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# Copyright 2023 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.
"""Keras metric for computing fraction of treated examples."""
import tensorflow as tf, tf_keras
from official.recommendation.uplift import types
@tf_keras.utils.register_keras_serializable(package="Uplift")
class TreatmentFraction(tf_keras.metrics.Metric):
"""Computes the fraction of treated examples.
Note that the prediction tensor is expected to be of type
`TwoTowerTrainingOutputs`.
Example standalone usage:
>>> treatment_fraction = TreatmentFraction()
>>> y_pred = types.TwoTowerTrainingOutputs(
... is_treatment=tf.constant([True, False, True, True]),
... )
>>> treatment_fraction(y_true=tf.zeros(4), y_pred=y_pred)
0.75
Example usage with the `model.compile()` API:
>>> model.compile(
... optimizer="sgd",
... loss=TrueLogitsLoss(tf_keras.losses.mean_squared_error),
... metrics=[TreatmentFraction()]
... )
"""
def __init__(self, **kwargs):
"""Initializes the instance.
Args:
**kwargs: base metric keyword arguments.
"""
super().__init__(**kwargs)
self._treatment_fraction = tf_keras.metrics.Mean(**kwargs)
def update_state(
self,
y_true: tf.Tensor,
y_pred: types.TwoTowerTrainingOutputs,
sample_weight: tf.Tensor | None = None,
) -> None:
"""Updates the treatment fraction.
Args:
y_true: tensor labels.
y_pred: two tower training outputs. The treatment indicator tensor is used
update the treatment fraction.
sample_weight: optional sample weight tensor for computing the weighted
treatment fraction. The unweighted treatment fraction is computed
instead if it is left as `None`.
Raises:
TypeError: if y_pred is not of type `TwoTowerTrainingOutputs`.
"""
if not isinstance(y_pred, types.TwoTowerTrainingOutputs):
raise TypeError(
"y_pred must be of type `TwoTowerTrainingOutputs` but got type"
f" {type(y_pred)} instead."
)
self._treatment_fraction.update_state(
values=y_pred.is_treatment, sample_weight=sample_weight
)
def result(self) -> tf.Tensor:
return self._treatment_fraction.result()
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