def _eval_metric_ops(
self, labels, probabilities, weights, unreduced_loss,
regularization_loss):
"""Returns a dict of metrics for eval_metric_ops."""
with ops.name_scope(
None, 'metrics',
[labels, probabilities, weights, unreduced_loss, regularization_loss]):
keys = metric_keys.MetricKeys
metric_ops = {
# Estimator already adds a metric for loss.
head_lib._summary_key(self._name, keys.LOSS_MEAN): # pylint:disable=protected-access
metrics_lib.mean(
values=unreduced_loss,
weights=weights,
name=keys.LOSS_MEAN),
head_lib._summary_key(self._name, keys.AUC): # pylint:disable=protected-access
metrics_lib.auc(labels=labels, predictions=probabilities,
weights=weights, name=keys.AUC),
head_lib._summary_key(self._name, keys.AUC_PR): # pylint:disable=protected-access
metrics_lib.auc(labels=labels, predictions=probabilities,
weights=weights, curve='PR',
name=keys.AUC_PR),
}
if regularization_loss is not None:
loss_regularization_key = head_lib._summary_key( # pylint:disable=protected-access
self._name, keys.LOSS_REGULARIZATION)
metric_ops[loss_regularization_key] = (
metrics_lib.mean(
values=regularization_loss,
name=keys.LOSS_REGULARIZATION))
for threshold in self._thresholds:
accuracy_key = keys.ACCURACY_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(self._name, accuracy_key)] = ( # pylint:disable=protected-access
head_lib._accuracy_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=accuracy_key))
# Precision for positive examples.
precision_key = keys.PRECISION_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(self._name, precision_key)] = ( # pylint:disable=protected-access
head_lib._precision_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=precision_key))
# Recall for positive examples.
recall_key = keys.RECALL_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(self._name, recall_key)] = ( # pylint:disable=protected-access
head_lib._recall_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=recall_key))
return metric_ops
def _eval_metric_ops(
self, labels, probabilities, weights, unreduced_loss,
regularization_loss):
"""Returns a dict of metrics for eval_metric_ops."""
with ops.name_scope(
None, 'metrics',
[labels, probabilities, weights, unreduced_loss, regularization_loss]):
keys = metric_keys.MetricKeys
metric_ops = {
# Estimator already adds a metric for loss.
head_lib._summary_key(self._name, keys.LOSS_MEAN): # pylint:disable=protected-access
metrics_lib.mean(
values=unreduced_loss,
weights=weights,
name=keys.LOSS_MEAN),
head_lib._summary_key(self._name, keys.AUC): # pylint:disable=protected-access
metrics_lib.auc(labels=labels, predictions=probabilities,
weights=weights, name=keys.AUC),
head_lib._summary_key(self._name, keys.AUC_PR): # pylint:disable=protected-access
metrics_lib.auc(labels=labels, predictions=probabilities,
weights=weights, curve='PR',
name=keys.AUC_PR),
}
if regularization_loss is not None:
loss_regularization_key = head_lib._summary_key( # pylint:disable=protected-access
self._name, keys.LOSS_REGULARIZATION)
metric_ops[loss_regularization_key] = (
metrics_lib.mean(
values=regularization_loss,
name=keys.LOSS_REGULARIZATION))
for threshold in self._thresholds:
accuracy_key = keys.ACCURACY_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(self._name, accuracy_key)] = ( # pylint:disable=protected-access
head_lib._accuracy_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=accuracy_key))
# Precision for positive examples.
precision_key = keys.PRECISION_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(self._name, precision_key)] = ( # pylint:disable=protected-access
head_lib._precision_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=precision_key))
# Recall for positive examples.
recall_key = keys.RECALL_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(self._name, recall_key)] = ( # pylint:disable=protected-access
head_lib._recall_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=recall_key))
return metric_ops
def _eval_metric_ops(self, labels, probabilities, weights,
weighted_sum_loss, example_weight_sum):
"""Returns a dict of metrics for eval_metric_ops."""
