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-rw-r--r--numpy/random/tests/test_direct.py4
-rw-r--r--numpy/random/tests/test_generator_mt19937.py190
-rw-r--r--numpy/random/tests/test_randomstate.py3
-rw-r--r--numpy/random/tests/test_randomstate_regression.py29
-rw-r--r--numpy/random/tests/test_seed_sequence.py2
-rw-r--r--numpy/random/tests/test_smoke.py22
6 files changed, 223 insertions, 27 deletions
diff --git a/numpy/random/tests/test_direct.py b/numpy/random/tests/test_direct.py
index 0f57c4bd4..34d7bd278 100644
--- a/numpy/random/tests/test_direct.py
+++ b/numpy/random/tests/test_direct.py
@@ -10,7 +10,7 @@ from numpy.random import (
Generator, MT19937, PCG64, Philox, RandomState, SeedSequence, SFC64,
default_rng
)
-from numpy.random.common import interface
+from numpy.random._common import interface
try:
import cffi # noqa: F401
@@ -120,7 +120,7 @@ def gauss_from_uint(x, n, bits):
return gauss[:n]
def test_seedsequence():
- from numpy.random.bit_generator import (ISeedSequence,
+ from numpy.random._bit_generator import (ISeedSequence,
ISpawnableSeedSequence,
SeedlessSeedSequence)
diff --git a/numpy/random/tests/test_generator_mt19937.py b/numpy/random/tests/test_generator_mt19937.py
index 853d86fba..526275dda 100644
--- a/numpy/random/tests/test_generator_mt19937.py
+++ b/numpy/random/tests/test_generator_mt19937.py
@@ -4,7 +4,7 @@ import pytest
import numpy as np
from numpy.testing import (
- assert_, assert_raises, assert_equal,
+ assert_, assert_raises, assert_equal, assert_allclose,
assert_warns, assert_no_warnings, assert_array_equal,
assert_array_almost_equal, suppress_warnings)
@@ -115,6 +115,140 @@ class TestMultinomial(object):
assert_array_equal(non_contig, contig)
+class TestMultivariateHypergeometric(object):
+
+ def setup(self):
+ self.seed = 8675309
+
+ def test_argument_validation(self):
+ # Error cases...
+
+ # `colors` must be a 1-d sequence
+ assert_raises(ValueError, random.multivariate_hypergeometric,
+ 10, 4)
+
+ # Negative nsample
+ assert_raises(ValueError, random.multivariate_hypergeometric,
+ [2, 3, 4], -1)
+
+ # Negative color
+ assert_raises(ValueError, random.multivariate_hypergeometric,
+ [-1, 2, 3], 2)
+
+ # nsample exceeds sum(colors)
+ assert_raises(ValueError, random.multivariate_hypergeometric,
+ [2, 3, 4], 10)
+
+ # nsample exceeds sum(colors) (edge case of empty colors)
+ assert_raises(ValueError, random.multivariate_hypergeometric,
+ [], 1)
+
+ # Validation errors associated with very large values in colors.
+ assert_raises(ValueError, random.multivariate_hypergeometric,
+ [999999999, 101], 5, 1, 'marginals')
+
+ int64_info = np.iinfo(np.int64)
+ max_int64 = int64_info.max
+ max_int64_index = max_int64 // int64_info.dtype.itemsize
+ assert_raises(ValueError, random.multivariate_hypergeometric,
+ [max_int64_index - 100, 101], 5, 1, 'count')
+
+ @pytest.mark.parametrize('method', ['count', 'marginals'])
+ def test_edge_cases(self, method):
+ # Set the seed, but in fact, all the results in this test are
+ # deterministic, so we don't really need this.
+ random = Generator(MT19937(self.seed))
+
+ x = random.multivariate_hypergeometric([0, 0, 0], 0, method=method)
+ assert_array_equal(x, [0, 0, 0])
+
+ x = random.multivariate_hypergeometric([], 0, method=method)
+ assert_array_equal(x, [])
+
+ x = random.multivariate_hypergeometric([], 0, size=1, method=method)
+ assert_array_equal(x, np.empty((1, 0), dtype=np.int64))
+
+ x = random.multivariate_hypergeometric([1, 2, 3], 0, method=method)
+ assert_array_equal(x, [0, 0, 0])
+
+ x = random.multivariate_hypergeometric([9, 0, 0], 3, method=method)
+ assert_array_equal(x, [3, 0, 0])
+
+ colors = [1, 1, 0, 1, 1]
+ x = random.multivariate_hypergeometric(colors, sum(colors),
+ method=method)
+ assert_array_equal(x, colors)
+
+ x = random.multivariate_hypergeometric([3, 4, 5], 12, size=3,
+ method=method)
+ assert_array_equal(x, [[3, 4, 5]]*3)
+
+ # Cases for nsample:
+ # nsample < 10
+ # 10 <= nsample < colors.sum()/2
+ # colors.sum()/2 < nsample < colors.sum() - 10
+ # colors.sum() - 10 < nsample < colors.sum()
+ @pytest.mark.parametrize('nsample', [8, 25, 45, 55])
+ @pytest.mark.parametrize('method', ['count', 'marginals'])
+ @pytest.mark.parametrize('size', [5, (2, 3), 150000])
+ def test_typical_cases(self, nsample, method, size):
+ random = Generator(MT19937(self.seed))
+
+ colors = np.array([10, 5, 20, 25])
+ sample = random.multivariate_hypergeometric(colors, nsample, size,
+ method=method)
+ if isinstance(size, int):
+ expected_shape = (size,) + colors.shape
+ else:
+ expected_shape = size + colors.shape
+ assert_equal(sample.shape, expected_shape)
+ assert_((sample >= 0).all())
+ assert_((sample <= colors).all())
+ assert_array_equal(sample.sum(axis=-1),
+ np.full(size, fill_value=nsample, dtype=int))
+ if isinstance(size, int) and size >= 100000:
+ # This sample is large enough to compare its mean to
+ # the expected values.
