feat: Add model-based explainability

src/sdqc_check/__init__.py

@@ -1,7 +1,7 @@
 from .causality import CausalAnalysis
 from .classification import ClassificationModel
 from .explainability import (
-    ShapFeatureImportance, PFIFeatureImportance
+    ShapFeatureImportance, PFIFeatureImportance, ModelBasedFeatureImportance
 )
 from .statistical_test import (
     data_corr, identify_data_types, CategoricalTest, NumericalTest
@@ -13,6 +13,7 @@
     'ClassificationModel',
     'ShapFeatureImportance',
     'PFIFeatureImportance',
+    'ModelBasedFeatureImportance',
     'data_corr',
     'identify_data_types',
     'CategoricalTest',

src/sdqc_check/explainability/__init__.py

@@ -1,9 +1,9 @@
 from .shap import ShapFeatureImportance
 from .pfi import PFIFeatureImportance
+from .model_based import ModelBasedFeatureImportance
 
 __all__ = [
     'ShapFeatureImportance',
     'PFIFeatureImportance',
+    'ModelBasedFeatureImportance',
 ]
-
-

src/sdqc_check/explainability/model_based.py

@@ -0,0 +1,45 @@
+from .base import BaseFeatureImportance
+import pandas as pd
+import numpy as np
+
+
+class ModelBasedFeatureImportance(BaseFeatureImportance):
+    """
+    Model-Based Feature Importance calculator.
+
+    This class uses model-specific attributes to compute feature importance scores.
+
+    Parameters
+    ----------
+    See base class BaseFeatureImportance for parameter details.
+    """
+
+    def compute_feature_importance(self) -> pd.DataFrame:
+        """
+        Compute Model-Based Feature Importance.
+
+        This method uses model-specific attributes to calculate feature importance.
+
+        Returns
+        -------
+        pd.DataFrame
+            A DataFrame with features and their model-based importance scores, sorted in descending order.
+
+        Raises
+        ------
+        AttributeError:
+            If the model does not have the required attribute for feature importance.
+        """
+        if hasattr(self.model, "feature_importances_"):
+            importances = self.model.feature_importances_
+        elif hasattr(self.model, "coef_"):
+            importances = np.abs(self.model.coef_[0])
+        else:
+            raise AttributeError(
+                "No attribute 'feature_importances_' or 'coef_' found for the model."
+            )
+        importance = pd.DataFrame({
+            'feature': self.X_train.columns,
+            'importance': importances
+        })
+        return importance.sort_values('importance', ascending=False)

src/sdqc_integration/sequential.py

@@ -10,6 +10,7 @@
 from sdqc_check import (
     CausalAnalysis,
     ClassificationModel,
+    ModelBasedFeatureImportance,
     ShapFeatureImportance,
     PFIFeatureImportance,
     data_corr,
@@ -272,7 +273,7 @@ def explainability_step(
         Perform explainability analysis on the best model.
 
         This method applies the specified explainability algorithm
-        (SHAP or PFI) to interpret the predictions of the best
+        (Model-Based, SHAP or PFI) to interpret the predictions of the best
         performing model.
 
         Parameters
@@ -308,21 +309,22 @@ def _get_explainability_class(self) -> Type:
         Returns
         -------
         Type
-            The explainability class (SHAP or PFI) to be used.
+            The explainability class (Model-Based, SHAP or PFI) to be used.
 
         Raises
         ------
         ValueError
             If the specified explainability algorithm is not supported.
         """
         explainability_classes = {
+            'model_based': ModelBasedFeatureImportance,
             'shap': ShapFeatureImportance,
             'pfi': PFIFeatureImportance
         }
         if self.explainability_algorithm not in explainability_classes:
             raise ValueError(
                 f"Algorithm {self.explainability_algorithm} not supported. "
-                "Please choose from 'shap' or 'pfi'."
+                "Please choose from 'model_based', 'shap' or 'pfi'."
             )
         return explainability_classes[self.explainability_algorithm]
 

tests/test_sdqc_check/test_explainability/test_explainability.py

@@ -4,7 +4,9 @@
 from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.svm import SVC
-from sdqc_check import ShapFeatureImportance, PFIFeatureImportance
+from sdqc_check import (
+    ShapFeatureImportance, PFIFeatureImportance, ModelBasedFeatureImportance
+)
 
 
 @pytest.fixture
@@ -22,7 +24,9 @@ def sample_data():
     return model, X_train, X_test, y_train, y_test
 
 
-@pytest.mark.parametrize("FeatureImportance", [ShapFeatureImportance, PFIFeatureImportance])
+@pytest.mark.parametrize("FeatureImportance", [
+    ShapFeatureImportance, PFIFeatureImportance, ModelBasedFeatureImportance
+])
 def test_feature_importance(sample_data, FeatureImportance):
     model, X_train, X_test, y_train, y_test = sample_data
     importance = FeatureImportance(model, X_train, X_test, y_test)
@@ -38,7 +42,9 @@ def test_feature_importance(sample_data, FeatureImportance):
     assert result['importance'].is_monotonic_decreasing
 
 
-@pytest.mark.parametrize("FeatureImportance", [ShapFeatureImportance, PFIFeatureImportance])
+@pytest.mark.parametrize("FeatureImportance", [
+    ShapFeatureImportance, PFIFeatureImportance, ModelBasedFeatureImportance
+])
 def test_feature_importance_random_seed(sample_data, FeatureImportance):
     model, X_train, X_test, y_train, y_test = sample_data
     importance1 = FeatureImportance(

tests/test_sdqc_integration/test_sequential.py

@@ -50,7 +50,7 @@ def test_causal_analysis_step(sample_data):
     assert isinstance(synthetic_matrix, np.ndarray)
 
 
-@pytest.mark.parametrize('explainability_algorithm', ['shap', 'pfi'])
+@pytest.mark.parametrize('explainability_algorithm', ['model_based', 'shap', 'pfi'])
 def test_explainability_step(sample_data, explainability_algorithm):
     raw_data, synthetic_data = sample_data
     analysis = SequentialAnalysis(