Add more vendored NumPy tests

mkl_random/mklrand.pyx

@@ -2189,17 +2189,17 @@ cdef class _MKLRandomState:
 
         if size is not None:
             if (np.prod(size) == 0):
-                return np.empty(size, dtype=np.dtype(dtype))
+                return np.empty(size, dtype=np.dtype(_dtype))
 
-        lowbnd, highbnd, randfunc = self._choose_randint_type(dtype)
+        lowbnd, highbnd, randfunc = self._choose_randint_type(_dtype)
 
         if low < lowbnd:
             raise ValueError(
-                f"low is out of bounds for {np.dtype(dtype).name}"
+                f"low is out of bounds for {np.dtype(_dtype).name}"
             )
         if high > highbnd:
             raise ValueError(
-                f"high is out of bounds for {np.dtype(dtype).name}"
+                f"high is out of bounds for {np.dtype(_dtype).name}"
             )
         if low >= high:
             raise ValueError("low >= high")
@@ -6303,29 +6303,55 @@ cdef class _MKLRandomState:
         array([100,   0])
 
         """
-        cdef cnp.npy_intp d
+        cdef cnp.npy_intp d, sz, niter
         cdef cnp.ndarray parr "arrayObject_parr", mnarr "arrayObject_mnarr"
         cdef double *pix
         cdef int *mnix
-        cdef cnp.npy_intp sz
-
-        d = len(pvals)
-        parr = <cnp.ndarray>cnp.PyArray_ContiguousFromObject(
-            pvals, cnp.NPY_DOUBLE, 1, 1
+        cdef long ni
+
+        parr = <cnp.ndarray>cnp.PyArray_FROMANY(
+            pvals,
+            cnp.NPY_DOUBLE,
+            0,
+            1,
+            cnp.NPY_ARRAY_ALIGNED | cnp.NPY_ARRAY_C_CONTIGUOUS
         )
+        if cnp.PyArray_NDIM(parr) == 0:
+            raise TypeError("pvals must be a 1-d sequence")
+        d = cnp.PyArray_SIZE(parr)
         pix = <double*>cnp.PyArray_DATA(parr)
-
-        if kahan_sum(pix, d-1) > (1.0 + 1e-12):
-            raise ValueError("sum(pvals[:-1]) > 1.0")
-
+        if (
+            not np.all(np.greater_equal(parr, 0))
+            or not np.all(np.less_equal(parr, 1))
+        ):
+            raise ValueError("pvals < 0, pvals > 1 or pvals is NaN")
+
+        if d and kahan_sum(pix, d - 1) > (1.0 + 1e-12):
+            # When floating, but not float dtype, and close, improve the error
+            # 1.0001 works for float16 and float32
+            if (isinstance(pvals, np.ndarray)
+                    and np.issubdtype(pvals.dtype, np.floating)
+                    and pvals.dtype != float
+                    and pvals.sum() < 1.0001):
+                msg = ("sum(pvals[:-1].astype(np.float64)) > 1.0. The pvals "
+                       "array is cast to 64-bit floating point prior to "
+                       "checking the sum. Precision changes when casting may "
+                       "cause problems even if the sum of the original pvals "
+                       "is valid.")
+            else:
+                msg = "sum(pvals[:-1]) > 1.0"
+            raise ValueError(msg)
         shape = _shape_from_size(size, d)
         multin = np.zeros(shape, np.int32)
-
         mnarr = <cnp.ndarray>multin
         mnix = <int*>cnp.PyArray_DATA(mnarr)
         sz = cnp.PyArray_SIZE(mnarr)
-
-        irk_multinomial_vec(self.internal_state, sz // d, mnix, n, d, pix)
+        ni = n
+        if (ni < 0):
+            raise ValueError("n < 0")
+        # numpy#20483: Avoids divide by 0
+        niter = sz // d if d else 0
+        irk_multinomial_vec(self.internal_state, niter, mnix, n, d, pix)
 
         return multin
 
@@ -6614,11 +6640,27 @@ cdef class _MKLRandomState:
 
         """
         if isinstance(x, (int, np.integer)):
-            arr = np.arange(x)
-        else:
-            arr = np.array(x)
-        self.shuffle(arr)
-        return arr
+            # keep using long as the default here (main numpy switched to intp)
+            arr = np.arange(x, dtype=np.result_type(x, np.long))
+            self.shuffle(arr)
+            return arr
+
+        arr = np.asarray(x)
+        if arr.ndim < 1:
+            raise IndexError("x must be an integer or at least 1-dimensional")
+
+        # shuffle has fast-path for 1-d
+        if arr.ndim == 1:
+            # Return a copy if same memory
+            if np.may_share_memory(arr, x):
+                arr = np.array(arr)
+            self.shuffle(arr)
+            return arr
+
+        # Shuffle index array, dtype to ensure fast path
+        idx = np.arange(arr.shape[0], dtype=np.intp)
+        self.shuffle(idx)
+        return arr[idx]
 
 
 cdef class MKLRandomState(_MKLRandomState):

mkl_random/tests/test_random.py

@@ -224,7 +224,7 @@ class RandIntData(NamedTuple):
 def randint():
     rfunc_method = rnd.randint
     integral_dtypes = [
-        np.bool_,
+        np.bool,
         np.int8,
         np.uint8,
         np.int16,
@@ -323,8 +323,9 @@ def test_randint_respect_dtype_singleton(randint):
         assert_equal(sample.dtype, np.dtype(dt))
 
     for dt in (bool, int):
-        lbnd = 0 if dt is bool else np.iinfo(np.dtype(dt)).min
-        ubnd = 2 if dt is bool else np.iinfo(np.dtype(dt)).max + 1
+        # The legacy rng uses "long" as the default integer:
+        lbnd = 0 if dt is bool else np.iinfo("long").min
+        ubnd = 2 if dt is bool else np.iinfo("long").max + 1
 
         # gh-7284: Ensure that we get Python data types
         sample = randint.rfunc(lbnd, ubnd, dtype=dt)

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)