sklearn: Extended and revised tests

andreArtelt · May 8, 2020 · 9875d97 · 9875d97
1 parent 5794716
commit 9875d97
Show file tree

Hide file tree

Showing 4 changed files with 28 additions and 3 deletions.
diff --git a/tests/sklearn/test_sklearn_linearregression.py b/tests/sklearn/test_sklearn_linearregression.py
@@ -38,6 +38,10 @@ def test_linearregression():
     assert y_target_done(y_cf)
     assert y_target_done(model.predict(np.array([x_cf])))
 
+    cf = generate_counterfactual(model, x_orig, y_target, done=y_target_done, features_whitelist=features_whitelist, regularization="l1", optimizer="mp", return_as_dict=True)
+    assert y_target_done(cf["y_cf"])
+    assert y_target_done(model.predict(np.array([cf["x_cf"]])))
+
     x_cf, y_cf, delta = generate_counterfactual(model, x_orig, y_target, done=y_target_done, features_whitelist=features_whitelist, regularization="l2", optimizer="mp", return_as_dict=False)
     assert y_target_done(y_cf)
     assert y_target_done(model.predict(np.array([x_cf])))

diff --git a/tests/sklearn/test_sklearn_lvq.py b/tests/sklearn/test_sklearn_lvq.py
@@ -179,6 +179,10 @@ def test_lgmlvq():
     assert y_cf == 0
     assert model.predict(np.array([x_cf])) == 0
 
+    cf = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l1", optimizer="mp", return_as_dict=True)
+    assert cf["y_cf"] == 0
+    assert model.predict(np.array([cf["x_cf"]])) == 0
+
     x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l2", optimizer="mp", return_as_dict=False)
     assert y_cf == 0
     assert model.predict(np.array([x_cf])) == 0
@@ -245,6 +249,10 @@ def test_lgmlvq_classwise():
     assert y_cf == 0
     assert model.predict(np.array([x_cf])) == 0
 
+    cf = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l1", optimizer="mp", return_as_dict=True)
+    assert cf["y_cf"] == 0
+    assert model.predict(np.array([cf["x_cf"]])) == 0
+
     x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l2", optimizer="mp", return_as_dict=False)
     assert y_cf == 0
     assert model.predict(np.array([x_cf])) == 0

diff --git a/tests/sklearn/test_sklearn_naivebayes.py b/tests/sklearn/test_sklearn_naivebayes.py
@@ -34,6 +34,10 @@ def test_gaussiannaivebayes():
     assert y_cf == 0
     assert model.predict(np.array([x_cf])) == 0
 
+    cf = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l1", optimizer="mp", return_as_dict=True)
+    assert cf["y_cf"] == 0
+    assert model.predict(np.array([cf["x_cf"]])) == 0
+
     x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l2", optimizer="mp", return_as_dict=False)
     assert y_cf == 0
     assert model.predict(np.array([x_cf])) == 0
@@ -97,23 +101,24 @@ def test_gaussiannaivebayes():
     model.fit(X_train, y_train)
 
     x_orig = X_test[1:4][0,:]
-    print(model.predict_proba(np.array([x_orig])))
     assert model.predict([x_orig]) == 0
 
     features_whitelist = None
 
     x_cf, y_cf, delta = generate_counterfactual(model, x_orig, y_target=1, features_whitelist=features_whitelist, optimizer="mp", return_as_dict=False)
     assert y_cf == 1
-    print(model.predict_proba(np.array([x_cf])))
     assert model.predict(np.array([x_cf])) == 1
 
+    cf = generate_counterfactual(model, x_orig, y_target=1, features_whitelist=features_whitelist, optimizer="mp", return_as_dict=True)
+    assert cf["y_cf"] == 1
+    assert model.predict(np.array([cf["x_cf"]])) == 1
+
     x_orig = X_test[0,:]
     print(model.predict_proba(np.array([x_orig])))
     assert model.predict([x_orig]) == 1
 
     x_cf, y_cf, delta = generate_counterfactual(model, x_orig, y_target=0, features_whitelist=features_whitelist, optimizer="mp", return_as_dict=False)
     assert y_cf == 0
-    print(model.predict_proba(np.array([x_cf])))
     assert model.predict(np.array([x_cf])) == 0
 
     # Other stuff

diff --git a/tests/sklearn/test_sklearn_softmaxregression.py b/tests/sklearn/test_sklearn_softmaxregression.py
@@ -62,6 +62,10 @@ def test_softmaxregression():
     assert y_cf == 0
     assert model.predict(np.array([x_cf])) == 0
 
+    cf = generate_counterfactual(model, x_orig, 0, return_as_dict=True) # Compute counterfactual explanation
+    assert cf["y_cf"]== 0
+    assert model.predict(np.array([cf["x_cf"]])) == 0
+
     # Multiclass classification problem
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4242)
 
@@ -85,6 +89,10 @@ def test_softmaxregression():
     assert y_cf == 0
     assert model.predict(np.array([x_cf])) == 0
 
+    cf = generate_counterfactual(model, x_orig, 0, return_as_dict=True)
+    assert cf["y_cf"] == 0
+    assert model.predict(np.array([cf["x_cf"]])) == 0
+
     x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization="l1", optimizer="mp", return_as_dict=False)
     assert y_cf == 0
     assert model.predict(np.array([x_cf])) == 0