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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 uplift_mean.""" | |
| from absl.testing import parameterized | |
| import numpy as np | |
| import tensorflow as tf, tf_keras | |
| from official.recommendation.uplift import keras_test_case | |
| from official.recommendation.uplift import types | |
| from official.recommendation.uplift.metrics import uplift_mean | |
| class UpliftMeanTest(keras_test_case.KerasTestCase, parameterized.TestCase): | |
| def _get_y_pred( | |
| self, uplift: tf.Tensor, is_treatment: tf.Tensor | |
| ) -> types.TwoTowerTrainingOutputs: | |
| # Only the uplift and is_treatment tensors are required for testing. | |
| return types.TwoTowerTrainingOutputs( | |
| shared_embedding=tf.ones_like(is_treatment), | |
| control_predictions=tf.ones_like(is_treatment), | |
| treatment_predictions=tf.ones_like(is_treatment), | |
| uplift=uplift, | |
| control_logits=tf.ones_like(is_treatment), | |
| treatment_logits=tf.ones_like(is_treatment), | |
| true_logits=tf.ones_like(is_treatment), | |
| is_treatment=is_treatment, | |
| ) | |
| def test_metric_computes_sliced_uplift_means( | |
| self, uplift, is_treatment, sample_weight, expected_result | |
| ): | |
| metric = uplift_mean.UpliftMean() | |
| y_pred = self._get_y_pred(uplift=uplift, is_treatment=is_treatment) | |
| metric( | |
| y_true=tf.zeros_like(uplift), y_pred=y_pred, sample_weight=sample_weight | |
| ) | |
| self.assertEqual(expected_result, metric.result()) | |
| def test_multiple_update_batches_returns_aggregated_uplift_means(self): | |
| metric = uplift_mean.UpliftMean(name="uplift") | |
| metric.update_state( | |
| y_true=tf.zeros(3), | |
| y_pred=self._get_y_pred( | |
| uplift=tf.constant([[1], [2], [4]]), | |
| is_treatment=tf.constant([[True], [True], [True]]), | |
| ), | |
| sample_weight=None, | |
| ) | |
| metric.update_state( | |
| y_true=tf.zeros(3), | |
| y_pred=self._get_y_pred( | |
| uplift=tf.constant([[-3], [0], [5]]), | |
| is_treatment=tf.constant([[False], [False], [False]]), | |
| ), | |
| sample_weight=None, | |
| ) | |
| metric.update_state( | |
| y_true=tf.zeros(3), | |
| y_pred=self._get_y_pred( | |
| uplift=tf.constant([[0], [1], [-5]]), | |
| is_treatment=tf.constant([[True], [False], [True]]), | |
| ), | |
| sample_weight=tf.constant([0.3, 0.25, 0.7]), | |
| ) | |
| expected_results = { | |
| "uplift": np.average( | |
| [1, 2, 4, -3, 0, 5, 0, 1, -5], | |
| weights=[1, 1, 1, 1, 1, 1, 0.3, 0.25, 0.7], | |
| ), | |
| "uplift/control": np.average([-3, 0, 5, 1], weights=[1, 1, 1, 0.25]), | |
| "uplift/treatment": np.average( | |
| [1, 2, 4, 0, -5], weights=[1, 1, 1, 0.3, 0.7] | |
| ), | |
| } | |
| self.assertEqual(expected_results, metric.result()) | |
| def test_initial_and_reset_state_return_zero_uplift_means(self): | |
| metric = uplift_mean.UpliftMean() | |
| expected_initial_result = { | |
| "uplift/mean": 0.0, | |
| "uplift/mean/control": 0.0, | |
| "uplift/mean/treatment": 0.0, | |
| } | |
| self.assertEqual(expected_initial_result, metric.result()) | |
| metric( | |
| y_true=tf.zeros(3), | |
| y_pred=self._get_y_pred( | |
| uplift=tf.constant([1, 2, 6]), | |
| is_treatment=tf.constant([[True], [False], [True]]), | |
| ), | |
| ) | |
| self.assertEqual( | |
| { | |
| "uplift/mean": 3.0, | |
| "uplift/mean/control": 2.0, | |
| "uplift/mean/treatment": 3.5, | |
| }, | |
| metric.result(), | |
| ) | |
| metric.reset_states() | |
| self.assertEqual(expected_initial_result, metric.result()) | |
| def test_metric_config_is_serializable(self): | |
| metric = uplift_mean.UpliftMean(name="test_name", dtype=tf.float16) | |
| y_pred = self._get_y_pred( | |
| uplift=tf.constant([[1], [2], [3], [4]]), | |
| is_treatment=tf.constant([[True], [False], [True], [False]]), | |
| ) | |
| self.assertLayerConfigurable( | |
| layer=metric, y_true=tf.zeros(4), y_pred=y_pred, serializable=True | |
| ) | |
| def test_invalid_prediction_tensor_type_raises_type_error(self): | |
| metric = uplift_mean.UpliftMean() | |
| with self.assertRaisesRegex( | |
| TypeError, "y_pred must be of type `TwoTowerTrainingOutputs`" | |
| ): | |
| metric.update_state(y_true=tf.ones((3, 1)), y_pred=tf.ones((3, 1))) | |
| if __name__ == "__main__": | |
| tf.test.main() | |