test_random.py
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import numpy as np
import pytest
import scipy.sparse as sp
from scipy.special import comb
from numpy.testing import assert_array_almost_equal
from sklearn.utils.random import _random_choice_csc, sample_without_replacement
from sklearn.utils._random import _our_rand_r_py
###############################################################################
# test custom sampling without replacement algorithm
###############################################################################
def test_invalid_sample_without_replacement_algorithm():
with pytest.raises(ValueError):
sample_without_replacement(5, 4, "unknown")
def test_sample_without_replacement_algorithms():
methods = ("auto", "tracking_selection", "reservoir_sampling", "pool")
for m in methods:
def sample_without_replacement_method(n_population, n_samples,
random_state=None):
return sample_without_replacement(n_population, n_samples,
method=m,
random_state=random_state)
check_edge_case_of_sample_int(sample_without_replacement_method)
check_sample_int(sample_without_replacement_method)
check_sample_int_distribution(sample_without_replacement_method)
def check_edge_case_of_sample_int(sample_without_replacement):
# n_population < n_sample
with pytest.raises(ValueError):
sample_without_replacement(0, 1)
with pytest.raises(ValueError):
sample_without_replacement(1, 2)
# n_population == n_samples
assert sample_without_replacement(0, 0).shape == (0, )
assert sample_without_replacement(1, 1).shape == (1, )
# n_population >= n_samples
assert sample_without_replacement(5, 0).shape == (0, )
assert sample_without_replacement(5, 1).shape == (1, )
# n_population < 0 or n_samples < 0
with pytest.raises(ValueError):
sample_without_replacement(-1, 5)
with pytest.raises(ValueError):
sample_without_replacement(5, -1)
def check_sample_int(sample_without_replacement):
# This test is heavily inspired from test_random.py of python-core.
#
# For the entire allowable range of 0 <= k <= N, validate that
# the sample is of the correct length and contains only unique items
n_population = 100
for n_samples in range(n_population + 1):
s = sample_without_replacement(n_population, n_samples)
assert len(s) == n_samples
unique = np.unique(s)
assert np.size(unique) == n_samples
assert np.all(unique < n_population)
# test edge case n_population == n_samples == 0
assert np.size(sample_without_replacement(0, 0)) == 0
def check_sample_int_distribution(sample_without_replacement):
# This test is heavily inspired from test_random.py of python-core.
#
# For the entire allowable range of 0 <= k <= N, validate that
# sample generates all possible permutations
n_population = 10
# a large number of trials prevents false negatives without slowing normal
# case
n_trials = 10000
for n_samples in range(n_population):
# Counting the number of combinations is not as good as counting the
# the number of permutations. However, it works with sampling algorithm
# that does not provide a random permutation of the subset of integer.
n_expected = comb(n_population, n_samples, exact=True)
output = {}
for i in range(n_trials):
output[frozenset(sample_without_replacement(n_population,
n_samples))] = None
if len(output) == n_expected:
break
else:
raise AssertionError(
"number of combinations != number of expected (%s != %s)" %
(len(output), n_expected))
def test_random_choice_csc(n_samples=10000, random_state=24):
# Explicit class probabilities
classes = [np.array([0, 1]), np.array([0, 1, 2])]
class_probabilities = [np.array([0.5, 0.5]), np.array([0.6, 0.1, 0.3])]
got = _random_choice_csc(n_samples, classes, class_probabilities,
random_state)
assert sp.issparse(got)
for k in range(len(classes)):
p = np.bincount(got.getcol(k).toarray().ravel()) / float(n_samples)
assert_array_almost_equal(class_probabilities[k], p, decimal=1)
# Implicit class probabilities
classes = [[0, 1], [1, 2]] # test for array-like support
class_probabilities = [np.array([0.5, 0.5]), np.array([0, 1/2, 1/2])]
got = _random_choice_csc(n_samples=n_samples,
classes=classes,
random_state=random_state)
assert sp.issparse(got)
for k in range(len(classes)):
p = np.bincount(got.getcol(k).toarray().ravel()) / float(n_samples)
assert_array_almost_equal(class_probabilities[k], p, decimal=1)
# Edge case probabilities 1.0 and 0.0
classes = [np.array([0, 1]), np.array([0, 1, 2])]
class_probabilities = [np.array([1.0, 0.0]), np.array([0.0, 1.0, 0.0])]
got = _random_choice_csc(n_samples, classes, class_probabilities,
random_state)
assert sp.issparse(got)
for k in range(len(classes)):
p = np.bincount(got.getcol(k).toarray().ravel(),
minlength=len(class_probabilities[k])) / n_samples
assert_array_almost_equal(class_probabilities[k], p, decimal=1)
# One class target data
classes = [[1], [0]] # test for array-like support
class_probabilities = [np.array([0.0, 1.0]), np.array([1.0])]
got = _random_choice_csc(n_samples=n_samples,
classes=classes,
random_state=random_state)
assert sp.issparse(got)
for k in range(len(classes)):
p = np.bincount(got.getcol(k).toarray().ravel()) / n_samples
assert_array_almost_equal(class_probabilities[k], p, decimal=1)
def test_random_choice_csc_errors():
# the length of an array in classes and class_probabilities is mismatched
classes = [np.array([0, 1]), np.array([0, 1, 2, 3])]
class_probabilities = [np.array([0.5, 0.5]), np.array([0.6, 0.1, 0.3])]
with pytest.raises(ValueError):
_random_choice_csc(4, classes, class_probabilities, 1)
# the class dtype is not supported
classes = [np.array(["a", "1"]), np.array(["z", "1", "2"])]
class_probabilities = [np.array([0.5, 0.5]), np.array([0.6, 0.1, 0.3])]
with pytest.raises(ValueError):
_random_choice_csc(4, classes, class_probabilities, 1)
# the class dtype is not supported
classes = [np.array([4.2, 0.1]), np.array([0.1, 0.2, 9.4])]
class_probabilities = [np.array([0.5, 0.5]), np.array([0.6, 0.1, 0.3])]
with pytest.raises(ValueError):
_random_choice_csc(4, classes, class_probabilities, 1)
# Given probabilities don't sum to 1
classes = [np.array([0, 1]), np.array([0, 1, 2])]
class_probabilities = [np.array([0.5, 0.6]), np.array([0.6, 0.1, 0.3])]
with pytest.raises(ValueError):
_random_choice_csc(4, classes, class_probabilities, 1)
def test_our_rand_r():
assert 131541053 == _our_rand_r_py(1273642419)
assert 270369 == _our_rand_r_py(0)