+ assert_allclose(sample.mean(axis=0),
+ nsample * colors / colors.sum(),
+ rtol=1e-3, atol=0.005)
+
+ def test_repeatability1(self):
+ random = Generator(MT19937(self.seed))
+ sample = random.multivariate_hypergeometric([3, 4, 5], 5, size=5,
+ method='count')
+ expected = np.array([[2, 1, 2],
+ [2, 1, 2],
+ [1, 1, 3],
+ [2, 0, 3],
+ [2, 1, 2]])
+ assert_array_equal(sample, expected)
+
+ def test_repeatability2(self):
+ random = Generator(MT19937(self.seed))
+ sample = random.multivariate_hypergeometric([20, 30, 50], 50,
+ size=5,
+ method='marginals')
+ expected = np.array([[ 9, 17, 24],
+ [ 7, 13, 30],
+ [ 9, 15, 26],
+ [ 9, 17, 24],
+ [12, 14, 24]])
+ assert_array_equal(sample, expected)
+
+ def test_repeatability3(self):
+ random = Generator(MT19937(self.seed))
+ sample = random.multivariate_hypergeometric([20, 30, 50], 12,
+ size=5,
+ method='marginals')
+ expected = np.array([[2, 3, 7],
+ [5, 3, 4],
+ [2, 5, 5],
+ [5, 3, 4],
+ [1, 5, 6]])
+ assert_array_equal(sample, expected)
+
+
class TestSetState(object):
def setup(self):
self.seed = 1234567890
@@ -732,6 +866,29 @@ class TestRandomDist(object):
desired = conv([4, 1, 9, 8, 0, 5, 3, 6, 2, 7])
assert_array_equal(actual, desired)
+ def test_shuffle_custom_axis(self):
+ random = Generator(MT19937(self.seed))
+ actual = np.arange(16).reshape((4, 4))
+ random.shuffle(actual, axis=1)
+ desired = np.array([[ 0, 3, 1, 2],
+ [ 4, 7, 5, 6],
+ [ 8, 11, 9, 10],
+ [12, 15, 13, 14]])
+ assert_array_equal(actual, desired)
+ random = Generator(MT19937(self.seed))
+ actual = np.arange(16).reshape((4, 4))
+ random.shuffle(actual, axis=-1)
+ assert_array_equal(actual, desired)
+
+ def test_shuffle_axis_nonsquare(self):
+ y1 = np.arange(20).reshape(2, 10)
+ y2 = y1.copy()
+ random = Generator(MT19937(self.seed))
+ random.shuffle(y1, axis=1)
+ random = Generator(MT19937(self.seed))
+ random.shuffle(y2.T)
+ assert_array_equal(y1, y2)
+
def test_shuffle_masked(self):
# gh-3263
a = np.ma.masked_values(np.reshape(range(20), (5, 4)) % 3 - 1, -1)
@@ -746,6 +903,16 @@ class TestRandomDist(object):
assert_equal(
sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask]))
+ def test_shuffle_exceptions(self):
+ random = Generator(MT19937(self.seed))
+ arr = np.arange(10)
+ assert_raises(np.AxisError, random.shuffle, arr, 1)
+ arr = np.arange(9).reshape((3, 3))
+ assert_raises(np.AxisError, random.shuffle, arr, 3)
+ assert_raises(TypeError, random.shuffle, arr, slice(1, 2, None))
+ arr = [[1, 2, 3], [4, 5, 6]]
+ assert_raises(NotImplementedError, random.shuffle, arr, 1)
+
def test_permutation(self):
random = Generator(MT19937(self.seed))
alist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0]
@@ -771,6 +938,27 @@ class TestRandomDist(object):
actual = random.permutation(integer_val)
assert_array_equal(actual, desired)
+ def test_permutation_custom_axis(self):
+ a = np.arange(16).reshape((4, 4))
+ desired = np.array([[ 0, 3, 1, 2],
+ [ 4, 7, 5, 6],
+ [ 8, 11, 9, 10],
+ [12, 15, 13, 14]])
+ random = Generator(MT19937(self.seed))
+ actual = random.permutation(a, axis=1)
+ assert_array_equal(actual, desired)
+ random = Generator(MT19937(self.seed))
+ actual = random.permutation(a, axis=-1)
+ assert_array_equal(actual, desired)
+
+ def test_permutation_exceptions(self):
+ random = Generator(MT19937(self.seed))
+ arr = np.arange(10)
+ assert_raises(np.AxisError, random.permutation, arr, 1)
+ arr = np.arange(9).reshape((3, 3))
+ assert_raises(np.AxisError, random.permutation, arr, 3)
+ assert_raises(TypeError, random.permutation, arr, slice(1, 2, None))
+
def test_beta(self):
random = Generator(MT19937(self.seed))
actual = random.beta(.1, .9, size=(3, 2))
diff --git a/numpy/random/tests/test_randomstate.py b/numpy/random/tests/test_randomstate.py
