCCbKi7pcvDFVmzJ0+I0xfItwJdCPBy9AGb02Kv1NoovXWqfGeC6V4Ja7L9FPMlwu+jp+a399ics+gzonQIRFXvfBKgU4usSpNjQoaBFz/nvpYoXWc6H2kRBgP3fq8vnqofzjtMAnj2df+dUxH8fcg+5x7W6x7WmKu5/bnQyp6CSUVWVVatWYbVaGTp0aInp/Pz8OHjwIGazmR49ejj9AwY4ePAgALVr1wZg+/btjvH37uYZFE8L51rg+/Xr55Ju9erVhISE0LFjR7f5nN9CXlQp37dvH/3790dVVT766CPCwsIYNmxYic94KeLi4ggODna7ItCxY8cA5+cqoqoqe/bsITQ01BGUHDt2DI1GQ2Sk64om//77r9OzHD16FKPRyMiRI12WNFUUBVVVZehQORDSKIBbnmvE1s/jqZJnYsyufSyKqsMpfx9aZ2UQ3cSHnw+oaM1W6p7JYNLtHRj71w5anDhNjqcHJo2OVodPM3bZVn5uH83mehFEpWTQ6EQqn329mlMB3hysGsDUPu1Bhccf7MWYP2Opdzod3xwzXplWWu88xv7qATRIzSAwP5/ayWcIzchiXYvGpPuf+2L1NVtpnZTOX7XtawvtqxHGnbvi3T6Xplhwo0XFVmx6sPPiourZjc8U0glHTyGBJKFgI4cgUokgl0K3LelF4UY+/uQSjIqCJ9ko2DDhjYoZHVZUbJjxhLMzAyzo0J4dXmQPRqqRRg28yUJztmR68jCjI48q6DGjwwyoeJKBgUxU9GefougHLHigxYQGT2yYz/ZWGFDQ4k0a+QSRSQ18OYWWQqx4nH2KQgJII4sgCvFESyFBHEdPPhQFQ41rwLwnS/wcGR5rj3XHCQq/jgWrDV2vBni+5dqjKIS4PmwyVMgtCQoqmbS0NFJSUujbty99+vQpMV10dDQrV64EcNsSHhcXR5UqVRwV3qKW/7fffpugoCC3eQYEnNv0yGg0EhIS4rZCbTQaadeunds8Dh48SGRkpGO8PkBgYCCRkZGOnoKlS5fy33//8b///c+p9eFyZWRkcOrUKTp16uT2fE6OfTiHv7/rGOXY2FhSUlK4//77ndL7+vq6XVlp9erVaLVax4TmoqCpUaNGLmmPHTtGYWGhBAXlRIthUTQeFElBRiF+1b35KKsQc44F7+r2SaKqqmI6VYDGMwL89CQej+DUD4egig81wnT88fUxvFMsjPttC9/HNMI/xURIer59TP6ZfBocTuerTs3J8vNif0RVxj3Qg+B8M/dsjqP31gPcvP8k5vhkMmt40yEhwVGuMwGun8ug/LOTaFWVBTGNmbhivVPb9/EAP5bXCufWbbsdxzSo6LFiRcFHk4rZ28zGdrfSsSpY1h1D6++NtkUIWg8N+QGtyFNU1PxCvIIMVKsbRPaiA9j+OuJmlwIF5eg0DD4+BH25BV1mJh4DmmL2D0QTfxTbkXQ0Vbzhh/VY8jXY7mgPhTYKE1pAtD+anBxMP8dTkKHgmZgBaNHqTGhsZmw2FTDgRTY6gw01qhpKmD8aLys5x3Lh2Bm8s9IABaunBzZPL3QFeaj5+diwoKBiRYdaPQTbqG5o/Xzxv7MZmqOn0QR6o1YLRKexQRVfyMpDn1mAV3gg+HuiHEuBXBMUmqFeOKTmQK2QC36GFJ0W79n34PlBXyi0ognzu2B6IYQoCxIUVDJFFdlq1arRvn37C6Ytmi/grmJqNBqdhtTk5trHJzdu3NhtS7i7vFu2bOly3GKxYDab3VacExMT2bNnj9ueiGbNmrFq1SrS0tL4/PPPHcOYrsSFhg6BvcsTID8/3+Xc/Pnz0Wq1DBo0yCl98SFURfbt28fOnTvp3bs3gYGBABQU2IcjuHsfipZGlaCg/NB769B7239d6v0N6P3PDXFRFAXPsHNBbL1antR74dxE8yb9Ixx/zr1vE9XSnT9PBquN137cRNyDzXmkbwAtWvtBgYGslCZMnBJMwb8pLG5UG12INwu/SCXqTDoANc+kcijCefWa094G9GYLVbPzmfTjapeKelhWNhN/Xk3kGefdkTXY2/X/HtyTO7/uyCjDpbeiVX2iNYeU99ycUSAyBB3gO7HbuecFaFSsEj3slhK/iAyvw+VWn73Oe13STgZux842cLODg68nSvVir2udl8b3/DuWTFOlfAwHFOJGJ5OK3ZM5BZVMaGgoBoOBdevWOU3sKZKRkYHFYp9QZzQaCQ0NdWn5P3XqFGlpaU6V0ho1agA4ehfOV3yS0IkTJ8jOznZb2dbpdISGhrJ3715HxRjsleQ33njDsaHY+Zo1a4bFYmHixImkp6fz7LPPXuhtuCQXm2Rcu3Zt9Ho927Ztczr+22+/sXHjRgYPHuzUE1KvXj0KCwsdQ4XAvh/CO++8g4+PD48//rjjeESEvaK4fft2p7z37t3rmLAtQUHlM/Zd9/NTGp9MZeqT1WjbLgCDToPBV09wbT9mTq7HvDXt2fZCAKNXbGRL3RpsrluDTC8PYowHqX/s3GRV36xcRvyyhXi/w7xjSKVJUorLffQqdOnoi6GEVXKq+evwvYyAQAghROUhPQWVjKenJ/fffz9ff/01DzzwAH379iUoKIgzZ86QkJDAzp07WbVqFaqqEh8fT9u2bV3ycFdZvv3225k7dy4zZ87k0KFDjgnBycnJbN26lRYtWvD8888D54YalVSpHThwINOmTWP06NH06dOHgoIClixZ4litwN11RZN3Y2NjGTBgQKkrzOvXr3f0kKxduxawV8wPHDiAXq9n+PDhjrSBgYHcddddLFq0iFdeeYXWrVuzb98+li5dys0338y4ceOc8h4yZAg//fQTEydO5IEHHgBg8eLFnDlzhvfff99pMlarVq2Iiopi4cKFFBYW0rhxYw4ePMivv/7qGK5U1FMhKo/AOv7YNKA5r06ubR6EZ4j7zawUjULjWl70TDhKzUznFYoGbtzKX50bc2p4e+rhzYB5DTFU8aTK1COc8fcn2M0KRGqu+2U5Aer0jijx3OVT4fd/4baWVzFPIYS4ctJT4J4EBZXQk08+Sd26dVm0aBHz58+noKCAqlWrEh0dzfjx41EUhWPHjpGbm+u2lbyoUl/8nJeXF1999RVz585l48aNbNiwAQ8PD0JCQmjTpo3TMBp31xc3bNgwCgsLWb58OVOmTCEqKooRI0Zw4MABFixY4NgIrLi6devi4eGBXq9nzJgxpX5vvvrqK8cqQUW+//57wD7PonhQAPD000+j0WhYvXo1f/75J5GRkYwfP57Bgwe7DP0JCwtjypQpfPrpp0ybNg0/Pz/atm3Lhx9+6Fj1qYhWq2XKlCm89957rF69mjVr1tCmTRumT5/OmDFjaNWqFaJyarfrTrbHLAOTDZsCSgN/Oi677YLXJGap2DQlfIkVWnhppPNE+R4PVmf3C9kuSVXAai15E/t6d118aOBl+W6DBAVCCFFBKKqqlvwNIcR1YjabGTBgADVr1uSLL75wOR8bG8tjjz3GM88842iFr4z+/vtvxo4dyxtvvEHfvn3LujjiGjKnFqD1N6DRX3wU5/5UlQONZ9PodLrLuR9vbcrEv3q7HN+jTMbbafdeO12/WhQsP+FyXAXqZTyJLsB1SdCLcT+nQCXq6WowedRl5yeEENfScwP2uD3+4c/uh3jeKGROgbjuzp/rYLPZ+OCDD0hOTubBBx90SW82m/noo4+Iiori3nvvvV7FvKaKz6cokpKSwrvvvktERMQVT6IW5Z++quclBQQAjaqe201AObszQBFDoZvNzczu5wwAaGwltwOZU0re26BUHu91dfMTQghxzcjwIXFdnTx5klGjRtGvXz8iIiJITU1l7dq1GI1Ghg8fzk033eRIu3//fg4ePMiKFSs4fPgws2bNcmyqdr60tDRsJUyeLOLr64unp/tx29fb119/TWxsLDfffDP+/v4cPnyYpUuXAjBt2rQrWmpVVE7Rp9Pw5xQmfDHjiYFsCvEiKMe1Ih8fm3V2B2GcViCyAarr5sAOXnVdlzm9JHoNnBeIaMJ8ILp6CRcIIUTZkY3K3JOgQFxXRasLLVu2jMzMTLy8vGjUqBEffvihYw3/ItOmTWPbtm3Url2b999/nyZNmpSY7z333ENmZuYF7z1u3Lgr3uzsaqlZsybr169n7ty5mEwmgoODue222xgxYoRjpSchivMnlWxCUM9umGXCDx0F1PBwreXbrODhsj/v2a5hneISLODm9eWocXg0x+tMdwQGSoCBWomln/sjhBDXkutvRwEyp0BUEjt27MBsdh0/XVytWrUICwu7YBohyqsk5VlycPP5vSOK+ksGOR0ym2wc8fzQ7fhQTbcaFPyZ7HJOBRqq46+ojOajmSieWnShvleUjxBCXEvjB+51e/zjxa4LndxIpKdAVApFS6QKUVlZFR24NOHY8PbSuqTVe5Q8V8FmvXYtZPpaARdPJIQQZcwmS5K6JRONhRCiAlDbNsCA874DWiwEv32r+/Ql5GPwlbYgIYQQriQoEEKICiBi43AK8cSDbAzkosGMrYo/HvXcb3KX7u1+Ur3voAaoWtdWMmk3E0LcKFRFcftzo5OgQAghKgCNXktUxtNoejfDUr0a/i/cQt2UJ0tMb/xfH5feAhUIHNGMGkvvcM2/hs/VLbAQQogKRfqRhRCigtAGeFLj13suKe2DT9Vjw28NCf8tDgV7QFDt+34oioJ/n7pYFvfl5NAVaArB7+761Py+/zUtuxBClBfSK+CeBAVCCFFJdfr1DlRTH6yn89DW8EMp9kXo1y+KvyfbVwkaOVI2GRNCiBudBAVCCFGJKR46dDVLuSmZEEJUQjbpKHBL5hQIIYQQQghxg5OeAiGEEEIIccOQOQXuSVAghBBCCCFuGDZZhNktGT4khBBCCCHEDU56CoQQohyyHMtENdvQRwWVdVEuic1iQ9EoKBppgRNClG8yfMg9CQqEEKIcseWZ2V97GvObNOFQSBU6Hklk9LLb0If6lnXR3DLnWZh/+x+YMswABNX35d6fu5ZxqYQQQlwuGT4khBDlyNFGn/NJ+7a0TjzJfVt3sal2JI+P2VvWxSrRV11WOQICgPQDOSweuqEMSySEEBdmU9z/3Oikp0AIIa6z7J+MmA9nEjC6BVpfD6dzuxRfXlv+p+N126MneGFgL7ILVfwM5e9by5pnczl2+t+M618QIYQQV0SCAiGEuE6seWYO+X8CVvvr1Of/InhWT4Ieau5IE56R7XLdkK3/kmeOwc9wnQoqhBCVmE3mFLglw4eEEOI6OdJ0tiMgKJLy8Cqn16FZOS7XRaZmEOpz7b/EbAUWjv/f3xy6fzUFCZnX