DevOps updates

.pre-commit-config.yaml

@@ -6,8 +6,8 @@ default_stages:
   - pre-push
 minimum_pre_commit_version: 2.12.0
 repos:
-  - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v4.0.0-alpha.4
+  - repo: https://github.com/rbubley/mirrors-prettier
+    rev: v3.6.2
     hooks:
       - id: prettier
         exclude: |
@@ -24,13 +24,13 @@ repos:
               docs/notes/
           )
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.7
+    rev: v0.12.10
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix, --unsafe-fixes]
       - id: ruff-format
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
+    rev: v6.0.0
     hooks:
       - id: detect-private-key
       - id: check-ast
@@ -44,7 +44,7 @@ repos:
       - id: trailing-whitespace
       - id: check-case-conflict
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.7.1
+    rev: v1.17.1
     hooks:
       - id: mypy
         args: [--no-strict-optional, --ignore-missing-imports]

docs/quickstart.ipynb

@@ -39,6 +39,7 @@
     "import scanpy as sc\n",
     "import pandas as pd\n",
     "import seaborn as sns\n",
+    "\n",
     "sns.set_theme()\n",
     "%config InlineBackend.figure_formats = ['svg']"
    ]
@@ -76,7 +77,9 @@
    },
    "outputs": [],
    "source": [
-    "artifact = ln.Artifact.using(\"laminlabs/arrayloader-benchmarks\").get(\"JNaxQe8zbljesdbK0000\")\n",
+    "artifact = ln.Artifact.using(\"laminlabs/arrayloader-benchmarks\").get(\n",
+    "    \"JNaxQe8zbljesdbK0000\"\n",
+    ")\n",
     "adata = artifact.load()\n",
     "sc.pp.log1p(adata)\n",
     "adata"
@@ -93,7 +96,7 @@
    },
    "outputs": [],
    "source": [
-    "keep = adata.obs[\"cell_line\"].value_counts().loc[lambda x: x>3].index\n",
+    "keep = adata.obs[\"cell_line\"].value_counts().loc[lambda x: x > 3].index\n",
     "adata = adata[adata.obs[\"cell_line\"].isin(keep)].copy()\n",
     "adata"
    ]
@@ -133,18 +136,14 @@
    "source": [
     "logreg = mn.models.SimpleLogReg(\n",
     "    adata=adata,\n",
-    "    label_column=\"cell_line\",    \n",
+    "    label_column=\"cell_line\",\n",
     "    learning_rate=1e-1,\n",
     "    weight_decay=1e-3,\n",
     ")\n",
     "logreg.fit(\n",
     "    adata_train=adata,\n",
     "    adata_val=adata[:20],\n",
-    "    train_dataloader_kwargs={\n",
-    "        \"batch_size\": 128,\n",
-    "        \"drop_last\": True,\n",
-    "        \"num_workers\": 4\n",
-    "    },\n",
+    "    train_dataloader_kwargs={\"batch_size\": 128, \"drop_last\": True, \"num_workers\": 4},\n",
     "    max_epochs=5,\n",
     ")"
    ]
@@ -212,8 +211,10 @@
    },
    "outputs": [],
    "source": [
-    "sc.tl.rank_genes_groups(adata, 'cell_line', method='logreg', key_added='sc_logreg')\n",
-    "df_scanpy_logreg = sc.get.rank_genes_groups_df(adata, group=None, key=\"sc_logreg\").pivot(index='group', columns='names', values='scores')\n",
+    "sc.tl.rank_genes_groups(adata, \"cell_line\", method=\"logreg\", key_added=\"sc_logreg\")\n",
+    "df_scanpy_logreg = sc.get.rank_genes_groups_df(\n",
+    "    adata, group=None, key=\"sc_logreg\"\n",
+    ").pivot(index=\"group\", columns=\"names\", values=\"scores\")\n",
     "df_scanpy_logreg.attrs[\"method_name\"] = \"scanpy_logreg\"\n",
     "df_scanpy_logreg.head()"
    ]
@@ -229,8 +230,10 @@
    },
    "outputs": [],
    "source": [
-    "sc.tl.rank_genes_groups(adata, 'cell_line', method='wilcoxon', key_added='sc_wilcoxon')\n",
-    "df_scanpy_wilcoxon = sc.get.rank_genes_groups_df(adata, group=None, key=\"sc_wilcoxon\").pivot(index='group', columns='names', values='scores')\n",
+    "sc.tl.rank_genes_groups(adata, \"cell_line\", method=\"wilcoxon\", key_added=\"sc_wilcoxon\")\n",
+    "df_scanpy_wilcoxon = sc.get.rank_genes_groups_df(\n",
+    "    adata, group=None, key=\"sc_wilcoxon\"\n",
+    ").pivot(index=\"group\", columns=\"names\", values=\"scores\")\n",
     "df_scanpy_wilcoxon.attrs[\"method_name\"] = \"scanpy_wilcoxon\"\n",
     "df_scanpy_wilcoxon.head()"
    ]
@@ -254,7 +257,9 @@
    },
    "outputs": [],
    "source": [
-    "compare = mn.eval.CompareScoresJaccard([df_modlyn_logreg, df_scanpy_logreg, df_scanpy_wilcoxon], n_top_values=[5, 10, 25])"
+    "compare = mn.eval.CompareScoresJaccard(\n",
+    "    [df_modlyn_logreg, df_scanpy_logreg, df_scanpy_wilcoxon], n_top_values=[5, 10, 25]\n",
+    ")"
    ]
   },
   {

modlyn/eval/__init__.py

@@ -6,6 +6,7 @@
    CompareScores
 
 """
+
 from ._jaccard import CompareScores
 
 CompareScoresJaccard = CompareScores  # backward compat

modlyn/eval/_jaccard.py

@@ -12,12 +12,9 @@ class CompareScores:
     def __init__(self, dataframes, n_top_values=None):
         """Initialize with dataframes and n_top values to compare.
 
