Christian comments 1

molpipeline/explainability/__init__.py → molpipeline/experimental/explainability/__init__.py

@@ -1,19 +1,23 @@
 """Explainability module for the molpipeline package."""
 
-from molpipeline.explainability.explainer import SHAPTreeExplainer
-from molpipeline.explainability.explanation import (
+from molpipeline.experimental.explainability.explainer import (
+    SHAPKernelExplainer,
+    SHAPTreeExplainer,
+)
+from molpipeline.experimental.explainability.explanation import (
     SHAPFeatureAndAtomExplanation,
     SHAPFeatureExplanation,
 )
-from molpipeline.explainability.visualization.visualization import (
+from molpipeline.experimental.explainability.visualization.visualization import (
     structure_heatmap,
     structure_heatmap_shap,
 )
 
 __all__ = [
+    "SHAPTreeExplainer",
+    "SHAPKernelExplainer",
     "SHAPFeatureExplanation",
     "SHAPFeatureAndAtomExplanation",
-    "SHAPTreeExplainer",
     "structure_heatmap",
     "structure_heatmap_shap",
 ]

molpipeline/explainability/explainer.py → molpipeline/experimental/explainability/explainer.py

@@ -15,7 +15,7 @@
 
 from molpipeline import Pipeline
 from molpipeline.abstract_pipeline_elements.core import OptionalMol
-from molpipeline.explainability.explanation import (
+from molpipeline.experimental.explainability.explanation import (
     AtomExplanationMixin,
     BondExplanationMixin,
     FeatureExplanationMixin,
@@ -24,12 +24,13 @@
     SHAPFeatureAndAtomExplanation,
     SHAPFeatureExplanation,
 )
-from molpipeline.explainability.fingerprint_utils import fingerprint_shap_to_atomweights
+from molpipeline.experimental.explainability.fingerprint_utils import (
+    fingerprint_shap_to_atomweights,
+)
 from molpipeline.mol2any import MolToMorganFP
 from molpipeline.utils.subpipeline import SubpipelineExtractor, get_model_from_pipeline
 
 
-# pylint: disable=C0103,W0613
 def _to_dense(
     feature_matrix: npt.NDArray[Any] | spmatrix,
 ) -> npt.NDArray[Any]:
@@ -170,13 +171,13 @@ def _convert_shap_feature_weights_to_atom_weights(
 ]
 
 
-# pylint: disable=R0903
 class AbstractSHAPExplainer(abc.ABC):
     """Abstract class for SHAP explainer objects."""
 
-    # pylint: disable=C0103,W0613
     @abc.abstractmethod
-    def explain(self, X: Any, **kwargs: Any) -> _SHAPExplainer_return_type_:
+    def explain(
+        self, X: Any, **kwargs: Any
+    ) -> _SHAPExplainer_return_type_:  # pylint: disable=invalid-name,unused-argument
         """Explain the predictions for the input data.
 
         Parameters
@@ -193,7 +194,6 @@ def explain(self, X: Any, **kwargs: Any) -> _SHAPExplainer_return_type_:
         """
 
 
-# pylint: disable=R0903
 class SHAPExplainerAdapter(AbstractSHAPExplainer, abc.ABC):
     """Adapter for SHAP explainer wrappers for handling molecules and pipelines."""
 
@@ -269,9 +269,10 @@ def _prediction_is_valid(prediction: Any) -> bool:
 
         return True
 
-    # pylint: disable=C0103,W0613
     @override
-    def explain(self, X: Any, **kwargs: Any) -> _SHAPExplainer_return_type_:
+    def explain(
+        self, X: Any, **kwargs: Any
+    ) -> _SHAPExplainer_return_type_:  # pylint: disable=invalid-name,unused-argument
         """Explain the predictions for the input data.
 
         If the calculation of the SHAP values for an input sample fails, the explanation will be invalid.

molpipeline/explainability/visualization/gauss.py → molpipeline/experimental/explainability/visualization/gauss.py

@@ -9,8 +9,7 @@
 import numpy.typing as npt
 
 
-# pylint: disable=too-few-public-methods
-class GaussFunctor2D:
+class GaussFunctor2D:  # pylint: disable=too-few-public-methods
     """2D Gaussian functor."""
 
     def __init__(

molpipeline/explainability/visualization/utils.py → molpipeline/experimental/explainability/visualization/utils.py

@@ -83,11 +83,10 @@ def get_color_map_from_input(
     # read user definer color scheme as ColorMap
     if color is None:
         coolwarm = (
-            (0.017, 0.50, 0.850, 0.5),
-            (1.0, 1.0, 1.0, 0.5),
             (1.0, 0.25, 0.0, 0.5),
+            (1.0, 1.0, 1.0, 0.5),
+            (0.017, 0.50, 0.850, 0.5),
         )
-        coolwarm = (coolwarm[2], coolwarm[1], coolwarm[0])
         color = coolwarm
     if isinstance(color, Colormap):
         color_map = color

molpipeline/explainability/visualization/visualization.py → molpipeline/experimental/explainability/visualization/visualization.py

@@ -20,20 +20,23 @@
 from rdkit.Chem.Draw import rdMolDraw2D
 
 from molpipeline.abstract_pipeline_elements.core import RDKitMol
-from molpipeline.explainability.explanation import SHAPFeatureAndAtomExplanation
-from molpipeline.explainability.visualization.gauss import GaussFunctor2D
-from molpipeline.explainability.visualization.heatmaps import (
+from molpipeline.experimental.explainability import (
+    SHAPFeatureAndAtomExplanation,
+)
+
+from molpipeline.experimental.explainability.visualization.gauss import GaussFunctor2D
+from molpipeline.experimental.explainability.visualization.heatmaps import (
     ValueGrid,
     color_canvas,
     get_color_normalizer_from_data,
 )
-from molpipeline.explainability.visualization.utils import (
-    RGBAtuple,
+from molpipeline.experimental.explainability.visualization.utils import (
     get_color_map_from_input,
-    get_mol_lims,
-    pad,
     plt_to_pil,
     to_png,
+    get_mol_lims,
+    pad,
+    RGBAtuple,
 )
 
