# Copyright 2022-2023 XProbe Inc.
# derived from copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from ... import opcodes as OperandDef
from ..array_utils import as_same_device, device
from .core import TensorReduction, TensorReductionMixin
from .sum import TensorSum
class TensorCountNonzero(TensorReduction, TensorReductionMixin):
_op_type_ = OperandDef.COUNT_NONZERO
def __init__(
self, axis=None, dtype=None, keepdims=None, combine_size=None, stage=None, **kw
):
if dtype is None:
dtype = np.dtype(np.intp)
stage = self._rewrite_stage(stage)
super().__init__(
_axis=axis,
_keepdims=keepdims,
_combine_size=combine_size,
dtype=dtype,
stage=stage,
**kw
)
@classmethod
def execute_map(cls, ctx, op):
(x,), device_id, xp = as_same_device(
[ctx[c.key] for c in op.inputs], op.device, ret_extra=True
)
axis = cls.get_arg_axis(op.axis, op.inputs[0].ndim)
keepdims = op.keepdims
with device(device_id):
nz = xp.count_nonzero(x, axis=axis)
if keepdims:
slcs = [slice(None)] * op.inputs[0].ndim
for ax in op.axis:
slcs[ax] = np.newaxis
nz = xp.asarray(nz)[tuple(slcs)]
ctx[op.outputs[0].key] = nz
@classmethod
def execute_agg(cls, ctx, op):
return TensorSum.execute_agg(ctx, op)
@classmethod
def execute_one_chunk(cls, ctx, op):
a = ctx[op.inputs[0].key]
(inp,), device_id, xp = as_same_device([a], device=op.device, ret_extra=True)
with device(device_id):
ctx[op.outputs[0].key] = xp.count_nonzero(inp, axis=op.axis)
[docs]def count_nonzero(a, axis=None, combine_size=None):
"""
Counts the number of non-zero values in the tensor ``a``.
The word "non-zero" is in reference to the Python 2.x
built-in method ``__nonzero__()`` (renamed ``__bool__()``
in Python 3.x) of Python objects that tests an object's
"truthfulness". For example, any number is considered
truthful if it is nonzero, whereas any string is considered
truthful if it is not the empty string. Thus, this function
(recursively) counts how many elements in ``a`` (and in
sub-tensors thereof) have their ``__nonzero__()`` or ``__bool__()``
method evaluated to ``True``.
Parameters
----------
a : array_like
The tensor for which to count non-zeros.
axis : int or tuple, optional
Axis or tuple of axes along which to count non-zeros.
Default is None, meaning that non-zeros will be counted
along a flattened version of ``a``.
combine_size: int, optional
The number of chunks to combine.
Returns
-------
count : int or tensor of int
Number of non-zero values in the array along a given axis.
Otherwise, the total number of non-zero values in the tensor
is returned.
See Also
--------
nonzero : Return the coordinates of all the non-zero values.
Examples
--------
>>> import mars.tensor as mt
>>> mt.count_nonzero(mt.eye(4)).execute()
4
>>> mt.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]]).execute()
5
>>> mt.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]], axis=0).execute()
array([1, 1, 1, 1, 1])
>>> mt.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]], axis=1).execute()
array([2, 3])
"""
op = TensorCountNonzero(
axis=axis, dtype=np.dtype(np.int_), keepdims=None, combine_size=combine_size
)
return op(a)