fix sanity and flakiness of tests

python/mxnet/ndarray/numpy/_op.py

@@ -2204,7 +2204,7 @@ def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable
 
 
 @set_module('mxnet.ndarray.numpy')
-def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
+def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=too-many-arguments
     """
     Compute the standard deviation along the specified axis.
     Returns the standard deviation, a measure of the spread of a distribution,
@@ -2271,7 +2271,7 @@ def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
 
 
 @set_module('mxnet.ndarray.numpy')
-def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
+def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=too-many-arguments
     """
     Compute the variance along the specified axis.
     Returns the variance of the array elements, a measure of the spread of a

python/mxnet/symbol/numpy/_symbol.py

@@ -2570,15 +2570,101 @@ def mean(a, axis=None, dtype=None, out=None, keepdims=False):  # pylint: disable
 
 
 @set_module('mxnet.symbol.numpy')
-def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
+def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=too-many-arguments
     """
+    Compute the standard deviation along the specified axis.
+
+    Returns the standard deviation, a measure of the spread of a distribution,
+    of the array elements. The standard deviation is computed for the
+    flattened array by default, otherwise over the specified axis.
+
+    Parameters
+    ----------
+    a : `_Symbol`
+        _Symbol containing numbers whose standard deviation is desired.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which the standard deviations are computed.
+        The default is to compute the standard deviation of the flattened array.
+        If this is a tuple of ints, computation is performed over multiple axes,
+        instead of a single axis or all the axes as before.
+    dtype : data-type, optional
+        Type to use in computing the standard deviation. For integer inputs, the default is float32;
+        for floating point inputs, it is the same as the input dtype.
+    out : _Symbol, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left in the result
+        as dimensions with size one. With this option, the result will broadcast correctly
+        against the input array.
+        If the default value is passed, then keepdims will not be passed through to the mean
+        method of sub-classes of _Symbol, however any non-default value will be. If the sub-class
+        method does not implement keepdims any exceptions will be raised.
+
+    Returns
+    -------
+    m : _Symbol, see dtype parameter above
+        If out=None, returns a new array containing the standard deviation values,
+        otherwise a reference to the output array is returned.
+
+    Notes
+    -----
+    This function differs from the original `numpy.std
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html>`_ in
+    the following way(s):
+
+    - only _Symbol is accepted as valid input, python iterables or scalar is not supported
+    - default output data type for integer input is float32
+
     """
     return _npi.std(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
 
 
 @set_module('mxnet.symbol.numpy')
-def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
+def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint: disable=too-many-arguments
     """
+    Compute the variance along the specified axis.
+
+    Returns the variance of the array elements, a measure of the spread of a
+    distribution.  The variance is computed for the flattened array by
+    default, otherwise over the specified axis.
+
+    Parameters
+    ----------
+    a : `_Symbol`
+        _Symbol containing numbers whose variance is desired.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which the variance is computed.
+        The default is to compute the variance of the flattened array.
+        If this is a tuple of ints, computation is performed over multiple axes,
+        instead of a single axis or all the axes as before.
+    dtype : data-type, optional
+        Type to use in computing the variance. For integer inputs, the default is float32;
+        for floating point inputs, it is the same as the input dtype.
+    out : _Symbol, optional
+        Dummy parameter to keep the consistency with the ndarray counterpart.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left in the result
+        as dimensions with size one. With this option, the result will broadcast correctly
+        against the input array.
+        If the default value is passed, then keepdims will not be passed through to the mean
+        method of sub-classes of _Symbol, however any non-default value will be. If the sub-class
+        method does not implement keepdims any exceptions will be raised.
+
+    Returns
+    -------
+    m : _Symbol, see dtype parameter above
+        If out=None, returns a new array containing the variance values,
+        otherwise a reference to the output array is returned.
+
+    Notes
+    -----
+    This function differs from the original `numpy.var
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html>`_ in
+    the following way(s):
+
+    - only _Symbol is accepted as valid input, python iterables or scalar is not supported
+    - default output data type for integer input is float32
+
     """
     return _npi.var(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
 

tests/python/unittest/test_numpy_op.py

@@ -415,7 +415,6 @@ def is_int(dtype):
             for axis in ([i for i in range(in_data_dim)] + [(), None]):
                 for itype in ['float16', 'float32', 'float64']:
                     for dtype in ['float16', 'float32', 'float64']:
-                        print(itype, dtype)
                         if is_int(dtype) and not is_int(itype):
                             continue
                         # test gluon
@@ -494,7 +493,7 @@ def legalize_shape(shape):
                             for dtype in ['float16', 'float32', 'float64']:
                                 if is_int(dtype) and not is_int(itype) or is_int(itype) and is_int(dtype):
                                     continue
-                                atol = 1e-4 if itype == 'float16' or dtype == 'float16' else 1e-5
+                                atol = 3e-4 if itype == 'float16' or dtype == 'float16' else 1e-5
                                 rtol = 1e-2 if itype == 'float16' or dtype == 'float16' else 1e-3
                                 # test gluon
                                 test_moment = TestMoment(name, axis=axis, dtype=dtype, keepdims=keepdims, ddof=ddof)