numpy doc enhancement (apache#16637)

* Change NDArray to ndarray for npx ops

Add nonzero

boolean mask supports boolean ndarray

Add argmin op and interoperability test for nonzero

Fix vdot, inner, outter docs

Add nonzero to mx.nd.np

Add docs

Fix

* Fix lint

* Fix

* Fix

* Fix get_constant

Author: reminisce

python/mxnet/_numpy_op_doc.py

@@ -34,6 +34,24 @@ def _np_ones_like(a):
     -------
     out : ndarray
         Array of ones with the same shape and type as `a`.
+
+    Examples
+    --------
+    >>> x = np.arange(6)
+    >>> x = x.reshape((2, 3))
+    >>> x
+    array([[0., 1., 2.],
+           [3., 4., 5.]])
+    >>> np.ones_like(x)
+    array([[1., 1., 1.],
+           [1., 1., 1.]])
+
+    >>> y = np.arange(3, dtype=float)
+    >>> y
+    array([0., 1., 2.], dtype=float64)
+    >>>
+    >>> np.ones_like(y)
+    array([1., 1., 1.], dtype=float64)
     """
     pass
 
@@ -52,6 +70,23 @@ def _np_zeros_like(a):
     -------
     out : ndarray
         Array of zeros with the same shape and type as `a`.
+
+    Examples
+    --------
+    >>> x = np.arange(6)
+    >>> x = x.reshape((2, 3))
+    >>> x
+    array([[0., 1., 2.],
+           [3., 4., 5.]])
+    >>> np.zeros_like(x)
+    array([[0., 0., 0.],
+           [0., 0., 0.]])
+    >>> y = np.arange(3, dtype=float)
+    >>> y
+    array([0., 1., 2.], dtype=float64)
+    >>>
+    >>> np.zeros_like(y)
+    array([0., 0., 0.], dtype=float64)
     """
     pass
 
@@ -477,6 +512,31 @@ def _np_reshape(a, newshape, order='C', out=None):
     See Also
     --------
     ndarray.reshape : Equivalent method.
+
+    Examples
+    --------
+    >>> a = np.arange(6).reshape((3, 2))
+    >>> a
+    array([[0., 1.],
+           [2., 3.],
+           [4., 5.]])
+
+    >>> np.reshape(a, (2, 3)) # C-like index ordering
+    array([[0., 1., 2.],
+           [3., 4., 5.]])
+
+    >>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
+    array([[0., 1., 2.],
+           [3., 4., 5.]])
+
+    >>> a = np.array([[1,2,3], [4,5,6]])
+    >>> np.reshape(a, 6)
+    array([1., 2., 3., 4., 5., 6.])
+
+    >>> np.reshape(a, (3,-1))       # the unspecified value is inferred to be 2
+    array([[1., 2.],
+           [3., 4.],
+           [5., 6.]])
     """
 
 

python/mxnet/base.py

@@ -20,6 +20,7 @@
 """ctypes library of mxnet and helper functions."""
 from __future__ import absolute_import
 
+import re
 import atexit
 import ctypes
 import os
@@ -853,3 +854,5 @@ def _init_np_op_module(root_module_name, np_module_name, mx_module_name, make_op
 
         if hasattr(_np_op_doc, name):
             function.__doc__ = getattr(_np_op_doc, name).__doc__
+        else:
+            function.__doc__ = re.sub('NDArray', 'ndarray', function.__doc__)

python/mxnet/gluon/parameter.py

@@ -674,7 +674,8 @@ def __init__(self, **kwargs):
     """
     def __init__(self, name, value):
         if not isinstance(value, ndarray.NDArray):
-            value = ndarray.array(value)
+            array_fn = _mx_np.array if is_np_array() else ndarray.array
+            value = array_fn(value)
         self.value = value
 
         class Init(initializer.Initializer):

python/mxnet/ndarray/numpy/_op.py

@@ -34,11 +34,11 @@
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
            'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate',
-           'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
+           'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'argmin',
            'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal',
-           'hsplit', 'rot90', 'einsum', 'true_divide']
+           'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -3165,8 +3165,6 @@ def clip(a, a_min, a_max, out=None):
 @set_module('mxnet.ndarray.numpy')
 def argmax(a, axis=None, out=None):
     r"""
-    argmax(a, axis=None, out=None)
-
     Returns the indices of the maximum values along an axis.
 
