Create initial version of wrappers for models (#1)

* New wrapper classes for models

tests/test_anonymizer.py

@@ -4,7 +4,7 @@ from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
 from sklearn.preprocessing import OneHotEncoder
 
 from apt.anonymization import Anonymize
-from apt.utils import get_iris_dataset, get_adult_dataset, get_nursery_dataset
+from apt.utils.dataset_utils import get_iris_dataset, get_adult_dataset, get_nursery_dataset
 from sklearn.datasets import load_diabetes
 from sklearn.model_selection import train_test_split
 

tests/test_minimizer.py

@@ -12,7 +12,7 @@ from sklearn.preprocessing import OneHotEncoder, StandardScaler
 
 from apt.minimization import GeneralizeToRepresentative
 from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
-from apt.utils import get_iris_dataset, get_adult_dataset, get_nursery_dataset, get_german_credit_dataset
+from apt.utils.dataset_utils import get_iris_dataset, get_adult_dataset, get_nursery_dataset, get_german_credit_dataset
 
 
 @pytest.fixture

tests/test_model.py

@@ -0,0 +1,44 @@
+import pytest
+
+from apt.utils.models import SklearnClassifier, SklearnRegressor
+from apt.utils import dataset_utils
+
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import RandomForestClassifier
+
+def test_sklearn_classifier():
+    (x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset()
+    underlying_model = RandomForestClassifier()
+    model = SklearnClassifier(underlying_model)
+    model.fit(x_train, y_train)
+    pred = model.predict(x_test)
+    assert(pred.shape[0] == x_test.shape[0])
+
+    score = model.score(x_test, y_test)
+    assert(0.0 <= score <= 1.0)
+
+def test_sklearn_regressor():
+    (x_train, y_train), (x_test, y_test) = dataset_utils.get_diabetes_dataset()
+    underlying_model = DecisionTreeRegressor()
+    model = SklearnRegressor(underlying_model)
+    model.fit(x_train, y_train)
+    pred = model.predict(x_test)
+    assert (pred.shape[0] == x_test.shape[0])
+
+    score = model.score(x_test, y_test)
+
+    losses = model.loss(x_test, y_test)
+    assert (losses.shape[0] == x_test.shape[0])
+
+
+# Probably not needed for now, as we will not be using these wrappers directly in ART.
+# def test_sklearn_decision_tree():
+#     (x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset()
+#     underlying_model = DecisionTreeClassifier()
+#     model = SklearnDecisionTreeClassifier(underlying_model)
+#     model.fit(x_train, y_train)
+#     pred = model.predict(x_test)
+#     assert(pred.shape[0] == x_test.shape[0])
+#
+#     score = model.score(x_test, y_test)
+#     assert(0.0 <= score <= 1.0)