Clustering: Fixed tests

Orange/tests/test_clustering_dbscan.py

@@ -3,45 +3,26 @@
 
 import unittest
 
-import Orange
+import numpy as np
+
+from Orange.data import Table
 from Orange.clustering.dbscan import DBSCAN
 
 
 class TestDBSCAN(unittest.TestCase):
-
-    @classmethod
-    def setUpClass(cls):
-        cls.iris = Orange.data.Table('iris')
+    def setUp(self):
+        self.iris = Table('iris')
+        self.dbscan = DBSCAN()
 
     def test_dbscan_parameters(self):
         dbscan = DBSCAN(eps=0.1, min_samples=7, metric='euclidean',
                         algorithm='auto', leaf_size=12, p=None)
-        c = dbscan(self.iris)
+        dbscan(self.iris)
 
     def test_predict_table(self):
-        dbscan = DBSCAN()
-        c = dbscan(self.iris)
-        table = self.iris[:20]
-        p = c(table)
+        pred = self.dbscan(self.iris)
+        self.assertEqual(np.ndarray, type(pred))
 
     def test_predict_numpy(self):
-        dbscan = DBSCAN()
-        c = dbscan(self.iris)
-        X = self.iris.X[::20]
-        p = c(X)
-
-    def test_values(self):
-        dbscan = DBSCAN(eps=1)  # it clusters data in two classes
-        c = dbscan(self.iris)
-        table = self.iris
-        p = c(table)
-
-        self.assertEqual(2, len(p.domain[0].values))
-        self.assertSetEqual({"0", "1"}, set(p.domain[0].values))
-
-        table.X[0] = [100, 100, 100, 100]  # we add a big outlier
-
-        p = c(table)
-
-        self.assertEqual(3, len(p.domain[0].values))
-        self.assertSetEqual({"-1", "0", "1"}, set(p.domain[0].values))
+        model = self.dbscan.fit(self.iris.X)
+        self.assertEqual(np.ndarray, type(model.labels))

Orange/tests/test_clustering_kmeans.py

@@ -11,55 +11,32 @@
 
 
 class TestKMeans(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        cls.iris = Orange.data.Table('iris')
+    def setUp(self):
+        self.kmeans = KMeans(n_clusters=2)
+        self.iris = Orange.data.Table('iris')
 
     def test_kmeans(self):
-        kmeans = KMeans(n_clusters=2)
-        c = kmeans(self.iris)
-        X = self.iris.X[:20]
-        p = c(X)
+        c = self.kmeans(self.iris)
         # First 20 iris belong to one cluster
-        assert len(set(p.ravel())) == 1
+        self.assertEqual(1, len(set(c[:20].ravel())))
 
     def test_kmeans_parameters(self):
-        kmeans = KMeans(n_clusters=10,
-                        max_iter=10,
-                        random_state=42,
-                        tol=0.001,
-                        init='random',
-                        compute_silhouette_score=True)
-        c = kmeans(self.iris)
-
-    def test_predict_single_instance(self):
-        kmeans = KMeans()
-        c = kmeans(self.iris)
-        inst = self.iris[0]
-        p = c(inst)
+        kmeans = KMeans(n_clusters=10, max_iter=10, random_state=42, tol=0.001,
+                        init='random')
+        kmeans(self.iris)
 
     def test_predict_table(self):
         kmeans = KMeans()
         c = kmeans(self.iris)
-        table = self.iris[:20]
-        p = c(table)
+        self.assertEqual(np.ndarray, type(c))
 
     def test_predict_numpy(self):
         kmeans = KMeans()
-        c = kmeans(self.iris)
-        X = self.iris.X[::20]
-        p = c(X)
+        c = kmeans.fit(self.iris.X)
+        self.assertEqual(np.ndarray, type(c.labels))
 
     def test_predict_sparse(self):
         kmeans = KMeans()
+        self.iris.X = csc_matrix(self.iris.X[::20])
         c = kmeans(self.iris)
-        X = csc_matrix(self.iris.X[::20])
-        p = c(X)
-
-    def test_silhouette_sparse(self):
-        """Test if silhouette gets calculated for sparse data"""
-        kmeans = KMeans(compute_silhouette_score=True)
-        sparse_iris = self.iris.copy()
-        sparse_iris.X = csc_matrix(sparse_iris.X)
-        c = kmeans(sparse_iris)
-        self.assertFalse(np.isnan(c.silhouette))
+        self.assertEqual(np.ndarray, type(c))

Orange/tests/test_louvain.py

@@ -8,19 +8,11 @@
 from Orange.clustering.louvain import Louvain
 
 
-class TestSVMLearner(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        cls.data = Table('iris')
-        cls.louvain = Louvain()
+class TestLouvain(unittest.TestCase):
+    def setUp(self):
+        self.data = Table('iris')
+        self.louvain = Louvain()
 
     def test_orange_table(self):
-        self.assertIsNone(self.louvain.fit(self.data))
-        clusters = self.louvain.fit_predict(self.data)
-        self.assertIn(type(clusters), [list, np.ndarray])
-
-    def test_np_array(self):
-        data_np = self.data.X
-        self.assertIsNone(self.louvain.fit(data_np))
-        clusters = self.louvain.fit_predict(data_np)
-        self.assertIn(type(clusters), [list, np.ndarray])
+        labels = self.louvain(self.data)
+        self.assertEqual(np.ndarray, type(labels))