chore: [pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ default_language_version:
   python: python3.10
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
       - id: check-added-large-files
       - id: check-toml
@@ -14,7 +14,7 @@ repos:
       - id: end-of-file-fixer
       - id: trailing-whitespace
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.2
+    rev: v0.16.4
     hooks:
       - id: ruff
         args:
@@ -23,17 +23,17 @@ repos:
         files: ^source/
       - id: ruff-format
   - repo: https://github.com/gitleaks/gitleaks
-    rev: v8.21.2
+    rev: v8.30.0
     hooks:
       - id: gitleaks
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.3.0
+    rev: v2.4.3
     hooks:
       - id: codespell
         additional_dependencies:
           - tomli
   - repo: https://github.com/compilerla/conventional-pre-commit
-    rev: v3.6.0
+    rev: v4.4.0
     hooks:
       - id: conventional-pre-commit
         stages: [commit-msg]

source/analysis/artifacts.py

@@ -1,5 +1,4 @@
 from pathlib import Path
-from typing import Optional
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -51,7 +50,7 @@ def _set_save_path(self, dir_name: str) -> Path:
         save_path.mkdir(parents=True, exist_ok=True)
         return save_path
 
-    def _get_save_path(self, dir_name: str, file_name: str) -> Optional[Path]:
+    def _get_save_path(self, dir_name: str, file_name: str) -> Path | None:
         save_path = self._artifacts_path / dir_name / file_name
         if save_path.exists():
             return save_path
@@ -61,25 +60,25 @@ def set_dir_params(self, params: DirParams):
         self._dir_params = params
         self._artifacts_path = OUT_DIR / params_to_path(params)
 
-    def save_study(self, study: Study, band_reduction: Optional[int] = None):
+    def save_study(self, study: Study, band_reduction: int | None = None):
         self.save_metric(study.best_value, band_reduction)
         self.save_params(study.best_params, band_reduction)
 
-    def save_metric(self, metric: float, band_reduction: Optional[int] = None):
+    def save_metric(self, metric: float, band_reduction: int | None = None):
         save_path = self._set_save_path(STUDY)
         if not band_reduction:
             write_txt(str(metric), save_path / STUDY_BEST_METRIC)
             logger.info(f"Metric saved: {metric}")
 
-    def save_params(self, params: dict, band_reduction: Optional[int] = None):
+    def save_params(self, params: dict, band_reduction: int | None = None):
         save_path = self._set_save_path(STUDY)
         if band_reduction:
             write_json(params, save_path / STUDY_BEST_PARAMS_REDUCED)
         else:
             write_json(params, save_path / STUDY_BEST_PARAMS)
         logger.info(f"Params saved: {params}")
 
-    def load_params(self, band_reduction: Optional[int] = None) -> Optional[dict]:
+    def load_params(self, band_reduction: int | None = None) -> dict | None:
         if band_reduction:
             save_path = self._get_save_path(STUDY, STUDY_BEST_PARAMS_REDUCED)
         else:
@@ -100,16 +99,14 @@ def load_encoder(self) -> LabelEncoder:
             "Encoder could not be found. Make sure you train the model first (cmd: train_model)"
         )
 
-    def load_unfit_model(self, band_reduction: Optional[int] = None) -> BaseEstimator:
+    def load_unfit_model(self, band_reduction: int | None = None) -> BaseEstimator:
         params = self.load_params(band_reduction)
         model = import_model(self._dir_params.estimator_name)
         if params:
             model.set_params(**params)
         return model
 
-    def save_metrics(
-        self, metrics: list[Metrics], band_reduction: Optional[int] = None
-    ):
+    def save_metrics(self, metrics: list[Metrics], band_reduction: int | None = None):
         table, metrics_all = present.generate_metrics_table(metrics)
         save_path = self._set_save_path(RESULTS)
         if band_reduction:
@@ -119,7 +116,7 @@ def save_metrics(
             for m in metrics_all:
                 write_txt(f"{m.mean:.2f}", save_path / f"{m.name}")
 
-    def load_metrics(self) -> Optional[dict[str, list[Metrics]]]:
+    def load_metrics(self) -> dict[str, list[Metrics]] | None:
         if not OUT_DIR:
             logger.warning("No output directory.")
             return None
@@ -163,7 +160,7 @@ def save_shap_values(self, values: np.ndarray):
         save_path = self._set_save_path(RESULTS)
         np.save(save_path / RESULT_SHAP_VALUES, values)
 
-    def load_shap_values(self) -> Optional[np.ndarray]:
+    def load_shap_values(self) -> np.ndarray | None:
         save_path = self._get_save_path(RESULTS, RESULT_SHAP_VALUES)
         if save_path:
             return np.load(save_path)
@@ -173,7 +170,7 @@ def load_shap_values(self) -> Optional[np.ndarray]:
         )
         return None
 
