Lint and format

check_shap.py

@@ -1,12 +1,14 @@
 """This script checks if the SV computed for an IsoForest model are the same for shap and shapiq."""
+
 import copy
 from dataclasses import dataclass
 
 import numpy as np
 import shap
-import shapiq
-from sklearn.model_selection import train_test_split
 from sklearn.ensemble import IsolationForest
+from sklearn.model_selection import train_test_split
+
+import shapiq
 
 
 def generate_random_perturb_features(settings):
@@ -46,8 +48,9 @@ def generate_random_perturb_features(settings):
     data = np.vstack(clusters)
 
     # Function to perturb a subset of features to create outliers
-    def create_outliers(data, n_outliers, n_features, n_perturb_features,
-                        always_perturb_same_features):
+    def create_outliers(
+        data, n_outliers, n_features, n_perturb_features, always_perturb_same_features
+    ):
         original_indices = rng.choice(len(data), n_outliers, replace=False)
         outliers = data[original_indices].copy()
 
@@ -62,8 +65,9 @@ def create_outliers(data, n_outliers, n_features, n_perturb_features,
         return outliers, original_indices
 
     # Create outliers and get the indices of the original points
-    outliers, original_indices = create_outliers(data, n_outliers, n_features, n_perturb_features,
-                                                 always_perturb_same_features)
+    outliers, original_indices = create_outliers(
+        data, n_outliers, n_features, n_perturb_features, always_perturb_same_features
+    )
 
     # Store original samples and perturbed samples separately
     original_samples = data[original_indices].copy()
@@ -84,12 +88,21 @@ def create_outliers(data, n_outliers, n_features, n_perturb_features,
     # print("Final Data:\n", final_data)
 
     # Split the data into training and test sets
-    X_train, X_test, y_train, y_test = train_test_split(final_data, labels, test_size=0.33,
-                                                        random_state=42)
+    X_train, X_test, y_train, y_test = train_test_split(
+        final_data, labels, test_size=0.33, random_state=42
+    )
 
     # Create a mapping between original points and their perturbed versions
     original_to_perturbed = {i: i for i in range(n_outliers)}
-    return X_train, X_test, y_train, y_test, original_samples, perturbed_samples, original_to_perturbed
+    return (
+        X_train,
+        X_test,
+        y_train,
+        y_test,
+        original_samples,
+        perturbed_samples,
+        original_to_perturbed,
+    )
 
 
 @dataclass
@@ -104,7 +117,7 @@ class SyntheticOutlierInlierSettings:
     random_state: int = None
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
 
     # create data
     settings = SyntheticOutlierInlierSettings(
@@ -114,7 +127,7 @@ class SyntheticOutlierInlierSettings:
         n_features=12,
         n_perturb_features=2,
         always_perturb_same_features=True,
-        random_state=0
+        random_state=0,
     )
     d = generate_random_perturb_features(settings)
     X_train, X_test, y_train, y_test, original_samples, perturbed_samples, original_to_perturbed = d

shapiq/explainer/tree/conversion/sklearn.py

@@ -4,7 +4,6 @@
 from typing import Optional
 
 import numpy as np
-
 from sklearn.ensemble._iforest import _average_path_length
 
 from shapiq.utils import safe_isinstance
@@ -33,6 +32,7 @@ def convert_sklearn_forest(
         for tree in tree_model.estimators_
     ]
 
+
 def convert_sklearn_tree(
     tree_model: Model, class_label: Optional[int] = None, scaling: float = 1.0
 ) -> TreeModel:
@@ -76,11 +76,15 @@ def convert_sklearn_tree(
         original_output_type=output_type,
     )
 
+
 def average_path_length(isolation_forest):
     max_samples = isolation_forest._max_samples
-    average_path_length = _average_path_length([max_samples]) # NOTE: _average_path_length func is equivalent to equation 1 in Isolation Forest paper Lui2008
+    average_path_length = _average_path_length(
+        [max_samples]
+    )  # NOTE: _average_path_length func is equivalent to equation 1 in Isolation Forest paper Lui2008
     return average_path_length
 
+
 def convert_sklearn_isolation_forest(
     tree_model: Model,
 ) -> list[TreeModel]:
@@ -100,8 +104,13 @@ def convert_sklearn_isolation_forest(
         for tree, features in zip(tree_model.estimators_, tree_model.estimators_features_)
     ]
 
+
 def convert_isolation_tree(
-            tree_model: Model, tree_features, class_label: Optional[int] = None, scaling: float = 1.0, average_path_length: float = 1.0 # TODO fix default value
+    tree_model: Model,
+    tree_features,
+    class_label: Optional[int] = None,
+    scaling: float = 1.0,
+    average_path_length: float = 1.0,  # TODO fix default value
 ) -> TreeModel:
     """Convert a scikit-learn decision tree to the format used by shapiq.
 