/H5CCCHKD+kpEEKI68R22LUX4Hw6VXU5FpBfcC2K48R0Mod9EfMcrzN+SCD05dZEvNXhmt9bCCGuJ1l9yD3pKRBCiHLuevyi/q/pDy7HTr294zrcWQghri+ZaOyeBAVCCFGGXPsFyoaaXuh6rAzKIYQQomxIUCCEENdJWVSyVauNgqPZ2AqtF053ncojhBBlTUVx+3OjkzkFQghRSe14ZzdZr/yD3qZiVSD8icbU/7Sj27QquP1K3L8nl0bNfK5pOYUQQpQ96SkQQohKKOm0iexX/sFgs7d/6VQ4/dl/5CVkXFY+WTmu+xAIIURFZlMUtz83OgkKRJnYvn07jz76KN26dSMmJobPPvusrIskxDV3Pb9yJo3ajd7mPChIAVY3X8byOovJ/jfN6ZxZ4750J6p4X6siCiGEKEdk+JC47o4dO8ZTTz1F/fr1efLJJ/H09KRp06aXfH1SUhJLly6la9euREdHX/b9jx8/ztKlS4mLi8NoNJKamkrPnj2ZNGlSidfs3r2bOXPmsHv3bsxmM61ateKZZ56hTp06LmlNJhPz5s3jt99+Izk5meDgYAYOHMjw4cNRpCVCnOdKPxEWm8p9Pxby834bqgqtqmvY/JCeIwZPjBFV+f7WJph0Wm7bcYhW8afxy7eSm5TH5ptWwVQc3wKHg/xomJrllHeBVsOR7IuUUFVBPtdCiApEegXck6BAXHe//PILZrOZd999l4iIiMu+ftOmTcycOZOOHd2Pjb6YlStX8tNPPxEdHU1UVBSpqak0bNiwxPTLly/nrbfeol69ejz66KPk5OTwzTff8MQTT7B48WI8PT0daXNychgzZgwJCQkMGjSI2rVrs27dOj777DM8PT257777SlVmIc53MtvGuuMq/1tnZk8K4KEFBXakgcf7Zjyb1+FoaCAv/vQ3kWcyyVMMaM52HPiYLJh0WoLnaPj7gVA2xVsILHBdfchgtdBp5VrWzzMRMqwDjdpWcTqf8ucJ94VTVXKzLOTuOE7eD3vwjA4n5N5oSM9FE+qHEuJ7ld8NIYQQV0qCAnHd7dy5k4iIiFIFBAB79+7FYDCUqpcAYMiQITz88MMATJs2jW3bttGoUSO3affv38+kSZNo3749H3/8MTqd/Z9MWFgYr7/+OsuXL+fuu+92pH/jjTcwGo18/vnntGnTBoABAwYwcOBA5s6dK0HBDSRvWzJnXt+CZ8sQQu4MRZOXT8nTeYHsPLDasOE6rjNfp2XIAxuorS/g/rWrGXv7ENbVb2LPzqyAl9b5ArMNs6Jl0vx1BOfkY0NxBARFPCxWZkW0I3AT/LZyN3VqBhMWd9xxPj60CpsaRGDZrKFJihV1WyyHj58kXadhbdMGeKLjZNWqhNSrQ8uDR1BUFb3ZigroC61M77IeX7Los3cDR/2rMHmliRT/QFof+od2xnh0WvBWTWR5eZPuGYBWp9KsRgGeDauR4++NoXUtqrSphlI9ACU7D06mQ8s6qPmF4Ovp0uumWqxgsqD4eFz+X5YQ4oYiexK4J0GBuG6+/PJLvvzyS8frmJgYAN5++21UVeWPP/7AaDSSlpZGQEAALVu25Mknn3QED0ajkQceeMBx/U033QSAoiisW7cOH59LWyHF2/vcGOm4uDgAGjRo4Dbt5MmT0Wq1vPTSS46AoHjZExISHMe2b9/O2rVruffeex0BAYBGo6FVq1YsW7aMtLQ0qlQ519p69OhRZsyYwT///IPZbCYmJoaJEycyduxY9Ho98+ad22FWVByH2n1HwbbTAOT/moDXO3H4k4aGFtg4f4y+Cq3GsyHDk8fvHMVPHuBjck6R6uPN943a0fD0Sdb2qEW23oemh0+zt1pV8DwvIADQKtRPTiM4Jx8ABZVzAYmKRmsjNciTJsdOsbR9I6I8c2i1P7GoNDw1rCeb69cAQGe1cdfBE9TNzOFESAiRJ05wm/EIP3S0//s7XTWQf22RtNt9EH+TvbchX6Ol1b9H8FBNVOE0NVMP02zdXozBDWiRdAQbGvIIADSE52RQqDnFhtAmLE+qys17t9Aw/RgqKmY8ULChI9deco0GbAaoHYr66YMo/VoBYPv0D9TXl0BaLvRojGbewyjhgVf0dyiEEDcaCQrEdXPTTTfh6enJ1KlTue222+jUqRMAbdu2ZejQoXTo0IF7770XX19f4uPj+eWXX4iPj2fBggXodDoCAwN59dVXefPNN2nbti39+/cHwGAwXHJAcL64uDgiIiLw8/NzOWc0GtmxYwcDBgwgNDTU6VzRkKHU1FTHse+//x6NRsOwYcNc8vLy8nKkLwoK4uLiGD16NAaDgUGDBlGtWjXWrFnD008/TWJiIn369CnVM4myZTqe5QgIAGzoOE4TGrCJkn7l5u89wV0Tv2DewmlUMWVhwt/p/MrG9l6xuGrVQavYf4AqmXmkad1PBD4V4INFo6A7u/qQFhtWtHhjwttqRsmz8V33FhwKrcJuYE2DmsyctpR37riF3TXPfd4tWg2ra4UxencCCrCvdiRBmTlO96pyKs0REAB42awcq+ZHrVPwL7dQjVNEmA/SMuk/VHsJKN4fYrBZaZh5nG2GBmwJaUe99ER0aNGTj4LFkU6x2VAxwZEzcM+nqImfwIHTqGO/O1eYP/7D9tg8tEvHun1fhBDCJnsSuCVBgbhumjVrxpkzZwDo2bMnXbp0AcBqtfLLL7/g4eHc7R8SEsLnn3/O0aNHqVu3LqGhoY4hQ7feeusVV5qTk5NJT093atUv7o8//nCU9XzZ2dkAjmAkPz+fv//+m5YtWxIWFnbR9CaTiRdeeIGAgADmzJlD1apVAftQo3vvvReTyVRi74Uo304/u97NUYUMQrHhplUf+CeyPhatlt7x/7LbtzXba9SlV9wBADbVieSTrre4v5migLtVg1SVdF8v5ndsyoj1ewAwYGVz7Wp0TkwEG3zXqRmHQs/1WmV7efDmPZ35L7Sqy05mWR56CrQavKw2CvQ6vAqduzKqZuW6FCHDx4NaZGNBTzqhZBNEczahwR4onc/bYs/TourIMXgRWFjo9mtbQUVFhQIz/Pkf6p6Trol+3e3mSiGEsFNlorFbsiSpuK6KhusUn9ir1WodAYHJZCIjI4OMjAz8/e2tpYWFhRe8/mqWpbjdu3ej1Wrdrox0+PBhAGrUsA+x+O+//zCbzTRv3txtXocPH8bDw4Nq1aoB8PPPP3PixAmef/55R0AA9veidevWAKWeM3EtpKWlYTKdqwjm5OQ4Ah2w/x0V7zUB+ypRF3qdnJyMqp6rfVaWe9gauPY6AegxuT0OUCv9DIUaDXl6AwoapnVsz9ImDVjZsB5TutxEpte5yezFa8pm3YV/hc/s0YpHR/Rk6m1teGpoD964uyNam33fgeIBgePZAnzcTnmokm/Cy2q/7kAVX7rv2Yt3sffxYHCgyzXZej0AGuzXWTCQQVEPhOv+yae87Hl4qCY8zwYI7nYYtR87e7x2MJmBbtq2ap37N1VZPldyD7nHjXQPUTakp0BcV3FxcQQGBjq1picmJjJ37lw2b95MSkqKyzU1a9Z0/NloNKIoylWpMF8sKDh+/DihoaFOqwsV2bVrF4AjCDh27BgAtWvXdkmbm5vLgQMHaNasmWNewurVqwkJCSlxBSWNRkP9+vUv74GuoeLzIAB8fZ1XjzEYDE7BDUB4ePgFX5/fo1JZ7lHrzc78N+lfKLZHgJZC/EnjVAntMHUyUnho+zo+7tiP0X/+y89zvkV39vrb4xJ46IGBbK8VRqa3j31c/Vk5Xga3+aHVUDSzeF/9MPbVCHGc+is6kq7xibRNOM6aZlFOl7U+ksyaxnVAUYvV21VapmRwzN+bfaGB1DydRJXcHF74eRnrmzTkQPXqHK8WRFxGDg1TMgDYFVaVVgdOo8eMrlgAkOnhSxVTFgbyMeF3dqaDwnGfYOIDamDTQsfT/+Bp42x/gA4rOnTkny0JgAFQ4O62KO3rEdisJrbvdsDORMeza/53bvJ/ZflcyT3kHjfSPa41mWjsngQF4royGo1Ow2L27t3LmDFj8Pf35+6776Z27dr4+vqiKAoffPABFovF6RdIXFwcNWvWLPUcguIuFhTk5OQ4egKKK5oUHRQU5GjVz8mxj7F2Nzfhzz//xGq10r17d8cxo9FIu3bt3N734MGDREZGOuYhiIonOukRjty2GHNcGvoIL2q3V+BwPfjHXWoF4j7ls7cW8adfVcx4OQICsHfnfvjLMo7UtLCqbmvW125CUmAgGX5e2LSakvcJKDqm1+CXV0DtpExu+e84jQ+kc8zPn077jrKh4VE2NYwERSEm4SQvL95A06On+bJba/INOhQVvGw2dlevQqxWQ/Sp03Q++C/h1r14mK1Ebd/NsvpdyTAEccjfh9VVQwnLy2Hg/p34FVjIJMAxZMqDXGqbjChoyPD04rSXPyHZueRrPTgQUh2DLp8mGQmYDL6kefvj5euJJromuubBqGnpKEFe0LUFHEqBpjWgtz0gV7w90Pz9EuriWDiZgdKvBUrDcNf3QwghxAVJUCCumzNnzpCamkrfvn0dx7744gtsNhvz588nMDDQcTwpKYnExESnirTNZuPAgQPceuutV6U8+/fvJywszOm+xQUFBZGfn+9yfM2aNZw4cYKHH37Y0fJf1BJyfnqr1cp3332Hr6+vYw6ExWLBbDaj0bi2GicmJrJnzx569OhxJY8mypiumjf1dg11PaG87ya1CtHV4ZtxdAOOffuWy6CZqrlmmv/3Mneerej/esjK2DUqp/Igx3zeMqfFuu29Cwp5avlW7th6gEKNBtWmwYYGr2yV5EAfgtIz8crNJdBDy7QZy9EBD27cw91b95Pm44VFo/DGyFsJTE8nz+BNb18zfafciUf4AJJ+iCd2RSqZp00oQN2sXFBVPCxmDoREUKeZJ2lJOvJPFpDt50VUAwvG/WbMnt7UntqfZl3qYLPY0OQVUOdMJtS9eEW+pMY9xVOPMqTDRa8XQgiQzctKIkGBuG7ctcwnJSURFBREQECA41h+fj4vv/wyNpvNKW1qaioFBQWl3t+guJSUFFJTU+natWuJaerXr8/69etJSkpydI+mpKQwefJkwsLCGDp0qFNasC9L2qtXL8fxr776igMHDjB+/HjHM+p0OkJDQ9m7dy8FBQWO4UkFBQW88cYb2Gw2mWRcSWkpxMrlr6OvgtO6/H2itPQ5O/LHbFWpNs1MhklxpJ59u4acAcuIOXzGcY3BZsOiUdkbEYyxRjChqdnc0Xk7/b22M3LkSPa8eC648C604F2YjQrMfbYmrcMiXcpU67kYaj0H05ssPXdQUTDpDZj0Bu5d2v+iz6XRacDf2/4jhBCiTElQIK4bd3sCxMTEsHjxYiZMmECHDh04ffo0S5cuJSgoCMBpU7GgoCC8vb1ZvXo1oaGheHt7U7du3UuuQCclJbFixQrHnwEyMjKYNWsWAD169HCaEzB8+HDWrVvH2LFjGTRoEHl5eSxatAiz2cxnn33mNKypfv363HLLLSxZsgS9Xk90dDRbtmxhzZo13HXXXQwZMsSpLAMHDmTatGmMHj2aPn36UFBQwJIlSxyTsyQouDGdzHadgHsxeq1C+lgDWSYbRzKhcbCCTqPwzfm7lQE6m8q+WsEEZhfS44V67LEcdZxTUdy2xK85aqN1mPtVkwD3Q5eEEKIckyVJ3ZOgQFw3RqMRHx8fIiPPtTqOGzcOi8XCX3/9xebNm2nUqBEvvfQSmzdvJi4uzqmnQKfTMWnSJKZNm8bHH39MYWEhL7300iVXoDdv3sz06dOdju3cuZOdO3cCOPZNKNK0aVPeffddvvzyS6ZMmUJQUBAdO3bk4Ycfdtm3AODNN9/ko48+YuXKlSxfvpy6devy5ptvul06ddiwYRQWFrJ8+XKmTJlCVFQUI0aM4MCBAyxYsMDtikei4rNe5FdusDecKmXe/h4amlc79/q2z2M42HOly1ffI108afPWzZjNZvbMPXe8pL2WG1QpxZenevnBjRBCiLKlqKr89haiPDCbzQwYMICaNWvyxRdflHVxxDVwQHkf16q3Sn31/xyvjimucwpUoKb6ymXfb0vQXGwZ5nMHDAo3mx4B7J+3uXPtUcHIkSPZbZjp9r4trY+jdbcXwlnTmyx17S1QVUbvu+OyyyuEENfDHQ8dc3t86eyabo/fKGSfAiHKQPE1m8E+ifqDDz4gOTmZBx98sIxKJa4999txXSvt00ZQ4802eDUNInx8U27KfajEtCW1Dv252XVjsotRFYW845d/nRBCXA82RXH7c6OT4UOiwsvNzXW7SlBxOp2uxFWGrreTJ08yatQo+vXrR0REBKmpqaxduxaj0cjw4cO56aabyrqIopJQFIXIV9oQ+Yr7XbsvxW/rcrjtFt8Sz6uK+7kIWi/5ehFCiIpEfmuLCm/mzJnMnz//gmmioqJYuHDhdSrRhRWtLrRs2TIyMzPx8vKiUaNGfPjhh3Tp0qWsiyduUFp/HWqWxelYvkGPl98FJhlfgFUvHdFCiPJJNi9zT4ICUeENHDjwoq3r3t7lZ8nDGjVqMGXKlLIuhii3XKf8KiUO7rl6GsUOZmuzRfgU2OcgZHt78mO3Njxzd2Cp8vP0ka8XIYSoSOS3tqjwIiMjnVY0EqLc8tRCgfWCScqqAcuzXiA3HRrCylGbOZCjIzWmBo8/U4u61fWlyk+jlaY4IUT5JEuSuidBgRBCXCc1Nt7P8RjnoW4e3cvPahf6cB/6/3Z5u2lf36nTQgghrhUZ9CmEENeJV5twIo0PYWhdDW1NP0Jm3U7kH/edl0qq2UIIcS1ZFcXtz41OegqEEOI68oiuQq3Y4WVdjGvKUMrJyUIIIcqO9BQIIUQ5EvjNnS7HfF7t5CZl+dBvTgfnAwoM+a172RRGCCEugU1x/3Ojk54CIYQoR3yHNkffrBppo5aByUrQtF54dq5d1sUqUY12ITyysy/GpcfxDNBRp3s4inTDCyFEhSNBgRBClDMeLcIIj32krItxybR6DY0HyQpgQoiKwSrztNySoEAIIYQQQtwwrBITuCVzCoQQQgghhLjBSU+BEEIIIYS4Ydhk3pNb0lMghBBCCCHEDU56CoQQQgghxA1DNipzT4ICIYS4AXX8AbZmjQTgg68g/mFVlhIVQogbmAwfEkKIG0zdLy1sPaUA9p+ETAXNR1bi0yxlXTQhhLjmLCX83OgkKBBCiBvMoSz3xxvOub7lEEIIUX7I8CEhhBAAqGVdACGEuA5kToF7EhQIIYQQQogbhkViArdk+JAQQgghhBA3OOkpEEIIIYQQNwwL0lXgjvQUCCGEEEIIcYOTngIhhBBCCHHDMEtHgVvSUyCEEMJBVWUNIiGEuBFJT4EQQggH2dVYCFHZmeX3nFvSUyBcTJgwgc6dO5d5i2HPnj155plnLvu63r17M2bMmGtQouvHaDQSExPD/Pnzy7ooQgghhLgBXHZQkJOTQ9u2bYmJiXH8dOrUifvuu4+FCxc60h04cMBxftasWS75qKpKly5dePDBB52OP//88448CwoKXK6bP38+MTExbNy40en46dOnmTx5Mvfccw+33HILnTp14s477+T555/njz/+uNzHrDB++uknYmJiaN++PYWFhVclz/3799OgQYMybTFMSUkhNTWVBg0aXNZ16enpnDlzhujo6GtUsuvjwIEDAJf9/OIG99t28L4PNAOhxdNgsZZ1iYQQotwxl/Bzo7vs4UNxcXGoqkqPHj249dZbAXsFbtGiRbz//vvYbDbuu+8+4uLiANBqtfz55588/PDDTvkcP36cnJwcGjZs6JK/VqslPz+fLVu20KVLF6fzRqMRwOm6HTt2MH78eAD69OlDVFQUZrOZo0ePsnbtWoKDg+nRo8flPmq5l5WVxRdffEFkZCSJiYkcPHiQRo0aXXG+ixYtQqMp206kos/P5Vbuiz4fFT0oSEhIACr+c4jr6N/D0Oedc693J4LnYLD8VHZlEkIIUWGUKigA+xCNzp07O463a9eOoUOHsmbNGqegoHfv3ixfvpzjx49To0YNR/r9+/cDOFViMzMzSUpKomvXrmzdupU1a9a4BAX79+8nJCSE4OBgAPLz85kwYQI+Pj589dVXhISEOKV/7rnnyMzMvNzHvGJWqxVVVdHprt20jenTp+Pt7c348eN5+umnMRqNVyUoMBgMV6F0V6aocn+5LeWlDSbKm/j4eMLDwwkICCjrooiKIuY512NWFVIyIfjSP0eqqsq8AiFEpZYnv+PcKnVQ0KRJE6fjRZX03NxcwF559/b2ZsSIESxfvpw//viDESNGuORTvMW/KFBo1qwZBoOBDRs2YDab0ev1gD0ASExM5JZbbnFc888//5CWlsawYcNcAgIAnU5H1apVL+sZk5OT6devHyNHjkSv17Nq1SqSkpKoWrUqffv25eGHH3aq7E+cOJENGzawaNEivvzySzZu3Eh6ejrLli0jPDyc3Nxcvv76a9asWUNycjI+Pj507tyZsWPH4ufnd1llK5KQkMBPP/3EpEmTHH8X8fHxbtN2796d5s2bM3r0aGbOnMmOHTuw2WzcdNNNTJw4EX9/f0faH3/8kf/973989dVXNG3a1OnY9OnTiY2N5ddffyUlJYW6devy4osv0rBhQ7Zs2cKcOXPYv38//v7+PPDAAwwZMsRtmWNjYzl16hQAtWrVYsSIEXTr1s0prdFoxNfXl4iIiMt6X4xGIwaDAR8fH9544w02btyIxWKhZcuWPP/881SvXt0p/W+//cYff/yB0WgkLS2NgIAAWrZsyZNPPuly7127djFv3jyMRiOpqan4+vpSp04dHnroIdq3b+9Id/DgQebNm8e2bdvIyMggPDyc/v37M3ToUJcgMSEhgRkzZrBt2zYAunXrxrPPPktCQgLNmze/rGcXN5j4k/DxUgpOZDC3Q2+2936C6LSTPLzzd6rmZ59L9+EyyMiB46lwcwM4ngZ1RkIJX4o/xlu5p4GsQSGEqLzyJSZwq1RBQWhoqCMIKLJ582bAHizYbDYOHDhAgwYNqF27NnXr1uXPP/90CQr0ej316tVzOgb2QCEiIoJVq1bxzz//0LFjR8Be4bPZbE6BRH5+PgCHDh1yCiCuRFHleunSpXh7e3PXXXfh4eHB8uXLmTVrFj4+PgwbNsyR3mg04uPjw4gRI2jdujWPPfaYozKYnJzMY489RmZmJgMGDCAyMpIDBw7w008/ER8fz5w5c9BqtZddxg8//JDmzZs7hkVVqVLFbfJmlMoAAE+6SURBVFCQnJxMZmYmKSkpPPnkk/Tr14+bb76Zbdu28fvvvxMQEMCECROcnkWr1Tr9vRS12k+dOpWgoCCGDBnCqVOn+Pbbb3nxxRcZPHgw8+fP56677qJHjx589913fPzxxzRv3twRWIB9/sORI0fo3r07oaGhpKWlsWTJEiZMmMD06dNp3bq10z1L09pvNBrx8/Pj0UcfpUOHDowZMwaj0cjixYs5evQoCxcudFTMbTYbU6dOpUOHDtx77734+voSHx/PL7/8Qnx8PAsWLHCkXbVqFS+//DItWrTgvvvuw9fXlzNnzrB582ZHIAywfv16Jk6cSEREBHfffTcBAQHs2LGDzz77jLS0NMcwN4Dt27czbtw4QkNDGTFiBF5eXqxYsYJnnnmGtLQ0mU8gSnYqA26aAGk5PNN7NL8nV4fa1fmrdjP+rN2MFd+/hU612dO+t/jcdStiUQHeH1li1oOXwVKdjf51ZR0KIYS4kVxWUJCXl0diYiIdOnQgIyMDsE/s/Oeff/jiiy8ICAhg5MiRHDlyhPz8fMdQlm7dujFz5kySk5MJCwsD7AFAVFSUUyW+eFBgMBjw8PDgzz//dAQFRT0JxYOCtm3b4ufnx6ZNm+jduzcdO3YkJiaGDh06uAQul6qoEly9enU+//xzvLy8AOjXrx933HEHS5YscQQFubm5HDt2DFVVeeutt+jdu7cjH4vFwjPPPENeXh7z5s0jMjLScS4gIICZM2eyefNmx/Ndqj/++IMdO3Ywb948x7G6devy33//uXT9Fz1LSkoK8+fPJzQ0FIABAwbw33//sXfvXpdnr1WrFp6eni55dOnShVGjRjmOJyUl8fvvv7N06VIWLFiAj48PYG/9f+KJJ9izZ49TUDB27FjHe1mkX79+9O3blw0bNjiCgpycHE6ePOmYs3Kp8vLyOH78OKqqMmXKFG6++WbHOS8vL7755hu2bNnieL9VVeWXX37Bw8PDKZ+QkBA+//xzjv5/e/cdFcX19gH8u7tUpYkiIIIUS1SQBBQTS1RUjF3siRortljQ2ILG6E+NsdeokcRC7EZjIWpsaFRUohQLvQiKoFIFpO7e9w/emTDs7IIVdJ/POZ4jd9q9M8tyn9smMREODg4AgN9++w3NmzeHr6+v4P56eXnx/3/w4AF8fHzQqVMnLF68mA/2BgwYAKlUioMHD2LKlCnQ0dFBRkYG5s6dCwcHB2zfvp2/33369EHfvn0B0CRjosaey0BGLh4b1MI5h08Em+JM6+GadVN0SLoveuj6tl+o7CXgbLhNQQEh5MNVBOoqEPNS3/rR0dFQKBQIDAxEly5d0KVLFwwaNAjr1q3DJ598gp07d6JevXp85Z6r1HBDQy