[Numpy] random.randint() implemented

python/mxnet/ndarray/numpy/random.py

@@ -20,7 +20,71 @@
 from ...context import current_context
 from . import _internal as _npi
 
-__all__ = ['uniform']
+
+__all__ = ['randint', 'uniform']
+
+
+def randint(low, high=None, size=None, dtype=None, **kwargs):
+    """Return random integers from `low` (inclusive) to `high` (exclusive).
+
+    Return random integers from the "discrete uniform" distribution of
+    the specified dtype in the "half-open" interval [`low`, `high`). If
+    `high` is None (the default), then results are from [0, `low`).
+
+    Parameters
+    ----------
+    low : int
+        Lowest (signed) integer to be drawn from the distribution (unless
+        ``high=None``, in which case this parameter is one above the
+        *highest* such integer).
+    high : int, optional
+        If provided, one above the largest (signed) integer to be drawn
+        from the distribution (see above for behavior if ``high=None``).
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    dtype : dtype, optional
+        Desired dtype of the result. All dtypes are determined by their
+        name, i.e., 'int64', 'int', etc, so byteorder is not available
+        and a specific precision may have different C types depending
+        on the platform. The default value is 'np.int'.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+    out : ndarray, optional
+        The output ndarray (default is `None`).
+
+    Returns
+    -------
+    out : ndarray of ints
+        `size`-shaped array of random integers from the appropriate
+        distribution, or a single such random int if `size` not provided.
+
+    Examples
+    --------
+    >>> np.random.randint(2, size=10)
+    array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
+    >>> np.random.randint(1, size=10)
+    array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
+
+    Generate a 2 x 4 array of ints between 0 and 4, inclusive:
+
+    >>> np.random.randint(5, size=(2, 4))
+    array([[4, 0, 2, 1],
+        [3, 2, 2, 0]])
+    """
+    ctx = kwargs.pop('ctx', None)
+    out = kwargs.pop('out', None)
+    if dtype is None:
+        dtype = 'int'
+    if ctx is None:
+        ctx = current_context()
+    if size is None:
+        size = 1
+    if high is None:
+        high = low
+        low = 0
+    return _npi.random_randint(low, high, shape=size, dtype=dtype, ctx=ctx, out=out)
 
 
 def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):

python/mxnet/numpy/random.py

@@ -20,7 +20,60 @@
 from __future__ import absolute_import
 from ..ndarray import numpy as _mx_nd_np
 
-__all__ = ['uniform']
+
+__all__ = ["randint", "uniform"]
+
+
+def randint(low, high=None, size=None, dtype=None, **kwargs):
+    """Return random integers from `low` (inclusive) to `high` (exclusive).
+
+    Return random integers from the "discrete uniform" distribution of
+    the specified dtype in the "half-open" interval [`low`, `high`). If
+    `high` is None (the default), then results are from [0, `low`).
+
+    Parameters
+    ----------
+    low : int
+        Lowest (signed) integer to be drawn from the distribution (unless
+        ``high=None``, in which case this parameter is one above the
+        *highest* such integer).
+    high : int, optional
+        If provided, one above the largest (signed) integer to be drawn
+        from the distribution (see above for behavior if ``high=None``).
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    dtype : dtype, optional
+        Desired dtype of the result. All dtypes are determined by their
+        name, i.e., 'int64', 'int', etc, so byteorder is not available
+        and a specific precision may have different C types depending
+        on the platform. The default value is 'np.int'.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+    out : ndarray, optional
+        The output ndarray (default is `None`).
+
+    Returns
+    -------
+    out : ndarray of ints
+        `size`-shaped array of random integers from the appropriate
+        distribution, or a single such random int if `size` not provided.
+
+    Examples
+    --------
+    >>> np.random.randint(2, size=10)
+    array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
+    >>> np.random.randint(1, size=10)
+    array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
+
+    Generate a 2 x 4 array of ints between 0 and 4, inclusive:
+
+    >>> np.random.randint(5, size=(2, 4))
+    array([[4, 0, 2, 1],
+        [3, 2, 2, 0]])
+    """
+    return _mx_nd_np.random.randint(low, high, size, dtype, **kwargs)
 
 
 def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):

python/mxnet/symbol/numpy/random.py

@@ -21,7 +21,71 @@
 from ...context import current_context
 from . import _internal as _npi
 