-    def load_spectral_bands(self) -> Optional[np.ndarray]:
+    def load_spectral_bands(self) -> np.ndarray | None:
         SPECTRAL_BANDS_DIR.mkdir(parents=True, exist_ok=True)
         save_path = SPECTRAL_BANDS_DIR / "bands.npy"
         if save_path.exists():

source/analysis/extensions.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 from source.analysis.base import BaseDataLoader, BaseOptParameters, BaseSklearnModel
 from source.core import settings
 from source.utils.utils import import_class
@@ -13,13 +11,13 @@
 DEFAULT_PARAMETERS_NAME = "ParamsSVC"
 
 
-def import_data_loader(data_loader_name: Optional[str]) -> BaseDataLoader:
+def import_data_loader(data_loader_name: str | None) -> BaseDataLoader:
     return import_class(data_loader_name, _DATA_LOADERS_DIR, DEFAULT_DATA_LOADER_NAME)
 
 
-def import_model(model_name: Optional[str]) -> BaseSklearnModel:
+def import_model(model_name: str | None) -> BaseSklearnModel:
     return import_class(model_name, _MODELS_DIR, DEFAULT_ESTIMATOR_NAME)
 
 
-def import_parameters(parameters_name: Optional[str]) -> BaseOptParameters:
+def import_parameters(parameters_name: str | None) -> BaseOptParameters:
     return import_class(parameters_name, _PARAMETERS_DIR, DEFAULT_PARAMETERS_NAME)

source/analysis/metrics.py

@@ -33,7 +33,7 @@
 @dataclass
 class MetricsContainer:
     def __init__(self):
-        self._metrics = {key: [] for key in METRIC_FUNC.keys()}
+        self._metrics = {key: [] for key in METRIC_FUNC}
 
     def calculate(self, y_test: ArrayLike, y_pred: ArrayLike):
         for metric_name, metric_func in METRIC_FUNC.items():

source/analysis/params.py

@@ -1,15 +1,14 @@
 from pathlib import Path
-from typing import Optional
 
 from pydantic import BaseModel, model_validator
 from source.analysis.extensions import DEFAULT_DATA_LOADER_NAME, DEFAULT_ESTIMATOR_NAME
 from typing_extensions import Self
 
 
 class DirParams(BaseModel):
-    estimator_name: Optional[str] = None
+    estimator_name: str | None = None
     estimator_is_optimized: bool = False
-    data_loader_name: Optional[str] = None
+    data_loader_name: str | None = None
 
     @model_validator(mode="after")
     def set_default_names(self) -> Self:

source/analysis/plots.py

@@ -1,9 +1,7 @@
-from typing import Optional
-
-import matplotlib.cm as cm
 import matplotlib.pyplot as plt
 import numpy as np
 import umap
+from matplotlib import cm
 from matplotlib.figure import Figure
 from matplotlib.lines import Line2D
 from sklearn.base import BaseEstimator, clone
@@ -16,7 +14,7 @@
 
 
 def relevant_features(
-    relevances: np.ndarray, bands: Optional[np.ndarray] = None
+    relevances: np.ndarray, bands: np.ndarray | None = None
 ) -> Figure:
     y = smooth_relevances(relevances)
     indices_by_relevance = np.argsort(y)[::-1]
@@ -55,7 +53,7 @@ def relevant_features(
 
 
 def relevant_amplitudes(
-    relevances: np.ndarray, bands: Optional[np.ndarray] = None
+    relevances: np.ndarray, bands: np.ndarray | None = None
 ) -> Figure:
     if bands is None:
         bands = np.arange(len(relevances))
@@ -121,7 +119,7 @@ def signatures_display(
     encoder: LabelEncoder,
     X: np.ndarray,
     y: np.ndarray,
-    bands: Optional[np.ndarray] = None,
+    bands: np.ndarray | None = None,
     *,
     x_label: str = "Spectral bands",
     y_label: str = "",
@@ -186,7 +184,7 @@ def umap_display(
     encoder: LabelEncoder,
     X: np.ndarray,
     y: np.ndarray,
-    meta: Optional[np.ndarray] = None,
+    meta: np.ndarray | None = None,
 ) -> Figure:
     y_encoded = np.array(encoder.fit_transform(y))
     classes = encoder.classes_

source/analysis/present.py

@@ -1,5 +1,4 @@
 from collections import defaultdict
-from typing import Optional
 
 from rich import print as RichPrint
 from rich.text import Text as RichText
@@ -22,9 +21,9 @@ def generate_metrics_table(metrics: list[Metrics]):
 
 def display_metrics(
     metrics: dict[str, list[Metrics]],
-    model: Optional[str] = None,
-    do_optimize: Optional[bool] = None,
-    data_loader: Optional[str] = None,
+    model: str | None = None,
+    do_optimize: bool | None = None,
+    data_loader: str | None = None,
 ):
     def check_filter(params: DirParams) -> bool:
         if model is not None and params.estimator_name != model:

source/cli/analysis.py

@@ -1,5 +1,4 @@
 from functools import reduce
-from typing import Optional
 
 import typer
 from rich import print
@@ -19,7 +18,7 @@
 
 @app.command()
 def test_load_data(
-    data_loader: Optional[str] = None,
+    data_loader: str | None = None,
 ):
     loader = import_data_loader(data_loader)
     loader.load_data()
@@ -32,10 +31,10 @@ def test_load_data(
 
