Increase test coverage

tests/sklearn/test_sklearn_isolationforest.py

@@ -1,6 +1,8 @@
 # -*- coding: utf-8 -*-
 import numpy as np
 np.random.seed(42)
+import pytest
+import sklearn
 from sklearn.ensemble import IsolationForest
 from sklearn.datasets import make_blobs
 
@@ -31,4 +33,16 @@ def test_isolationforest():
 
     x_cf, y_cf, _ = generate_counterfactual(model, x, y_target=y_target, return_as_dict=False)
     assert y_cf == y_target
-    assert model.predict(np.array([x_cf])) == y_target
+    assert model.predict(np.array([x_cf])) == y_target
+
+    cf = generate_counterfactual(model, x, y_target=y_target, return_as_dict=True)
+    assert cf["y_cf"] == y_target
+    assert model.predict(np.array([cf["x_cf"]])) == y_target
+
+    # Other stuff
+    from ceml.sklearn import IsolationForest as IsolationForestCf
+    model_cf = IsolationForestCf(model)
+    assert model.predict([x]) == model_cf.predict(x)
+
+    with pytest.raises(TypeError):
+        IsolationForestCf(sklearn.linear_model.LogisticRegression())

tests/sklearn/test_sklearn_lda.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 np.random.seed(42)
+import pytest
 import sklearn
 from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
@@ -83,4 +84,9 @@ def test_lda():
     x_cf, y_cf, delta = generate_counterfactual(model, x_orig, 0, features_whitelist=features_whitelist, regularization=None, optimizer="nelder-mead", return_as_dict=False)
     assert y_cf == 0
     assert model.predict(np.array([x_cf])) == 0
-    assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x_orig.shape[0])])
+    assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x_orig.shape[0])])
+
+    # Other stuff
+    from ceml.sklearn import LdaCounterfactual
+    with pytest.raises(TypeError):
+        LdaCounterfactual(sklearn.linear_model.LogisticRegression())

tests/sklearn/test_sklearn_linearregression.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 np.random.seed(42)
+import pytest
 import sklearn
 from sklearn.datasets import load_boston
 from sklearn.model_selection import train_test_split
@@ -87,4 +88,9 @@ def test_linearregression():
     x_cf, y_cf, delta = generate_counterfactual(model, x_orig, y_target, done=y_target_done, features_whitelist=features_whitelist, regularization=None, optimizer="nelder-mead", return_as_dict=False)
     assert y_target_done(y_cf)
     assert y_target_done(model.predict(np.array([x_cf])))
-    assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x_orig.shape[0])])
+    assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x_orig.shape[0])])
+
+    # Other stuff
+    from ceml.sklearn import LinearRegressionCounterfactual
+    with pytest.raises(TypeError):
+        LinearRegressionCounterfactual(sklearn.naive_bayes.GaussianNB())

tests/sklearn/test_sklearn_lvq.py

@@ -4,12 +4,14 @@
 
 import numpy as np
 np.random.seed(42)
+import pytest
 import sklearn
 from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
 from sklearn_lvq import GlvqModel, GmlvqModel, LgmlvqModel
 
 from ceml.sklearn import generate_counterfactual
+from ceml.sklearn import LVQ as LvqCf
 
 
 def test_glvq():
@@ -81,6 +83,10 @@ def test_glvq():
     assert model.predict(np.array([x_cf])) == 0
     assert all([True if i in features_whitelist else delta[i] == 0. for i in range(x_orig.shape[0])])
 
+    # Other stuff
+    with pytest.raises(TypeError):
+        LvqCf(sklearn.linear_model.LogisticRegression())
+
 
 def test_gmlvq():
     # Load data

tests/sklearn/test_sklearn_naivebayes.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 np.random.seed(42)
+import pytest
 import sklearn
 from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
@@ -113,4 +114,9 @@ def test_gaussiannaivebayes():
     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
+    assert model.predict(np.array([x_cf])) == 0
+
+    # Other stuff
+    from ceml.sklearn import GaussianNbCounterfactual
+    with pytest.raises(TypeError):
+        GaussianNbCounterfactual(sklearn.linear_model.LogisticRegression())

tests/sklearn/test_sklearn_qda.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 np.random.seed(42)
+import pytest
 import sklearn
 from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
@@ -107,4 +108,9 @@ def test_qda():
 
     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
-    assert model.predict(np.array([x_cf])) == 0
+    assert model.predict(np.array([x_cf])) == 0
+
+    # Other stuff
+    from ceml.sklearn import QdaCounterfactual
+    with pytest.raises(TypeError):
+        QdaCounterfactual(sklearn.linear_model.LogisticRegression())

tests/sklearn/test_sklearn_softmaxregression.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 np.random.seed(42)
+import pytest
 import sklearn
 from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
@@ -156,4 +157,12 @@ def test_softmaxregression():
 
     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
+    assert model.predict(np.array([x_cf])) == 0
+
+    # Other stuff
+    from ceml.sklearn import SoftmaxCounterfactual
+    with pytest.raises(TypeError):
+        SoftmaxCounterfactual(sklearn.linear_model.LinearRegression())
+
+    with pytest.raises(ValueError):
+        SoftmaxCounterfactual(LogisticRegression(multi_class="ovr"))