-__all__ = ['uniform']
+
+__all__ = ['randint', 'uniform']
+
+
+def randint(low, high=None, size=None, dtype=None, **kwargs):
+    """Return random integers from `low` (inclusive) to `high` (exclusive).
+
+    Return random integers from the "discrete uniform" distribution of
+    the specified dtype in the "half-open" interval [`low`, `high`). If
+    `high` is None (the default), then results are from [0, `low`).
+
+    Parameters
+    ----------
+    low : int
+        Lowest (signed) integer to be drawn from the distribution (unless
+        ``high=None``, in which case this parameter is one above the
+        *highest* such integer).
+    high : int, optional
+        If provided, one above the largest (signed) integer to be drawn
+        from the distribution (see above for behavior if ``high=None``).
+    size : int or tuple of ints, optional
+        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
+        ``m * n * k`` samples are drawn.  Default is None, in which case a
+        single value is returned.
+    dtype : dtype, optional
+        Desired dtype of the result. All dtypes are determined by their
+        name, i.e., 'int64', 'int', etc, so byteorder is not available
+        and a specific precision may have different C types depending
+        on the platform. The default value is 'np.int'.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+    out : symbol, optional
+        The output symbol (default is `None`).
+
+    Returns
+    -------
+    out : symbol
+        `size`-shaped array of random integers from the appropriate
+        distribution, or a single such random int if `size` not provided.
+
+    Examples
+    --------
+    >>> np.random.randint(2, size=10)
+    array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
+    >>> np.random.randint(1, size=10)
+    array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
+
+    Generate a 2 x 4 array of ints between 0 and 4, inclusive:
+
+    >>> np.random.randint(5, size=(2, 4))
+    array([[4, 0, 2, 1],
+        [3, 2, 2, 0]])
+    """
+    ctx = kwargs.pop('ctx', None)
+    out = kwargs.pop('out', None)
+    if dtype is None:
+        dtype = 'int'
+    if ctx is None:
+        ctx = current_context()
+    if size is None:
+        size = 1
+    if high is None:
+        high = low
+        low = 0
+    return _npi.random_randint(low, high, shape=size, dtype=dtype, ctx=ctx, out=out)
 
 
 def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):

src/operator/random/sample_op.cc

@@ -182,6 +182,7 @@ Example::
 
 MXNET_OPERATOR_REGISTER_SAMPLE(_random_randint, SampleRandIntParam)
 .add_alias("random_randint")
+.add_alias("_npi_random_randint")
 .describe(R"code(Draw random samples from a discrete uniform distribution.
 
 Samples are uniformly distributed over the half-open interval *[low, high)*

tests/python/unittest/test_numpy_op.py

@@ -28,6 +28,8 @@
 from common import assertRaises, with_seed
 import random
 import collections
+import scipy.stats as ss
+from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, retry
 
 
 @with_seed()
@@ -931,6 +933,50 @@ def hybrid_forward(self, F, a, *args):
                 assert same(mx_out.asnumpy(), np_out)
 
 
+@with_seed()
+@use_np
+def test_np_randint():
+    ctx = mx.context.current_context()
+    # test shapes
+    params = [
+        (0, 10),
+        (5, None)
+    ]
+    shapes = [
+        (3, 3),
+        (3, 4),
+        (0, 0),
+        (3, 3, 3),
+        (0, 0, 0),
+        (2, 2, 4, 3),
+        (2, 2, 4, 3),
+        (2, 0, 3, 0),
+        (2, 0, 2, 3)
+    ]
+    for shape in shapes:
+        for (low, high) in params:
+            data_mx = np.random.randint(low, high, size=shape)
+            assert data_mx.shape == shape
+
+    # test generator
+    for dtype in ['int32', 'int64']:
+        for low, high in [(50000000, 50001000),(-50000100,-50000000),(-500,199)]:
+            scale = high - low
+            buckets, probs = gen_buckets_probs_with_ppf(lambda x: ss.uniform.ppf(x, loc=low, scale=scale), 5)
+            # Quantize bucket boundaries to reflect the actual dtype and adjust probs accordingly
+            buckets = _np.array(buckets, dtype=dtype).tolist()
+            probs = [(buckets[i][1] - buckets[i][0]) / float(scale) for i in range(5)]
+            generator_mx = lambda x: np.random.randint(low, high, size=x, dtype=dtype, ctx=ctx).asnumpy()
+            verify_generator(generator=generator_mx, buckets=buckets, probs=probs, nrepeat=100)
+            # Scipy uses alpha = 0.01 for testing discrete distribution generator but we are using default alpha=0.05 (higher threshold ensures robustness)
+            # Refer - https://github.com/scipy/scipy/blob/9f12af697763fb5f9767d5cb1280ce62456a3974/scipy/stats/tests/test_discrete_basic.py#L45
+            generator_mx_same_seed = \
+                lambda x: _np.concatenate(
+                    [np.random.randint(low, high, size=x // 10, dtype=dtype, ctx=ctx).asnumpy()
+                        for _ in range(10)])
+            verify_generator(generator=generator_mx_same_seed, buckets=buckets, probs=probs, nrepeat=100)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()