Comments and tests to setUpClass, other test fixes.

biolab · sstanovnik · Jun 10, 2016 · Jun 17, 2016 · Jun 17, 2016 · Jun 22, 2016
commit d0a03dd4ca37843e09219e5ab1eab1f2c5fa7e68
diff --git a/Orange/data/table/impl.py b/Orange/data/table/impl.py
@@ -298,7 +298,7 @@ def _any_to_coo(mat):
         return result
 
     def _compute_distributions(self, columns=None):
-        # this needs reimplementing because of possible columns without values
+        # FIXME: this needs reimplementing because of possible columns without values
         if columns is None:
             columns = self.domain.attributes + self.domain.class_vars + self.domain.metas
         distributions = []

diff --git a/Orange/data/variable.py b/Orange/data/variable.py
@@ -42,9 +42,6 @@ class Variable(str, metaclass=VariableMeta):
     ----------
     name : str
         The name of the variable.
-    unknown_str : set
-        A set of values that represent unknowns in conversion from textual
-        formats. Default is Variable.MISSING_VALUES.
     compute_value : Callable
         A function for computing the variable's value when converting from
         another domain which does not contain this variable. The base class
@@ -72,7 +69,6 @@ def __init__(self, name="", compute_value=None):
         super().__init__()
         self.name = name
         self._compute_value = compute_value
-        self.unknown_str = Variable.MISSING_VALUES
         self.source_variable = None
         self.attributes = {}
         self.master = self
@@ -216,7 +212,7 @@ def to_val(self, s):
         """
         if not self.is_primitive():
             return s
-        if s in self.unknown_str:
+        if s in self.MISSING_VALUES:
             return Unknown
         raise RuntimeError(
             "primitive variable descriptors must overload to_val()")
@@ -711,6 +707,7 @@ def column_to_datetime(self, column):
             # handle missing values like they don't exist
             if val in Variable.MISSING_VALUES or (isinstance(val, Number) and np.isnan(val)):
                 continue
+            # for multiple timezones, use the last one for display
             self.timezone = TimeVariable._detect_timezone(val) if not np.issubdtype(column.dtype, np.number) else None
             # if any value doesn't have a timezone, permanently strip display timezones for the column
             if self.timezone is None:

diff --git a/Orange/tests/test_ada_boost.py b/Orange/tests/test_ada_boost.py
@@ -11,10 +11,11 @@
 
 
 class TestSklAdaBoostLearner(unittest.TestCase):
-    def setUp(self):
-        self.iris = Table("iris")
-        self.housing = Table("housing")
-        self.titanic = Table("titanic")
+    @classmethod
+    def setUpClass(cls):
+        cls.iris = Table("iris")
+        cls.housing = Table("housing")
+        cls.titanic = Table("titanic")
 
     def test_adaboost(self):
         learn = SklAdaBoostLearner()

diff --git a/Orange/tests/test_basic_stats.py b/Orange/tests/test_basic_stats.py
@@ -9,8 +9,9 @@
 
 
 class TestBasicStats(TestCase):
-    def setUp(self):
-        self.zoo = Table("zoo")
+    @classmethod
+    def setUpClass(cls):
+        cls.zoo = Table("zoo")
 
     def test_domain_basic_stats(self):
         domain = self.zoo.domain

diff --git a/Orange/tests/test_classification.py b/Orange/tests/test_classification.py
@@ -225,7 +225,8 @@ def test_continuous(self):
 
 
 class UnknownValuesInPrediction(unittest.TestCase):
-    def setUp(self):
+    @classmethod
+    def setUpClass(cls):
         Variable._clear_all_caches()
 
     def test_unknown(self):
@@ -248,8 +249,8 @@ def test_missing_class(self):
 
 
 class LearnerAccessibility(unittest.TestCase):
-
-    def setUp(self):
+    @classmethod
+    def setUpClass(cls):
         Variable._clear_all_caches()
 
     def all_learners(self):

