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gaussian_toy_dataset_test.py
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gaussian_toy_dataset_test.py
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# Copyright 2024 DeepMind Technologies Limited
#
# 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 gaussian_toy_dataset."""
from absl.testing import absltest
from absl.testing import parameterized
import jax.numpy as jnp
import numpy as np
import gaussian_toy_dataset as gtd
class GaussianToyDatasetTest(parameterized.TestCase):
@parameterized.parameters([
dict(
class_weights=np.array([0.5, 0.5]),
class_sigmas=0.1,
dimensionality=10,
sigma=1),
dict(
class_weights=np.array([0.5, 0.5]),
class_sigmas=0.1,
dimensionality=10,
sigma=0.01),
dict(
class_weights=np.array([0.5, 0.5]),
class_sigmas=0.1,
dimensionality=1000,
sigma=1),
dict(
class_weights=np.array([1, 1]),
class_sigmas=0.1,
dimensionality=10,
sigma=1),
dict(
class_weights=np.array([0.1, 0.1]),
class_sigmas=0.1,
dimensionality=10,
sigma=1),
dict(
class_weights=np.array([0.1, 0.1]),
class_sigmas=np.array([0.1, 0.5]),
dimensionality=10,
sigma=1),
])
def test_constructor(self, class_weights, class_sigmas, dimensionality,
sigma):
model = gtd.GaussianToyDataset(
gtd.PRNGSequence(0), class_weights, class_sigmas, dimensionality, sigma
)
self.assertIsNotNone(model.means)
self.assertTrue(jnp.all(model.sigmas > 0))
self.assertTrue(jnp.isclose(1, jnp.sum(model.class_probabilities)))
@parameterized.parameters([
dict(
class_weights=np.array([]),
class_sigmas=0.1,
dimensionality=10,
sigma=1),
dict(
class_weights=np.array([0.5, 0.5]),
class_sigmas=np.array([1]),
dimensionality=10,
sigma=1),
])
def test_constructor_errors(self, class_weights, class_sigmas, dimensionality,
sigma):
with self.assertRaises(ValueError):
gtd.GaussianToyDataset(
gtd.PRNGSequence(0),
class_weights,
class_sigmas,
dimensionality,
sigma,
)
@parameterized.parameters([
dict(
class_weights=np.array([1]),
class_sigmas=0.1,
dimensionality=10,
num_examples=100),
dict(
class_weights=np.array([1, 1]),
class_sigmas=0.1,
dimensionality=10,
num_examples=100),
dict(
class_weights=np.array([1, 1, 1, 1]),
class_sigmas=0.1,
dimensionality=10,
num_examples=100),
dict(
class_weights=np.array([1, 1, 1, 1]),
class_sigmas=np.array([0.5, 0.5, 0.3, 0.1]),
dimensionality=10,
num_examples=100),
])
def test_sample_points(self, class_weights, class_sigmas, dimensionality,
num_examples):
model = gtd.GaussianToyDataset(
gtd.PRNGSequence(0), class_weights, class_sigmas, dimensionality, 1
)
examples, ground_truth = model.sample_points(num_examples)
self.assertEqual(examples.shape[0], num_examples)
self.assertEqual(examples.shape[1], dimensionality)
if class_weights.shape[0] == 1:
self.assertTrue(jnp.all(ground_truth == 0))
else:
for k in range(class_weights.shape[0]):
self.assertTrue(jnp.any(ground_truth == k))
@parameterized.parameters([
dict(num_examples=0),
dict(num_examples=-1),
])
def test_sample_points_errors(self, num_examples):
with self.assertRaises(ValueError):
model = gtd.GaussianToyDataset(
gtd.PRNGSequence(0), jnp.array([1, 1]), 0.1, 10, 1
)
model.sample_points(num_examples)
@parameterized.parameters([
dict(class_weights=np.array([1]), dimensionality=10, num_examples=100),
dict(class_weights=np.array([1, 1]), dimensionality=10, num_examples=100),
dict(
class_weights=np.array([1, 1, 1, 1]),
dimensionality=10,
num_examples=100),
])
def test_evaluate_points(self, class_weights, dimensionality, num_examples):
model = gtd.GaussianToyDataset(
gtd.PRNGSequence(0), class_weights, 0.1, dimensionality, 1
)
points, _ = model.sample_points(num_examples)
probabilities = model.evaluate_points(points)
self.assertEqual(probabilities.shape[0], points.shape[0])
self.assertEqual(probabilities.shape[1], class_weights.shape[0])
self.assertTrue(jnp.all(jnp.isclose(jnp.sum(probabilities, axis=1), 1)))
self.assertTrue(jnp.all(probabilities >= 0))
def test_evaluate_points_priors(self):
model = gtd.GaussianToyDataset(
gtd.PRNGSequence(0), jnp.array([0.1, 0.9]), 1, 1
)