5cuAAASE5OxvPnz5XGv0dGRsLKygpGRkbQ09NDmzZtcPnyZZSUlPDbAeE8hDp16sDPzw+enp6QSqXw9/fHokWL0KNHD3z33XfIycnBy4qOjoZEIsEPP/wgqMQaGBigWbNmSElJ4dNiYmLAGIOHh4cgIACAs2fPIiYmBpMmTRIEBADg6uoKAEhMTHypvBUUFGDDhg3o2bOnIDhycHBAXl4ekpOTBftzFXpvb28+IABK1yKXyWSCCnFJSQni4uIElVEurWHDhhg9Wti6aGBgAADw8fHhA4Ky6eV7QLh7KZfLkZOTg6ysLGhpaUFLSwuFhYWCPDPGXrpSzH0+3d3dBQEBAH7I2aNHj/i0suUvLCxEVlYWsrKy+OFUZVdzev78OdLT05Xub1k///wztLW1MXnyZL583L+GDRtCLpfj8ePHAIDdu3cjOzsb3377rSAA09fXh5OTE4DqNS8iIyND8Ixyc3MFv1tFRUVIT08XHFP290Ts59TUVMGyt3SNyl/jRXbp+Uqk4r2MqtIBoLASPZPFig/nXtE16Bp0jffvGqRqvFRPAddS7+3tjUaNGkEqlaJGjRqwtbUVVArLt+g3atQI1tbWuHjxIoYNGyba4p+bm4vk5GR06tSJT3N3d0dAQABu376N1q1bIyIiAqampqhbt64gX9bW1pg/fz58fHzw4MEDBAYGYv/+/Th37hz09fWxcOHClykmoqKi4OzsDFtbW6VtCoVCEChwgUrv3r2V9j1//jx0dHTQo0cPldcq33Jekd27d+PZs2fo27cvHj58yKebmJjweS87oTsqKgq6urqCSeFAaXCRnJyMTz75r5UxISEBhYWFgso4l9a5c2elyYcPHjyAhYWFoDeAOwYo7THgFBYW4uDBgzhz5gzi4uIglwuXSiwbNHHDoF4lKACgNJcBAL+aUtn7nZSUhJ07d+L69etIS0tTOsba2pr//4QJE7By5Ur0798fLVq0QLt27fDFF1/wPV95eXm4evUqiouL0adPH5V55OaQnD17Fk5OTnB2dlbaR6FQwNjYmD93dWBqair4mQv8ODo6OkpzdywtLdX+XL58dI3KX6PGWA9g7SnYPE/Dp48icaP+f9+l9Z6no11SOFSZdfU0vuv1tdregvEtpB/MvaJr0DXoGu/fNd466igQ9VJBQdkVhdQ9QK4iamdnx6e5u7vDz88PaWlpopOMuaVOy6a1b98e2trauHjxIpydnZGYmCiY0FmeRCKBnZ0d7Ozs4OrqiuHDhyM4OPhlioicnBw8fvxYqRINlLaaR0VFCfLIjcEvW7nmxMXFoV69eoKWYM6rLDmZkpICPz8/lJSUKC3xyomOjkbnzp0F+WvUqJFSHmJiYiCXywW9LmLLvXJpXOs1hzGG6OhofPbZZ0p5KH+e4uJiTJ48Gffu3UPv3r3x1VdfoVatWtDS0sLVq1exf/9+pWtqa2sLPj+VwV1XbAUmbpgUt+3evXuYPHkyjIyMMHDgQNja2sLAwAASiQSrVq1CSUmJ4IvL09MT7dq1w6VLl3Dt2jVs3boVW7duxdKlS9G1a1ckJCSguLgYX3/9tcrPqEQiQe3atZGWloZnz54JnhNHLpcjPDychg4R9RrUBQIWAz8ewZaUv7GxsQGCHijQJCMZ026ehK6i5L99p34BPMwonWjs1hBaqVmqz8sYNrVjGNaMJhoTQoimeemeAlNTU7UBAWOMnyRadvhIp06dsHv3bgQEBPDvImjUqJHg3ICwQmdgYIBWrVrh0qVL6N69O+RyudJ7DVThltV82dV9uIql2FKi/v7+yM7ORrdu3QT729railb8AYhOfGaMwd/fH+bm5mjWrFml87Zu3ToYGBhg8eLFott9fHwEk42fP3+O1NRUtG/fXmlfscBMbBlQsUABKB32lJeXJ/o8IiMjUa9ePX45zQsXLiAsLAzz5s3DwIEDBfvu3r0bUqlUEBxFRUXBwcHhpZdzjYqKglQqVXrHQklJCf744w/Y29vz19m6dSsUCgX27NnD97IApYFXUlKSaIXdzMwMgwYNwqBBg5CQkICvvvoKx48fR9euXZGbmwugtHdBXeAKgH8pn9gbo0+fPo309HS1vUuEAABaNQL+nAcjAAsAQNYfUIjsN7svYG0uTFtdIrJjqSmfVf2SxIQQ8lbRkqSiKj2noKCgAImJiYJVacQkJSWJVhYdHR1hbm6OCxcu8JOMy45nF6ukAqU9DOnp6Thw4AAAYdAQFBQk+hZfhUIBX19fAFAa518RrhJ8+/ZtQXpaWhp8fX3h4ODAV9iKi4sRHx+vMlCxt7dHUlKS0tCU7du3IzIyEhMmTKj0S8Ju3brFr+DEzeco/8/c3FwQFKiq0AP/vSSu/CpDVlZWgkAqKioKFhYWgoozd7zYubkehLKBBTfesPxwrH379iEoKAgNGjRAjRo1AJSOLUxISHjp8fQlJSWIj4+HQqHArVu3BNt27dqFx48f45tvvhHkqVatWoL3AOTn52PBggVKK1xlZmYqXU9XVxcKhYKfp8EN2Tp79qxoZb/seEkLCwvIZDL8+++/UCj+q8WlpaVh69atAGiSMXkFf6sYJlkuIBD7fPLoDyUhhGisSjfFRkdHQy6XC1r3xXAt/mKVGnd3dxw4cACMMaXW64iICJibm6NWrVqC9I4dO2L58uX8JOWylbVly5YhLy8PnTp1QqNGjaCjo4PU1FScPXsWSUlJ6N69O4YMGVLZIvLlNDY2RkZGBmbMmIF27dohPT0dR44cAWMMq1at4luw4+LiUFJSojIoGDNmDK5du4ZJkybB09MT2trauHTpEm7evIkRI0aoHXtellwux6pVq2BmZob+/fur3M/GxgY3b95EVlYWTExM1A6niYyMFPRwcD08bm5u/D5cWqtWrUSPB5SDArGg0MXFBRKJBMuWLcPgwYPBGMO1a9fw/PlzpXNw9/RlK8Xx8fEoKipC8+bNsWDBAgwfPhwmJia4cuUKLl++jNGjRwuGhLVs2RJHjx7FvHnz8Omnn+Lp06c4ceIE//nj7llycjIGDBiAdu3aoWnTpqhduzYeP36M48ePw9DQkJ98Xb9+fXTt2hXnzp3jAzcDAwM8efIEERERSEtLw969ewGU9kL16tULx48fxzfffIPOnTsjIyOD/4yJ3VdCKtTlY2DDaGDGztIeAzNDIHar0m70YjJCCCFiKh0UcJXAioICda3T7u7u2L9/PwBh0MAtJSm2NKeJiQlcXFzw77//wtjYWDChZcKECQgMDERwcDDOnTuHgoICmJiYwNHRETNnznzppT65/Ddp0gQzZszAihUrsHbtWujr66Nt27aYNGmSYAKNqooxx8nJCWvWrIGvry82bdoEPT09NG3aFGvWrBGds6DK4cOHERcXh9mzZystn1lWgwYNcPPmTURHR8PNzY1/kZe9vb1gP65V3cPDg09LTk5GXl6e4LlwaWLli4iIEO1BEHv+zs7O+OGHH/Dbb79h06ZNsLCwQI8ePeDm5oYxY8ZUOISpMsqushQWFoZDhw7xq/4sW7ZMMOQLAKZPn46SkhJcvnwZ169fR9OmTTF//nxcv34dkZGRfJ60tLTg6emJ0NBQ3Lp1C8XFxbC0tISHhwdGjhwpWPZ2yZIlcHZ2xsmTJ/Hbb79BLpejdu3acHR0VApOZ82aBS0tLVy6dAl37txBgwYN4OXlhdu3b+PKlStKq1URUinTepf+I4QQoho1joiSMLV9yZqlqKgIn3/+OQYPHix4yRQhhHwoGGOQrpGr3j6LJhkTQj5skm+zRNPZGpN3mo/qhr79y+CGrlQ0b4IQQt5XNHyIEKLx6GtQlMYEBVlZWfxL0FQJDQ0FUPEQqTctIyNDMOFUjIGBgcoVjj5kRUVF/NwDdWrXrk2VHUIIIYSQV6QxQcHUqVP5SdCqDBo0CFKp9KXXx39dgwYNQnZ2ttp9pk+fjhEjRryjHFUfN2/exIwZMyrc7/z580rzGwghhBBClFEjohiNmVNw79495OXlqd3H3Nxc9C3Gb1twcDCKi4vV7tOgQYNq9YbbdyUrK4ufRKyOq6vrS7/XgBBNJVHzngKaU0AI+dBJZok3xLLVxqLpmkJjvv0dHR2rOgsqubi4VHUWqi0TE5MKXwZGCCGEEFJp1FEgqtIvLyOEEEIIIYR8mDSmp4AQQgghhBDqKRBHPQWEEEIIIYRoOAoKCCFEwzQzFU+/P/rd5oMQQqqGRMU/zUZBASGEaJj7Y7TQwYoBKP1nZ8Qg/1aGZrVpRCkhRANQTCCKggJCCNFA5wYCvxjtxC9GOxE1GpDSy/8IIUSjUbMQIYQQQgjRINQIIoZ6CgghhBBCCNFw1FNACCGEEEI0B3UUiKKeAkIIIYQQQjQc9RQQQgghhBDNQT0FoigoIIQQDVNUokCLXUDU81HQQz4GFgKm2lWdK0IIIVWJhg8RQoiG0V2vQFS2BIAUBaiJ2tuqOkeEEPIu0YsKxFBQQAghGuR/V0tEUiUwWieWTgghRFPQ8CFCCNEgP9wQT8+Rv9t8EEJIlaFOAVEUFBBCCCGEEM1Bb3AXRcOHCCGEEEII0XAUFBBCCCGEEKLhKCgghBBCCCFEw9GcAkIIIYQQojloSoEo6ikghBBCCCFEw1FPASGEEEII0SDUVSCGegoIIYQQQgjRcBQUEKLC1atX0bJlS1y8ePGVzzFy5EgMGTLkDeaKEEIIIa9FouKfhqPhQ29Abm4uOnXqBMYYn6avrw8rKyv0798fgwcPBgDExMTgyy+/BABMnDgR48aNE5yHMYZOnTrBxsYGfn5+fPrs2bMREBAAfX19nDt3Dnp6eoLj9uzZg/Xr12P9+vVo164dn/706VPs3bsXgYGBePz4MaRSKUxNTdG4cWN069YNXbp0eeP3oqoMGzYMUVFR/M9aWlowNjaGg4MD3N3d0adPH+jo6LzUOSMjIwEATZo0eaU8yeVyxMbGwt3d/ZWOJ4QQQshbQAGAKAoK3oDIyEgwxtClSxd8/vnnAIC0tDQcPnwYK1euhEKhwNChQ/lKpkwmw8WLF5WCgkePHiE3NxcfffSR0vllMhny8/Nx48YNdOzYUbCdqwyXPS44OBgzZ84EAPTo0QP29vYoLi5GYmIiAgICUKdOnQ8mKCguLkZ8fDzs7e0xatQoPu3p06cIDAzETz/9hMOHD2PLli2oXbt2pc87atQojBgxArq6uq+Ur8TERBQWFr5yUEHIG5f2HNr5ChTrG1R1TgghhFQzFBS8AVxlv3v37ujQoQOf7ubmhuHDh+PChQuCoKB79+7w9/fHo0ePUL9+fX7/iIgIAEDTpk35tOzsbKSkpKBTp