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multilabel_confusion_matrix.py
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multilabel_confusion_matrix.py
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# Copyright 2019 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.
# ==============================================================================
"""Implements Multi-label confusion matrix scores."""
import warnings
import tensorflow as tf
from tensorflow.keras.metrics import Metric
import numpy as np
from typeguard import typechecked
from tensorflow_addons.utils.types import AcceptableDTypes, FloatTensorLike
class MultiLabelConfusionMatrix(Metric):
"""Computes Multi-label confusion matrix.
Class-wise confusion matrix is computed for the
evaluation of classification.
If multi-class input is provided, it will be treated
as multilabel data.
Consider classification problem with two classes
(i.e num_classes=2).
Resultant matrix `M` will be in the shape of (num_classes, 2, 2).
Every class `i` has a dedicated 2*2 matrix that contains:
- true negatives for class i in M(0,0)
- false positives for class i in M(0,1)
- false negatives for class i in M(1,0)
- true positives for class i in M(1,1)
```python
# multilabel confusion matrix
y_true = tf.constant([[1, 0, 1], [0, 1, 0]],
dtype=tf.int32)
y_pred = tf.constant([[1, 0, 0],[0, 1, 1]],
dtype=tf.int32)
output = MultiLabelConfusionMatrix(num_classes=3)
output.update_state(y_true, y_pred)
print('Confusion matrix:', output.result().numpy())
# Confusion matrix: [[[1 0] [0 1]] [[1 0] [0 1]]
[[0 1] [1 0]]]
# if multiclass input is provided
y_true = tf.constant([[1, 0, 0], [0, 1, 0]],
dtype=tf.int32)
y_pred = tf.constant([[1, 0, 0],[0, 0, 1]],
dtype=tf.int32)
output = MultiLabelConfusionMatrix(num_classes=3)
output.update_state(y_true, y_pred)
print('Confusion matrix:', output.result().numpy())
# Confusion matrix: [[[1 0] [0 1]] [[1 0] [1 0]] [[1 1] [0 0]]]
```
"""
@typechecked
def __init__(
self,
num_classes: FloatTensorLike,
name: str = "Multilabel_confusion_matrix",
dtype: AcceptableDTypes = None,
**kwargs
):
super().__init__(name=name, dtype=dtype)
self.num_classes = num_classes
self.true_positives = self.add_weight(
"true_positives",
shape=[self.num_classes],
initializer="zeros",
dtype=self.dtype,
)
self.false_positives = self.add_weight(
"false_positives",
shape=[self.num_classes],
initializer="zeros",
dtype=self.dtype,
)
self.false_negatives = self.add_weight(
"false_negatives",
shape=[self.num_classes],
initializer="zeros",
dtype=self.dtype,
)
self.true_negatives = self.add_weight(
"true_negatives",
shape=[self.num_classes],
initializer="zeros",
dtype=self.dtype,
)
def update_state(self, y_true, y_pred, sample_weight=None):
if sample_weight is not None:
warnings.warn(
"`sample_weight` is not None. Be aware that MultiLabelConfusionMatrix "
"does not take `sample_weight` into account when computing the metric "
"value."
)
y_true = tf.cast(y_true, tf.int32)
y_pred = tf.cast(y_pred, tf.int32)
# true positive
true_positive = tf.math.count_nonzero(y_true * y_pred, 0)
# predictions sum
pred_sum = tf.math.count_nonzero(y_pred, 0)
# true labels sum
true_sum = tf.math.count_nonzero(y_true, 0)
false_positive = pred_sum - true_positive
false_negative = true_sum - true_positive
y_true_negative = tf.math.not_equal(y_true, 1)
y_pred_negative = tf.math.not_equal(y_pred, 1)
true_negative = tf.math.count_nonzero(
tf.math.logical_and(y_true_negative, y_pred_negative), axis=0
)
# true positive state update
self.true_positives.assign_add(tf.cast(true_positive, self.dtype))
# false positive state update
self.false_positives.assign_add(tf.cast(false_positive, self.dtype))
# false negative state update
self.false_negatives.assign_add(tf.cast(false_negative, self.dtype))
# true negative state update
self.true_negatives.assign_add(tf.cast(true_negative, self.dtype))
def result(self):
flat_confusion_matrix = tf.convert_to_tensor(
[
self.true_negatives,
self.false_positives,
self.false_negatives,
self.true_positives,
]
)
# reshape into 2*2 matrix
confusion_matrix = tf.reshape(tf.transpose(flat_confusion_matrix), [-1, 2, 2])
return confusion_matrix
def get_config(self):
"""Returns the serializable config of the metric."""
config = {
"num_classes": self.num_classes,
}
base_config = super().get_config()
return {**base_config, **config}
def reset_states(self):
self.true_positives.assign(np.zeros(self.num_classes), np.int32)
self.false_positives.assign(np.zeros(self.num_classes), np.int32)
self.false_negatives.assign(np.zeros(self.num_classes), np.int32)
self.true_negatives.assign(np.zeros(self.num_classes), np.int32)