@@ -117,7 +126,9 @@ def convert_isolation_tree(
     output_type = "raw"
     tree_values = tree_model.tree_.value.copy()
     tree_values = tree_values.flatten()
-    features_updated, values_updated = isotree_value_traversal(tree_model.tree_, tree_features, normalize=False, scaling=1.0)
+    features_updated, values_updated = isotree_value_traversal(
+        tree_model.tree_, tree_features, normalize=False, scaling=1.0
+    )
     values_updated = values_updated * scaling
     values_updated = values_updated.flatten()
 
@@ -132,20 +143,23 @@ def convert_isolation_tree(
         original_output_type=output_type,
     )
 
-def isotree_value_traversal(tree, tree_features, normalize=False, scaling=1.0, data=None, data_missing=None):
+
+def isotree_value_traversal(
+    tree, tree_features, normalize=False, scaling=1.0, data=None, data_missing=None
+):
     features = tree.feature.copy()
     corrected_values = tree.value.copy()
     if safe_isinstance(tree, "sklearn.tree._tree.Tree"):
 
-        def _recalculate_value(tree, i , level):
+        def _recalculate_value(tree, i, level):
             if tree.children_left[i] == -1 and tree.children_right[i] == -1:
                 value = level + _average_path_length(np.array([tree.n_node_samples[i]]))[0]
-                corrected_values[i, 0] =  value
+                corrected_values[i, 0] = value
                 return value * tree.n_node_samples[i]
             else:
-                value_left = _recalculate_value(tree, tree.children_left[i] , level + 1)
-                value_right = _recalculate_value(tree, tree.children_right[i] , level + 1)
-                corrected_values[i, 0] =  (value_left + value_right) / tree.n_node_samples[i]
+                value_left = _recalculate_value(tree, tree.children_left[i], level + 1)
+                value_right = _recalculate_value(tree, tree.children_right[i], level + 1)
+                corrected_values[i, 0] = (value_left + value_right) / tree.n_node_samples[i]
                 return value_left + value_right
 
         _recalculate_value(tree, 0, 0)
@@ -154,4 +168,4 @@ def _recalculate_value(tree, i , level):
         corrected_values = corrected_values * scaling
         # re-number the features if each tree gets a different set of features
         features = np.where(features >= 0, tree_features[features], features)
-    return features, corrected_values
+    return features, corrected_values

shapiq/explainer/tree/validation.py

@@ -6,7 +6,11 @@
 
 from .base import TreeModel
 from .conversion.lightgbm import convert_lightgbm_booster
-from .conversion.sklearn import convert_sklearn_forest, convert_sklearn_tree, convert_sklearn_isolation_forest
+from .conversion.sklearn import (
+    convert_sklearn_forest,
+    convert_sklearn_isolation_forest,
+    convert_sklearn_tree,
+)
 from .conversion.xgboost import convert_xgboost_booster
 
 SUPPORTED_MODELS = {
@@ -71,9 +75,8 @@ def validate_tree_model(
         or safe_isinstance(model, "sklearn.ensemble._forest.ExtraTreesClassifier")
     ):
         tree_model = convert_sklearn_forest(model, class_label=class_label)
-    elif (
-        safe_isinstance(model, "sklearn.ensemble.IsolationForest")
-        or safe_isinstance(model, "sklearn.ensemble._iforest.IsolationForest")
+    elif safe_isinstance(model, "sklearn.ensemble.IsolationForest") or safe_isinstance(
+        model, "sklearn.ensemble._iforest.IsolationForest"
     ):
         tree_model = convert_sklearn_isolation_forest(model)
     elif safe_isinstance(model, "lightgbm.sklearn.LGBMRegressor") or safe_isinstance(

shapiq/interaction_values.py

@@ -655,7 +655,6 @@ def plot_force(
         Returns:
             The force plot as a matplotlib figure (if show is ``False``).
         """
-        # from shapiq import force_plot
         from .plot import force_plot
 
         return force_plot(