04ICgrChQsXlIKCiIgImJmZoU6dOgCA/Px8zJs3DzVr1sSuXbtgZmYm2H/WrFnIzs5+czegkuRyORhj0NJ6sx+7uLg4FBcXo2XLlujRo4dgm5eXF/z8/LBx40YsW7YMa9euVXsuxhiKi4uho6MDLS2t18orF6xRUEDeuZQM4FwYUFAM6GgB7ZsBLbyBF0WY7T4AP3YXGdLGGKBQAOk5wN+heGJsisC6DWFVWwa3hi/Xy0YIIdUbdRWIoaDgDeAq+82bNxekc5X0vLw8AKWV9xo1amDUqFHw9/fH+fPn+Zbtsucp2+LPBQpOTk7Q0dHBlStXUFxcDG1tbQClAUBSUhLatm3LH3Pz5k1kZGRgxIgRSgEBUDq05mVazAEgNTUVvXr1wujRo6GtrY2///4bKSkpqF27Nnr27Ilx48YJKtDfffcdrly5gsOHD2P79u24evUqMjMzcfLkSVhaWiIvLw+7d+/GhQsXkJqaipo1a6JDhw6YNm0aDA0NXypv3D0q38PCGTFiBI4dO4YrV64gPT2dL/sff/yBn376Cdu2bcPt27dx6tQppKSkYOHChejcuTM6dOiA/v37Y968eQBKn2PHjh0xaNAguLu7Y+fOnbh37x60tbXh7u6OWbNmCYYocUFB48aN+bTc3FwsWbIE//zzD7y9vfn5BmFhYfDz80NUVBTS09NhYGAAOzs7jB07Fq1bt36p+0E03J83gKFrgaIS8c0ff6by0Hz7b6D/NBNnLZtherdxKJblAgDcHXWw1csIMin9ISWEkA8VBQVvQGRkJMzNzfkggHP9+nUApcGCQqFATEwMmjRpAltbWzg4OODixYtKQYG2tjYaNmwoSANKK7xWVlb4+++/cfPmTX7uQFRUFBQKhaBCnJ+fDwCIj48XBBCvIzo6GgBw4sQJ1KhRA/369YOuri78/f3x66+/ombNmhgxYgS/f1RUFGrWrIlRo0bBxcUFEyZMQFZWFiwtLZGamooJEyYgOzsbnp6esLGxQUxMDI4cOYLo6Gjs2LEDMpms0nmraOy/RCJB8+bNkZSUhAcPHvBBAVdpX7NmDfT19TFo0CBIJBI4OzsjOjoaCoVCcM7o6GgwxhAREYFLly6hT58+6Ny5My5cuICjR4/CysoKI0eOFNwDc3NzGBsb8z/PmzcPRUVF8PX1haOjIwDg77//xoIFC+Ds7IyhQ4fCwMAAz549w/Xr1/mAkpBKYQzw3qEyIACAF1qqW/2fZRWhfn4xlrQfgmLZf98bF+8V4dL9InR2erWhdIQQUq1Q+4YoWn3oNb148QJJSUlwcHBAVlYWsrKykJCQgAMHDmD16tUwNjbG6NGj8eDBA+Tn5/NDg9zd3REeHo7U1FT+XJGRkbC3txdU4ssGBW3btoWurq5gNRyxVvJWrVrB0NAQ165dQ/fu3bFo0SL4+/sjLS3tlcvJVaDr1auHffv2YeTIkRg6dCi2bdsGIyMjHD9+nN83Ly8PDx8+RHp6Ory9vbF8+XIMHDgQ48aNQ0lJCWbMmIEXL17Az88P06dPh6enJ+bMmYMxY8bg/v37fDBVWREREdDW1oaDg4PKfbggo+xkcK5MTk5O+PXXXzF8+HAMGzYM1tbWokN/uLT8/HwcOnQIEydORP/+/bFu3Tro6uri3r17gmtGR0fzvQRHjx7FmDFjYGVlhb179/IBAQD89ttvaN68OXx9fTF8+HD069cPXl5e2LFjR7WZpJyRkYHCwkL+59zcXOTk5PA/FxUVIT09XXBMSkqK2p9TU1MFz4Ou8QaukVcAJKn/Pe9776b4BokEL7T18EJbBymGpkqbw5NefFj3iq5B16BrVNtrkKpBPQWviWtRDgwMFEzclclk+OyzzzBz5kzUq1cPp06dAvBfJdPd3R2+vr64cOEChg0bhuTkZDx//lypEhgZGQkrKysYGRkBANq0aYPLly+jpKQEWlpafNBQdh5CnTp14OfnBz8/P1y+fBn+/v7w9/eHVCpF586d4ePj89JDdKKjoyGRSPDDDz9AX1+fTzcwMECzZs0QEhLCp8XExIAxBg8PD3Tv3l1wnrNnzyImJgY+Pj6wsbERbHN1dYWvry8SExMFqyipU1JSgri4ODg4OKgd/5+RkQHgvyFd3HH16tXDrFmzIJUK4+OoqCjIZDJBoMEFBfPnz4eBwX8TNbnrll0VKiUlBdnZ2bCxscGCBQtw9uxZjB07Fl5eXkrXev78OfLz85GcnCyYY1KdmJoKK4llyw8AOjo6SkPSLC0t1f5sYWFB13gb12jpANyKgyqGRYXiGxiDccELGBQXwvFpIu7VbSDY/HlzAxga/tdg8UHcK7oGXYOuUS2vQaoGBQWviWup9/b2RqNGjSCVSlGjRg3Y2tqiZs2aSvtxLfqNGjWCtbU1Ll68iGHDhom2+Ofm5iI5ORmdOnXi09zd3REQEIDbt2+jdevWiIiIgKmpKerWrSvIl7W1NebPnw8fHx88ePAAgYGB2L9/P86dOwd9fX0sXLjwpcoZFRUFZ2dn2NraKm1TKBSCQIELVHr37q207/nz56Gjo6M0IbissueqSHx8fIUr/DDGEB4eDlNTUzRoUFrRSUhIQGFhITw8PESXKo2MjISdnZ1g5aGoqChYWlrCyclJsO/Dhw9RWFjIn5vbFwAOHjwIiUSC9evXo02bNqL5mzBhAlauXIn+/fujRYsWaNeuHb744gulL1ZCKmXXVGDgKiAy+b+0erWAx5kAgGQjE5WHWn7RDIhJwcrzOzG1+0TE1bKAvg7wTbeacLZ9/WGIhBBCqi8KCl5T2RWF1EW6UVFR0NXVhZ2dHZ/m7u4OPz8/pKWliU4y5pY6LZvWvn17aGtr4+LFi3B2dkZiYqLaiagSiQR2dnaws7ODq6srhg8fjuDg4JcqY05ODh4/fixYWYlTUlKCqKgoQR65VvZPPvlEaX+udb78uxYAIDY2FoBwYm5FxO5bef/88w+ysrIwePBgSCQSPo8A0LJlS9EyxcfHo1u3bnwat+xp2QCtfB7K9tZw5/f09MThw4cRHR2tMijw9PREu3btcOnSJVy7dg1bt27F1q1bsXTpUnTt2lVt+QlR0twGiNgExKcCBvrA8xeAXV1g+m/Az2cQVs9O9bGHZwMAGic8wRmTmkgq0UdtQwkM9GikKSHkA0JzCkTRN/1r4lrq1QUEjDFERUWhcePGggm03AvPAgIC+HcRNGrUSHBuQFjZNDAwQKtWrXDp0iVERkZCLperrRCXxbWIv+zQIa6CKzY8x9/fH9nZ2YIKdFRUFGxtbUUr/gBEJz4zxuDv7w9zc3M0a9as0nnj7pGqnoLc3FysW7cOenp6SpOAAfFgglvitOw54+LiUFJSIngWHLHAJCoqCsbGxpg7dy769OmDn3/+GWfPnlVZDjMzMwwaNAjr16/HgQMHIJVKBfM0CHlp9hZAXWOgoSUgkwGbxwPsKEIaNBLfX1Lmr6SdOSS1DNDATEYBASGEaAj6tn8NBQUFSExMFKwWJCYpKQl5eXlKFVBHR0eYm5vjwoUL/CTjssNVVLWCu7u7Iz09HQcOHAAgDBqCgoJQVFSklAeFQgFfX18AUBrnXxGuAn379m1BelpaGnx9feHg4MAPB+Ja1FUFKvb29khKSlKa9Lx9+3ZERkZiwoQJSmPuK8qbVCoVBFOcBw8eYPz48UhOTsb3338Pc3NzwXEWFhYwMTFRWV6xScZiQUFUVBTMzMwEgWFUVBR/vI+PD1q1aoVFixYhNDRUcGxmZqbS+XR1daFQKAT5JYQQQsgbIpGI/9NwNHzoNURHR0Mul4tWSMtS15rt7u6OAwcOgDGG9u3bKx1nbm6OWrVqCdI7duyI5cuX48KFCwCEQcOyZcuQl5eHTp06oVGjRtDR0UFqairOnj2LpKQkdO/enV8b/2XKaWxsjIyMDMyYMQPt2rVDeno6jhw5AsYYVq1axfcicC3qqoKCMWPG4Nq1a5g0aRI8PT2hra2NS5cu4ebNmxgxYgT69OlT6XzJ5XJER0fDyMgIAQEBAEqDkvT0dISGhuLGjRswNDTEqlWrBC9843puWrVqJXreqKgoSCQSwTAmdT0LUVFRaNGiBf9zVlYWnjx5wg/90dLSwsqVKzFmzBh8++232LlzJ2xsbJCcnIwBAwagXbt2aNq0KWrXro3Hjx/j+PHjMDQ0xOjRoyt9LwghhBBCXgcFBa+Ba8mvKChQV6F0d3fH/v37AQiDhhcvXuDRo0eiq/CYmJjAxcUF//77L4yNjQUz/ydMmIDAwEAEBwfj3LlzKCgogImJCRwdHTFz5sxKr+pTPv9NmjTBjBkzsGLFCqxduxb6+vpo27YtJk2aJJgQW9EYfycnJ6xZswa+vr7YtGkT9PT00LRpU6xZs0Z0zoI6Dx48QEFBAQoKCrBw4UJIJBLUqFEDJiYmaNy4MebMmYOePXsqTVxOTk4W7bkpW14rKyvBCglcWvmhV9wqQ+WHDgHCuREGBgbYsGEDRo0ahenTp2Pnzp3Q0tKCp6cnQkNDcevWLRQXF8PS0hIeHh4YOXKk0nsvCCGEEELeFgkru5gsIeUUFRXh888/x+DBgzFz5syqzg4h5DVJVqt+sRmbRe1EhJAPn+R/BaLpbKH4XEhNQXMKiFrccKCK5k0QQgghhJD3FzULabCsrCyUlKhuNQTAT4ytaIjUm5aRkQGFQqF2HwMDA5UrHBFCCCGEiKNJxWIoKNBgU6dO5SdBqzJo0CBIpVLB+xXehUGDBiE7O1vtPtOnT8eIESPeUY4IIYQQQj5cNKdAg927dw95eXlq9zE3Nxd9i/HbFhwcjOLiYrX7NGjQgN76S8hLojkFhBBNJ1lSKJrOvtcVTdcU9BdAgzk6OlZ1FlRycXGp6iwQQgghhGgMmmhMCCGEEEKIhqOeAkII0SBaANQvL0AIIR84mmcsinoKCCFEg4SpmJv/hfW7zQchhJDqhYICQgjRIM3MtVBTCgDcGhMMAMPpIdRxTAghmoyCAkII0TC5M7VwcyjQVPIQY3XOo2h6VeeIEEJIVaOmIUII0UCfmAPehueqOhuEEPLuSWhSgRjqKSCEEEIIIUTDUU8BIYQQQgjRHNRRIIp6CgghhBBCCNFwFBQQQgghhBCi4SgoIIQQQgghRMPRnAJCCCGEEKI5aE6BKAoKCCGEEEKIBqGoQAwNHyKEEEIIIUTDUU8BIYQQQgjRHNRRIIp6CgghhBBCCNFwFBQQQgghhBCi4SgoIIQQQgghRMPRnAJCCCGEEKI5aE6BKOopIIQQQgghRMNRUEAIIYQQQoiGo+FDhBBCCCFEc9DwIVHUU0AIIYQQQoiGo6CAEEIIIYQQNebNmwc9PT2UlJRUdVbeGgoKCCGEEEIIUePOnTto2rQptLQ+3JH3FBQQQgghhBDNIZGI/1Pjzp07aNGixTvKYNWgoIAQQgghhBAVMjMzkZycDCcnJ5X73LhxAx4eHjA2NoalpSW++eYb5OXl8dtdXV3x7bff8j/fvHkTEokEo0eP5tNCQkIgkUhw9+7dt1OQClBQQAghhBBCNIdExT8VwsLCAEBlT4G/vz/at28PW1tbHDlyBCtWrMAff/yBb775ht/HxMQEOTk5/M8bNmyAgYEBMjMz+bStW7eiQ4cOaoOPt+nDHRhFCHkjGGOCLzLyYSguLkZ+fj4A4Pnz59DW1q7iHBFCCGBoaAhJBUN53rU7d+4AEA8KMjIyMGLECHz77bf46aef+PSsrCx8++232L59O3R0dGBiYoLc3FwAQEpKCo4ePYqpU6fi33//BVD6Pbxv3z7s3r37HZRIHAUFhBC1cnJyYGxsXNXZIG+Rt7d3VWeBEEIAANnZ2TAyMnqr12CzXq76e+fOHdSpUwcWFhZK2/z8/FBcXIx58+YJ0u3t7VFSUoInT57A2toaJiYmePbsGQBgy5Yt6Ny5M1xdXXHu3Dn+PKampujXr9+rFeoNoKCAEKKWoaEhsrOzX+qY3Nxc9OzZE3/99RcMDAzeUs7eLSpT9fehlQegMr0vqExvjqGh4Tu7VmWpm2R86dIltGrVCiYmJoL0R48eQUtLC2ZmZgBKhw8lJCSgsLAQv/zyC/z8/MAYQ1ZWFgBg27ZtmDRpEmQy2dssiloUFBBC1JJIJC/daiOVSiGTyWBkZPTB/IGkMlV/H1p5ACrT+4LK9OFSKBS4f/8+xo8fL7r92bNnsLGxUUq/dOkS3NzcoKenBwD88KH9+/ejVq1a6NatGwIDA5GVlYV//vkHsbGx8PLyeqtlqQgFBYQQQgghhIiIjY3FixcvVE7+tbGxQVxcnCAtJCQEhw8fhp+fH5/GBQUbN27ElClTIJFIYGxsjOfPn+Pnn3/Gl19+iTp16rzVslSEggJCCCGEEEJEcJOMnz59imPHjgm2OTg44Ouvv0aPHj0wb948dOvWDeHh4Vi0aBG+/PJLDBs2jN/XxMQE0dHRqFGjBkaNGgUAMDIyAmMMR44cwc2bN99VkVSioIAQ8sbp6OjAy8sLOjo6VZ2VN4bKVP19aOUBqEzvCyrTh4t7Z8B3332ntG3dunXw9vbGr7/+ilWrVmHDhg2wtbXFd999p7SAQ61atSCXyzFq1Ch+3gS3iIebmxtcXV3fbkEqQcIYY1WdCUIIIYQQQkjVoZeXEUIIIYQQouEoKCCEEEIIIUTD0ZwCQsgb8fvvv2PDhg1K6aNHjxa86l1McXExtm/fjr/++gvZ2dlwdXXF/PnzYW5u/rayW6GSkhIcO3YMly9fRmxsLPLy8tCwYUNMmzYNH3/8cYXHDx48GPHx8UrpR44cQYMGDd5CjpUlJCRg48aNCA4Ohq6uLgYMGIDx48dX+LZQf39/+Pn54dGjR2jQoAFmz54NFxeXd5JnVa5evYozZ87g7t27SEtLg6WlJYYPH17hi35WrFiBw4cPK6X7+Pigf//+bym3lRceHo6vv/5aKb1FixbYsWOH2mOr43N68OABBg4cqHL7tGnTRMsLVJ9nFR4ejiNHjiA8PBzx8fFo2rQpdu3apbTf1atXsX37dsTFxaFu3bqYMmUKOnfuXOH5q+L7rqIyfQjfd+T1UVBACHkjoqOj0aJFC8yYMUOQbmVlpfY4uVyO6dOnIzExEVOmTEGNGjWwdu1aLF68GFu2bHmbWVYrPj4e27dvR58+fTB06FAwxvDLL79g6tSp2L9/P+rXr6/y2KKiIiQmJmLSpElwc3MTbBNbz/ptiI2Nxbhx4+Di4oIVK1YgIiICW7ZsgY2NDbp3767yOF9fX+zcuRPjx4/HRx99hD179mDWrFnw9/dHjRo13knexSxduhSfffYZpk2bBkNDQ5w8eRJLly5FzZo10bVrV5XHxcTEoFOnTkoVUTs7u7ed5UqJjo6Grq4utm3bJkgv/yKk8qrrc6pTpw527typlL5kyRJkZGSordxXl2cVEBCA5ORkfPbZZ3j48CEaNWqktA/3+Rs+fDi++eYb+Pv7w8fHB8eOHYOlpaXKc1fV911FZXrfv+/IG8IIIeQNGDx4MFu7du1LH+fn58c+/fRTFh8fz6edOnWKubq6suTk5DeZxZeSnZ3NCgsLBWmxsbHM1dWV7d27V+2x4eHhzNXVlcXExLzNLKokl8vZ0KFDmZeXF5PL5Xz6pEmTmJeXl8rj7t69y1q1asX+/PNPPu3JkyfM1dWVnThx4m1mWa2SkhKWmZmplObh4cFmzpyp8jiFQsE+//xztn///recw1e3YsUKNmzYsJc6pro+J1UCAwOZq6srO3jwoMp9quOzysrKYq6uruzAgQOC9JSUFNa2bVu2ZcsWPi0/P599/vnnbNu2bWrPWdXfd6rK9D5/35E3h+YUEEJeW1FRER48eCDaoqZOYWEhdu/ejb59+wpaA+3t7QGUvia+qhgZGSktxce9fr6oqEjtsdHR0dDW1oatre3byp5aFy9eRExMDKZNmwap9L+veQcHByQnJ6s87tdff4WtrS369u3Lp9WtWxcGBgZV+ixkMplSy7lMJoNEIkFxcbHK45KTk5GXl/fSn8t3KSYm5qXzV12fkxi5XI4NGzbAzs5ObS9BdXxWMTExAKCUp927d0NPT49fax4A9PT0YGVlpfb+V4fvO1Vlep+/78ibQ0EBIeS1xcfHQy6XY926dXBzc0PPnj2xZcsWtRU2APwr3nv37i1IVygUAEr/iFYnhw8fhkQiQZs2bdTuFx0dDYVCgS5duqBNmzYYMWIEbty48Y5yCZw6dQr29vZwdHQUpMvlchQUFIgek5mZievXr6NXr15Kcw4UCkW1exYXL15EWlqa2mcRHR0NAJgzZw5at26Nfv36Yd++fWDVaCXumJgYBAQEoHXr1ujcuTN8fHyQlpamcv/37TmdOHECsbGxmD59OrS0VI9Yro7PSqwCXVJSgrNnz6Jr167Q19cX7C+Xy9Xe/+rwfacqKBDzvnzfkTeH5hQQQl7b8+fPMXHiRDRr1gwymQx///03duzYAcaY2knGQUFBMDQ0RPPmzQXpmZmZACoeV/0uHT9+HIcOHcKQIUPQuHFjtfvWqlULPj4+sLa2Rnp6Onx9fTFjxgzs2bMHDg4ObzWfjDHcunVL0IrMyczMVHlPb9++DblcjtatWwvSCwoK8OLFi2r1LCIjI7F06VI0a9YMAwYMULlfSUkJvL290bhxY5SUlODw4cNYu3Yt9PX14enp+Q5zLC4jIwM9evSAm5sbjIyMEBYWBl9fXzx9+hS+vr6iE8Lfp+f04sULbNu2DW5ubmjXrp3afavjs4qOjoa5uTn/oikAiIqKQnZ2ttL9B4CsrCw4OzurPF91+L4TK5OY9+X7jrxZFBQQQpTk5uaqba3k1KtXDzo6OnBzcxNMMGvdujWSkpJw5swZtUFBXFycaItVdHQ0JBLJG51g+LJlKuvgwYNYvXo1evXqhZkzZ1Z4jnHjxgl+btq0Kfr164fz58+/9T+SKSkpePHiheh9jYmJUXn9uLg4yGQype2xsbEAUG3+uIeGhmLGjBmoV68eNm7cCG1tbZX7enh4CH52c3PDgAEDcPr06WoRFJiammLOnDn8zy4uLsjPz8eOHTuQkpKCevXqKR3zvjwnAPDz80NmZqbS4gNiquOzio2NFZ2QCyi3tKenpyM9PV3t/X+X33eqiJWpvPfp+468WRQUEEKUBAQEYPHixRXut3fvXjRp0kR0m4ODAyIiItQen5WVJfqHMCgoCM2aNYOBgUHlMlwJr1qmX375Bb6+vhg+fDimT59e4XKeYurXrw9dXV2+RfBtysrKAlBa4SzryZMnSExMxNChQ1UeZ2hoqDTE48aNG9DS0sInn3zyVvL7Mq5evYq5c+fC0dERa9aseenPh5aWFho0aICUlJS3lMPX17BhQwClvQhiQcH78JwA4OnTp9izZw/69OnzSvMEqvpZyeVyxMfH49NPPxWkq/r9unnzJgAorb5T/th39X0nRlWZynrfvu/Im0VBASFESe/evZXGvb6shIQEvoKjiq6urmAiLACkpqbi9u3blWpdfBkvWyaFQoFVq1bh8OHDmDJlimBS4ctKTk5GYWFhhffjTeB6Ocrf11OnTkFHRwfu7u6ix+nq6vITCzkKhQJnzpxB+/bt33qFpSKnTp3C4sWL0b59e/z4449KvTmVwRhDQkKC0lyL6iQhIQHa2toq13av7s+Js2XLFkilUkycOPGVjq/qZ/Xw4UPR31l1v19NmjRR29r/Lr/vxKgqE/D+ft+RN4smGhNC3rirV68iODgYgwYNUrtf/fr1lV548/PPP8PU1LTCl1K9TSUlJViwYAGOHj2KBQsWvNYfSMYYNm/eDGNjY6UhEm+DpaUlZDKZ4L6mp6djz5496N+/v1ILJ6d+/frIzMwUtO4dP34cDx8+xNixY996vtXZv38/fvjhB/Ts2RMrVqx4pYAAKJ04mZKSovblWlXpyZMnOHjwIL744guVY76r83PiREZG4tSpUxg5ciTq1KnzSueo6mfFTcgtP56eW6+/7O/XjRs3cOPGDXh5eak9Z1V/36kq0/v8fUfeLOopIIS8lh9++AHW1tb46KOPAAC3bt3CgQMH0L17d/Ts2ZPf7/z585g/fz5++eUX/g2Zffv2xZQpU7Bp0ya0bt0aZ86cwYULF7Bx40bo6elVRXEAADNnzkRgYCC+/vprODg44O7du/w2Ozs7vjW2fJkeP36MZcuWwcPDA1ZWVnj27BmOHTuGsLAwrF69GkZGRm897zVr1kSXLl3g5+eHunXrQktLC9u2bYO5uTk/vyMjIwPdu3fH+PHj+Ypk165dsXHjRvzvf//D0KFDERERge3bt2PChAn8s60Ku3fvxqZNm+Dm5gZPT0+Eh4fz28zMzGBhYSFanmnTpsHNzQ0NGzZEQUEBrl69iuPHj2P06NFwdXWtquLw9uzZg8ePH6Nly5YwMDBAdHQ0du3aBTMzM3h7ewN4v55TWRs2bICZmRmGDRumtK06PyuFQoGLFy8CKB1uKJFIEBMTg7i4ODRv3hyWlpZo3bo1LCwssGLFCkyYMAHJycn4+eef4enpiY4dO/Lnqi7fd5Up0/v8fUfeLAoKCCGvxdDQEKdOncLOnTshkUhgb2+P2bNno3///oLxqFFRUZDL5YIu5U8//RTTpk3D3r17cejQITg6OuKXX36