# Source code for mars.tensor.reduction.nanmean

```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# 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 ..datasource import tensor as astensor
from ..array_utils import as_same_device, device
from .core import TensorReduction, TensorReductionMixin, nannumel
from .mean import TensorMean
class TensorNanMean(TensorReduction, TensorReductionMixin):
_op_type_ = OperandDef.NANMEAN
def __init__(self, axis=None, keepdims=None, combine_size=None, stage=None, **kw):
stage = self._rewrite_stage(stage)
super().__init__(_axis=axis, _keepdims=keepdims,
_combine_size=combine_size, stage=stage, **kw)
@classmethod
def execute_map(cls, ctx, op):
(in_chunk,), device_id, xp = as_same_device(
[ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True)
axis = cls.get_axis(op.axis)
with device(device_id):
chunk_count = nannumel(in_chunk, axis=axis, dtype=np.int64,
keepdims=bool(op.keepdims))
chunk_sum = xp.nansum(in_chunk, axis=axis, dtype=op.dtype,
keepdims=bool(op.keepdims))
ctx[op.outputs[0].key] = (chunk_sum, chunk_count)
@classmethod
def execute_agg(cls, ctx, op):
axis = cls.get_axis(op.axis)
a = ctx[op.inputs[0].key]
if not isinstance(a, (list, tuple)):
(inp,), device_id, xp = as_same_device(
[a], device=op.device, ret_extra=True)
with device(device_id):
ctx[op.outputs[0].key] = xp.nanmean(inp, axis=axis, dtype=op.dtype,
keepdims=bool(op.keepdims))
else:
(_data, _count), device_id, xp = as_same_device(
a, device=op.device, ret_extra=True)
with device(device_id):
chunk_count = xp.sum(_count, axis=axis, dtype=op.dtype,
keepdims=bool(op.keepdims))
chunk_sum = xp.sum(_data, axis=axis, dtype=op.dtype,
keepdims=bool(op.keepdims))
ctx[op.outputs[0].key] = xp.true_divide(chunk_sum, chunk_count,
dtype=op.dtype)
@classmethod
def execute_combine(cls, ctx, op):
TensorMean.execute_combine(ctx, op)
[docs]def nanmean(a, axis=None, dtype=None, out=None, keepdims=None, combine_size=None):
"""
Compute the arithmetic mean along the specified axis, ignoring NaNs.
Returns the average of the tensor elements. The average is taken over
the flattened tensor by default, otherwise over the specified axis.
`float64` intermediate and return values are used for integer inputs.
For all-NaN slices, NaN is returned and a `RuntimeWarning` is raised.
Parameters
----------
a : array_like
Tensor containing numbers whose mean is desired. If `a` is not an
tensor, a conversion is attempted.
axis : int, optional
Axis along which the means are computed. The default is to compute
the mean of the flattened tensor.
dtype : data-type, optional
Type to use in computing the mean. For integer inputs, the default
is `float64`; for inexact inputs, it is the same as the input
dtype.
out : Tensor, optional
Alternate output tensor in which to place the result. The default
is ``None``; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary. See
`doc.ufuncs` for details.
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 original `a`.
If the value is anything but the default, then
`keepdims` will be passed through to the `mean` or `sum` methods
of sub-classes of `Tensor`. If the sub-classes methods
does not implement `keepdims` any exceptions will be raised.
combine_size: int, optional
The number of chunks to combine.
Returns
-------
m : Tensor, see dtype parameter above
If `out=None`, returns a new array containing the mean values,
otherwise a reference to the output array is returned. Nan is
returned for slices that contain only NaNs.
See Also
--------
average : Weighted average
mean : Arithmetic mean taken while not ignoring NaNs
var, nanvar
Notes
-----
The arithmetic mean is the sum of the non-NaN elements along the axis
divided by the number of non-NaN elements.
Note that for floating-point input, the mean is computed using the same
precision the input has. Depending on the input data, this can cause
the results to be inaccurate, especially for `float32`. Specifying a
higher-precision accumulator using the `dtype` keyword can alleviate
this issue.
Examples
--------
>>> import mars.tensor as mt
>>> a = mt.array([[1, mt.nan], [3, 4]])
>>> mt.nanmean(a).execute()
2.6666666666666665
>>> mt.nanmean(a, axis=0).execute()
array([ 2., 4.])
>>> mt.nanmean(a, axis=1).execute()
array([ 1., 3.5])
"""
a = astensor(a)
if dtype is None:
dtype = np.nanmean(np.empty((1,), dtype=a.dtype)).dtype
op = TensorNanMean(axis=axis, dtype=dtype, keepdims=keepdims, combine_size=combine_size)
return op(a, out=out)
```