diff --git a/Orange/tests/test_continuize.py b/Orange/tests/test_continuize.py
@@ -12,9 +12,10 @@
 
 
 class TestDomainContinuizer(unittest.TestCase):
-    def setUp(self):
+    @classmethod
+    def setUpClass(cls):
         Variable._clear_all_caches()
-        self.data = Table(test_filename("test4"))
+        cls.data = Table(test_filename("test4"))
 
     def test_default(self):
         for inp in (self.data, self.data.domain):

diff --git a/Orange/tests/test_discretize.py b/Orange/tests/test_discretize.py
@@ -114,9 +114,10 @@ def test_entropy(self):
 
 # noinspection PyPep8Naming
 class TestDiscretizer(TestCase):
-    def setUp(self):
-        self.var = Mock(data.ContinuousVariable, number_of_decimals=1)
-        self.var.name = "x"
+    @classmethod
+    def setUpClass(cls):
+        cls.var = Mock(data.ContinuousVariable, number_of_decimals=1)
+        cls.var.name = "x"
 
     def test_create_discretized_var(self):
         dvar = discretize.Discretizer.create_discretized_var(

diff --git a/Orange/tests/test_distances.py b/Orange/tests/test_distances.py
@@ -22,13 +22,14 @@
 
 def tables_equal(tab1, tab2):
     # catch none
-    return (tab1 is None and tab1 == tab2) or tab1.equals(tab2)
+    return (tab1 is None and tab1 is tab2) or tab1.equals(tab2)
 
 
 class TestDistMatrix(TestCase):
-    def setUp(self):
-        self.iris = Table('iris')
-        self.dist = Euclidean(self.iris)
+    @classmethod
+    def setUpClass(cls):
+        cls.iris = Table('iris')
+        cls.dist = Euclidean(cls.iris)
 
     def test_submatrix(self):
         sub = self.dist.submatrix([2, 3, 4])
@@ -173,10 +174,11 @@ def test_save(self):
 
 
 class TestEuclidean(TestCase):
-    def setUp(self):
-        self.iris = Table('iris')
-        self.sparse = Table(csr_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]]))
-        self.dist = Euclidean
+    @classmethod
+    def setUpClass(cls):
+        cls.iris = Table('iris')
+        cls.sparse = Table(csr_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]]))
+        cls.dist = Euclidean
 
     def test_euclidean_distance_one_example(self):
         np.testing.assert_almost_equal(self.dist(self.iris.iloc[0]), np.array([[0]]))
@@ -241,10 +243,11 @@ def test_euclidean_distance_numpy(self):
 
 
 class TestManhattan(TestCase):
-    def setUp(self):
-        self.iris = Table('iris')
-        self.sparse = Table(csr_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]]))
-        self.dist = Manhattan
+    @classmethod
+    def setUpClass(cls):
+        cls.iris = Table('iris')
+        cls.sparse = Table(csr_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]]))
+        cls.dist = Manhattan
 
     def test_manhattan_distance_one_example(self):
         np.testing.assert_almost_equal(self.dist(self.iris.iloc[0]), np.array([[0]]))
@@ -308,10 +311,11 @@ def test_manhattan_distance_numpy(self):
 
 
 class TestCosine(TestCase):
-    def setUp(self):
-        self.iris = Table('iris')
-        self.sparse = Table(csr_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]]))
-        self.dist = Cosine
+    @classmethod
+    def setUpClass(cls):
+        cls.iris = Table('iris')
+        cls.sparse = Table(csr_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]]))
+        cls.dist = Cosine
 
     def test_cosine_distance_one_example(self):
         np.testing.assert_almost_equal(self.dist(self.iris.iloc[0]), np.array([[0]]))
@@ -375,9 +379,10 @@ def test_cosine_distance_numpy(self):
 