Bk5NTVRQFAJCWlobAwEAApRMl/fz8BNuPHTvG/5EsX6bi4mLIZDJs2bIF2dnZMDQ0xMcff4xdu3a90wrbrFmzsHTpUixevBgGBgZwd3fHN998w1c8oqOjIZfLBZMAjY2NsWLFCqxZswYzZsyAjY0N5s+fLwjsqsKJEycAlI67DgoKEmxbsGAB+vXrp1QehUIBQ0NDHDp0CGlpadDW1kbjxo2xfPlydOnS5Z2XQYyBgQHu3LmDv/76C0VFRbC0tES/fv0wevRo1KxZE8D79Zw4//zzD/79918sWbJEtKJbnZ9VUlIS5s2bJ0jz8fEBAGzduhWWlpbQ0tLCypUrsXz5csycORMWFhYYO3asUgBUXb7vKiqTtrb2e/99R94cCatOCzYTQgghhBBC3jmaU0AIIYQQQoiGo6CAEEIIIYQQDUdBASGEEEIIIRqOggJCCCGEEEI0HAUFhBBCCCGEaDgKCgghhBBCCNFwFBQQQgghhBCi4SgoIIQQQgghRMNRUEAIIRrCwsICvXv3rupsAACePXuGxo0bY9q0aXzanDlzIJFIkJ6eXoU5I9VBVlYWJBIJpk6d+krH5+XlQSaTvfLxH6r9+/dDV1cXycnJVZ0VUg1RUEAIeW9lZWXhhx9+wCeffAIDAwPo6+vD1tYWPXv2xC+//CLYd+bMmZBIJLh27ZrouY4dOwaJRIL//e9/Kq/33XffQSKRwNraWuU+3HW4f1KpFFZWVhg8eDBiYmJeraBvQEpKCp48eYJPPvmkyvLAKSoqQp8+fWBkZITVq1fz6bdv34aNjQ1q165dhbl7O8LCwrBo0SIkJiZWdVbeC3fu3AGAV/683rlzBwqFoso/77t378aGDRve+XWPHz+OJUuWgDEmSB80aBCMjIywcePGd54nUv1pVXUGCCHkVYSHh8PDwwMZGRn48ssvMXr0aMhkMsTGxuL48eMAgAkTJvD7BwcHQ0tLCy4uLqLnu3nzJgCo3B4XF4d169ahUaNGiImJQXp6umjllbvOjh07IJFIUFRUhH/++Qe7d+9GUFAQ7t27BwMDg9ct/ksLCQkBAHz88cfv/NrlzZo1C8HBwQgODoaOjg6ffvr06SrM1du1b98+rFy5Et98801VZ+W98LpBQXX5vM+dOxeurq6YPn36O73ujz/+iNzcXHz//feCdC0tLfTo0QP79u3DTz/9BIlE8k7zRao3CgoIIe8dxhgGDx6MnJwcBAUFwdHRUbB97dq1SElJEewfGhqKFi1aQF9fX/ScXFDg6uoqut3b2xsuLi4YO3Ysxo0bh9DQUHTu3FkpX6GhoXBycsKIESP49DFjxiAjIwMnT55EUFAQ3N3dX6ncr4OrJFV1y+mlS5ewefNmzJs3D82bNxdsKxsgfGhu3rwJW1tbmJmZVXVW3gthYWHQ1tZW+oxUVkhIyGsd/yY8ePAAT548gZub2zu9blFREUJDQ/HVV1+Jbm/VqhX8/PwQHh5epfeHVD80fIgQ8t65c+cO7t+/Dw8PD6WAAAAkEgnq1avH/xwbG4vs7Gy0bt1a9HwKhQK3bt2ChYUFLC0tlbafOXMGf/31F9avXw8nJycAQGhoqNJ+3HVatWqltI3Lz/Pnz1WWa8mSJZBIJLh+/brStpycHJibm6NTp0582r179zBlyhQ4OjrC2NgYxsbGcHNzw9GjR5WODwkJgbGxMezs7Pg0e3t7tGnTRmnfoKAgSCQSpSFYJSUl+Pnnn9G6dWsYGBigTp066NGjB+7evauyTGUpFApMmzYNtWvXho+Pj2Db6dOnIZFIcOjQIaW0ffv2YePGjWjevDn09fXRvHlznDt3DgDw77//omfPnjA2Noa5uTnmzp2rNGRi7Nix0NXVRWBgIHr27AlTU1MYGhqiQ4cOCAsLU8rn3r170a9fPzRo0AB6enqwsrLC0KFDkZCQIFquv/76Cz179oSZmRl0dXVhZ2eHsWPHIjs7mx+WdvnyZTx48IAfVlaZ4CAnJwcLFizARx99BH19fZibm2PChAnIysoS7Hfy5ElIJBL88ccf8PX1hYuLC2rUqIEGDRpg8+bNFV4H+G8M/sSJE3H69Gm0b98eNWvWhJWVFdauXQsASE1NhZeXFywsLGBkZIQBAwaIzv948uQJpk2bBnt7e+jq6sLa2hpz585FYWGh0r6JiYkYOXIkzMzMYGhoiP79+yM1NRV37txB8+bNlQLF06dPo3v37jA1NYWBgQFatWqFY8eOKZ03JCQETZs2ha6uboVlZ4zB19cXn376KYyMjGBoaIiePXsiPDxcsN8///wDiUSC7du3K51j8uTJkMlkePHiBQCgX79+/O/aokWL+Oc+e/ZsAMDvv/8OiUSCAwcOYNy4cahfvz709PTg6OiIw4cPC86dlJQEiUSi9DsDACtXroREIkFUVBSA0sYLXV1dFBUVYdeuXfx1Bw0axB/D5Uvss080G/UUEELeO7m5uQBKK+G5ubkVDscJDg4GAFhbWyM2NlZpe3x8PHJycvD5558rbSsuLoa3tzeGDRsGNzc35OXlQSKRiAYF3HXKtwwWFRUhICAAUqkULVu2VJnPFi1aACit7H/22WeCbcuXL8ezZ8+wZs0aPm3r1q2IjIzEwIEDUb9+fTx9+hS//fYbBg0ahICAAEF5QkJCBEMpsrKykJCQgJ49eyrl49atWwCEvQqFhYXo0aMHrl69iuHDh2P06NHIyMjA1q1b0b59e9y9e1ftXAugdJzz3bt3sWTJEqVnJtaTwaWtW7cOurq6GDduHHJzc7FixQoMHDgQW7duxcyZMzF27Fj07t0bv/76K1auXAlXV1cMHjyYP09oaCh0dXXRrVs3DBgwACtWrEB4eDh+/vlndOvWDbGxsXx+FAoF5syZAw8PD0ydOhXGxsYIDQ3Fr7/+itDQUNy7dw9aWv/96fT29saGDRvg6uqKefPmwcDAAPfv38fBgwexdetWfPTRR1i5ciXmzJmDIUOGoFevXgBQ4byJhw8fomPHjkhPT4eXlxcaN26MO3fuYNu2bQgNDUVgYCBkMhlfPqC0h6ykpARDhw5FjRo1sHHjRkydOhWtWrVSGRBzwsLC+OD477//xpgxYzBkyBCsW7cO3377LWrXro0FCxage/fuWLx4MS5evIhDhw7B3NwcW7ZsEZynS5cu0NLSgpeXF+rXr49r165h1apViIuLwx9//MHve//+fXTo0AG6urqYMmUKzM3NceTIEfTu3Rvh4eEYMmSIII8LFizAsmXL0K1bNyxcuBAymQz79u2Dp6cnjh8/jj59+gAoDV7v3buHoUOHqi0zUPq76enpiTNnzmDYsGEYM2YMnjx5go0bN6Jdu3YIDQ2FjY0NgP9+v8V6E2/duoUmTZqgRo0aAIDx48dDLpfD398fmzdvhrGxMYD/vhu4Z+bt7Y2mTZti/vz5yMrKwvr16zFkyBDY2tryjQsVXbdmzZpo1KgRAMDT0xPPnz/Hzp07sWjRIjg4OAAAmjVrxh9Ts2ZNAEBaWlqF94doGEYIIe+ZvLw8ZmtrywAwIyMjNnjwYLZt2zYWHx8vuv/s2bMZgAr/ff/990rHrly5ktWoUYM9evSIT7O3t2eOjo4qrxMQEMCePXvGHj16xAICApiHhwcDwL799lu15UpISGAA2LRp0wTpDx8+ZPr6+mzkyJGC9NzcXKVzPHr0iEkkEjZr1iw+LSsri0kkEubt7c2nXbhwgQFgO3fuVDrH6NGjmUwmYy9evODTxo4dy4yMjNjNmzcF+8bExKi8d+X16NGDyWQy9vjxY6VtAwcOZIaGhkyhUAjSALAJEyYI0ufOncsAsPr167Pk5GSlvJQtZ3FxMdPV1WUSiYT98ccfgmsuWbKEAWAHDhzg00pKSlh+fr5S/pYtW8YAsHv37vFpa9asYQDY/PnzmVwuF+xf9hxHjx5lANixY8fU3p+yeW7RogUzMzNj0dHRgm0//PADA8D++usvPs3T05MBYKNGjRLk49KlSwwA27hxY4XX3Lx5MwPAXFxcWE5ODp9++vRpBoBpaWmxCxcu8OlyuZzZ2Niwjz/+mE/LyspilpaWrHHjxuzp06eC848cOZIBYPfv32eMMVZQUMAaNWrE7OzsBPvK5XLm6OiolO/ff/+dAWBbt24VnLegoIDZ29uz9u3b82lhYWEMAFu/fn2F5Z48eTKTSqXs6NGjgvSAgAAGgM2ePZtPGzFiBNPR0WGFhYWCfYuKipiuri776quvBOl9+vRhtWrVEr1up06dGAA2depUQfqVK1cYADZx4kQ+7fvvv2cAWEJCgtJ57OzsWJs2bQRp06ZNYwBYZmam6LVPnDjBALBt27aJbieai4YPEULeOzVq1EBgYCBmzpwJExMTHDp0CBMnToS9vT08PDzw8OFDwf7BwcGoUaMGDh8+LPqva9euAJRb4lJTU7FkyRLMnTsXVlZWfLqjoyMiIyOVhkNwLXqdOnWCmZkZ6tevj06dOuH+/ftYt24dVq1apbZctra2MDIywr179wTpPj4+kEgkWLZsmSCda/GTy+XIyspCWloatLW1oa2tjYKCAn6/0NBQMMYEPQW3b98WLTNQ2vrIDVkBSodr7dixA1OmTIG9vT3S0tL4fyYmJqhdu3aFKysVFBTg4sWL+Oyzz0SHaIWEhMDZ2Vkw8TEkJAR16tTBhg0bBOlcq+vq1asFw8S49LIt+eHh4SgsLMTQoUMxYMAAwTW5OSEPHjzg02QyGfT09Pg8p6enIy0tDbVq1eLTAODp06dYuHAhOnfujKVLl0IqFf455c4B/Pe5UDWJvbwDBw7gzp07WLp0Kd8CzOnYsSMA8MNFgNL7ZGJigi1btgjywQ29UTWPpiyuV2b79u2CXhzuno4bN04wF0YqlcLAwEBwrzdt2oSUlBRs2rRJaXhU+Xxv374dMTExWLZsmWBfqVTKPxfu81pUVIQ5c+agQ4cOGDhwoODzl5OTA0dHR8Hnr7KTjBMTE7Ft2zYMGTIEnp6egm3t2rWDTCYT3Ofbt2/D0dFRaUjT3bt3UVhYqDRfJzg4WOUcnrCwMDRo0ECw+hYAtG3bFrq6uoLP5O3bt2FqagpbW1vBvhkZGUhISBC9rr29PUxMTESvHR0dDQAV9uwRzUPDhwgh7yVLS0usWbMGa9asQUJCAv7++29s3rwZ586dw6hRo3DhwgV+3+DgYDg5OWHgwIGi5+LGXZevtM2bNw9aWlrw9PQUDDuytLTkhyiUrVQHBwfD2dmZ/0Ovq6sLS0tLODg4VHqVDycnJ9y/f5//OSQkBHv27MH3338vCEwKCgqwadMm7N27F/fv30dJSYngPGUrk