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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 utils."""
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.recommendation.uplift import utils
class UtilsTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.named_parameters(
{
"testcase_name": "treatment_only",
"values": tf.constant([1.5, 2, 3]),
"is_treatment": tf.ones((3, 1)),
"expected_control_values": tf.zeros((0,)),
"expected_treatment_values": tf.constant([1.5, 2, 3]),
},
{
"testcase_name": "control_only",
"values": tf.constant([1.5, 2, 3]),
"is_treatment": tf.zeros((3, 1)),
"expected_control_values": tf.constant([1.5, 2, 3]),
"expected_treatment_values": tf.zeros((0,)),
},
{
"testcase_name": "control_and_treatment",
"values": tf.concat(
values=[tf.ones((2, 2, 3)), 2.0 * tf.ones((1, 2, 3))], axis=0
),
"is_treatment": tf.constant([[0], [0], [1]]),
"expected_control_values": tf.ones((2, 2, 3)),
"expected_treatment_values": 2.0 * tf.ones((1, 2, 3)),
},
{
"testcase_name": "one_dimensional_is_treatment",
"values": tf.concat(
values=[tf.ones((2, 2, 3)), 2.0 * tf.ones((1, 2, 3))], axis=0
),
"is_treatment": tf.constant([0, 0, 1]),
"expected_control_values": tf.ones((2, 2, 3)),
"expected_treatment_values": 2.0 * tf.ones((1, 2, 3)),
},
{
"testcase_name": "empty_values",
"values": tf.raw_ops.Empty(shape=(0,), dtype=tf.float32, init=True),
"is_treatment": tf.raw_ops.Empty(
shape=(0,), dtype=tf.float32, init=True
),
"expected_control_values": tf.zeros((0,)),
"expected_treatment_values": tf.zeros((0,)),
},
)
def test_split_by_treatment(
self,
values,
is_treatment,
expected_control_values,
expected_treatment_values,
):
control_values, treatment_values = utils.split_by_treatment(
values=values, is_treatment=is_treatment
)
self.assertAllEqual(expected_control_values, control_values)
self.assertAllEqual(expected_treatment_values, treatment_values)
@parameterized.named_parameters(
{
"testcase_name": "decimal_values",
"is_treatment": tf.constant([1.0, 0.3, 0.0]),
"expected_error": tf.errors.InvalidArgumentError,
},
{
"testcase_name": "string_values",
"is_treatment": tf.constant(["a", "b", "c"]),
"expected_error": ValueError,
},
)
def test_invalid_treatment_indicator_tensor(
self, is_treatment, expected_error
):
values = tf.ones((3, 1))
with self.assertRaises(expected_error):
utils.split_by_treatment(values, is_treatment)
def test_shape_mismatch(self):
values = tf.ones((4, 1))
is_treatment = tf.constant([0, 0, 1])
with self.assertRaises(ValueError):
utils.split_by_treatment(values, is_treatment)
if __name__ == "__main__":
tf.test.main()
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