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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.
"""Tests for true_logits_loss."""
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.recommendation.uplift import types
from official.recommendation.uplift.losses import true_logits_loss
class TrueLogitsLossTest(tf.test.TestCase, parameterized.TestCase):
def _get_y_pred(self, **kwargs):
# The shared embedding and control/treatment/uplift predictions are
# distracting from the test logic.
return types.TwoTowerTrainingOutputs(
shared_embedding=tf.zeros((3, 1)),
control_predictions=tf.zeros((3, 1)),
treatment_predictions=tf.zeros((3, 1)),
uplift=tf.zeros((3, 1)),
**kwargs,
)
@parameterized.product(
(
dict(
reduction_strategy=tf_keras.losses.Reduction.NONE,
reduction_op=tf.identity,
),
dict(
reduction_strategy=tf_keras.losses.Reduction.SUM,
reduction_op=tf.reduce_sum,
),
dict(
reduction_strategy=tf_keras.losses.Reduction.SUM_OVER_BATCH_SIZE,
reduction_op=tf.reduce_mean,
),
),
(
dict(
loss_fn=tf_keras.losses.mean_squared_error, loss_fn_kwargs=dict()
),
dict(
loss_fn=tf_keras.losses.mean_absolute_percentage_error,
loss_fn_kwargs=dict(),
),
dict(
loss_fn=tf_keras.losses.huber,
loss_fn_kwargs=dict(delta=0.2),
),
dict(
loss_fn=tf_keras.losses.categorical_crossentropy,
loss_fn_kwargs=dict(from_logits=True),
),
),
)
def test_correctness(
self, reduction_strategy, reduction_op, loss_fn, loss_fn_kwargs
):
loss = true_logits_loss.TrueLogitsLoss(
loss_fn=loss_fn,
reduction=reduction_strategy,
**loss_fn_kwargs,
)
y_true = tf.constant([[0.4], [1.0], [0.0]])
y_pred = self._get_y_pred(
control_logits=tf.constant([[0.6], [4.3], [-0.3]]),
treatment_logits=tf.constant([[-2.0], [-0.1], [0.5]]),
true_logits=tf.constant([[-2.0], [4.3], [0.5]]),
is_treatment=tf.constant([[True], [False], [True]]),
)
expected_loss = reduction_op(
loss_fn(y_true, y_pred.true_logits, **loss_fn_kwargs)
)
self.assertAllEqual(expected_loss, loss(y_true, y_pred))
if __name__ == "__main__":
tf.test.main()
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