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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,
)
@parameterized.named_parameters(
{
"testcase_name": "unweighted",
"uplift": tf.constant([0, 1, 5, 6]),
"is_treatment": tf.constant([[True], [False], [True], [False]]),
"sample_weight": None,
"expected_result": {
"uplift/mean": 3.0,
"uplift/mean/control": 3.5,
"uplift/mean/treatment": 2.5,
},
},
{
"testcase_name": "weighted",
"uplift": tf.constant([0, 1, 5, 6, -7]),
"is_treatment": tf.constant(
[[True], [False], [True], [True], [False]]
),
"sample_weight": tf.constant([0.5, 0.5, 0, 0.7, 1.8]),
"expected_result": {
"uplift/mean": np.average(
[0, 1, 5, 6, -7], weights=[0.5, 0.5, 0, 0.7, 1.8]
),
"uplift/mean/control": np.average([1, -7], weights=[0.5, 1.8]),
"uplift/mean/treatment": np.average(
[0, 5, 6], weights=[0.5, 0, 0.7]
),
},
},
{
"testcase_name": "only_control",
"uplift": tf.constant([[0], [1], [5]]),
"is_treatment": tf.constant([[False], [False], [False]]),
"sample_weight": tf.constant([1, 0, 1]),
"expected_result": {
"uplift/mean": 2.5,
"uplift/mean/control": 2.5,
"uplift/mean/treatment": 0.0,
},
},
{
"testcase_name": "only_treatment",
"uplift": tf.constant([[0], [1], [5]]),
"is_treatment": tf.constant([[True], [True], [True]]),
"sample_weight": tf.constant([0, 1, 1]),
"expected_result": {
"uplift/mean": 3.0,
"uplift/mean/control": 0.0,
"uplift/mean/treatment": 3.0,
},
},
{
"testcase_name": "one_entry",
"uplift": tf.constant([2.5]),
"is_treatment": tf.constant([True]),
"sample_weight": tf.constant([1]),
"expected_result": {
"uplift/mean": 2.5,
"uplift/mean/control": 0.0,
"uplift/mean/treatment": 2.5,
},
},
{
"testcase_name": "no_entry",
"uplift": tf.constant([]),
"is_treatment": tf.constant([], dtype=tf.bool),
"sample_weight": tf.constant([]),
"expected_result": {
"uplift/mean": 0.0,
"uplift/mean/control": 0.0,
"uplift/mean/treatment": 0.0,
},
},
)
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()
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