with ops.name_scope(None, 'metrics', [
labels, probabilities, weights, weighted_sum_loss,
example_weight_sum
]):
keys = metric_keys.MetricKeys
metric_ops = {
# Estimator already adds a metric for loss.
head_lib._summary_key(self._name, keys.LOSS_MEAN): # pylint:disable=protected-access
metrics_lib.mean(
# Both values and weights here are reduced, scalar Tensors.
# values is the actual mean we want, but we pass the scalar
# example_weight_sum in order to return the correct update_op
# alongside the value_op for streaming metrics.
values=(weighted_sum_loss / example_weight_sum),
weights=example_weight_sum,
name=keys.LOSS_MEAN),
head_lib._summary_key(self._name, keys.AUC): # pylint:disable=protected-access
metrics_lib.auc(labels=labels, predictions=probabilities,
weights=weights, name=keys.AUC),
head_lib._summary_key(self._name, keys.AUC_PR): # pylint:disable=protected-access
metrics_lib.auc(labels=labels, predictions=probabilities,
weights=weights, curve='PR',
name=keys.AUC_PR),
}
for threshold in self._thresholds:
accuracy_key = keys.ACCURACY_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(self._name, accuracy_key)] = ( # pylint:disable=protected-access
head_lib._accuracy_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=accuracy_key))
# Precision for positive examples.
precision_key = keys.PRECISION_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(
self._name, precision_key)] = ( # pylint:disable=protected-access
head_lib._precision_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=precision_key))
# Recall for positive examples.
recall_key = keys.RECALL_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(self._name, recall_key)] = ( # pylint:disable=protected-access
head_lib._recall_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=recall_key))
return metric_ops
def _eval_metric_ops(self, labels, probabilities, weights, weighted_sum_loss,
example_weight_sum):
"""Returns a dict of metrics for eval_metric_ops."""
with ops.name_scope(
None, 'metrics',
[labels, probabilities, weights, weighted_sum_loss, example_weight_sum
]):
keys = metric_keys.MetricKeys
metric_ops = {
# Estimator already adds a metric for loss.
head_lib._summary_key(self._name, keys.LOSS_MEAN): # pylint:disable=protected-access
metrics_lib.mean(
# Both values and weights here are reduced, scalar Tensors.
# values is the actual mean we want, but we pass the scalar
# example_weight_sum in order to return the correct update_op
# alongside the value_op for streaming metrics.
values=(weighted_sum_loss / example_weight_sum),
weights=example_weight_sum,
name=keys.LOSS_MEAN),
head_lib._summary_key(self._name, keys.AUC): # pylint:disable=protected-access
metrics_lib.auc(labels=labels, predictions=probabilities,
weights=weights, name=keys.AUC),
head_lib._summary_key(self._name, keys.AUC_PR): # pylint:disable=protected-access
metrics_lib.auc(labels=labels, predictions=probabilities,
weights=weights, curve='PR',
name=keys.AUC_PR),
}
for threshold in self._thresholds:
accuracy_key = keys.ACCURACY_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(self._name, accuracy_key)] = ( # pylint:disable=protected-access
head_lib._accuracy_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=accuracy_key))
# Precision for positive examples.
precision_key = keys.PRECISION_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(self._name, precision_key)] = ( # pylint:disable=protected-access
head_lib._precision_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=precision_key))
# Recall for positive examples.
recall_key = keys.RECALL_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(self._name, recall_key)] = ( # pylint:disable=protected-access
head_lib._recall_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=recall_key))
return metric_ops
def model_fn(self, mode, features, labels, params):
c = variable_scope.get_variable('c',
initializer=constant_op.constant(
10, dtype=dtypes.float64),
dtype=dtypes.float64)
predictions = math_ops.multiply(features, c)
loss = None
if mode is not model_fn_lib.ModeKeys.PREDICT:
loss = losses.absolute_difference(labels=labels,
predictions=predictions,
reduction=losses.Reduction.SUM)
loss = math_ops.reduce_sum(loss)
metrics = {
'accuracy': metrics_lib.accuracy(labels, predictions),
'auc': metrics_lib.auc(labels, predictions)
}
return model_fn_lib.EstimatorSpec(
mode=mode,
loss=loss,
eval_metric_ops=metrics,
predictions={'probabilities': predictions},
train_op=control_flow_ops.no_op(
)) # This train_op isn't actually used.
def model_fn(self, mode, features, labels, params):
c = variable_scope.get_variable(
'c',
initializer=constant_op.constant(10, dtype=dtypes.float64),
dtype=dtypes.float64)
predictions = math_ops.multiply(features, c)
loss = None
if mode is not model_fn_lib.ModeKeys.PREDICT:
loss = losses.absolute_difference(
labels=labels,
predictions=predictions,
reduction=losses.Reduction.SUM)
loss = math_ops.reduce_sum(loss)
metrics = {
'accuracy': metrics_lib.accuracy(labels, predictions),
'auc': metrics_lib.auc(labels, predictions)
}
return model_fn_lib.EstimatorSpec(
mode=mode,
loss=loss,
eval_metric_ops=metrics,
predictions={'probabilities': predictions},
train_op=control_flow_ops.no_op()) # This train_op isn't actually used.
