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# Licensed under the Apache License, Version 2.0 (the "License");
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"""Keras metric for computing the mean uplift sliced by treatment group."""

import tensorflow as tf, tf_keras

from official.recommendation.uplift import types
from official.recommendation.uplift.metrics import treatment_sliced_metric


@tf_keras.utils.register_keras_serializable(package="Uplift")
class UpliftMean(tf_keras.metrics.Metric):
  """Computes the overall and treatment sliced uplift mean.

  Note that the prediction tensor is expected to be of type
  `TwoTowerTrainingOutputs`.

  Example standalone usage:

  >>> uplift_mean = UpliftMean()
  >>> y_pred = types.TwoTowerTrainingOutputs(
  ...     uplift=tf.constant([1, 2, 3, 4])
  ...     is_treatment=tf.constant([True, False, True, False]),
  ... )
  >>> uplift_mean(y_true=tf.zeros(4), y_pred=y_pred)
  {
      "uplift/mean": 2.5
      "uplift/mean/control": 3.0
      "uplift/mean/treatment": 2.0
  }

  Example usage with the `model.compile()` API:

  >>> model.compile(
  ...     optimizer="sgd",
  ...     loss=TrueLogitsLoss(tf_keras.losses.mean_squared_error),
  ...     metrics=[UpliftMean()]
  ... )
  """

  def __init__(self, name: str = "uplift/mean", **kwargs):
    """Initializes the instance.

    Args:
      name: name for the overall uplift mean metric result. The control and
        treatment uplift means will have "/control" and "/treatment" appended to
        the result name.
      **kwargs: other base metric keyword arguments.
    """
    super().__init__(name=name, **kwargs)
    self._sliced_uplift = treatment_sliced_metric.TreatmentSlicedMetric(
        metric=tf_keras.metrics.Mean(name=name, **kwargs)
    )

  def update_state(
      self,
      y_true: tf.Tensor,
      y_pred: types.TwoTowerTrainingOutputs,
      sample_weight: tf.Tensor | None = None,
  ) -> None:
    """Updates the overall, control and treatment uplift means.

    Args:
      y_true: tensor labels.
      y_pred: two tower training outputs. The treatment indicator tensor is used
        to slice the uplift prediction into control and treatment groups.
      sample_weight: optional sample weight to compute weighted uplift means. If
        given, the sample weight will also be sliced by the treatment indicator
        tensor to compute the weighted control and treatment uplift means.

    Raises:
      TypeError: if y_pred is not of type `TwoTowerTrainingOutputs`.
    """
    del y_true

    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._sliced_uplift.update_state(
        values=y_pred.uplift,
        is_treatment=y_pred.is_treatment,
        sample_weight=sample_weight,
    )

  def result(self) -> dict[str, tf.Tensor]:
    return self._sliced_uplift.result()