 
 class TestJaccard(TestCase):
-    def setUp(self):
-        self.titanic = Table('titanic')[173:177]
-        self.dist = Jaccard
+    @classmethod
+    def setUpClass(cls):
+        cls.titanic = Table('titanic')[173:177]
+        cls.dist = Jaccard
 
     def test_jaccard_distance_one_example(self):
         np.testing.assert_almost_equal(self.dist(self.titanic.iloc[0]), np.array([[0]]))
@@ -428,9 +433,10 @@ def test_jaccard_distance_numpy(self):
 
 
 class TestSpearmanR(TestCase):
-    def setUp(self):
-        self.breast = Table("breast-cancer-wisconsin-cont")
-        self.dist = SpearmanR
+    @classmethod
+    def setUpClass(cls):
+        cls.breast = Table("breast-cancer-wisconsin-cont")
+        cls.dist = SpearmanR
 
     def test_spearmanr_distance_one_example(self):
         np.testing.assert_almost_equal(self.dist(self.breast.iloc[0]), np.array([[0]]))
@@ -506,9 +512,10 @@ def test_spearmanr_distance_numpy(self):
 
 
 class TestSpearmanRAbsolute(TestCase):
-    def setUp(self):
-        self.breast = Table("breast-cancer-wisconsin-cont")
-        self.dist = SpearmanRAbsolute
+    @classmethod
+    def setUpClass(cls):
+        cls.breast = Table("breast-cancer-wisconsin-cont")
+        cls.dist = SpearmanRAbsolute
 
     def test_spearmanrabsolute_distance_one_example(self):
         np.testing.assert_almost_equal(self.dist(self.breast.iloc[0]), np.array([[0]]))
@@ -582,9 +589,10 @@ def test_spearmanrabsolute_distance_numpy(self):
 
 
 class TestPearsonR(TestCase):
-    def setUp(self):
-        self.breast = Table("breast-cancer-wisconsin-cont")
-        self.dist = PearsonR
+    @classmethod
+    def setUpClass(cls):
+        cls.breast = Table("breast-cancer-wisconsin-cont")
+        cls.dist = PearsonR
 
     def test_pearsonr_distance_one_example(self):
         np.testing.assert_almost_equal(self.dist(self.breast.iloc[0]), np.array([[0]]))
@@ -655,9 +663,10 @@ def test_pearsonr_distance_numpy(self):
 
 
 class TestPearsonRAbsolute(TestCase):
-    def setUp(self):
-        self.breast = Table("breast-cancer-wisconsin-cont")
-        self.dist = PearsonRAbsolute
+    @classmethod
+    def setUpClass(cls):
+        cls.breast = Table("breast-cancer-wisconsin-cont")
+        cls.dist = PearsonRAbsolute
 
     def test_pearsonrabsolute_distance_one_example(self):
         np.testing.assert_almost_equal(self.dist(self.breast.iloc[0]), np.array([[0]]))
@@ -726,11 +735,12 @@ def test_pearsonrabsolute_distance_numpy(self):
 
 
 class TestMahalanobis(TestCase):
-    def setUp(self):
-        self.n, self.m = 10, 5
-        self.X = np.random.rand(self.n, self.m)
-        self.x1 = np.random.rand(self.m)
-        self.x2 = np.random.rand(self.m)
+    @classmethod
+    def setUpClass(cls):
+        cls.n, cls.m = 10, 5
+        cls.X = np.random.rand(cls.n, cls.m)
+        cls.x1 = np.random.rand(cls.m)
+        cls.x2 = np.random.rand(cls.m)
 
     def test_correctness(self):
         mah = MahalanobisDistance(self.X)
@@ -769,8 +779,9 @@ def test_axis(self):
 
 
 class TestDistances(TestCase):
-    def setUp(self):
-        self.test5 = Table(test_filename('test5.tab'))
+    @classmethod
+    def setUpClass(cls):
+        cls.test5 = Table(test_filename('test5.tab'))
 
     def test_preprocess(self):
         domain = Domain([ContinuousVariable("c"),

diff --git a/Orange/tests/test_distribution.py b/Orange/tests/test_distribution.py
@@ -11,11 +11,13 @@
 from Orange.data import Table, DiscreteVariable, ContinuousVariable, Domain, Variable
 from Orange.tests import test_filename
 