 @app.command()
 def train_model(
-    model: Optional[str] = None,
-    data_loader: Optional[str] = None,
+    model: str | None = None,
+    data_loader: str | None = None,
     do_optimize: bool = False,
-    parameters: Optional[list[str]] = None,
+    parameters: list[str] | None = None,
 ):
     artifacts.set_dir_params(
         DirParams(
@@ -69,8 +68,8 @@ def train_model(
 
 @app.command()
 def generate_metrics(
-    model: Optional[str] = None,
-    data_loader: Optional[str] = None,
+    model: str | None = None,
+    data_loader: str | None = None,
     do_optimize: bool = False,
 ):
     artifacts.set_dir_params(
@@ -93,8 +92,8 @@ def generate_metrics(
 
 @app.command()
 def generate_plots(
-    model: Optional[str] = None,
-    data_loader: Optional[str] = None,
+    model: str | None = None,
+    data_loader: str | None = None,
     do_optimize: bool = False,
 ):
     artifacts.set_dir_params(
@@ -127,8 +126,8 @@ def generate_plots(
 
 @app.command()
 def calculate_relevances(
-    model: Optional[str] = None,
-    data_loader: Optional[str] = None,
+    model: str | None = None,
+    data_loader: str | None = None,
     do_optimize: bool = False,
 ):
     artifacts.set_dir_params(
@@ -149,8 +148,8 @@ def calculate_relevances(
 
 @app.command()
 def display_metrics(
-    model: Optional[str] = None,
-    data_loader: Optional[str] = None,
+    model: str | None = None,
+    data_loader: str | None = None,
     do_optimize: bool = False,
 ):
     metrics_ = artifacts.load_metrics()

source/cli/misc.py

@@ -3,7 +3,6 @@
 from source.misc.display_image import (
     display_spectral_image,
 )
-from typing import Optional
 
 app = typer.Typer()
 
@@ -14,5 +13,5 @@ def check_images():
 
 
 @app.command()
-def display_image(label: Optional[str] = None):
+def display_image(label: str | None = None):
     display_spectral_image(label)

source/cli/segment.py

@@ -1,14 +1,4 @@
-from typing import Optional
-
 import typer
-from source.segmentation.artifacts import (
-    load_all_selected_areas,
-    load_model,
-    load_transformation_matrix,
-    save_model,
-    save_selected_areas,
-    save_transformation_matrix,
-)
 from source.misc.display_image import (
     display_spectral_images_with_areas,
 )
@@ -21,6 +11,14 @@
     select_areas_on_images,
     train_xgboost_model,
 )
+from source.segmentation.artifacts import (
+    load_all_selected_areas,
+    load_model,
+    load_transformation_matrix,
+    save_model,
+    save_selected_areas,
+    save_transformation_matrix,
+)
 
 app = typer.Typer()
 
@@ -52,7 +50,7 @@ def train_model():
 
 
 @app.command()
-def segment_images(label: Optional[str] = None):
+def segment_images(label: str | None = None):
     encoder_cam1, model_cam1, encoder_cam2, model_cam2 = load_model()
     matx = load_transformation_matrix()
     perform_segmentation(

source/core/logger.py

@@ -12,7 +12,7 @@ def emit(self, record: logging.LogRecord) -> None:
         logger_opt.log(record.levelname, record.getMessage())
 
 
-@lru_cache()
+@lru_cache
 def setup_logger(debug: bool = False) -> loguru._Logger:  # type: ignore
     LOGGING_LEVEL = logging.DEBUG if debug else logging.INFO
     logging.basicConfig(

source/misc/check_images.py

@@ -32,6 +32,6 @@ def check_spectral_images():
         f"   Number of labels: {len(labels_cam1)} (cam1), {len(labels_cam2)} (cam2)\n"
     )
     msg += f"   Number of unique labels: {len(labels_unique)} \n"
-    msg += f"   Labels: \n{str(labels_unique)} \n"
-    msg += f"   Duplicated labels: \n{str(labels_duplicated)} \n"
+    msg += f"   Labels: \n{labels_unique!s} \n"
+    msg += f"   Duplicated labels: \n{labels_duplicated!s} \n"
     logger.info(f"Report: \n{msg}")