def _metric_fn(x):
labels = x["labels"]
predictions = x["predictions"]
return metrics.auc(labels,
predictions,
num_thresholds=8,
curve="PR",
summation_method="careful_interpolation")
def _auc(labels, predictions, weights=None, curve='ROC', name=None):
with ops.name_scope(name, 'auc', (predictions, labels, weights)) as scope:
predictions = math_ops.to_float(predictions, name='predictions')
if weights is not None:
weights = weights_broadcast_ops.broadcast_weights(weights, predictions)
return metrics_lib.auc(
labels=labels, predictions=predictions, weights=weights, curve=curve,
name=scope)
def _auc(labels, predictions, weights=None, curve='ROC', name=None):
with ops.name_scope(name, 'auc', (predictions, labels, weights)) as scope:
predictions = math_ops.to_float(predictions, name='predictions')
if weights is not None:
weights = weights_broadcast_ops.broadcast_weights(weights, predictions)
return metrics_lib.auc(
labels=labels, predictions=predictions, weights=weights, curve=curve,
name=scope)
def create_eval_metrics(self, noise):
predictions = np.array([0.1, 0.2, 0.3, 0.6 + noise])
labels = np.array([0.1, 0.2, 0.3, 0.6])
metrics = {
'accuracy': metrics_lib.accuracy(labels, predictions),
'auc': metrics_lib.auc(labels, predictions)
}
return metrics
def create_eval_metrics(self, noise):
predictions = np.array([0.1, 0.2, 0.3, 0.6 + noise])
labels = np.array([0.1, 0.2, 0.3, 0.6])
metrics = {
'accuracy': metrics_lib.accuracy(labels, predictions),
'auc': metrics_lib.auc(labels, predictions)
}
return metrics
def _auc(labels, predictions, weights=None, curve='ROC', name=None):
with ops.name_scope(name, 'auc', (predictions, labels, weights)) as scope:
predictions = math_ops.to_float(predictions, name='predictions')
if labels.dtype.base_dtype != dtypes.bool:
logging.warning('Casting %s labels to bool.', labels.dtype)
labels = math_ops.cast(labels, dtypes.bool)
if weights is not None:
weights = weights_broadcast_ops.broadcast_weights(weights, predictions)
return metrics_lib.auc(
labels=labels, predictions=predictions, weights=weights, curve=curve,
name=scope)
def _auc(labels, predictions, weights=None, curve='ROC', name=None):
with ops.name_scope(name, 'auc', (predictions, labels, weights)) as scope:
predictions = math_ops.to_float(predictions, name='predictions')
if labels.dtype.base_dtype != dtypes.bool:
logging.warning('Casting %s labels to bool.', labels.dtype)
labels = math_ops.cast(labels, dtypes.bool)
if weights is not None:
weights = weights_broadcast_ops.broadcast_weights(weights, predictions)
return metrics_lib.auc(
labels=labels, predictions=predictions, weights=weights, curve=curve,
name=scope)
def model_fn(self, mode, features, labels, params):
c = variable_scope.get_variable('c',
initializer=constant_op.constant(
10, dtype=dtypes.float64),
dtype=dtypes.float64)
predictions = {'probabilities': math_ops.multiply(features, c)}
loss = losses.absolute_difference(
labels=labels,
predictions=predictions['probabilities'],
reduction=losses.Reduction.SUM)
metrics = {
'accuracy': metrics_lib.accuracy(labels,
predictions['probabilities']),
'auc': metrics_lib.auc(labels, predictions['probabilities'])
}
tensor_string_repr = str(features)
classes = constant_op.constant(re.search('(split_inputs/split:[0-9])',
tensor_string_repr).group(1),
dtype=dtypes.string)
export_outputs = {
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
export_output.PredictOutput(predictions),
'classification_output':
export_output.ClassificationOutput(predictions['probabilities'],
classes),
'classification_scores':
export_output.ClassificationOutput(
scores=predictions['probabilities']),
'classification_classes':
export_output.ClassificationOutput(classes=classes),
'regression_output':
export_output.RegressionOutput(predictions['probabilities']),
}
return model_fn_lib.EstimatorSpec(
mode=mode,
loss=math_ops.reduce_sum(loss),
eval_metric_ops=metrics,
predictions=predictions,
train_op=loss, # This train_op isn't actually used.
