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# Copyright 2016 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
import numpy as np
from tensorflow.python.platform import test
from tensorflow.contrib.learn.python.learn import datasets
from tensorflow.contrib.learn.python.learn.datasets import synthetic
class SyntheticTest(test.TestCase):
"""Test synthetic dataset generation"""
def test_make_dataset(self):
"""Test if the synthetic routine wrapper complains about the name"""
self.assertRaises(
ValueError, datasets.make_dataset, name='_non_existing_name')
def test_all_datasets_callable(self):
"""Test if all methods inside the `SYNTHETIC` are callable"""
self.assertIsInstance(datasets.SYNTHETIC, dict)
if len(datasets.SYNTHETIC) > 0:
for name, method in six.iteritems(datasets.SYNTHETIC):
self.assertTrue(callable(method))
def test_circles(self):
"""Test if the circles are generated correctly
Tests:
- return type is `Dataset`
- returned `data` shape is (n_samples, n_features)
- returned `target` shape is (n_samples,)
- set of unique classes range is [0, n_classes)
TODO:
- all points have the same radius, if no `noise` specified
"""
n_samples = 100
n_classes = 2
circ = synthetic.circles(
n_samples=n_samples, noise=None, n_classes=n_classes)
self.assertIsInstance(circ, datasets.base.Dataset)
self.assertTupleEqual(circ.data.shape, (n_samples, 2))
self.assertTupleEqual(circ.target.shape, (n_samples,))
self.assertSetEqual(set(circ.target), set(range(n_classes)))
def test_circles_replicable(self):
"""Test if the data generation is replicable with a specified `seed`
Tests:
- return the same value if raised with the same seed
- return different values if noise or seed is different
"""
seed = 42
noise = 0.1
circ0 = synthetic.circles(
n_samples=100, noise=noise, n_classes=2, seed=seed)
circ1 = synthetic.circles(
n_samples=100, noise=noise, n_classes=2, seed=seed)
np.testing.assert_array_equal(circ0.data, circ1.data)
np.testing.assert_array_equal(circ0.target, circ1.target)
circ1 = synthetic.circles(
n_samples=100, noise=noise, n_classes=2, seed=seed + 1)
self.assertRaises(AssertionError, np.testing.assert_array_equal, circ0.data,
circ1.data)
self.assertRaises(AssertionError, np.testing.assert_array_equal,
circ0.target, circ1.target)
circ1 = synthetic.circles(
n_samples=100, noise=noise / 2., n_classes=2, seed=seed)
self.assertRaises(AssertionError, np.testing.assert_array_equal, circ0.data,
circ1.data)
def test_spirals(self):
"""Test if the circles are generated correctly
Tests:
- if mode is unknown, ValueError is raised
- return type is `Dataset`
- returned `data` shape is (n_samples, n_features)
- returned `target` shape is (n_samples,)
- set of unique classes range is [0, n_classes)
"""
self.assertRaises(
ValueError, synthetic.spirals, mode='_unknown_mode_spiral_')
n_samples = 100
modes = ('archimedes', 'bernoulli', 'fermat')
for mode in modes:
spir = synthetic.spirals(n_samples=n_samples, noise=None, mode=mode)
self.assertIsInstance(spir, datasets.base.Dataset)
self.assertTupleEqual(spir.data.shape, (n_samples, 2))
self.assertTupleEqual(spir.target.shape, (n_samples,))
self.assertSetEqual(set(spir.target), set(range(2)))
def test_spirals_replicable(self):
"""Test if the data generation is replicable with a specified `seed`
Tests:
- return the same value if raised with the same seed
- return different values if noise or seed is different
"""
seed = 42
noise = 0.1
modes = ('archimedes', 'bernoulli', 'fermat')
for mode in modes:
spir0 = synthetic.spirals(n_samples=1000, noise=noise, seed=seed)
spir1 = synthetic.spirals(n_samples=1000, noise=noise, seed=seed)
np.testing.assert_array_equal(spir0.data, spir1.data)
np.testing.assert_array_equal(spir0.target, spir1.target)
spir1 = synthetic.spirals(n_samples=1000, noise=noise, seed=seed + 1)
self.assertRaises(AssertionError, np.testing.assert_array_equal,
spir0.data, spir1.data)
self.assertRaises(AssertionError, np.testing.assert_array_equal,
spir0.target, spir1.target)
spir1 = synthetic.spirals(n_samples=1000, noise=noise / 2., seed=seed)
self.assertRaises(AssertionError, np.testing.assert_array_equal,
spir0.data, spir1.data)
def test_spirals_synthetic(self):
synthetic.spirals(3)
if __name__ == '__main__':
test.main()
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