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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 treatment_fraction."""
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 treatment_fraction
class TreatmentFractionTest(
keras_test_case.KerasTestCase, parameterized.TestCase
):
def _get_y_pred(
self, is_treatment: tf.Tensor
) -> types.TwoTowerTrainingOutputs:
# Only the is_treatment tensor is 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=tf.ones_like(is_treatment),
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",
"is_treatment": tf.constant([[True], [False], [True], [False]]),
"sample_weight": None,
"expected_result": 0.5,
},
{
"testcase_name": "weighted",
"is_treatment": tf.constant(
[[True], [False], [True], [True], [False]]
),
"sample_weight": tf.constant([0.5, 0.5, 0, 0.7, 1.8]),
"expected_result": np.average(
[1, 0, 1, 1, 0], weights=[0.5, 0.5, 0, 0.7, 1.8]
),
},
{
"testcase_name": "only_control",
"is_treatment": tf.constant([[False], [False], [False]]),
"sample_weight": tf.constant([1, 0, 1]),
"expected_result": 0.0,
},
{
"testcase_name": "only_treatment",
"is_treatment": tf.constant([[True], [True], [True]]),
"sample_weight": tf.constant([0, 1, 1]),
"expected_result": 1.0,
},
{
"testcase_name": "one_entry",
"is_treatment": tf.constant([True]),
"sample_weight": None,
"expected_result": 1.0,
},
{
"testcase_name": "no_entry",
"is_treatment": tf.constant([], dtype=tf.bool),
"sample_weight": tf.constant([]),
"expected_result": 0.0,
},
)
def test_treatment_fraction_computes_weighted_mean_of_is_treatment_tensor(
self, is_treatment, sample_weight, expected_result
):
metric = treatment_fraction.TreatmentFraction()
y_true = tf.zeros_like(is_treatment)
y_pred = self._get_y_pred(is_treatment)
metric.update_state(
y_true=y_true, y_pred=y_pred, sample_weight=sample_weight
)
self.assertEqual(expected_result, metric.result())
def test_multiple_update_batches_returns_aggregated_treatment_fractions(self):
metric = treatment_fraction.TreatmentFraction()
metric.update_state(
y_true=tf.zeros(3),
y_pred=self._get_y_pred(tf.constant([[True], [True], [True]])),
sample_weight=None,
)
metric.update_state(
y_true=tf.zeros(3),
y_pred=self._get_y_pred(tf.constant([[False], [False], [False]])),
sample_weight=None,
)
metric.update_state(
y_true=tf.zeros(3),
y_pred=self._get_y_pred(tf.constant([[True], [False], [True]])),
sample_weight=tf.constant([0.3, 0.25, 0.7]),
)
expected_treatment_fraction = np.average(
[1, 1, 1, 0, 0, 0, 1, 0, 1], weights=[1, 1, 1, 1, 1, 1, 0.3, 0.25, 0.7]
)
self.assertEqual(expected_treatment_fraction, metric.result())
def test_initial_and_reset_state_return_zero_treatment_fraction(self):
metric = treatment_fraction.TreatmentFraction()
self.assertEqual(0.0, metric.result())
metric(
y_true=tf.zeros(3),
y_pred=self._get_y_pred(tf.constant([[True], [False], [True]])),
)
self.assertEqual(2 / 3, metric.result())
metric.reset_states()
self.assertEqual(0.0, metric.result())
def test_metric_config_is_serializable(self):
metric = treatment_fraction.TreatmentFraction(
name="test_name", dtype=tf.float16
)
y_pred = self._get_y_pred(
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 = treatment_fraction.TreatmentFraction()
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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