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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Masks one `Series` based on the content of another `Series`."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.learn.python.learn.dataframe import series
from tensorflow.contrib.learn.python.learn.dataframe import transform
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor as sparse_tensor_py
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import functional_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import sparse_ops
def sparse_boolean_mask(sparse_tensor, mask, name="sparse_boolean_mask"):
"""Boolean mask for `SparseTensor`s.
Args:
sparse_tensor: a `SparseTensor`.
mask: a 1D boolean dense `Tensor` whose length is equal to the 0th dimension
of `sparse_tensor`.
name: optional name for this operation.
Returns:
A `SparseTensor` that contains row `k` of `sparse_tensor` iff `mask[k]` is
`True`.
"""
# TODO(jamieas): consider mask dimension > 1 for symmetry with `boolean_mask`.
with ops.name_scope(name, values=[sparse_tensor, mask]):
mask = ops.convert_to_tensor(mask)
mask_rows = array_ops.where(mask)
first_indices = array_ops.squeeze(array_ops.slice(sparse_tensor.indices,
[0, 0], [-1, 1]))
# Identify indices corresponding to the rows identified by mask_rows.
sparse_entry_matches = functional_ops.map_fn(
lambda x: math_ops.equal(first_indices, x),
mask_rows,
dtype=dtypes.bool)
# Combine the rows of index_matches to form a mask for the sparse indices
# and values.
to_retain = array_ops.reshape(
functional_ops.foldl(math_ops.logical_or, sparse_entry_matches), [-1])
return sparse_ops.sparse_retain(sparse_tensor, to_retain)
@series.Series.register_binary_op("select_rows")
class BooleanMask(transform.TensorFlowTransform):
"""Apply a boolean mask to a `Series`."""
@property
def name(self):
return "BooleanMask"
@property
def input_valency(self):
return 2
@property
def _output_names(self):
return "output",
def _apply_transform(self, input_tensors, **kwargs):
"""Applies the transformation to the `transform_input`.
Args:
input_tensors: a list of Tensors representing the input to
the Transform.
**kwargs: Additional keyword arguments, unused here.
Returns:
A namedtuple of Tensors representing the transformed output.
"""
input_tensor = input_tensors[0]
mask = input_tensors[1]
if mask.get_shape().ndims > 1:
mask = array_ops.squeeze(mask)
if isinstance(input_tensor, sparse_tensor_py.SparseTensor):
mask_fn = sparse_boolean_mask
else:
mask_fn = array_ops.boolean_mask
# pylint: disable=not-callable
return self.return_type(mask_fn(input_tensor, mask))
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