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# Test methods with long descriptive names can omit docstrings
# pylint: disable=missing-docstring
import pickle
import unittest
from unittest.mock import Mock
import numpy as np
from scipy.sparse import csr_matrix
from Orange.data import Table, Domain, ContinuousVariable
from Orange.preprocess import EntropyMDL, DoNotImpute, Default, Average, \
SelectRandomFeatures, EqualFreq, RemoveNaNColumns, DropInstances, \
EqualWidth, SelectBestFeatures, RemoveNaNRows, Preprocess, Scale, \
Randomize, Continuize, Discretize, Impute, SklImpute, Normalize, \
ProjectCUR, ProjectPCA, RemoveConstant, AdaptiveNormalize, RemoveSparse
class TestPreprocess(unittest.TestCase):
def test_read_data_calls_reader(self):
class MockPreprocessor(Preprocess):
__init__ = Mock(return_value=None)
__call__ = Mock()
@classmethod
def reset(cls):
cls.__init__.reset_mock()
cls.__call__.reset_mock()
table = Mock(Table)
MockPreprocessor(1, 2, a=3)(table)
MockPreprocessor.__init__.assert_called_with(1, 2, a=3)
MockPreprocessor.__call__.assert_called_with(table)
MockPreprocessor.reset()
MockPreprocessor(1, 2, a=3)
MockPreprocessor.__init__.assert_called_with(1, 2, a=3)
self.assertEqual(MockPreprocessor.__call__.call_count, 0)
MockPreprocessor(a=3)
MockPreprocessor.__init__.assert_called_with(a=3)
self.assertEqual(MockPreprocessor.__call__.call_count, 0)
MockPreprocessor()
MockPreprocessor.__init__.assert_called_with()
self.assertEqual(MockPreprocessor.__call__.call_count, 0)
class TestRemoveConstant(unittest.TestCase):
def test_remove_columns(self):
X = np.random.rand(6, 5)
X[:, (1, 3)] = 5
X[3, 1] = np.nan
X[1, 1] = np.nan
X[:, 4] = np.nan
data = Table.from_numpy(None, X)
d = RemoveConstant()(data)
self.assertEqual(len(d.domain.attributes), 2)
pp_rc = RemoveConstant()
d = pp_rc(data)
self.assertEqual(len(d.domain.attributes), 2)
def test_nothing_to_remove(self):
data = Table("iris")
d = RemoveConstant()(data)
self.assertEqual(len(d.domain.attributes), 4)
class TestRemoveNaNRows(unittest.TestCase):
def test_remove_row(self):
data = Table("iris")
with data.unlocked():
data.X[0, 0] = np.nan
pp_data = RemoveNaNRows()(data)
self.assertEqual(len(pp_data), len(data) - 1)
self.assertFalse(np.isnan(pp_data.X).any())
class TestRemoveNaNColumns(unittest.TestCase):
def test_column_filtering(self):
data = Table("iris")
with data.unlocked():
data.X[:, (1, 3)] = np.nan
new_data = RemoveNaNColumns()(data)
self.assertEqual(len(new_data.domain.attributes),
len(data.domain.attributes) - 2)
data = Table("iris")
with data.unlocked():
data.X[0, 0] = np.nan
new_data = RemoveNaNColumns()(data)
self.assertEqual(len(new_data.domain.attributes),
len(data.domain.attributes))
def test_column_filtering_sparse(self):
data = Table("iris")
with data.unlocked():
data.X = csr_matrix(data.X)
new_data = RemoveNaNColumns()(data)
self.assertEqual(data, new_data)
class TestScaling(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.table = Table.from_numpy(None, [[1, 2, 3],
[2, 3, 4],
[3, 4, 5],
[4, 5, 6]])
def test_scaling_mean_span(self):
table = Scale(center=Scale.Mean, scale=Scale.Span)(self.table)
np.testing.assert_almost_equal(np.mean(table, 0), 0)
np.testing.assert_almost_equal(np.ptp(table, 0), 1)
def test_scaling_median_stddev(self):
table = Scale(center=Scale.Median, scale=Scale.Std)(self.table)
np.testing.assert_almost_equal(np.std(table, 0), 1)
