add more vendored numpy tests

these are for various regressions

Author: ndgrigorian

mkl_random/tests/third_party/test_numpy_random.py

@@ -1,6 +1,7 @@
 # This file includes tests from numpy.random module:
 # https://github.com/numpy/numpy/blob/main/numpy/random/tests/test_random.py
 
+import sys
 import warnings
 
 import numpy as np
@@ -1128,3 +1129,178 @@ def test_three_arg_funcs(self):
 
             out = func(argOne, argTwo[0], argThree)
             assert_equal(out.shape, tgtShape)
+
+
+class TestRegression:
+
+    def test_VonMises_range(self):
+        # Make sure generated random variables are in [-pi, pi].
+        # Regression test for ticket #986.
+        for mu in np.linspace(-7.0, 7.0, 5):
+            r = mkl_random.vonmises(mu, 1, 50)
+            assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
+
+    def test_hypergeometric_range(self):
+        # Test for ticket #921
+        assert_(np.all(mkl_random.hypergeometric(3, 18, 11, size=10) < 4))
+        assert_(np.all(mkl_random.hypergeometric(18, 3, 11, size=10) > 0))
+
+        # Test for ticket #5623
+        args = [
+            (2**20 - 2, 2**20 - 2, 2**20 - 2),  # Check for 32-bit systems
+        ]
+        for arg in args:
+            assert_(mkl_random.hypergeometric(*arg) > 0)
+
+    def test_logseries_convergence(self):
+        # Test for ticket #923
+        N = 1000
+        mkl_random.seed(0)
+        rvsn = mkl_random.logseries(0.8, size=N)
+        # these two frequency counts should be close to theoretical
+        # numbers with this large sample
+        # theoretical large N result is 0.49706795
+        freq = np.sum(rvsn == 1) / N
+        msg = f"Frequency was {freq:f}, should be > 0.45"
+        assert_(freq > 0.45, msg)
+        # theoretical large N result is 0.19882718
+        freq = np.sum(rvsn == 2) / N
+        msg = f"Frequency was {freq:f}, should be < 0.23"
+        assert_(freq < 0.23, msg)
+
+    def test_shuffle_mixed_dimension(self):
+        # Test for trac ticket #2074
+        # only check that shuffle does not raise an error
+        for t in [
+            [1, 2, 3, None],
+            [(1, 1), (2, 2), (3, 3), None],
+            [1, (2, 2), (3, 3), None],
+            [(1, 1), 2, 3, None],
+        ]:
+            shuffled = list(t)
+            mkl_random.shuffle(shuffled)
+
+    def test_call_within_randomstate(self):
+        # Check that custom RandomState does not call into global state
+        m = mkl_random.RandomState()
+        m.seed(1234)
+        res = m.choice(10, size=10, p=np.ones(10) / 10.0)
+        for i in range(3):
+            mkl_random.seed(i)
+            m.seed(1234)
+            # If m.state is not honored, the result will change
+            assert_array_equal(m.choice(10, size=10, p=np.ones(10) / 10.0), res)
+
+    def test_multivariate_normal_size_types(self):
+        # Test for multivariate_normal issue with 'size' argument.
+        # Check that the multivariate_normal size argument can be a
+        # numpy integer.
+        mkl_random.multivariate_normal([0], [[0]], size=1)
+        mkl_random.multivariate_normal([0], [[0]], size=np.int_(1))
+        mkl_random.multivariate_normal([0], [[0]], size=np.int64(1))
+
+    def test_beta_small_parameters(self):
+        # Test that beta with small a and b parameters does not produce
+        # NaNs due to roundoff errors causing 0 / 0, gh-5851
+        mkl_random.seed(1234567890)
+        x = mkl_random.beta(0.0001, 0.0001, size=100)
+        assert_(not np.any(np.isnan(x)), "Nans in mkl_random.beta")
+
+    def test_choice_sum_of_probs_tolerance(self):
+        # The sum of probs should be 1.0 with some tolerance.
+        # For low precision dtypes the tolerance was too tight.
+        # See numpy github issue 6123.
+        mkl_random.seed(1234)
+        a = [1, 2, 3]
+        counts = [4, 4, 2]
+        for dt in np.float16, np.float32, np.float64:
+            probs = np.array(counts, dtype=dt) / sum(counts)
+            c = mkl_random.choice(a, p=probs)
+            assert_(c in a)
+            assert_raises(ValueError, mkl_random.choice, a, p=probs * 0.9)
+
+    def test_shuffle_of_array_of_different_length_strings(self):
+        # Test that permuting an array of different length strings
+        # will not cause a segfault on garbage collection
+        # Tests gh-7710
+        mkl_random.seed(1234)
+
+        a = np.array(["a", "a" * 1000])
+
+        for _ in range(100):
+            mkl_random.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+
+        gc.collect()
+
+    def test_shuffle_of_array_of_objects(self):
+        # Test that permuting an array of objects will not cause
+        # a segfault on garbage collection.
+        # See gh-7719
+        mkl_random.seed(1234)
+        a = np.array([np.arange(1), np.arange(4)], dtype=object)
+
+        for _ in range(1000):
+            mkl_random.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+
+        gc.collect()
+
+    def test_permutation_subclass(self):
+        class N(np.ndarray):
+            pass
+
+        rng = mkl_random.RandomState()
+        orig = np.arange(3).view(N)
+        rng.permutation(orig)
+        assert_array_equal(orig, np.arange(3).view(N))
+
+        class M:
+            a = np.arange(5)
+
+            def __array__(self, dtype=None, copy=None):
+                return self.a
+
+        m = M()
+        rng.permutation(m)
+        assert_array_equal(m.__array__(), np.arange(5))
+
+    def test_warns_byteorder(self):
+        # GH 13159
+        other_byteord_dt = "<i4" if sys.byteorder == "big" else ">i4"
+        with pytest.deprecated_call(match="non-native byteorder is not"):
+            mkl_random.randint(0, 200, size=10, dtype=other_byteord_dt)
+
+    def test_named_argument_initialization(self):
+        # GH 13669
+        rs1 = mkl_random.RandomState(123456789)
+        rs2 = mkl_random.RandomState(seed=123456789)
+        assert rs1.randint(0, 100) == rs2.randint(0, 100)
+
+    def test_choice_return_dtype(self):
+        # GH 9867, now long since the NumPy default changed.
+        c = mkl_random.choice(10, p=[0.1] * 10, size=2)
+        assert c.dtype == np.dtype(np.long)
+        c = mkl_random.choice(10, p=[0.1] * 10, replace=False, size=2)
+        assert c.dtype == np.dtype(np.long)
+        c = mkl_random.choice(10, size=2)
+        assert c.dtype == np.dtype(np.long)
+        c = mkl_random.choice(10, replace=False, size=2)
+        assert c.dtype == np.dtype(np.long)
+
+
+def test_multinomial_empty():
+    # gh-20483
+    # Ensure that empty p-vals are correctly handled
+    assert mkl_random.multinomial(10, []).shape == (0,)
+    assert mkl_random.multinomial(3, [], size=(7, 5, 3)).shape == (7, 5, 3, 0)
+
+
+def test_multinomial_1d_pval():
+    # gh-20483
+    with pytest.raises(TypeError, match="pvals must be a 1-d"):
+        mkl_random.multinomial(10, 0.3)