Refactor tests

pyod/test/test_abod.py

@@ -30,22 +30,17 @@
 
 class TestFastABOD(unittest.TestCase):
     def setUp(self):
-        self.n_train = 100
-        self.n_test = 50
+        self.n_train = 200
+        self.n_test = 100
         self.contamination = 0.1
-        self.roc_floor = 0.6
+        self.roc_floor = 0.8
         self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
             n_train=self.n_train, n_test=self.n_test,
             contamination=self.contamination, random_state=42)
 
         self.clf = ABOD(contamination=self.contamination)
         self.clf.fit(self.X_train)
 
-    def test_sklearn_estimator(self):
-        # TODO: sklearn check does not support Numba optimization
-        # check_estimator(self.clf)
-        pass
-
     def test_parameters(self):
         assert_true(hasattr(self.clf, 'decision_scores_') and
                     self.clf.decision_scores_ is not None)
@@ -118,19 +113,14 @@ def setUp(self):
         self.n_train = 50
         self.n_test = 50
         self.contamination = 0.2
-        self.roc_floor = 0.6
+        self.roc_floor = 0.8
         self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
             n_train=self.n_train, n_test=self.n_test,
             contamination=self.contamination, random_state=42)
 
         self.clf = ABOD(contamination=self.contamination, method='default')
         self.clf.fit(self.X_train)
 
-    def test_sklearn_estimator(self):
-        # TODO: sklearn check does not support Numba optimization
-        # check_estimator(self.clf)
-        pass
-
     def test_parameters(self):
         if not hasattr(self.clf,
                        'decision_scores_') or self.clf.decision_scores_ is None:

pyod/test/test_cblof.py

@@ -30,22 +30,17 @@
 
 class TestLOF(unittest.TestCase):
     def setUp(self):
-        self.n_train = 100
-        self.n_test = 50
+        self.n_train = 200
+        self.n_test = 100
         self.contamination = 0.1
-        self.roc_floor = 0.6
+        self.roc_floor = 0.8
         self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
             n_train=self.n_train, n_test=self.n_test,
             contamination=self.contamination, random_state=42)
 
         self.clf = CBLOF(contamination=self.contamination, random_state=42)
         self.clf.fit(self.X_train)
 
-    def test_sklearn_estimator(self):
-        # TODO: sklearn examples are too small to form valid
-        # check_estimator(self.clf)
-        pass
-
     def test_parameters(self):
         assert_true(hasattr(self.clf, 'decision_scores_') and
                     self.clf.decision_scores_ is not None)

pyod/test/test_feature_bagging.py

@@ -31,21 +31,17 @@
 
 class TestFeatureBagging(unittest.TestCase):
     def setUp(self):
-        self.n_train = 100
-        self.n_test = 50
+        self.n_train = 200
+        self.n_test = 100
         self.contamination = 0.1
-        self.roc_floor = 0.6
+        self.roc_floor = 0.8
         self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
             n_train=self.n_train, n_test=self.n_test,
             contamination=self.contamination, random_state=42)
 
         self.clf = FeatureBagging(contamination=self.contamination)
         self.clf.fit(self.X_train)
 
-    # TODO: failed due to sklearn uses 2 feature examples.
-    # def test_sklearn_estimator(self):
-    #     check_estimator(self.clf)
-
     def test_parameters(self):
         assert_true(hasattr(self.clf, 'decision_scores_') and
                     self.clf.decision_scores_ is not None)

pyod/test/test_hbos.py

@@ -30,10 +30,10 @@
 
 class TestHBOS(unittest.TestCase):
     def setUp(self):
-        self.n_train = 100
-        self.n_test = 50
+        self.n_train = 200
+        self.n_test = 100
         self.contamination = 0.1
-        self.roc_floor = 0.6
+        self.roc_floor = 0.8
         self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
             n_train=self.n_train, n_test=self.n_test,
             contamination=self.contamination, random_state=42)

pyod/test/test_iforest.py

@@ -31,10 +31,10 @@
 
 class TestIForest(unittest.TestCase):
     def setUp(self):
-        self.n_train = 100
-        self.n_test = 50
+        self.n_train = 200
+        self.n_test = 100
         self.contamination = 0.1
-        self.roc_floor = 0.6
+        self.roc_floor = 0.8
         self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
             n_train=self.n_train, n_test=self.n_test,
             contamination=self.contamination, random_state=42)