 
@@ -129,15 +132,14 @@ def _add_gaussians_for_atoms(
     return v_map
 
 
-# pylint: disable=too-many-locals
 def _add_gaussians_for_bonds(
     mol: Chem.Mol,
     conf: Chem.Conformer,
     v_map: ValueGrid,
     bond_weights: npt.NDArray[np.float64],
     bond_width: float,
     bond_length: float,
-) -> ValueGrid:
+) -> ValueGrid:  # pylint: disable=too-many-locals
     """Add Gauss-functions centered at bonds to the grid.
 
     Parameters
@@ -475,7 +477,7 @@ def structure_heatmap_shap(  # pylint: disable=too-many-branches
             f"$Prediction = {explanation.prediction[-1]:.2f}$ ="
             "\n"
             "\n"
-            f"  $expected \ value={explanation.expected_value[-1]:.2f}$   +   "  # noqa: W605 # pylint: disable=W1401
+            f"  $expected \ value={explanation.expected_value[-1]:.2f}$   +   "  # noqa: W605 # pylint: disable=anomalous-backslash-in-string
             f"$features_{{present}}= {sum_present_shap:.2f}$   +   "
             f"$features_{{absent}}={sum_absent_shap:.2f}$"
         )

tests/test_explainability/test_shap_explainers.py → tests/test_experimental/test_explainability/test_shap_explainers.py

@@ -18,20 +18,23 @@
 from molpipeline import ErrorFilter, FilterReinserter, Pipeline, PostPredictionWrapper
 from molpipeline.abstract_pipeline_elements.core import RDKitMol
 from molpipeline.any2mol import SmilesToMol
-from molpipeline.explainability.explainer import SHAPKernelExplainer, SHAPTreeExplainer
-from molpipeline.explainability.explanation import (
-    AtomExplanationMixin,
+from molpipeline.experimental.explainability import (
     SHAPFeatureAndAtomExplanation,
     SHAPFeatureExplanation,
+    SHAPTreeExplainer,
+    SHAPKernelExplainer,
 )
+from molpipeline.experimental.explainability.explanation import AtomExplanationMixin
 from molpipeline.mol2any import (
     MolToConcatenatedVector,
     MolToMorganFP,
     MolToRDKitPhysChem,
 )
 from molpipeline.mol2mol import SaltRemover
 from molpipeline.utils.subpipeline import SubpipelineExtractor
-from tests.test_explainability.utils import construct_kernel_shap_kwargs
+from tests.test_experimental.test_explainability.utils import (
+    construct_kernel_shap_kwargs,
+)
 
 TEST_SMILES = ["CC", "CCO", "COC", "c1ccccc1(N)", "CCC(-O)O", "CCCN"]
 CONTAINS_OX = [0, 1, 1, 0, 1, 0]
@@ -171,7 +174,9 @@ def test_explanations_fingerprint_pipeline(  # pylint: disable=too-many-locals
         ]
         explainer_estimators = [tree_estimators + other_estimators, tree_estimators]
 
-        for estimators, explainer_type in zip(explainer_estimators, explainer_types):
+        for estimators, explainer_type in zip(
+            explainer_estimators, explainer_types, strict=True
+        ):
 
             # test explanations with different estimators
             for estimator in estimators:

tests/test_explainability/test_visualization/test_gaussian_grid.py → tests/test_experimental/test_explainability/test_visualization/test_gaussian_grid.py

@@ -8,15 +8,15 @@
 from rdkit.Chem import Draw
 
 from molpipeline import Pipeline
-from molpipeline.explainability import (
+from molpipeline.experimental.explainability import (
     SHAPFeatureAndAtomExplanation,
     SHAPFeatureExplanation,
     SHAPTreeExplainer,
 )
-from molpipeline.explainability.visualization.visualization import (
+from molpipeline.experimental.explainability.visualization.visualization import (
     make_sum_of_gaussians_grid,
 )
-from tests.test_explainability.test_visualization.test_visualization import (
+from tests.test_experimental.test_explainability.test_visualization.test_visualization import (
     _get_test_morgan_rf_pipeline,
 )
 

tests/test_explainability/test_visualization/test_visualization.py → tests/test_experimental/test_explainability/test_visualization/test_visualization.py

@@ -9,16 +9,18 @@
 
 from molpipeline import Pipeline
 from molpipeline.any2mol import SmilesToMol
-from molpipeline.explainability import (
+from molpipeline.experimental.explainability import (
     SHAPFeatureAndAtomExplanation,
     SHAPFeatureExplanation,
+    SHAPKernelExplainer,
     SHAPTreeExplainer,
     structure_heatmap,
     structure_heatmap_shap,
 )
-from molpipeline.explainability.explainer import SHAPKernelExplainer
 from molpipeline.mol2any import MolToMorganFP
-from tests.test_explainability.utils import construct_kernel_shap_kwargs
+from tests.test_experimental.test_explainability.utils import (
+    construct_kernel_shap_kwargs,
+)
 
 TEST_SMILES = ["CC", "CCO", "COC", "c1ccccc1(N)", "CCC(-O)O", "CCCN"]
 CONTAINS_OX = [0, 1, 1, 0, 1, 0]  # classification labels