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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 variance metric."""
from typing import Optional
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.metrics import variance
class VarianceTest(keras_test_case.KerasTestCase, parameterized.TestCase):
def _compute_variance(
self, values: tf.Tensor, weights: Optional[tf.Tensor] = None
) -> float:
values = values.numpy()
if weights is None:
return values.var()
weights = weights.numpy()
weights = np.broadcast_to(weights, shape=values.shape)
weighted_mean = np.average(values, weights=weights)
return np.average((values - weighted_mean) ** 2, weights=weights)
@parameterized.named_parameters(
{
"testcase_name": "unweighted",
"values": tf.constant([-2, 0, 3, 5]),
"sample_weight": None,
},
{
"testcase_name": "weighted",
"values": tf.constant([-2, 0, 3, 5]),
"sample_weight": tf.constant([1, 0.3, 0.0, 1.5]),
},
{
"testcase_name": "negative_weights",
"values": tf.constant([-2, 0, 3, 5]),
"sample_weight": tf.constant([1, 0.3, 0.0, -1.5]),
},
)
def test_single_batch_correctness(self, values, sample_weight):
metric = variance.Variance()
metric(values=values, sample_weight=sample_weight)
expected_variance = self._compute_variance(values, sample_weight)
self.assertAllClose(expected_variance, metric.result())
@parameterized.named_parameters(
{
"testcase_name": "unweighted",
"values_batches": [tf.constant([-2, 0, 3, 5]), tf.constant([10])],
"sample_weight_batches": [None, None],
"all_values": tf.constant([-2, 0, 3, 5, 10]),
"all_weights": tf.ones((5,)),
},
{
"testcase_name": "weighted",
"values_batches": [tf.constant([-2, 0, 3, 5]), tf.constant([10, -4])],
"sample_weight_batches": [
tf.constant([1, 0.3, 0.0, -1.5]),
tf.constant([-4.0]),
],
"all_values": tf.constant([-2, 0, 3, 5, 10, -4]),
"all_weights": tf.constant([1, 0.3, 0.0, -1.5, -4.0, -4.0]),
},
{
"testcase_name": "mix_weighted_and_unweighted",
"values_batches": [
tf.constant([-2.2, 0, 3, 5]),
tf.constant([10.5, -4]),
tf.ones((3,), dtype=tf.float32),
],
"sample_weight_batches": [
tf.constant([1, 0.3, 0.0, -1.5]),
None,
None,
],
"all_values": tf.constant([-2.2, 0, 3, 5, 10.5, -4, 1, 1, 1]),
"all_weights": tf.constant([1, 0.3, 0.0, -1.5, 1, 1, 1, 1, 1]),
},
)
def test_multi_batch_correctness(
self, values_batches, sample_weight_batches, all_values, all_weights
):
metric = variance.Variance()
for values, sample_weight in zip(values_batches, sample_weight_batches):
metric(values=values, sample_weight=sample_weight)
expected_variance = self._compute_variance(all_values, all_weights)
self.assertAllClose(expected_variance, metric.result())
self.assertAllGreaterEqual(metric.result(), 0.0)
@parameterized.named_parameters(
{
"testcase_name": "unit_weight",
"values": tf.constant([0, 1, 2, 3]),
"sample_weight": tf.constant([1.0]),
"expected_variance": 1.25,
},
{
"testcase_name": "zero_weight",
"values": tf.constant([0, 1, 2, 3]),
"sample_weight": tf.constant([0.0]),
"expected_variance": 0.0,
},
{
"testcase_name": "decimal_weight",
"values": tf.constant([0, 1, 2, 3]),
"sample_weight": tf.constant([0.2]),
"expected_variance": 1.25,
},
{
"testcase_name": "negative_weight",
"values": tf.constant([0, 1, 2, 3]),
"sample_weight": tf.constant([-0.2]),
"expected_variance": 1.25,
},
)
def test_float_sample_weight(self, values, sample_weight, expected_variance):
metric = variance.Variance()
metric(values, sample_weight=sample_weight)
self.assertEqual(expected_variance, metric.result())
def test_empty_input(self):
metric = variance.Variance()
values = tf.constant([0, 1, 2, 3])
metric(values)
self.assertEqual(1.25, metric.result())
metric(tf.ones(shape=(0,)), sample_weight=None)
self.assertEqual(1.25, metric.result())
def test_initial_state(self):
metric = variance.Variance()
self.assertEqual(0.0, metric.result())
def test_dtype_correctness(self):
# 1 << 128 overflows for float32 but fits in float64.
value = tf.constant([1 << 128], dtype=tf.float64)
metric = variance.Variance(dtype=tf.float32)
metric(value)
self.assertAllEqual(np.nan, metric.result().numpy())
metric = variance.Variance(dtype=tf.float64)
metric(value)
self.assertAllEqual(0.0, metric.result().numpy())
def test_invalid_dtype(self):
with self.assertRaises(ValueError):
metric = variance.Variance(dtype=tf.string)
metric(tf.constant(["hello, world!"], tf.string))
@parameterized.named_parameters(
{
"testcase_name": "squeeze_dimension_invalid",
"values": tf.ones((10, 10)),
"weights": tf.ones((10, 10, 10)),
},
{
"testcase_name": "dimension_mismatch",
"values": tf.ones((10, 10)),
"weights": tf.ones((10, 7)),
},
)
def test_invalid_weight_shape(self, values, weights):
metric = variance.Variance()
with self.assertRaises(tf.errors.InvalidArgumentError):
metric(values, weights)
def test_name(self):
metric = variance.Variance(name="test_name")
self.assertEqual("test_name", metric.name)
def test_multiple_result_calls(self):
metric = variance.Variance()
values = tf.constant([1, 2, 1, 4])
metric.update_state(values)
self.assertEqual(values.numpy().var(), metric.result())
self.assertEqual(values.numpy().var(), metric.result())
metric.update_state(tf.constant([-1, -2, 0]))
self.assertEqual(np.array([1, 2, 1, 4, -1, -2, 0]).var(), metric.result())
def test_reset_state(self):
metric = variance.Variance()
values = tf.constant([1, 2, 1, 4])
metric.update_state(values)
self.assertEqual(1.5, metric.result())
metric.reset_state()
metric.update_state(values, sample_weight=tf.constant([1, 0, 1, 0]))
self.assertEqual(0.0, metric.result())
def test_numpy_correctness(self):
metric = variance.Variance()
values = np.array([-1.3, 2.4, 1, 4])
weights = np.array([0.7, 0, 1.3, 1.0])
metric.update_state(values, weights)
expected_variance = self._compute_variance(
tf.convert_to_tensor(values), tf.convert_to_tensor(weights)
)
self.assertAllClose(expected_variance, metric.result())
def test_metric_config(self):
metric = variance.Variance()
self.assertLayerConfigurable(layer=metric)
if __name__ == "__main__":
tf.test.main()
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