-        Parameters:
-        -----------
-        dataframes : list of pd.DataFrame
-            List of dataframes with method results. Each should have df.attrs["method_name"]
-        n_top_values : list of int
-            List of top-N values to compare across
+        Args:
+            dataframes: List of dataframes with method results. Each should have df.attrs["method_name"]
+            n_top_values: List of top-N values to compare across
         """
         if n_top_values is None:
             n_top_values = [25, 50, 100, 200]
@@ -45,7 +42,7 @@ def compute_jaccard_comparison(self):
             for cell_line in common_cells:
                 scores = {
                     name: df.loc[cell_line]
-                    for df, name in zip(dfs_aligned, method_names)
+                    for df, name in zip(dfs_aligned, method_names, strict=False)
                 }
                 top_features = {
                     name: set(scores[name].abs().nlargest(n_top).index)
@@ -121,7 +118,7 @@ def plot_jaccard_comparison(self):
             )
 
             # Add value labels
-            for bar, value in zip(bars, values):
+            for bar, value in zip(bars, values, strict=False):
                 if not np.isnan(value):
                     ax.text(
                         bar.get_x() + bar.get_width() / 2,
@@ -164,7 +161,9 @@ def plot_heatmaps(self):
             axes = [axes]
 
         # Plot heatmaps
-        for i, (df, method_name) in enumerate(zip(dfs_sorted, method_names)):
+        for i, (df, method_name) in enumerate(
+            zip(dfs_sorted, method_names, strict=False)
+        ):
             sns.heatmap(df, ax=axes[i], cmap="viridis", vmin=vmin, vmax=vmax, cbar=True)
             axes[i].set_title(method_name)
 

modlyn/models/__init__.py

@@ -6,4 +6,5 @@
    SimpleLogReg
 
 """
+
 from ._simple_logreg_model import SimpleLogReg

modlyn/models/_simple_logreg_datamodule.py

@@ -15,8 +15,8 @@ class SimpleLogRegDataModule(L.LightningDataModule):
     """A simple LightningDataModule for classification tasks using TensorDataset.
 
     Args:
-        adata_train: anndata.AnnData object containing the training data.
-        adata_val: anndata.AnnData object containing the validation data.
+        adata_train: `AnnData` object containing the training data.
+        adata_val: `AnnData` object containing the validation data.
         label_column: Name of the column in `obs` that contains the target values.
         train_dataloader_kwargs: Additional keyword arguments passed to the torch DataLoader for the training dataset.
         val_dataloader_kwargs: Additional keyword arguments passed to the torch DataLoader for the validation dataset.

modlyn/models/_simple_logreg_model.py

@@ -26,7 +26,6 @@ class SimpleLogReg(L.LightningModule):
         label_column: Name of the column in `obs` that contains the target values.
         learning_rate: Learning rate for the optimizer.
         weight_decay: Weight decay for the optimizer.
-
     """
 
     def __init__(

noxfile.py

@@ -26,5 +26,4 @@ def docs(session):
     if not IS_PR:
         subprocess.run(
             "lndocs --strip-prefix --format text --error-on-index",  # --strict back
-            shell=True,  # noqa: S602
         )

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "flit_core.buildapi"
 
 [project]
 name = "modlyn"
-requires-python = ">=3.9,<3.13"
+requires-python = ">=3.11,<3.14"
 authors = [{name = "Lamin Labs", email = "open-source@lamin.ai"}]
 readme = "README.md"
 dynamic = ["version", "description"]
@@ -41,7 +41,7 @@ filterwarnings = [
 [tool.ruff]
 src = ["src"]
 line-length = 88
-select = [
+lint.select = [
     "F",  # Errors detected by Pyflakes
     "E",  # Error detected by Pycodestyle
     "W",  # Warning detected by Pycodestyle
@@ -58,7 +58,7 @@ select = [
     "PTH",  # Use pathlib
     "S"  # Security
 ]
-ignore = [
+lint.ignore = [
     # Do not catch blind exception: `Exception`
     "BLE001",
     # Errors from function calls in argument defaults. These are fine when the result is immutable.
@@ -129,10 +129,10 @@ ignore = [
     "S607"
 ]
 
-[tool.ruff.pydocstyle]
+[tool.ruff.lint.pydocstyle]
 convention = "google"
 
-[tool.ruff.per-file-ignores]
+[tool.ruff.lint.per-file-ignores]
 "docs/*" = ["I"]
 "tests/**/*.py" = [
     "D",  # docstrings are allowed to look a bit off

tests/test_notebooks.py

@@ -4,7 +4,6 @@
 
 
 def test_notebooks():
-    # assuming this is in the tests folder
     docs_folder = Path(__file__).parents[1] / "docs/"
 
     for check_folder in docs_folder.glob("./**"):