Add test for PCA preprocessing

tests/sklearn/test_sklearn_pipeline.py

@@ -5,9 +5,9 @@
 import numpy as np
 np.random.seed(42)
 import sklearn
-from sklearn.datasets import load_iris
+from sklearn.datasets import load_iris, load_boston
 from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LogisticRegression
+from sklearn.linear_model import LogisticRegression, Lasso
 from sklearn.preprocessing import StandardScaler, RobustScaler, PolynomialFeatures, Normalizer, MinMaxScaler, MaxAbsScaler
 from sklearn.decomposition import PCA
 from sklearn.pipeline import make_pipeline
@@ -305,4 +305,31 @@ def test_pipeline_scaler_poly_softmaxregression():
     assert model.predict([x_orig]) == 2
 
     # Compute counterfactual
-    compute_counterfactuals_poly(model, x_orig, 0)
+    compute_counterfactuals_poly(model, x_orig, 0)
+
+
+def test_pipeline_pca_linearregression():
+    # Load data
+    X, y = load_boston(True)
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4242)
+
+    # Create and fit model
+    pca = PCA(n_components=4)
+    model = Lasso()
+
+    model = make_pipeline(pca, model)
+    model.fit(X_train, y_train)
+
+    # Select data point for explaining its prediction
+    x_orig = X_test[1:4][0,:]
+    y_orig_pred = model.predict([x_orig])
+    assert y_orig_pred >= 25 and y_orig_pred < 26
+
+    # Compute counterfactual
+    y_target = 20.
+    y_target_done = lambda z: np.abs(z - y_target) < 3.
+
+    x_cf, y_cf, _ = generate_counterfactual(model, x_orig, y_target=y_target, done=y_target_done, regularization="l1", C=0.1, features_whitelist=None, optimizer="bfgs", return_as_dict=False)
+    assert y_target_done(y_cf)
+    assert y_target_done(model.predict(np.array([x_cf])))