     Parameters
@@ -3234,6 +3232,75 @@ def argmax(a, axis=None, out=None):
     return _npi.argmax(a, axis=axis, keepdims=False, out=out)
 
 
+@set_module('mxnet.ndarray.numpy')
+def argmin(a, axis=None, out=None):
+    r"""
+    Returns the indices of the maximum values along an axis.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array. Only support ndarrays of dtype `float16`, `float32`, and `float64`.
+    axis : int, optional
+        By default, the index is into the flattened array, otherwise
+        along the specified axis.
+    out : ndarray or None, optional
+        If provided, the result will be inserted into this array. It should
+        be of the appropriate shape and dtype.
+
+    Returns
+    -------
+    index_array : ndarray of indices whose dtype is same as the input ndarray.
+        Array of indices into the array. It has the same shape as `a.shape`
+        with the dimension along `axis` removed.
+
+    Notes
+    -----
+    In case of multiple occurrences of the maximum values, the indices
+    corresponding to the first occurrence are returned.
+
+    This function differs from the original `numpy.argmax
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html>`_ in
+    the following aspects:
+
+    - Input type does not support Python native iterables(list, tuple, ...).
+    - Output has dtype that is same as the input ndarray.
+    - ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be the same as the expected output.
+    - ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the same as the expected output.
+    - ``out`` param does not support scalar input case.
+
+    Examples
+    --------
+    >>> a = np.arange(6).reshape(2,3) + 10
+    >>> a
+    array([[10., 11., 12.],
+           [13., 14., 15.]])
+    >>> np.argmin(a)
+    array(0.)
+    >>> np.argmin(a, axis=0)
+    array([0., 0., 0.])
+    >>> np.argmin(a, axis=1)
+    array([0., 0.])
+
+    >>> b = np.arange(6)
+    >>> b[2] = 0
+    >>> b
+    array([0., 1., 0., 3., 4., 5.])
+    >>> np.argmax(b)  # Only the first occurrence is returned.
+    array(0.)
+
+    Specify ``out`` ndarray:
+
+    >>> a = np.arange(6).reshape(2,3) + 10
+    >>> b = np.zeros((2,))
+    >>> np.argmin(a, axis=1, out=b)
+    array([0., 0.])
+    >>> b
+    array([0., 0.])
+    """
+    return _npi.argmin(a, axis=axis, keepdims=False, out=out)
+
+
 @set_module('mxnet.ndarray.numpy')
 def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable=arguments-differ
     """
@@ -4761,3 +4828,84 @@ def einsum(*operands, **kwargs):
     subscripts = operands[0]
     operands = operands[1:]
     return _npi.einsum(*operands, subscripts=subscripts, out=out, optimize=int(optimize_arg))
+
+
+@set_module('mxnet.ndarray.numpy')
+def nonzero(a):
+    """
+    Return the indices of the elements that are non-zero.
+
+    Returns a tuple of arrays, one for each dimension of `a`,
+    containing the indices of the non-zero elements in that
+    dimension. The values in `a` are always returned in
+    row-major, C-style order.
+
+    To group the indices by element, rather than dimension, use `argwhere`,
+    which returns a row for each non-zero element.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array.
+
+    Returns
+    -------
+    tuple_of_arrays : tuple
+        Indices of elements that are non-zero.
+
+    See Also
+    --------
+    ndarray.nonzero :
+        Equivalent ndarray method.
+
+    Notes
+    -----
+    While the nonzero values can be obtained with ``a[nonzero(a)]``, it is
+    recommended to use ``x[x.astype(bool)]`` or ``x[x != 0]`` instead, which
+    will correctly handle 0-d arrays.
+
+    Examples
+    --------
+    >>> x = np.array([[3, 0, 0], [0, 4, 0], [5, 6, 0]])
+    >>> x
+    array([[3, 0, 0],
+           [0, 4, 0],
+           [5, 6, 0]], dtype=int32)
+    >>> np.nonzero(x)
+    (array([0, 1, 2, 2], dtype=int64), array([0, 1, 0, 1], dtype=int64))
+
+    >>> x[np.nonzero(x)]
+    array([3, 4, 5, 6])
+    >>> np.transpose(np.stack(np.nonzero(x)))
+    array([[0, 0],
+           [1, 1],
+           [2, 0],
+           [2, 1]], dtype=int64)
+
+    A common use for ``nonzero`` is to find the indices of an array, where
+    a condition is True.  Given an array `a`, the condition `a` > 3 is a
+    boolean array and since False is interpreted as 0, np.nonzero(a > 3)
+    yields the indices of the `a` where the condition is true.
+
+    >>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32)
+    >>> a > 3
+    array([[False, False, False],
+           [ True,  True,  True],
+           [ True,  True,  True]])
+    >>> np.nonzero(a > 3)
+    (array([1, 1, 1, 2, 2, 2], dtype=int64), array([0, 1, 2, 0, 1, 2], dtype=int64))
+
+    Using this result to index `a` is equivalent to using the mask directly:
+
+    >>> a[np.nonzero(a > 3)]
+    array([4, 5, 6, 7, 8, 9], dtype=int32)
+    >>> a[a > 3]
+    array([4, 5, 6, 7, 8, 9], dtype=int32)
+
+    ``nonzero`` can also be called as a method of the array.
+
+    >>> (a > 3).nonzero()
+    (array([1, 1, 1, 2, 2, 2], dtype=int64), array([0, 1, 2, 0, 1, 2], dtype=int64))
+    """
+    out = _npi.nonzero(a).transpose()
+    return tuple([out[i] for i in range(len(out))])

