Merge pull request #2396 from ales-erjavec/numpy-1.13.0

[FIX] tests: Fix test errors when running with numpy 1.13.0

Orange/tests/__init__.py

@@ -3,6 +3,7 @@
 import tempfile
 from contextlib import contextmanager
 
+import numpy as np
 import Orange
 
 
@@ -19,6 +20,31 @@ def named_file(content, encoding=None, suffix=''):
         os.remove(name)
 
 
+@np.vectorize
+def naneq(a, b):
+    try:
+        return (np.isnan(a) and np.isnan(b)) or a == b
+    except TypeError:
+        return a == b
+
+
+def assert_array_nanequal(a, b, *args, **kwargs):
+    """
+    Similar as np.testing.assert_array_equal but with better handling of
+    object arrays.
+
+    Note
+    ----
+    Is not fast!
+
+    Parameters
+    ----------
+    a : array-like
+    b : array-like
+    """
+    return np.testing.utils.assert_array_compare(naneq, a, b, *args, **kwargs)
+
+
 def test_dirname():
     """
     Return the absolute path to the Orange.tests package.

Orange/tests/test_instance.py

@@ -12,6 +12,7 @@
 from Orange.data import \
     Instance, Domain, Unknown, Value, \
     DiscreteVariable, ContinuousVariable, StringVariable
+from Orange.tests import assert_array_nanequal
 
 
 class TestInstance(unittest.TestCase):
@@ -75,11 +76,10 @@ def test_init_xym_no_data(self):
         self.assertEqual(inst._metas.shape, (3, ))
         self.assertTrue(all(isnan(x) for x in inst._x))
         self.assertTrue(all(isnan(x) for x in inst._y))
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore", FutureWarning)
-            assert_array_equal(inst._metas,
-                               np.array([var.Unknown for var in domain.metas],
-                                        dtype=object))
+
+        assert_array_nanequal(inst._metas,
+                              np.array([var.Unknown for var in domain.metas],
+                                       dtype=object))
 
     def test_init_x_arr(self):
         domain = self.create_domain(["x", DiscreteVariable("g", values="MF")])
@@ -162,12 +162,11 @@ def test_init_inst(self):
                                      domain.class_vars,
                                      [self.metas[0], "w", domain[0]])
         inst2 = Instance(domain2, inst)
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore", FutureWarning)
-            assert_array_equal(inst2._x, np.array([Unknown, 0, 43]))
-            self.assertEqual(inst2._y[0], 1)
-            assert_array_equal(inst2._metas, np.array([0, Unknown, 42],
-                                                      dtype=object))
+
+        assert_array_nanequal(inst2._x, np.array([Unknown, 0, 43]))
+        self.assertEqual(inst2._y[0], 1)
+        assert_array_nanequal(inst2._metas, np.array([0, Unknown, 42],
+                                                     dtype=object))
 
     def test_get_item(self):
         domain = self.create_domain(["x", DiscreteVariable("g", values="MF")],

Orange/tests/test_table.py

@@ -14,21 +14,7 @@
 from Orange import data
 from Orange.data import (filter, Unknown, Variable, Table, DiscreteVariable,
                          ContinuousVariable, Domain, StringVariable)
-from Orange.tests import test_dirname
-
-
-@np.vectorize
-def naneq(a, b):
-    try:
-        return (isnan(a) and isnan(b)) or a == b
-    except TypeError:
-        return a == b
-
-
-def assert_array_nanequal(*args, **kwargs):
-    # similar as np.testing.assert_array_equal but with better handling of
-    # object arrays
-    return np.testing.utils.assert_array_compare(naneq, *args, **kwargs)
+from Orange.tests import test_dirname, assert_array_nanequal
 
 
 class TableTestCase(unittest.TestCase):

Orange/tests/test_util.py

@@ -46,10 +46,10 @@ def test_reprable(self):
         var = ContinuousVariable('x')
         transform = ReplaceUnknownsRandom(var, Continuous(1, var))
 
-        self.assertEqual(repr(transform).replace('\n       ', ' '),
+        self.assertEqual(repr(transform).replace('\n', '').replace(' ', ''),
                          "ReplaceUnknownsRandom("
-                         "variable=ContinuousVariable(name='x', number_of_decimals=3), "
-                         "distribution=Continuous([[ 0.], [ 0.]]))")
+                         "variable=ContinuousVariable(name='x',number_of_decimals=3),"
+                         "distribution=Continuous([[0.],[0.]]))")
 
         # GH 2275
         logit = LogisticRegressionLearner()