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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
from Orange.preprocess import Randomize
class TestRandomizer(unittest.TestCase):
@classmethod
def setUpClass(cls):
np.random.seed(42)
cls.zoo = Table("zoo")
def test_randomize_default(self):
data = self.zoo
randomizer = Randomize()
data_rand = randomizer(data)
self.assertTrue((data.X == data_rand.X).all())
self.assertTrue((data.metas == data_rand.metas).all())
self.assertTrue((data.Y != data_rand.Y).any())
self.assertTrue((np.sort(data.Y, axis=0) == np.sort(
data_rand.Y, axis=0)).all())
def test_randomize_classes(self):
data = self.zoo
randomizer = Randomize(rand_type=Randomize.RandomizeClasses)
data_rand = randomizer(data)
self.assertTrue((data.X == data_rand.X).all())
self.assertTrue((data.metas == data_rand.metas).all())
self.assertTrue((data.Y != data_rand.Y).any())
self.assertTrue((np.sort(data.Y, axis=0) == np.sort(
data_rand.Y, axis=0)).all())
def test_randomize_attributes(self):
data = self.zoo
randomizer = Randomize(rand_type=Randomize.RandomizeAttributes)
data_rand = randomizer(data)
self.assertTrue((data.Y == data_rand.Y).all())
self.assertTrue((data.metas == data_rand.metas).all())
self.assertTrue((data.X != data_rand.X).any())
self.assertTrue((np.sort(data.X, axis=0) == np.sort(
data_rand.X, axis=0)).all())
def test_randomize_metas(self):
data = self.zoo
randomizer = Randomize(rand_type=Randomize.RandomizeMetas)
data_rand = randomizer(data)
self.assertTrue((data.X == data_rand.X).all())
self.assertTrue((data.Y == data_rand.Y).all())
self.assertTrue((data.metas != data_rand.metas).any())
self.assertTrue((np.sort(data.metas, axis=0) == np.sort(
data_rand.metas, axis=0)).all())
def test_randomize_all(self):
data = self.zoo
rand_type = Randomize.RandomizeClasses | Randomize.RandomizeAttributes \
| Randomize.RandomizeMetas
randomizer = Randomize(rand_type=rand_type)
data_rand = randomizer(data)
self.assertTrue((data.Y != data_rand.Y).any())
self.assertTrue((np.sort(data.Y, axis=0) == np.sort(
data_rand.Y, axis=0)).all())
self.assertTrue((data.X != data_rand.X).any())
self.assertTrue((np.sort(data.X, axis=0) == np.sort(
data_rand.X, axis=0)).all())
self.assertTrue((data.metas != data_rand.metas).any())
self.assertTrue((np.sort(data.metas, axis=0) == np.sort(
data_rand.metas, axis=0)).all())
def test_randomize_keep_original_data(self):
data_orig = self.zoo
data = Table("zoo")
_ = Randomize(rand_type=Randomize.RandomizeClasses)(data)
_ = Randomize(rand_type=Randomize.RandomizeAttributes)(data)
_ = Randomize(rand_type=Randomize.RandomizeMetas)(data)
self.assertTrue((data.X == data_orig.X).all())
self.assertTrue((data.metas == data_orig.metas).all())
self.assertTrue((data.Y == data_orig.Y).all())
def test_randomize_replicate(self):
randomizer1 = Randomize(rand_seed=1)
rand_data11 = randomizer1(self.zoo)
rand_data12 = randomizer1(self.zoo)
randomizer2 = Randomize(rand_seed=1)
rand_data2 = randomizer2(self.zoo)
np.testing.assert_array_equal(rand_data11.Y, rand_data12.Y)
np.testing.assert_array_equal(rand_data11.Y, rand_data2.Y)
def test_randomize(self):
x = np.arange(10000, dtype=int).reshape((100, 100))
randomized = Randomize().randomize(x.copy())
# Do not mix data between columns
np.testing.assert_equal(randomized % 100, x % 100)
# Do not shuffle entire rows:
# lexical sorting of rows should not equal the original table
randomized = np.array(sorted(list(map(list, randomized))), dtype=int)
self.assertFalse(np.all(randomized == x))
def test_randomize_sparse(self):
x = np.array([[0, 0, 3, 0],
[1, 0, 2, 0],
[4, 5, 6, 7]])
randomize = Randomize().randomize
randomized = randomize(sp.csr_matrix(x), rand_state=1)
randomized = randomized.toarray()
# Data is shuffled (rand_seed=1 should always shuffle it)
self.assertFalse(np.all(x == randomized))
# Data remains within a column
self.assertTrue(all(sorted(x[:, i]) == sorted(randomized[:, i])
for i in range(4)))
# Do not shuffle entire rows
randomized = np.array(sorted(list(map(list, randomized))), dtype=int)
self.assertFalse(np.all(randomized == x))
# Test that shuffle is not sparse structure dependent
x = np.array([[1, 2, 3, 4],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]])
randomized = randomize(sp.csr_matrix(x), rand_state=0x393f)
self.assertFalse(np.all(x == randomized.todense()))
# Do not just assign some indices. I.e. make sure that the shuffling is
# dependent in the input's non-zero indices.
r_once = randomize(sp.csr_matrix(x), rand_state=1)
r_twice = randomize(r_once.copy(), rand_state=1)
self.assertFalse(np.all(r_once.todense() == r_twice.todense()))
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