9xQhbKVh+DgYOjr66Np06aC4/Lz8xEeHo4vv/yST9u/fz8YY/jxxx/x448/iuZbVQWEc+/ePRQUFKB9+/ZK27KzsxEfH48ePXoopY0aNUppXHhERAR0dXWV3rkQEREBAGjSpAmfxlUQx44dq3RdhUIBAIJKcExMDJYvX44zZ84IJqtzGjZsCAD4888/kZeXhxkzZqgtN1B6r+vUqVPpStjhw4ehq6srmKzOYf8/X4ILCLOysvDgwQOMHDlSqfLPzfX46KOPKpVHOzs7pSCRu6dlx6QDpcPq4uLiBL9Thw8fRoMGDeDh4VFhvvfv3w8rKyulIUJAaZArkUjg7OwMAAgICEBKSgpSUlJUzsUoW0bu815RUHD06FEoFAp4eXkpbZNKpVAoFHx+X7x4gaioKIwZM0ZpX7HhdmlpaXj06JHoEKbExERkZGRg8uTJopPrGWOCz2RwcLDK4L38dRljCAsLQ7du3VSW+8qVK5BIJCoXVSCai4ICQsh7z87ODhMnTsSgQYNgZmaGK1eugDEGiUSChIQEZGZm8uP1xdy5cwdmZmaCStvNmzfh5+cHxpjKY0NDQ/k/rNx1hgwZgi5durxyWZydnXHt2jWkpaWhTp06mDVrFiwsLDBnzhx+n6KiInTp0gU3b97EqFGjMGPGDNStWxfa2trw9/fHhg0bBAFOSEgIdHR0BAEAF8CUbenlyi2XywUVqtDQUFhYWOD3339XmW9u7LIqXO9N48aNlbZxPRll88ylffrpp0r73759Gy1atODHb5ctEyDs/eAqiGJzObgVp7hKVVBQEDp37gxTU1NMmjQJTZs2hZGREaRSKaZOnYri4mK+5ZwLNsTyV15wcHClewmA0sq8nZ2daAs/14vEVZjV5SM4OBhSqbTCynFxcTHu37+v1JMClN5rqVSqNHn+/v37KCws5O+1QqHA/fv3+V43VfnmfpdCQkLg4eGh1MMClD4XBwcHGBkZAfjvGe7duxd169YVPb+pqSn//5CQENjZ2fHPShUuaCo73r5s+Rhj/H0OCwuDXC4XrUgHBgYCgGhPnNhzV/eZDAkJQVFREf+ZTE1NRWpqKkaNGlWp60ZHRyMnJ0fl5y0nJwfnz59Hy5YtYW5uLroP0VwUFBBCPhg6OjqQSqUwMjLiW+a5P87cH/fykpKSkJmZKWhZY4xh2rRpcHR0xMKFC5WOefbsGSZPniyYbMxdR13wURllJxvn5ubi4sWL+O233/gWSwD4448/cO3aNWzZsgWTJk0SHP/TTz8pVQRDQkLg6OgIbW1tAKVBRWxsLMaNG6d0/f379wMQtj5mZ2dDKpW+VrBTXFwMADA0NFTapm6ScfnKDddiK5Z37r0HZZdZ5M5TPvgpKSnB5s2bYWdnx1eoFyxYAIVCgdu3b6NOnTr8vomJiYiOjha0inOrSFXUA6SuUqeKRCJR2YK8a9cuWFtb85V07jMoVlkNDg5G48aNK5yIf//+fRQVFYlWJIODg9GoUSOl51Z+SBRjDDKZTDTf+fn5OHDgANq2bQsLCwuUlJSgsLBQaZUooHSFn3///VfQM5GdnQ2gdCnN8sOpxIgtFyyGe3Zied6xYwcAoH///gDAr0RU/vf7xYsXOHHiBKytrQWTx1V9fstuK/+ZBEon1ctkMr78XK9h+esqFAocOnQIWlpaghXY1F0XAPbs2YO8vDzR3x9CaE4BIeS9cvXqVZXLei5ZsgRyuRzDhw/n07jKi6qgQKxStXPnTgQFBWHJkiUYOHCg0j8vLy9oaWkJggLuOq8bFJRtmZwzZw6cnZ2VKpTcW3HLDwtZv349Lly4gCZNmvAVwcLCQkRERAiChIKCAigUCn4VJ85ff/2FX3/9FYCw9bFhw4Z4/PgxLl++rJRfuVxeqVVMuApTXl6e0jauJ6Nsiy23zjy3BCwnNDRUZYttcHCw0phvbtnF8nn/6aefkJCQgB9//JFvrU5MTISZmZmgcpeXl4dhw4ZBoVAIrsn1jJw6dUopH0VFRfz/4+PjAUCwFGxFmjdvjujoaKSmpgrSFy1ahODgYCxevJjPM3efyn/uSkpKcPfu3Ur1UHAVyfL3VC6XIywsTOW9lkgkfCAnk8nQpEkTBAUFCeazKBQKTJ48GSkpKfzbwrW0tGBtbY3r168jJyeH3zc3Nxfe3t4AhAEiN2Rr3759ovl/8uQJ///4+HhkZ2dX6qVlXPBY/rNx9uxZbN68GV9//TUfhHCf27K/MyUlJRg/fjwyMzOVxvWre+7c/S5/3cuXL2P//v0YN26c2usCpfOMIiIi0KxZM8HwOnXXLS4uxqpVq2BtbY2vv/5a+YYQjUc9BYSQ98rChQtx69Yt9O3bFy1btoShoSGSk5Px559/IiQkBF26dBFMyK2oBZ+rNHKVp+fPn8PHxwcuLi7o27ev6DFaWlqws7NDWFgYP0zp9u3bkEgkSpXYl+Xk5ASJRILly5fjyZMnOH/+vNIQiw4dOkAikWD8+PGYMmUKGGM4deoUMjIyAAgrd/fu3UNxcbGg0mJkZISmTZvi4MGDMDMzg62tLW7cuIEbN26gQYMGUCgUguEYM2bMwOHDh9GjRw+MHTsWTk5OeP78ORISEnD8+HFs2LCBb1FVVy4Aomv9l+/J4NKaN2+uNJ9A1aTd/Px8REZGYvTo0XxaQkICsrKy4OLighEjRmDGjBkwMzPDqVOncOLECXh7ewvGfHfq1Am//PILBg8eDA8PDzx69Ag7duzgh6yUva9eXl7YvHkzxo8fj6CgIDg5OSEzMxOBgYFwcHDgl461tbWFVCrF1q1bIZVKoaenh9atW6tt8fbx8cFff/0Fd3d3jB8/Hjo6Ojh27BjOnTuH2bNnC8qo6j6Fh4ejoKDgpYKC8hXbiIgI5Ofnq+xBKDvEBwC+//57DB48GJ06dcKwYcMgl8uxb98+3Lp1Cxs3bhRMVPby8sL333+Pjh07YsyYMcjNzYWvry8fJJTNy5AhQ/Djjz9i8eLF/DKmQGkv3/nz59G2bVts3LhRbVnEjB07FqtXr8aYMWMQFhYGKysrXL9+Hbt370bbtm2xbds2fl+uN2nChAmYPHkyXrx4gT/++IMPIMtfz97eHgAwbdo0tGnTBjKZDF9++SWkUilCQ0Ph7OyMjRs34sWLF/j4448RGhoKX19fuLm5Yf369fx5XFxcoKOjg/nz5+Pp06eQyWTw9/eHTCYTBGXlrzt//nz06tULMpkMffr0gZGRETZv3oyEhAQcPHhQMBGeEN47X++IEEJew4kTJ9jYsWNZs2bNmKmpKdPS0mJmZmasW7dubM+ePYKlKxljzMzMjNna2qo834ABAxgAfjnTmTNnMgDs5MmTavPRq1cvBoDFxsZW6jovo2HDhgwA69Wrl8p9du3axRo2bMh0dXVZ48aN2f/+9z8WGBjIALC1a9fy+/n6+jIA7MqVK4Ljw8LCWNu2bZm+vj6zsrJiU6dOZc+ePWMmJiasb9++Ste7d+8eGzx4MGvQoAHT0dFh5ubm7LPPPmMLFy5kGRkZlSpXixYtWI8ePQRpBQUFTEtLi40dO1YpbcyYMUrnGDVqFNPW1lZaFvLGjRsMANuyZQufduTIEQaAXb58mS1cuJBZWloyXV1d5uzszHbs2KF07ufPn7MxY8aw2rVrMwMDA9axY0d2+vRpNn36dAZAqZwxMTHsyy+/ZDY2NkxHR4dZWlqyPn36sH///Vew344dO1ijRo2YtrY2A8DOnj1b4b06efIka9myJdPR0WEmJiasc+fO7Pjx46L3Tuw+7dq1iwFgFy9erPBa7dq1Y/b29pU+h1wuZzVr1mSDBw9WOmbHjh2sWbNmTFtbm9WuXZv17t2b/fPPP0r7FRcXs/nz5zNra2umq6vLPvroI7ZkyRI2Y8YMBoClpKQI9n/27BmbOXMma9KkCdPX12e1atVijo6ObMqUKfwyp4wxNn/+fAaAPXz4sMJyM8ZYeHg469mzJzMwMGA1a9ZkH3/8MVu7di0rLi5W2nfz5s3MxsaG6enpMRcXF/bbb7/xy83++eefgn3z8vLYiBEjWJ06dRgAVq9ePcYYYxkZGQwAW7JkCTt48CD76KOPmI6ODrO1tWXz588XLAPMOXToEGvcuDHT09NjTZs2ZatXr2ZBQUEMAFu3bp1gX7lczry9vZmFhQWTSCT80sJJSUnMyMiIDRo0qFL3hWgmCWMig/oIIYSQN2z16tVYsGABnj59KmhhflsWLlyIJUuWICsrq8JJp4S8CxcvXkTnzp0FL1t72xQKBTw8PJCcnIygoCDReT2EADSngBBCyDsyfvx46OvrY+fOne/keiEhIbCxsaGAgFQb3PCm1x1m+DKWLl2K8PBwnD59mgICohb1FBBCCPkgWVtb4+OPP8bJkyerOiuEAABGjBiB48ePIzs7u9LvLiHkXaGeAkIIIR+c9PR0PHr06J22yBJSEW5SPQUEpDqingJCCCGEEEI0HPUUEEIIIYQQouEoKCCEEEIIIUTDUVBACCGEEEKIhqOggBBCCCGEEA1HQQEhhBBCCCEajoICQgghhBBCNBwFBYQQQgghhGg4CgoIIYQQQgjRcBQUEEIIIYQQouH+D8+XkKnh+H2qAAAAAElFTkSuQmCC", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import shap\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Take the optimized model\n", - "model = best_rf # ou grid_search.best_estimator_ si tu gardes grid_search\n", - "\n", - "# Initialize SHAP explainer\n", - "explainer = shap.TreeExplainer(model)\n", - "\n", - "# Compute SHAP values\n", - "shap_values = explainer(X_train) \n", - "shap.summary_plot(shap_values.values, X_train, feature_names=X_train.columns)\n", - "shap.summary_plot(shap_values.values, X_train, feature_names=X_train.columns, plot_type=\"bar\")\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "demo_desu", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}