# Ensure that there is not much overlap to have probabilities be
# determined primarily by the class priors.
model.means = jnp.array([[-0.9], [0.9]])
num_examples = 10000
_, labels = model.sample_points(num_examples)
self.assertLessEqual(jnp.sum(labels == 0), (0.1 + 1e-2) * num_examples)
self.assertGreaterEqual(jnp.sum(labels == 1), (0.9 - 1e-2) * num_examples)
@parameterized.parameters([
dict(points=np.zeros((100))),
dict(points=np.zeros((100, 5))),
])
def test_evaluate_points_errors(self, points):
with self.assertRaises(ValueError):
model = gtd.GaussianToyDataset(
gtd.PRNGSequence(0), jnp.array([1, 1]), 0.1, 10, 1
)
model.evaluate_points(jnp.array(points))
@parameterized.parameters([
dict(num_examples=10, readers=np.array([0, 0, 0]), expected_length=None),
])
def test_sample_rankings(self, num_examples, readers, expected_length):
model = gtd.GaussianToyDataset(
gtd.PRNGSequence(0), jnp.array([1, 1]), 0.1, 10, 1
)
points, _ = model.sample_points(num_examples)
probabilities = model.evaluate_points(points)
rankings, groups = model.sample_rankings(probabilities, jnp.array(readers),
expected_length)
self.assertEqual(rankings.shape[0], points.shape[0])
self.assertEqual(rankings.shape[1], readers.shape[0])
self.assertEqual(rankings.shape[2], model.num_classes)
self.assertTrue(jnp.all(rankings < model.num_classes))
self.assertTrue(jnp.all(rankings >= 0))
np.testing.assert_array_equal(rankings.shape, groups.shape)
@parameterized.parameters([
dict(
probabilities=np.zeros((100)),
readers=np.zeros((2)),
expected_length=None,
grouping_threshold=None),
dict(
probabilities=np.zeros((100, 1)),
readers=np.array([]),
expected_length=None,
grouping_threshold=None),
dict(
probabilities=np.zeros((100, 3)),
readers=np.array([]),
expected_length=None,
grouping_threshold=None),
dict(
probabilities=np.zeros((100, 2)),
readers=np.array([]),
expected_length=None,
grouping_threshold=None),
dict(
probabilities=np.zeros((100, 2)),
readers=np.zeros((2, 2)),
expected_length=None,
grouping_threshold=None),
dict(
probabilities=np.zeros((100, 2)),
readers=np.zeros((2)),
expected_length=0,
grouping_threshold=None),
dict(
probabilities=np.zeros((100, 2)),
readers=np.zeros((2)),
expected_length=0,
grouping_threshold=0),
])
def test_sample_rankings_errors(self, probabilities, readers, expected_length,
grouping_threshold):
with self.assertRaises(ValueError):
model = gtd.GaussianToyDataset(
gtd.PRNGSequence(0), jnp.array([1, 1]), 0.1, 10, 1
)
model.sample_rankings(probabilities, readers, expected_length,
grouping_threshold)
def test_sample_partial_rankings(self):
num_classes = 5
probabilities = jnp.array([[
[0.4, 0.35, 0.15, 0.1, 0],
[0.6, 0.1, 0.1, 0.1, 0.1],
[0.525, 0.475, 0, 0, 0],
[0.6, 0.3, 0.1, 0, 0],
[0.4, 0.3, 0.2, 0.1, 0],
]])
rankings = jnp.tile(
jnp.expand_dims(jnp.arange(num_classes), axis=0), num_classes)
expected_groups_001 = jnp.array([[
[0, 1, 2, 3, 4],
[0, 1, 1, 1, 1],
[0, 1, 2, 2, 2],
[0, 1, 2, 3, 3],
[0, 1, 2, 3, 4],
]])
expected_groups_01 = jnp.array([[
[0, 0, 1, 1, 1],
[0, 1, 1, 1, 1],
[0, 0, 1, 1, 1],
[0, 1, 2, 2, 2],
[0, 0, 0, 0, 0],
]])
expected_groups_005 = jnp.array([[
[0, 0, 1, 1, 2],
[0, 1, 1, 1, 1],
[0, 0, 1, 1, 1],
[0, 1, 2, 3, 3],
[0, 1, 2, 3, 4],
]])
model = gtd.GaussianToyDataset(
gtd.PRNGSequence(0), jnp.ones(num_classes), 0.1, 10, 1
)
groups = model._sample_partial_rankings(
probabilities, rankings, expected_length=None, grouping_threshold=0.01)
np.testing.assert_equal(np.array(groups), np.array(expected_groups_001))
groups = model._sample_partial_rankings(
probabilities, rankings, expected_length=None, grouping_threshold=0.1)
np.testing.assert_equal(np.array(groups), np.array(expected_groups_01))
groups = model._sample_partial_rankings(
probabilities, rankings, expected_length=None, grouping_threshold=0.05)
np.testing.assert_equal(np.array(groups), np.array(expected_groups_005))
def test_vary_number_of_readers(self):
num_classes = 5
num_examples = 10
groups = jnp.array([
[0, 1, 2, 3, 4],
[0, 1, 1, 1, 1],
[0, 1, 2, 2, 2],
[0, 1, 2, 3, 3],
[0, 1, 2, 3, 4],
])
groups = jnp.repeat(jnp.expand_dims(groups, axis=0), num_examples, axis=0)
model = gtd.GaussianToyDataset(
gtd.PRNGSequence(0), jnp.ones(num_classes), 0.1, 10, 1
)
groups, num_readers = model.vary_number_of_readers(groups, 2)
for n in range(num_examples):
actual_readers = jnp.sum(groups[n] >= 0, axis=1)
actual_readers = jnp.sum(actual_readers > 0, axis=0)
self.assertEqual(num_readers[n], actual_readers)
if __name__ == "__main__":
absltest.main()