# NB: This test just covers. The following fails. You figure it out.
# np.testing.assert_almost_equal(np.median(table, 0), 0)
class TestReprs(unittest.TestCase):
def test_reprs(self):
preprocs = [Continuize, Discretize, Impute, SklImpute, Normalize,
Randomize, ProjectPCA, ProjectCUR, Scale,
EqualFreq, EqualWidth, EntropyMDL, SelectBestFeatures,
SelectRandomFeatures, RemoveNaNColumns, DoNotImpute, DropInstances,
Average, Default, RemoveSparse]
for preproc in preprocs:
repr_str = repr(preproc())
new_preproc = eval(repr_str)
self.assertEqual(repr(new_preproc), repr_str)
class TestEnumPickling(unittest.TestCase):
def test_continuize_pickling(self):
c = Continuize(multinomial_treatment=Continuize.FirstAsBase)
s = pickle.dumps(c, -1)
c1 = pickle.loads(s)
self.assertIs(c1.multinomial_treatment, c.multinomial_treatment)
def test_randomize_pickling(self):
c = Randomize(rand_type=Randomize.RandomizeMetas)
s = pickle.dumps(c, -1)
c1 = pickle.loads(s)
self.assertIs(c1.rand_type, c.rand_type)
def test_scaling_pickling(self):
c = Scale(center=Scale.Median, scale=Scale.Span)
s = pickle.dumps(c, -1)
c1 = pickle.loads(s)
self.assertIs(c1.center, c.center)
self.assertIs(c1.scale, c.scale)
class TestAdaptiveNormalize(unittest.TestCase):
"""
Checks if output for sparse data is the same as for Scale
preprocessor. For dense data the output should match that
of Normalize preprocessor.
"""
def setUp(self):
self.data = Table("iris")
def test_dense_pps(self):
true_out = Normalize()(self.data)
out = AdaptiveNormalize()(self.data)
np.testing.assert_array_equal(out, true_out)
def test_sparse_pps(self):
with self.data.unlocked():
self.data.X = csr_matrix(self.data.X)
out = AdaptiveNormalize()(self.data)
true_out = Scale(center=Scale.NoCentering, scale=Scale.Span)(self.data)
np.testing.assert_array_equal(out, true_out)
self.data = self.data.X.toarray()
class TestRemoveSparse(unittest.TestCase):
def setUp(self):
domain = Domain([ContinuousVariable('a'), ContinuousVariable('b')])
self.data = Table.from_numpy(domain, np.zeros((3, 2)))
def test_0_dense(self):
with self.data.unlocked():
self.data[1:, 1] = 7
true_out = self.data[:, 1].copy()
with true_out.unlocked(true_out.X):
true_out.X = true_out.X.reshape(-1, 1)
out = RemoveSparse(0.5, True)(self.data)
np.testing.assert_array_equal(out, true_out)
out = RemoveSparse(2, True)(self.data)
np.testing.assert_array_equal(out, true_out)
def test_0_sparse(self):
with self.data.unlocked():
self.data[1:, 1] = 7
true_out = self.data[:, 1].copy()
self.data.X = csr_matrix(self.data.X)
with true_out.unlocked(true_out.X):
true_out.X = csr_matrix(true_out.X)
out = RemoveSparse(0.5, True)(self.data).X
np.testing.assert_array_equal(out, true_out)
out = RemoveSparse(1, True)(self.data).X
np.testing.assert_array_equal(out, true_out)
def test_nan_dense(self):
with self.data.unlocked():
self.data[1:, 1] = np.nan
self.data.X[:, 0] = 7
true_out = self.data[:, 0].copy()
with true_out.unlocked(true_out.X):
true_out.X = true_out.X.reshape(-1, 1)
out = RemoveSparse(0.5, False)(self.data)
np.testing.assert_array_equal(out, true_out)
out = RemoveSparse(1, False)(self.data)
np.testing.assert_array_equal(out, true_out)
def test_nan_sparse(self):
with self.data.unlocked():
self.data[1:, 1] = np.nan
self.data.X[:, 0] = 7
true_out = self.data[:, 0].copy()
with true_out.unlocked(true_out.X):
true_out.X = true_out.X.reshape(-1, 1)
true_out.X = csr_matrix(true_out.X)
self.data.X = csr_matrix(self.data.X)
out = RemoveSparse(0.5, False)(self.data)
np.testing.assert_array_equal(out, true_out)
out = RemoveSparse(1, False)(self.data)
np.testing.assert_array_equal(out, true_out)
if __name__ == '__main__':
unittest.main()
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