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# Test methods with long descriptive names can omit docstrings
# pylint: disable=missing-docstring
import unittest
import numpy as np
import scipy.sparse as sp
from Orange.data import Table, Domain, ContinuousVariable
from Orange.preprocess import Normalize
from Orange.tests import test_filename
class TestNormalizer(unittest.TestCase):
def compare_tables(self, dataNorm, solution):
for i in range(len(dataNorm)):
for j in range(len(dataNorm[i])):
if type(solution[i][j]) == float:
self.assertAlmostEqual(dataNorm[i, j], solution[i][j], places=3)
else:
self.assertEqual(dataNorm[i, j], solution[i][j])
self.assertEqual([attr.name for attr in dataNorm.domain.attributes],
["c1", "c2", "d1", "d2", "n1", "n2", "c3", "d3", "c4"])
self.assertEqual([attr.name for attr in dataNorm.domain.class_vars],
["cl1", "cl2"])
@classmethod
def setUpClass(cls):
cls.data = Table(test_filename("datasets/test5.tab"))
def test_normalize_default(self):
normalizer = Normalize()
data_norm = normalizer(self.data)
solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],
[0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],
[0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]
self.compare_tables(data_norm, solution)
def test_normalize_transform_by_sd(self):
normalizer = Normalize(zero_based=False,
norm_type=Normalize.NormalizeBySD,
transform_class=False)
data_norm = normalizer(self.data)
solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],
[0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],
[0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]
self.compare_tables(data_norm, solution)
def test_normalize_transform_class(self):
normalizer = Normalize(zero_based=True,
norm_type=Normalize.NormalizeBySD,
transform_class=True)
data_norm = normalizer(self.data)
solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 1.225],
[0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', -1.225],
[0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 0.]]
self.compare_tables(data_norm, solution)
def test_normalize_transform_by_span(self):
normalizer = Normalize(zero_based=False,
norm_type=Normalize.NormalizeBySpan,
transform_class=False)
data_norm = normalizer(self.data)
solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 2.],
[0., -1., 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0.],
[0., 0., 'a', 'b', 1., 'b', -1., 'c', '?', 'c', 1.]]
self.compare_tables(data_norm, solution)
def test_normalize_transform_by_span_zero(self):
normalizer = Normalize(zero_based=True,
norm_type=Normalize.NormalizeBySpan,
transform_class=False)
data_norm = normalizer(self.data)
solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 2.],
[0., 0., 'a', 'b', 0., '?', 0.5, 'b', '?', 'b', 0.],
[0., 0.5, 'a', 'b', 1., 'b', 0., 'c', '?', 'c', 1.]]
self.compare_tables(data_norm, solution)
def test_normalize_transform_by_span_class(self):
normalizer = Normalize(zero_based=False,
norm_type=Normalize.NormalizeBySpan,
transform_class=True)
data_norm = normalizer(self.data)
solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 1.],
[0., -1., 'a', 'b', -1., '?', 0., 'b', '?', 'b', -1.],
[0., 0., 'a', 'b', 1., 'b', -1., 'c', '?', 'c', 0.]]
self.compare_tables(data_norm, solution)
def test_normalize_transform_by_span_zero_class(self):
normalizer = Normalize(zero_based=True,
norm_type=Normalize.NormalizeBySpan,
transform_class=True)
data_norm = normalizer(self.data)
solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 1.],
[0., 0., 'a', 'b', 0., '?', 0.5, 'b', '?', 'b', 0.],
[0., 0.5, 'a', 'b', 1., 'b', 0., 'c', '?', 'c', 0.5]]
self.compare_tables(data_norm, solution)
def test_normalize_sparse(self):
domain = Domain([ContinuousVariable(str(i)) for i in range(3)])
# pylint: disable=bad-whitespace
X = np.array([
[0, -1, -2],
[0, 1, 2],
])
data = Table.from_numpy(domain, X).to_sparse()
# pylint: disable=bad-whitespace
solution = sp.csr_matrix(np.array([
[0, -1, -1],
[0, 1, 1],
]))
normalizer = Normalize()
normalized = normalizer(data)
self.assertEqual((normalized.X != solution).nnz, 0)
# raise error for non-zero offsets
with data.unlocked():
data.X = sp.csr_matrix(np.array([
[0, 0, 0],
[0, 1, 3],
[0, 2, 4],
]))
with self.assertRaises(ValueError):
normalizer(data)
def test_skip_normalization(self):
data = self.data.copy()
for attr in data.domain.attributes:
attr.attributes = {'skip-normalization': True}
normalizer = Normalize()
normalized = normalizer(data)
np.testing.assert_array_equal(data.X, normalized.X)
def test_datetime_normalization(self):
data = Table(test_filename("datasets/test10.tab"))
normalizer = Normalize(zero_based=False,
norm_type=Normalize.NormalizeBySD,
transform_class=False)
data_norm = normalizer(data)
solution = [[0., '1995-01-21', 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],
[0., '2003-07-23', 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],
[0., '1967-03-12', 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]
self.compare_tables(data_norm, solution)
def test_retain_vars_attributes(self):
data = Table("iris")
attributes = {"foo": "foo", "baz": 1}
data.domain.attributes[0].attributes = attributes
self.assertDictEqual(
Normalize(norm_type=Normalize.NormalizeBySD)(
data).domain.attributes[0].attributes, attributes)
self.assertDictEqual(
Normalize(norm_type=Normalize.NormalizeBySpan)(
data).domain.attributes[0].attributes, attributes)
def test_number_of_decimals(self):
foo = ContinuousVariable("Foo", number_of_decimals=0)
data = Table.from_list(Domain((foo,)), [[1], [2], [3]])
normalized = Normalize()(data)
norm_foo: ContinuousVariable = normalized.domain.attributes[0]
self.assertGreater(norm_foo.number_of_decimals, 0)
for val1, val2 in zip(normalized[:, "Foo"],
["-1.225", "0.0", "1.225"]):
self.assertEqual(str(val1[0]), val2)
if __name__ == "__main__":
unittest.main()
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