export_outputs=export_outputs)
def model_fn(self, mode, features, labels, params):
c = variable_scope.get_variable(
'c',
initializer=constant_op.constant(10, dtype=dtypes.float64),
dtype=dtypes.float64)
predictions = {'probabilities': math_ops.multiply(features, c)}
loss = losses.absolute_difference(
labels=labels,
predictions=predictions['probabilities'],
reduction=losses.Reduction.SUM)
metrics = {
'accuracy': metrics_lib.accuracy(labels, predictions['probabilities']),
'auc': metrics_lib.auc(labels, predictions['probabilities'])
}
tensor_string_repr = str(features)
classes = constant_op.constant(
re.search('(split_inputs/split:[0-9])', tensor_string_repr).group(1),
dtype=dtypes.string)
export_outputs = {
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
export_output.PredictOutput(predictions),
'classification_output':
export_output.ClassificationOutput(predictions['probabilities'],
classes),
'classification_scores':
export_output.ClassificationOutput(
scores=predictions['probabilities']),
'classification_classes':
export_output.ClassificationOutput(classes=classes),
'regression_output':
export_output.RegressionOutput(predictions['probabilities']),
}
return model_fn_lib.EstimatorSpec(
mode=mode,
loss=math_ops.reduce_sum(loss),
eval_metric_ops=metrics,
predictions=predictions,
train_op=loss, # This train_op isn't actually used.
export_outputs=export_outputs)
def _eval_metric_ops(
self, labels, probabilities, weights, unreduced_loss,
regularization_loss):
"""Returns a dict of metrics for eval_metric_ops."""
with ops.name_scope(
None, 'metrics',
[labels, probabilities, weights, unreduced_loss, regularization_loss]):
keys = metric_keys.MetricKeys
metric_ops = {
# Estimator already adds a metric for loss.
head_lib._summary_key(self._name, keys.LOSS_MEAN): # pylint:disable=protected-access
metrics_lib.mean(
values=unreduced_loss,
weights=weights,
name=keys.LOSS_MEAN),
head_lib._summary_key(self._name, keys.AUC): # pylint:disable=protected-access
metrics_lib.auc(labels=labels, predictions=probabilities,
weights=weights, name=keys.AUC),
head_lib._summary_key(self._name, keys.AUC_PR): # pylint:disable=protected-access
metrics_lib.auc(labels=labels, predictions=probabilities,
weights=weights, curve='PR',
name=keys.AUC_PR),
}
if regularization_loss is not None:
loss_regularization_key = head_lib._summary_key( # pylint:disable=protected-access
self._name, keys.LOSS_REGULARIZATION)
metric_ops[loss_regularization_key] = (
metrics_lib.mean(
values=regularization_loss,
name=keys.LOSS_REGULARIZATION))
for threshold in self._thresholds:
accuracy_key = keys.ACCURACY_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(self._name, accuracy_key)] = ( # pylint:disable=protected-access
head_lib._accuracy_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=accuracy_key))
# Precision for positive examples.
precision_key = keys.PRECISION_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(self._name, precision_key)] = ( # pylint:disable=protected-access
head_lib._precision_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=precision_key))
# Recall for positive examples.
recall_key = keys.RECALL_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(self._name, recall_key)] = ( # pylint:disable=protected-access
head_lib._recall_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=recall_key))
for class_id in self._classes_for_class_based_metrics:
batch_rank = array_ops.rank(probabilities) - 1
begin = array_ops.concat(
[array_ops.zeros([batch_rank], dtype=dtypes.int32), [class_id]],
axis=0)
size = array_ops.concat(
[-1 * array_ops.ones([batch_rank], dtype=dtypes.int32), [1]],
axis=0)
class_probabilities = array_ops.slice(
probabilities, begin=begin, size=size)
class_labels = array_ops.slice(labels, begin=begin, size=size)
prob_key = keys.PROBABILITY_MEAN_AT_CLASS % class_id
metric_ops[head_lib._summary_key(self._name, prob_key)] = ( # pylint:disable=protected-access
head_lib._predictions_mean( # pylint:disable=protected-access
predictions=class_probabilities,
weights=weights,
name=prob_key))
auc_key = keys.AUC_AT_CLASS % class_id
metric_ops[head_lib._summary_key(self._name, auc_key)] = ( # pylint:disable=protected-access
head_lib._auc( # pylint:disable=protected-access
labels=class_labels,
predictions=class_probabilities,
weights=weights,
name=auc_key))
auc_pr_key = keys.AUC_PR_AT_CLASS % class_id
metric_ops[head_lib._summary_key(self._name, auc_pr_key)] = ( # pylint:disable=protected-access
head_lib._auc( # pylint:disable=protected-access
labels=class_labels,
predictions=class_probabilities,
weights=weights,
curve='PR',
name=auc_pr_key))
return metric_ops
def _eval_metric_ops(self, labels, probabilities, weights, unreduced_loss,
regularization_loss):
"""Returns a dict of metrics for eval_metric_ops."""