pyod/test/test_knn.py

@@ -32,10 +32,10 @@
 
 class TestKnn(unittest.TestCase):
     def setUp(self):
-        self.n_train = 100
-        self.n_test = 50
+        self.n_train = 200
+        self.n_test = 100
         self.contamination = 0.1
-        self.roc_floor = 0.6
+        self.roc_floor = 0.8
         self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
             n_train=self.n_train, n_test=self.n_test,
             contamination=self.contamination, random_state=42)
@@ -132,10 +132,10 @@ def tearDown(self):
 class TestKnnMean(unittest.TestCase):
 
     def setUp(self):
-        self.n_train = 100
-        self.n_test = 50
+        self.n_train = 200
+        self.n_test = 100
         self.contamination = 0.1
-        self.roc_floor = 0.75
+        self.roc_floor = 0.8
         self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
             n_train=self.n_train, n_test=self.n_test,
             contamination=self.contamination, random_state=42)
@@ -160,10 +160,10 @@ def tearDown(self):
 class TestKnnMedian(unittest.TestCase):
 
     def setUp(self):
-        self.n_train = 100
-        self.n_test = 50
+        self.n_train = 200
+        self.n_test = 100
         self.contamination = 0.1
-        self.roc_floor = 0.75
+        self.roc_floor = 0.8
         self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
             n_train=self.n_train, n_test=self.n_test,
             contamination=self.contamination, random_state=42)
@@ -187,10 +187,10 @@ def tearDown(self):
 
 class TestKnnMahalanobis(unittest.TestCase):
     def setUp(self):
-        self.n_train = 100
-        self.n_test = 50
+        self.n_train = 200
+        self.n_test = 100
         self.contamination = 0.1
-        self.roc_floor = 0.6
+        self.roc_floor = 0.8
         self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
             n_train=self.n_train, n_test=self.n_test,
             contamination=self.contamination, random_state=42)
@@ -268,7 +268,7 @@ def test_predict_rank(self):
         pred_ranks = self.clf._predict_rank(self.X_test)
 
         # assert the order is reserved
-        assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
+        assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=3)
         assert_array_less(pred_ranks, self.X_train.shape[0] + 1)
         assert_array_less(-0.1, pred_ranks)
 
@@ -277,7 +277,7 @@ def test_predict_rank_normalized(self):
         pred_ranks = self.clf._predict_rank(self.X_test, normalized=True)
 
         # assert the order is reserved
-        assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
+        assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=3)
         assert_array_less(pred_ranks, 1.01)
         assert_array_less(-0.1, pred_ranks)
 

pyod/test/test_loci.py

@@ -31,10 +31,10 @@
 
 class TestLOCI(unittest.TestCase):
     def setUp(self):
-        self.n_train = 100
-        self.n_test = 50
+        self.n_train = 200
+        self.n_test = 100
         self.contamination = 0.1
-        self.roc_floor = 0.6
+        self.roc_floor = 0.8
         self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
             n_train=self.n_train, n_test=self.n_test,
             contamination=self.contamination, random_state=42)

pyod/test/test_lof.py

@@ -30,10 +30,10 @@
 
 class TestLOF(unittest.TestCase):
     def setUp(self):
-        self.n_train = 100
-        self.n_test = 50
+        self.n_train = 200
+        self.n_test = 100
         self.contamination = 0.1
-        self.roc_floor = 0.6
+        self.roc_floor = 0.8
         self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
             n_train=self.n_train, n_test=self.n_test,
             contamination=self.contamination, random_state=42)
@@ -110,8 +110,6 @@ def test_fit_predict_score(self):
     def test_predict_rank(self):
         pred_socres = self.clf.decision_function(self.X_test)
         pred_ranks = self.clf._predict_rank(self.X_test)
-        print(pred_ranks)
-        print(pred_ranks)
 
