- test whether cloning works, not all work now, WIP

skmultilearn/ensemble/tests/test_fixed.py

@@ -40,5 +40,10 @@ def test_if_dense_classification_works_on_dense_base_classifier(self):
 
         self.assertClassifierWorksWithSparsity(classifier, 'dense')
 
+    def test_if_works_with_cross_validation(self):
+        classifier =  self.get_classifier(self.get_labelpowerset_with_nb())
+
+        self.assertClassifierWorksWithCV(classifier)
+
 if __name__ == '__main__':
     unittest.main()

skmultilearn/ensemble/tests/test_partition.py

@@ -40,6 +40,11 @@ def test_if_dense_classification_works_on_dense_base_classifier(self):
         classifier =  self.get_classifier(self.get_labelpowerset_with_nb())
 
         self.assertClassifierWorksWithSparsity(classifier, 'dense')
+
+    def test_if_works_with_cross_validation(self):
+        classifier =  self.get_classifier(self.get_labelpowerset_with_nb())
+
+        self.assertClassifierWorksWithCV(classifier)
 
 if __name__ == '__main__':
     unittest.main()

skmultilearn/ensemble/tests/test_rakeld.py

@@ -36,6 +36,11 @@ def test_if_dense_classification_works_on_dense_base_classifier(self):
         classifier = RakelD(classifier = self.get_labelpowerset_with_nb(), labelset_size = 3)
 
         self.assertClassifierWorksWithSparsity(classifier, 'dense')
+
+    def test_if_works_with_cross_validation(self):
+        classifier = RakelD(classifier = self.get_labelpowerset_with_nb(), labelset_size = 3)
+
+        self.assertClassifierWorksWithCV(classifier)
 
 if __name__ == '__main__':
     unittest.main()

skmultilearn/ensemble/tests/test_rakelo.py

@@ -36,6 +36,11 @@ def test_if_dense_classification_works_on_dense_base_classifier(self):
         classifier = RakelO(classifier = self.get_labelpowerset_with_nb(), models = 20, labelset_size = 5)
 
         self.assertClassifierWorksWithSparsity(classifier, 'dense')
+
+    def test_if_works_with_cross_validation(self):
+        classifier = RakelO(classifier = self.get_labelpowerset_with_nb(), models = 20, labelset_size = 5)
+
+        self.assertClassifierWorksWithCV(classifier)
 
 if __name__ == '__main__':
     unittest.main()

skmultilearn/ext/tests/test_meka.py

@@ -20,6 +20,8 @@ def test_if_meka_classification_works_on_sparse_input(self):
     def test_if_meka_classification_works_on_dense_input(self):
         self.assertClassifierWorksWithSparsity(self.classifier(), 'dense')
 
+    def test_if_works_with_cross_validation(self):
+        self.assertClassifierWorksWithCV(self.classifier())
 
 if __name__ == '__main__':
     unittest.main()

skmultilearn/problem_transform/tests/test_br.py

@@ -29,5 +29,10 @@ def test_if_dense_classification_works_on_dense_base_classifier(self):
 
         self.assertClassifierWorksWithSparsity(classifier, 'dense')
 
+    def test_if_works_with_cross_validation(self):
+        classifier = BinaryRelevance(classifier = GaussianNB(), require_dense = [True, True])
+
+        self.assertClassifierWorksWithCV(classifier)
+
 if __name__ == '__main__':
     unittest.main()

skmultilearn/problem_transform/tests/test_cc.py

@@ -28,6 +28,11 @@ def test_if_dense_classification_works_on_dense_base_classifier(self):
         classifier = ClassifierChain(classifier = GaussianNB(), require_dense = [True, True])
 
         self.assertClassifierWorksWithSparsity(classifier, 'dense')
+
+    def test_if_works_with_cross_validation(self):
+        classifier = ClassifierChain(classifier = GaussianNB(), require_dense = [True, True])
+
+        self.assertClassifierWorksWithCV(classifier)
 
 if __name__ == '__main__':
     unittest.main()

skmultilearn/problem_transform/tests/test_lp.py

@@ -1,33 +1,38 @@
 import unittest
 
-from skmultilearn.problem_transform.cc import ClassifierChain
+from skmultilearn.problem_transform.lp import LabelPowerset
 from skmultilearn.tests.classifier_basetest import ClassifierBaseTest
 from sklearn.naive_bayes import GaussianNB
 from sklearn.svm import SVC
 from sklearn.datasets import make_multilabel_classification
 from sklearn.cross_validation import train_test_split
 from sklearn.utils.estimator_checks import check_estimator
 
-class CCTest(ClassifierBaseTest):
+class LPTest(ClassifierBaseTest):
     def test_if_sparse_classification_works_on_non_dense_base_classifier(self):
-        classifier = ClassifierChain(classifier = SVC(), require_dense = [False, True])
+        classifier = LabelPowerset(classifier = SVC(), require_dense = [False, True])
 
         self.assertClassifierWorksWithSparsity(classifier, 'sparse')
 
     def test_if_dense_classification_works_on_non_dense_base_classifier(self):
-        classifier = ClassifierChain(classifier = SVC(), require_dense = [False, True])
+        classifier = LabelPowerset(classifier = SVC(), require_dense = [False, True])
 
         self.assertClassifierWorksWithSparsity(classifier, 'dense')
 
     def test_if_sparse_classification_works_on_dense_base_classifier(self):
-        classifier = ClassifierChain(classifier = GaussianNB(), require_dense = [True, True])
+        classifier = LabelPowerset(classifier = GaussianNB(), require_dense = [True, True])
 
         self.assertClassifierWorksWithSparsity(classifier, 'sparse')
 
     def test_if_dense_classification_works_on_dense_base_classifier(self):
-        classifier = ClassifierChain(classifier = GaussianNB(), require_dense = [True, True])
+        classifier = LabelPowerset(classifier = GaussianNB(), require_dense = [True, True])
 
         self.assertClassifierWorksWithSparsity(classifier, 'dense')
 
+    def test_if_works_with_cross_validation(self):
+        classifier = LabelPowerset(classifier = GaussianNB(), require_dense = [True, True])
+
+        self.assertClassifierWorksWithCV(classifier)
+
 if __name__ == '__main__':
     unittest.main()

skmultilearn/tests/classifier_basetest.py

@@ -1,19 +1,28 @@
 import unittest
 
 from sklearn.datasets import make_multilabel_classification
-from sklearn.cross_validation import train_test_split
+from sklearn import cross_validation
 from sklearn.utils.estimator_checks import check_estimator
 
 class ClassifierBaseTest(unittest.TestCase):
     def assertClassifierWorksWithSparsity(self, classifier, sparsity_indicator = 'sparse'):
         feed_sparse = sparsity_indicator == 'sparse'
         X, y = make_multilabel_classification(sparse = feed_sparse, return_indicator = sparsity_indicator)
-        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
+        X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.33, random_state=42)
         classifier.fit(X_train, y_train)
         result = classifier.predict(X_test)
 
         self.assertEqual(result.shape, y_test.shape)
 
+    def assertClassifierWorksWithCV(self, classifier):
+        # all the nice stuff is tested here - whether the classifier is clonable, etc.
+        X, y = make_multilabel_classification()
+        n_iterations = 3
+        cv = cross_validation.ShuffleSplit(X.shape[0], n_iter=n_iterations, test_size=0.3, random_state=0)
+        scores = cross_validation.cross_val_score(classifier, X, y, cv=cv, scoring='f1_macro')
+
+        self.assertEqual(len(scores), n_iterations)
+
 
 
 if __name__ == '__main__':