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classification.py
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classification.py
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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.
# ==============================================================================
"""Classification metrics library."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import metrics_impl
from tensorflow.python.ops import variable_scope
from tensorflow.python.training import distribute as distribute_lib
# TODO(nsilberman): move into metrics/python/ops/
def accuracy(predictions, labels, weights=None, name=None):
"""Computes the percentage of times that predictions matches labels.
Args:
predictions: the predicted values, a `Tensor` whose dtype and shape
matches 'labels'.
labels: the ground truth values, a `Tensor` of any shape and
bool, integer, or string dtype.
weights: None or `Tensor` of float values to reweight the accuracy.
name: A name for the operation (optional).
Returns:
Accuracy `Tensor`.
Raises:
ValueError: if dtypes don't match or
if dtype is not bool, integer, or string.
"""
if not (labels.dtype.is_integer or
labels.dtype in (dtypes.bool, dtypes.string)):
raise ValueError(
'Labels should have bool, integer, or string dtype, not %r' %
labels.dtype)
if not labels.dtype.is_compatible_with(predictions.dtype):
raise ValueError('Dtypes of predictions and labels should match. '
'Given: predictions (%r) and labels (%r)' %
(predictions.dtype, labels.dtype))
with ops.name_scope(name, 'accuracy', values=[predictions, labels]):
is_correct = math_ops.cast(
math_ops.equal(predictions, labels), dtypes.float32)
if weights is not None:
is_correct = math_ops.multiply(is_correct, weights)
num_values = math_ops.multiply(weights, array_ops.ones_like(is_correct))
return math_ops.div(math_ops.reduce_sum(is_correct),
math_ops.reduce_sum(num_values))
return math_ops.reduce_mean(is_correct)
def f1_score(labels, predictions, weights=None, num_thresholds=200,
metrics_collections=None, updates_collections=None, name=None):
"""Computes the approximately best F1-score across different thresholds.
The f1_score function applies a range of thresholds to the predictions to
convert them from [0, 1] to bool. Precision and recall are computed by
comparing them to the labels. The F1-Score is then defined as
2 * precision * recall / (precision + recall). The best one across the
thresholds is returned.
Disclaimer: In practice it may be desirable to choose the best threshold on
the validation set and evaluate the F1 score with this threshold on a
separate test set. Or it may be desirable to use a fixed threshold (e.g. 0.5).
This function internally creates four local variables, `true_positives`,
`true_negatives`, `false_positives` and `false_negatives` that are used to
compute the pairs of recall and precision values for a linearly spaced set of
thresholds from which the best f1-score is derived.
This value is ultimately returned as `f1-score`, an idempotent operation that
computes the F1-score (computed using the aforementioned variables). The
`num_thresholds` variable controls the degree of discretization with larger
numbers of thresholds more closely approximating the true best F1-score.
For estimation of the metric over a stream of data, the function creates an
`update_op` operation that updates these variables and returns the F1-score.
Example usage with a custom estimator:
def model_fn(features, labels, mode):
predictions = make_predictions(features)
loss = make_loss(predictions, labels)
train_op = tf.contrib.training.create_train_op(
total_loss=loss,
optimizer='Adam')
eval_metric_ops = {'f1': f1_score(labels, predictions)}
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops,
export_outputs=export_outputs)
estimator = tf.estimator.Estimator(model_fn=model_fn)
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
labels: A `Tensor` whose shape matches `predictions`. Will be cast to
`bool`.
predictions: A floating point `Tensor` of arbitrary shape and whose values
are in the range `[0, 1]`.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`labels`, and must be broadcastable to `labels` (i.e., all dimensions must
be either `1`, or the same as the corresponding `labels` dimension).
num_thresholds: The number of thresholds to use when discretizing the roc
curve.
metrics_collections: An optional list of collections that `f1_score` should
be added to.
updates_collections: An optional list of collections that `update_op` should
be added to.
name: An optional variable_scope name.
Returns:
f1_score: A scalar `Tensor` representing the current best f1-score across
different thresholds.
update_op: An operation that increments the `true_positives`,
`true_negatives`, `false_positives` and `false_negatives` variables
appropriately and whose value matches the `f1_score`.
Raises:
ValueError: If `predictions` and `labels` have mismatched shapes, or if
`weights` is not `None` and its shape doesn't match `predictions`, or if
either `metrics_collections` or `updates_collections` are not a list or
tuple.
"""
with variable_scope.variable_scope(
name, 'f1', (labels, predictions, weights)):
predictions, labels, weights = metrics_impl._remove_squeezable_dimensions( # pylint: disable=protected-access
predictions=predictions, labels=labels, weights=weights)
# To account for floating point imprecisions / avoid division by zero.
epsilon = 1e-7
thresholds = [(i + 1) * 1.0 / (num_thresholds - 1)
for i in range(num_thresholds - 2)]
thresholds = [0.0 - epsilon] + thresholds + [1.0 + epsilon]
# Confusion matrix.
values, update_ops = metrics_impl._confusion_matrix_at_thresholds( # pylint: disable=protected-access
labels, predictions, thresholds, weights, includes=('tp', 'fp', 'fn'))
# Compute precision and recall at various thresholds.
def compute_best_f1_score(tp, fp, fn, name):
precision_at_t = math_ops.div(tp, epsilon + tp + fp,
name='precision_' + name)
recall_at_t = math_ops.div(tp, epsilon + tp + fn, name='recall_' + name)
# Compute F1 score.
f1_at_thresholds = (
2.0 * precision_at_t * recall_at_t /
(precision_at_t + recall_at_t + epsilon))
return math_ops.reduce_max(f1_at_thresholds)
def f1_across_towers(_, values):
best_f1 = compute_best_f1_score(tp=values['tp'], fp=values['fp'],
fn=values['fn'], name='value')
if metrics_collections:
ops.add_to_collections(metrics_collections, best_f1)
return best_f1
best_f1 = distribute_lib.get_tower_context().merge_call(
f1_across_towers, values)
update_op = compute_best_f1_score(tp=update_ops['tp'], fp=update_ops['fp'],
fn=update_ops['fn'], name='update')
if updates_collections:
ops.add_to_collections(updates_collections, update_op)
return best_f1, update_op