with ops.name_scope(None, 'metrics', [
labels, probabilities, weights, unreduced_loss,
regularization_loss
]):
keys = metric_keys.MetricKeys
metric_ops = {
# Estimator already adds a metric for loss.
head_lib._summary_key(self._name, keys.LOSS_MEAN): # pylint:disable=protected-access
metrics_lib.mean(
values=unreduced_loss,
weights=weights,
name=keys.LOSS_MEAN),
head_lib._summary_key(self._name, keys.AUC): # pylint:disable=protected-access
metrics_lib.auc(labels=labels, predictions=probabilities,
weights=weights, name=keys.AUC),
head_lib._summary_key(self._name, keys.AUC_PR): # pylint:disable=protected-access
metrics_lib.auc(labels=labels, predictions=probabilities,
weights=weights, curve='PR',
name=keys.AUC_PR),
}
if regularization_loss is not None:
loss_regularization_key = head_lib._summary_key( # pylint:disable=protected-access
self._name, keys.LOSS_REGULARIZATION)
metric_ops[loss_regularization_key] = (metrics_lib.mean(
values=regularization_loss, name=keys.LOSS_REGULARIZATION))
for threshold in self._thresholds:
accuracy_key = keys.ACCURACY_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(self._name, accuracy_key)] = ( # pylint:disable=protected-access
head_lib._accuracy_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=accuracy_key))
# Precision for positive examples.
precision_key = keys.PRECISION_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(
self._name, precision_key)] = ( # pylint:disable=protected-access
head_lib._precision_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=precision_key))
# Recall for positive examples.
recall_key = keys.RECALL_AT_THRESHOLD % threshold
metric_ops[head_lib._summary_key(self._name, recall_key)] = ( # pylint:disable=protected-access
head_lib._recall_at_threshold( # pylint:disable=protected-access
labels=labels,
predictions=probabilities,
weights=weights,
threshold=threshold,
name=recall_key))
for class_id in self._classes_for_class_based_metrics:
batch_rank = array_ops.rank(probabilities) - 1
begin = array_ops.concat([
array_ops.zeros([batch_rank], dtype=dtypes.int32),
[class_id]
],
axis=0)
size = array_ops.concat([
-1 * array_ops.ones([batch_rank], dtype=dtypes.int32), [1]
],
axis=0)
class_probabilities = array_ops.slice(probabilities,
begin=begin,
size=size)
class_labels = array_ops.slice(labels, begin=begin, size=size)
if self._label_vocabulary is None:
prob_key = keys.PROBABILITY_MEAN_AT_CLASS % class_id
else:
prob_key = (keys.PROBABILITY_MEAN_AT_NAME %
self._label_vocabulary[class_id])
metric_ops[head_lib._summary_key(self._name, prob_key)] = ( # pylint:disable=protected-access
head_lib._predictions_mean( # pylint:disable=protected-access
predictions=class_probabilities,
weights=weights,
name=prob_key))
if self._label_vocabulary is None:
auc_key = keys.AUC_AT_CLASS % class_id
else:
auc_key = keys.AUC_AT_NAME % self._label_vocabulary[
class_id]
metric_ops[head_lib._summary_key(self._name, auc_key)] = ( # pylint:disable=protected-access
head_lib._auc( # pylint:disable=protected-access
labels=class_labels,
predictions=class_probabilities,
weights=weights,
name=auc_key))
if self._label_vocabulary is None:
auc_pr_key = keys.AUC_PR_AT_CLASS % class_id
else:
auc_pr_key = keys.AUC_PR_AT_NAME % self._label_vocabulary[
class_id]
metric_ops[head_lib._summary_key(self._name, auc_pr_key)] = ( # pylint:disable=protected-access
head_lib._auc( # pylint:disable=protected-access
labels=class_labels,
predictions=class_probabilities,
weights=weights,
curve='PR',
name=auc_pr_key))
return metric_ops
def _auc(probs, targets, weights=None):
return metrics.auc(labels=targets,
predictions=array_ops.slice(probs, [0, 1], [-1, 1]),
weights=weights)
def my_auc(labels, predictions):
from tensorflow.python.ops import metrics as metrics_lib
auc = metrics_lib.auc(
labels=labels, predictions=predictions['logistic'], curve='ROC',
name='my_auc')
return {'my_auc': auc}
def _metric_fn(x):
labels = x["labels"]
predictions = x["predictions"]
return metrics.auc(labels, predictions, num_thresholds=8, curve="PR",
summation_method="careful_interpolation")
def _auc(probs, targets, weights=None):
return metrics.auc(
labels=targets,
predictions=array_ops.slice(probs, [0, 1], [-1, 1]),
weights=weights)