         # assert the order is reserved
         assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)

pyod/test/test_lscp.py

@@ -32,10 +32,10 @@
 
 class TestLSCP(unittest.TestCase):
     def setUp(self):
-        self.n_train = 100
-        self.n_test = 50
+        self.n_train = 200
+        self.n_test = 100
         self.contamination = 0.1
-        self.roc_floor = 0.6
+        self.roc_floor = 0.8
         self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
             n_train=self.n_train, n_test=self.n_test,
             contamination=self.contamination, random_state=42)

pyod/test/test_mcd.py

@@ -30,10 +30,10 @@
 
 class TestMCD(unittest.TestCase):
     def setUp(self):
-        self.n_train = 100
-        self.n_test = 50
+        self.n_train = 200
+        self.n_test = 100
         self.contamination = 0.1
-        self.roc_floor = 0.6
+        self.roc_floor = 0.8
         self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
             n_train=self.n_train, n_test=self.n_test,
             contamination=self.contamination, random_state=42)
@@ -124,7 +124,7 @@ def test_predict_rank(self):
         pred_ranks = self.clf._predict_rank(self.X_test)
 
         # assert the order is reserved
-        assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
+        assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2.5)
         assert_array_less(pred_ranks, self.X_train.shape[0] + 1)
         assert_array_less(-0.1, pred_ranks)
 
@@ -133,7 +133,7 @@ def test_predict_rank_normalized(self):
         pred_ranks = self.clf._predict_rank(self.X_test, normalized=True)
 
         # assert the order is reserved
-        assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
+        assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2.5)
         assert_array_less(pred_ranks, 1.01)
         assert_array_less(-0.1, pred_ranks)
 

pyod/test/test_ocsvm.py

@@ -31,10 +31,10 @@
 
 class TestOCSVM(unittest.TestCase):
     def setUp(self):
-        self.n_train = 100
-        self.n_test = 50
+        self.n_train = 200
+        self.n_test = 100
         self.contamination = 0.1
-        self.roc_floor = 0.6
+        self.roc_floor = 0.8
         self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
             n_train=self.n_train, n_test=self.n_test,
             contamination=self.contamination, random_state=42)
@@ -118,7 +118,7 @@ def test_predict_rank(self):
         pred_ranks = self.clf._predict_rank(self.X_test)
 
         # assert the order is reserved
-        assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
+        assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=3.5)
         assert_array_less(pred_ranks, self.X_train.shape[0] + 1)
         assert_array_less(-0.1, pred_ranks)
 
@@ -127,7 +127,7 @@ def test_predict_rank_normalized(self):
         pred_ranks = self.clf._predict_rank(self.X_test, normalized=True)
 
         # assert the order is reserved
-        assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
+        assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=3.5)
         assert_array_less(pred_ranks, 1.01)
         assert_array_less(-0.1, pred_ranks)
 

pyod/test/test_pca.py

@@ -30,15 +30,16 @@
 
 class TestPCA(unittest.TestCase):
     def setUp(self):
-        self.n_train = 100
-        self.n_test = 50
+        self.n_train = 200
+        self.n_test = 100
         self.contamination = 0.1
-        self.roc_floor = 0.5
+        self.roc_floor = 0.8
         self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
-            n_train=self.n_train, n_test=self.n_test,
+            n_train=self.n_train, n_test=self.n_test, n_features=10,
             contamination=self.contamination, random_state=42)
 
-        self.clf = PCA(contamination=self.contamination, random_state=42)
+        self.clf = PCA(contamination=self.contamination,
+                       random_state=42)
         self.clf.fit(self.X_train)
 
     def test_sklearn_estimator(self):

pyod/test/test_sos.py

@@ -32,12 +32,12 @@
 
 class TestSOS(unittest.TestCase):
     def setUp(self):
-        self.n_train = 100
-        self.n_test = 50
+        self.n_train = 200
+        self.n_test = 100
         self.contamination = 0.1
-        self.roc_floor = 0.6
+        # self.roc_floor = 0.8
         self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
-            n_train=self.n_train, n_test=self.n_test,
+            n_train=self.n_train, n_test=self.n_test, n_features=5,
             contamination=self.contamination, random_state=42)
 
         self.clf = SOS(contamination=self.contamination)