python/mxnet/ndarray/numpy/random.py

@@ -23,11 +23,11 @@
 from ..ndarray import NDArray
 
 
-__all__ = ['randint', 'uniform', 'normal', "choice", "rand"]
+__all__ = ['randint', 'uniform', 'normal', "choice", "rand", "multinomial"]
 
 
 def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
-    """Return random integers from `low` (inclusive) to `high` (exclusive).
+    r"""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
@@ -88,7 +88,7 @@ def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
 
 
 def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
-    """Draw samples from a uniform distribution.
+    r"""Draw samples from a uniform distribution.
 
     Samples are uniformly distributed over the half-open interval
     ``[low, high)`` (includes low, but excludes high).  In other words,
@@ -143,7 +143,7 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
 
 
 def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
-    """Draw random samples from a normal (Gaussian) distribution.
+    r"""Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
     by *loc* (mean) and *scale* (standard deviation).
@@ -194,7 +194,7 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
 
 
 def multinomial(n, pvals, size=None):
-    """multinomial(n, pvals, size=None)
+    r"""multinomial(n, pvals, size=None)
 
     Draw samples from a multinomial distribution.
 
@@ -246,7 +246,7 @@ def multinomial(n, pvals, size=None):
 
 
 def choice(a, size=None, replace=True, p=None, ctx=None, out=None):
-    """Generates a random sample from a given 1-D array
+    r"""Generates a random sample from a given 1-D array
 
     Parameters
     -----------

python/mxnet/numpy/linalg.py

@@ -54,10 +54,35 @@ def norm(x, ord=None, axis=None, keepdims=False):
     n : float or ndarray
         Norm of the matrix or vector(s).
 
+    Notes
+    -----
+    This operator differs from NumPy in the aspect that it always returns a
+    zero-dim tensor for the cases where Python float values are expected
+    in NumPy.
+
     References
     ----------
     .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
            Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
+
+    Examples
+    --------
+    >>> from numpy import linalg as LA
+    >>> a = np.arange(9) - 4
+    >>> a
+    array([-4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.])
+    >>> b = a.reshape((3, 3))
+    >>> b
+    array([[-4., -3., -2.],
+           [-1.,  0.,  1.],
+           [ 2.,  3.,  4.]])
+    >>> LA.norm(a)
+    array(7.745967)
+    >>>
+    >>> LA.norm(b)
+    array(7.745967)
+    >>> LA.norm(b, 'fro')
+    array(7.745967)
     """
     return _mx_nd_np.linalg.norm(x, ord, axis, keepdims)