Add tests for isolation forest conversion

tests/conftest.py

@@ -9,7 +9,7 @@
 import pytest
 from PIL import Image
 from sklearn.datasets import make_classification, make_regression
-from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
+from sklearn.ensemble import IsolationForest, RandomForestClassifier, RandomForestRegressor
 from sklearn.linear_model import LinearRegression, LogisticRegression
 from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
 
@@ -142,6 +142,25 @@ def rf_clf_model() -> RandomForestClassifier:
     return model
 
 
+# Isolationforest model
+@pytest.fixture
+def if_clf_model() -> IsolationForest:
+    n_samples, n_outliers = 120, 40
+    rng = np.random.RandomState(0)
+    covariance = np.array([[0.5, -0.1], [0.7, 0.4]])
+    cluster_1 = 0.4 * rng.randn(n_samples, 2) @ covariance + np.array([2, 2])  # general
+    cluster_2 = 0.3 * rng.randn(n_samples, 2) + np.array([-2, -2])  # spherical
+    outliers = rng.uniform(low=-4, high=4, size=(n_outliers, 2))
+
+    X = np.concatenate([cluster_1, cluster_2, outliers])
+    y = np.concatenate([np.ones((2 * n_samples), dtype=int), -np.ones((n_outliers), dtype=int)])
+
+    # X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)
+    model = IsolationForest(random_state=42, n_estimators=3)
+    model.fit(X, y)
+    return model
+
+
 @pytest.fixture
 def xgb_reg_model():
     """Return a simple xgboost regression model."""

tests/tests_explainer/tests_tree_explainer/test_tree_explainer_conversion.py

@@ -5,7 +5,11 @@
 
 from shapiq.explainer.tree.base import TreeModel
 from shapiq.explainer.tree.conversion.edges import create_edge_tree
-from shapiq.explainer.tree.conversion.sklearn import convert_sklearn_forest, convert_sklearn_tree
+from shapiq.explainer.tree.conversion.sklearn import (
+    convert_sklearn_forest,
+    convert_sklearn_isolation_forest,
+    convert_sklearn_tree,
+)
 from shapiq.utils import safe_isinstance
 
 
@@ -123,3 +127,14 @@ def test_skleanr_rf_conversion(rf_clf_model, rf_reg_model):
     assert isinstance(tree_model, list)
     assert safe_isinstance(tree_model[0], tree_model_class_path_str)
     assert tree_model[0].empty_prediction is not None
+
+
+def test_sklearn_if_conversion(if_clf_model):
+    """Test the conversion of a scikit-learn isolation forest model."""
+    tree_model_class_path_str = ["shapiq.explainer.tree.base.TreeModel"]
+
+    # test the isolation forest model
+    tree_model = convert_sklearn_isolation_forest(if_clf_model)
+    assert isinstance(tree_model, list)
+    assert safe_isinstance(tree_model[0], tree_model_class_path_str)
+    assert tree_model[0].empty_prediction is not None

tests/tests_explainer/tests_tree_explainer/test_tree_explainer_validate.py

@@ -7,7 +7,7 @@
 from shapiq.explainer.tree.validation import validate_tree_model
 
 
-def test_validate_model(dt_clf_model, dt_reg_model, rf_reg_model, rf_clf_model):
+def test_validate_model(dt_clf_model, dt_reg_model, rf_reg_model, rf_clf_model, if_clf_model):
     """Test the validation of the model."""
     class_path_str = ["shapiq.explainer.tree.base.TreeModel"]
     # sklearn dt models are supported
@@ -20,6 +20,10 @@ def test_validate_model(dt_clf_model, dt_reg_model, rf_reg_model, rf_clf_model):
     for tree in tree_model:
         assert safe_isinstance(tree, class_path_str)
     tree_model = validate_tree_model(rf_reg_model)
+    for tree in tree_model:
+        assert safe_isinstance(tree, class_path_str)
+    # sklearn isolation forest is supported
+    tree_model = validate_tree_model(if_clf_model)
     for tree in tree_model:
         assert safe_isinstance(tree, class_path_str)