remove test_regression.py and replace with test_regression_randomstate

test_regression tests were vendored from numpy but outdated and harder to maintain

also remove vendored regression tests from test_numpy_random

Author: ndgrigorian

mkl_random/tests/test_regression.py

@@ -1129,178 +1129,3 @@ 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)