+
 class Distribution_DiscreteTestCase(unittest.TestCase):
-    def setUp(self):
-        self.freqs = [4.0, 20.0, 13.0, 8.0, 10.0, 41.0, 5.0]
-        s = sum(self.freqs)
-        self.rfreqs = [x/s for x in self.freqs]
+    @classmethod
+    def setUpClass(cls):
+        cls.freqs = [4.0, 20.0, 13.0, 8.0, 10.0, 41.0, 5.0]
+        s = sum(cls.freqs)
+        cls.rfreqs = [x/s for x in cls.freqs]
 
     def test_from_table(self):
         d = Table("zoo")
@@ -191,9 +193,7 @@ class Distribution_ContinuousTestCase(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
         cls.iris = Table("iris")
-
-    def setUp(self):
-        self.freqs = np.array([(1.0, 1), (1.1, 1), (1.2, 2), (1.3, 7), (1.4, 12),
+        cls.freqs = np.array([(1.0, 1), (1.1, 1), (1.2, 2), (1.3, 7), (1.4, 12),
                                (1.5, 14), (1.6, 7), (1.7, 4), (1.9, 2), (3.0, 1),
                                (3.3, 2), (3.5, 2), (3.6, 1), (3.7, 1), (3.8, 1),
                                (3.9, 3), (4.0, 5), (4.1, 3), (4.2, 4), (4.3, 2),

diff --git a/Orange/tests/test_domain.py b/Orange/tests/test_domain.py
@@ -403,8 +403,9 @@ def test_domain_conversion_is_fast_enough(self):
 
 
 class TestDomainFilter(unittest.TestCase):
-    def setUp(self):
-        self.iris = Table('iris')
+    @classmethod
+    def setUpClass(cls):
+        cls.iris = Table('iris')
 
     def test_filter_visible(self):
         n_feats = len(self.iris.domain.attributes)

diff --git a/Orange/tests/test_normalize.py b/Orange/tests/test_normalize.py
@@ -22,8 +22,9 @@ def compare_tables(self, dataNorm, solution):
         self.assertEqual([attr.name for attr in dataNorm.domain.class_vars],
                          ["cl1", "cl2"])
 
-    def setUp(self):
-        self.data = Table(test_filename("test5.tab"))
+    @classmethod
+    def setUpClass(cls):
+        cls.data = Table(test_filename("test5.tab"))
 
     def test_normalize_default(self):
         normalizer = Normalize()

diff --git a/Orange/tests/test_remove.py b/Orange/tests/test_remove.py
@@ -10,8 +10,9 @@
 
 
 class TestRemover(unittest.TestCase):
-    def setUp(self):
-        self.test8 = Table(test_filename('test8.tab'))
+    @classmethod
+    def setUpClass(cls):
+        cls.test8 = Table(test_filename('test8.tab'))
 
     def test_remove(self):
         data = Table("iris").iloc[:5]

diff --git a/Orange/tests/test_score_feature.py b/Orange/tests/test_score_feature.py
@@ -12,11 +12,12 @@
 
 
 class FeatureScoringTest(unittest.TestCase):
-    def setUp(self):
-        self.zoo = Table("zoo")  # disc. features, disc. class
-        self.housing = Table("housing")  # cont. features, cont. class
-        self.monk = Table("monks-1")
-        self.adult = Table("adult_sample")
+    @classmethod
+    def setUpClass(cls):
+        cls.zoo = Table("zoo")  # disc. features, disc. class
+        cls.housing = Table("housing")  # cont. features, cont. class
+        cls.monk = Table("monks-1")
+        cls.adult = Table("adult_sample")
 
     def test_info_gain(self):
         scorer = InfoGain()