index a0edc5c23..5131f1839 100644
--- a/numpy/random/tests/test_randomstate.py
+++ b/numpy/random/tests/test_randomstate.py
@@ -11,7 +11,8 @@ from numpy.testing import (
suppress_warnings
)
-from numpy.random import MT19937, PCG64, mtrand as random
+from numpy.random import MT19937, PCG64
+from numpy import random
INT_FUNCS = {'binomial': (100.0, 0.6),
'geometric': (.5,),
diff --git a/numpy/random/tests/test_randomstate_regression.py b/numpy/random/tests/test_randomstate_regression.py
index 29870534a..bdc2214b6 100644
--- a/numpy/random/tests/test_randomstate_regression.py
+++ b/numpy/random/tests/test_randomstate_regression.py
@@ -8,7 +8,7 @@ from numpy.testing import (
from numpy.compat import long
import numpy as np
-from numpy.random import mtrand as random
+from numpy import random
class TestRegression(object):
@@ -181,3 +181,30 @@ class TestRegression(object):
assert c.dtype == np.dtype(int)
c = np.random.choice(10, replace=False, size=2)
assert c.dtype == np.dtype(int)
+
+ @pytest.mark.skipif(np.iinfo('l').max < 2**32,
+ reason='Cannot test with 32-bit C long')
+ def test_randint_117(self):
+ # GH 14189
+ random.seed(0)
+ expected = np.array([2357136044, 2546248239, 3071714933, 3626093760,
+ 2588848963, 3684848379, 2340255427, 3638918503,
+ 1819583497, 2678185683], dtype='int64')
+ actual = random.randint(2**32, size=10)
+ assert_array_equal(actual, expected)
+
+ def test_p_zero_stream(self):
+ # Regression test for gh-14522. Ensure that future versions
+ # generate the same variates as version 1.16.
+ np.random.seed(12345)
+ assert_array_equal(random.binomial(1, [0, 0.25, 0.5, 0.75, 1]),
+ [0, 0, 0, 1, 1])
+
+ def test_n_zero_stream(self):
+ # Regression test for gh-14522. Ensure that future versions
+ # generate the same variates as version 1.16.
+ np.random.seed(8675309)
+ expected = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [3, 4, 2, 3, 3, 1, 5, 3, 1, 3]])
+ assert_array_equal(random.binomial([[0], [10]], 0.25, size=(2, 10)),
+ expected)
diff --git a/numpy/random/tests/test_seed_sequence.py b/numpy/random/tests/test_seed_sequence.py
index 8d6d604a2..fe23680ed 100644
--- a/numpy/random/tests/test_seed_sequence.py
+++ b/numpy/random/tests/test_seed_sequence.py
@@ -1,7 +1,7 @@
import numpy as np
from numpy.testing import assert_array_equal
-from numpy.random.bit_generator import SeedSequence
+from numpy.random import SeedSequence
def test_reference_data():
diff --git a/numpy/random/tests/test_smoke.py b/numpy/random/tests/test_smoke.py
index 84d261e5e..6e641b5f4 100644
--- a/numpy/random/tests/test_smoke.py
+++ b/numpy/random/tests/test_smoke.py
@@ -5,7 +5,7 @@ from functools import partial
import numpy as np
import pytest
from numpy.testing import assert_equal, assert_, assert_array_equal
-from numpy.random import (Generator, MT19937, PCG64, Philox, SFC64, entropy)
+from numpy.random import (Generator, MT19937, PCG64, Philox, SFC64)
@pytest.fixture(scope='module',
params=(np.bool, np.int8, np.int16, np.int32, np.int64,
@@ -806,23 +806,3 @@ class TestDefaultRNG(RNG):
np.random.default_rng(-1)
with pytest.raises(ValueError):
np.random.default_rng([12345, -1])
-
-
-class TestEntropy(object):
- def test_entropy(self):
- e1 = entropy.random_entropy()
- e2 = entropy.random_entropy()
- assert_((e1 != e2))
- e1 = entropy.random_entropy(10)
- e2 = entropy.random_entropy(10)
- assert_((e1 != e2).all())
- e1 = entropy.random_entropy(10, source='system')
- e2 = entropy.random_entropy(10, source='system')
- assert_((e1 != e2).all())
-
- def test_fallback(self):
- e1 = entropy.random_entropy(source='fallback')
- time.sleep(0.1)
- e2 = entropy.random_entropy(source='fallback')
- assert_((e1 != e2))
-
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