Merge pull request #818 from thocevar/tests

Compatibility with newest Numpy and Scipy.

Orange/classification/naive_bayes.py

@@ -32,8 +32,8 @@ def fit_storage(self, table):
             raise NotImplementedError("Only discrete variables are supported.")
 
         cont = contingency.get_contingencies(table)
-        class_freq = np.diag(
-            contingency.get_contingency(table, table.domain.class_var))
+        class_freq = np.array(np.diag(
+            contingency.get_contingency(table, table.domain.class_var)))
         return NaiveBayesModel(cont, class_freq, table.domain)
 
 

Orange/preprocess/discretize.py

@@ -17,9 +17,13 @@ def __init__(self, variable, points):
         super().__init__(variable)
         self.points = points
 
+    @staticmethod
+    def digitize(x, bins):
+        return np.digitize(x, bins) if bins else [0]*len(x)
+
     def transform(self, c):
         if c.size:
-            return np.where(np.isnan(c), np.NaN, np.digitize(c, self.points))
+            return np.where(np.isnan(c), np.NaN, self.digitize(c, self.points))
         else:
             return np.array([], dtype=int)
 

Orange/tests/test_clustering_kmeans.py

@@ -17,7 +17,7 @@ def test_kmeans(self):
 
     def test_kmeans_parameters(self):
         table = Orange.data.Table('iris')
-        kmeans = KMeans(n_clusters=20,
+        kmeans = KMeans(n_clusters=10,
                         max_iter=10,
                         random_state=42,
                         tol=0.001,

Orange/tests/test_distances.py

@@ -336,7 +336,7 @@ def test_spearmanr_distance_many_examples(self):
                                                  [ 0.25,  0.  ,  0.25,  0.  ,  0.  ,  0.  ,  0.25,  0.  ,  0.75],
                                                  [ 0.25,  0.75,  0.25,  0.75,  0.75,  0.75,  1.  ,  0.75,  0.  ]]))
 
-    def test_spearmanr_distacne_numpy(self):
+    def test_spearmanr_distance_numpy(self):
         np.testing.assert_almost_equal(self.dist(self.breast[0].x, self.breast[1].x, axis=0), np.array([[0.5083333333333333]]))
         np.testing.assert_almost_equal(self.dist(self.breast[:2].X),
                                        np.array([[ 0.                ,  0.5083333333333333],

Orange/tests/test_domain.py

@@ -425,7 +425,7 @@ def test_conversion(self):
         x, y, metas = domain.convert([42, 13, "White"])
         assert_array_equal(x, np.array([42, 13]))
         assert_array_equal(y, np.array([0]))
-        assert_array_equal(metas, np.array([Unknown, Unknown, Unknown], dtype=object))
+        self.assertTrue(all(np.isnan(np.array(metas, dtype=float))))
 
         x, y, metas = domain.convert([42, 13, "White", "M", "HS", "1234567"])
         assert_array_equal(x, np.array([42, 13]))

Orange/tests/test_pca.py

@@ -100,9 +100,9 @@ def test_transformed_domain_does_not_pickle_data(self):
     def test_chain(self):
         zoo = Orange.data.Table('zoo')
         zoo_c = Continuize(zoo)
-        pca = PCA()(zoo_c)(zoo)
-        pca2 = PCA()(zoo_c)(zoo_c)
-        pca3 = PCA(preprocessors=[Continuize()])(zoo)(zoo)
+        pca = PCA(n_components=3)(zoo_c)(zoo)
+        pca2 = PCA(n_components=3)(zoo_c)(zoo_c)
+        pca3 = PCA(n_components=3, preprocessors=[Continuize()])(zoo)(zoo)
         np.testing.assert_almost_equal(pca.X, pca2.X)
         np.testing.assert_almost_equal(pca.X, pca3.X)
 

Orange/tests/test_softmax_regression.py

@@ -15,19 +15,12 @@ def test_SoftmaxRegression(self):
         self.assertTrue(0.9 < ca < 1.0)
 
     def test_SoftmaxRegressionPreprocessors(self):
-        np.random.seed(42)
         table = Table('iris')
-        new_attrs = (ContinuousVariable('c0'),) + table.domain.attributes
-        new_domain = Domain(new_attrs,
-                            table.domain.class_vars,
-                            table.domain.metas)
-        new_table = np.hstack((
-            1000000 * np.random.random((table.X.shape[0], 1)),
-            table))
-        table = table.from_numpy(new_domain, new_table)
+        table.X[:,2] = table.X[:,2] * 0.001
+        table.X[:,3] = table.X[:,3] * 0.001
         learners = [SoftmaxRegressionLearner(preprocessors=[]),
                     SoftmaxRegressionLearner()]
-        results = CrossValidation(table, learners, k=3)
+        results = CrossValidation(table, learners, k=10)
         ca = CA(results)
         self.assertTrue(ca[0] < ca[1])
 

Orange/widgets/utils/colorpalette.py

@@ -548,7 +548,7 @@ def getRGB(self, values):
         values = np.clip(values, 0, 1 - EPS)
         bin = np.digitize(values, self.bins)
         nans = bin >= len(self.bins)
-        bin[nans] = 0  # just so that the next two lines pass
+        values[nans] = bin[nans] = 0  # just so that the next two lines pass
         p = (values - self.bins[bin - 1]) / self.deriv
         results = np.round((1 - p) * self.colors[bin - 1].T + p * self.colors[bin].T).T.astype(int)
         results[nans] = NAN_GREY