/*
* 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.
*/
package com.facebook.presto.ml;
import com.google.common.collect.ImmutableMap;
import com.google.common.collect.ImmutableSet;
import org.testng.annotations.Test;
import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
import java.util.Set;
import static com.facebook.presto.ml.TestUtils.getDataset;
import static org.testng.Assert.assertEquals;
import static org.testng.Assert.assertTrue;
public class TestFeatureTransformations
{
@Test
public void testUnitNormalizer()
throws Exception
{
FeatureTransformation transformation = new FeatureUnitNormalizer();
Dataset dataset = getDataset();
boolean valueGreaterThanOne = false;
for (FeatureVector vector : dataset.getDatapoints()) {
for (double value : vector.getFeatures().values()) {
if (value > 1) {
valueGreaterThanOne = true;
break;
}
}
}
// Make sure there is a feature that needs to be normalized
assertTrue(valueGreaterThanOne);
transformation.train(dataset);
for (FeatureVector vector : transformation.transform(dataset).getDatapoints()) {
for (double value : vector.getFeatures().values()) {
assertTrue(value <= 1);
}
}
}
@Test
public void testUnitNormalizerSimple()
throws Exception
{
FeatureTransformation transformation = new FeatureUnitNormalizer();
List<Double> labels = new ArrayList<>();
List<FeatureVector> features = new ArrayList<>();
for (int i = 0; i < 3; i++) {
labels.add(0.0);
features.add(new FeatureVector(0, (double) i));
}
Dataset dataset = new Dataset(labels, features, ImmutableMap.of());
transformation.train(dataset);
Set<Double> featureValues = new HashSet<>();
for (FeatureVector vector : transformation.transform(dataset).getDatapoints()) {
for (double value : vector.getFeatures().values()) {
featureValues.add(value);
}
}
assertEquals(featureValues, ImmutableSet.of(0.0, 0.5, 1.0));
}
}