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# Copyright 2015 Google Inc. 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import ops
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import sparse_ops
"""Confusion matrix related metrics."""
def confusion_matrix(predictions, targets, num_classes=None, name=None):
"""Computes the confusion matrix from predictions and targets
Calculate the Confusion Matrix for a pair of prediction and
target 1-D int arrays.
Considering a prediction array such as: `[1, 2, 3]`
And a target array such as: `[2, 2, 3]`
The confusion matrix returned would be the following one:
[[0, 0, 0]
[0, 1, 0]
[0, 1, 0]
[0, 0, 1]]
Where the matrix rows represent the prediction labels and the columns
represents the target labels. The confusion matrix is always a 2-D array
of shape [n, n], where n is the number of valid labels for a given
classification task. Both prediction and target must be 1-D arrays of
the same shape in order for this function to work.
Args:
predictions: A 1-D array represeting the predictions for a given
classification.
targets: A 1-D represeting the real labels for the classification task.
num_classes: The possible number of labels the classification task can
have. If this value is not provided, it will be calculated
using both predictions and targets array.
Returns:
A l X l matrix represeting the confusion matrix, where l in the number of
possible labels in the classification task.
Raises:
ValueError: If both predictions and targets are not 1-D vectors and do not
have the same size.
"""
with ops.op_scope([predictions, targets, num_classes], name,
'confusion_matrix') as name:
predictions = ops.convert_to_tensor(
predictions, name="predictions", dtype=dtypes.int64)
targets = ops.convert_to_tensor(
targets, name="targets", dtype=dtypes.int64)
if num_classes is None:
num_classes = math_ops.maximum(math_ops.reduce_max(predictions),
math_ops.reduce_max(targets)) + 1
shape = array_ops.pack([num_classes, num_classes])
indices = array_ops.transpose(
array_ops.pack([predictions, targets]))
values = array_ops.ones_like(predictions, dtype=dtypes.int32)
cm_sparse = ops.SparseTensor(
indices=indices, values=values, shape=shape)
zero_matrix = array_ops.zeros(math_ops.to_int32(shape), dtypes.int32)
return sparse_ops.sparse_add(zero_matrix, cm_sparse)
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