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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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 org.apache.spark.ml.classification;
import java.io.Serializable;
import java.util.List;
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import static org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInputAsList;
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SQLContext;
public class JavaLogisticRegressionSuite implements Serializable {
private transient JavaSparkContext jsc;
private transient SQLContext jsql;
private transient Dataset<Row> dataset;
private transient JavaRDD<LabeledPoint> datasetRDD;
private double eps = 1e-5;
@Before
public void setUp() {
jsc = new JavaSparkContext("local", "JavaLogisticRegressionSuite");
jsql = new SQLContext(jsc);
List<LabeledPoint> points = generateLogisticInputAsList(1.0, 1.0, 100, 42);
datasetRDD = jsc.parallelize(points, 2);
dataset = jsql.createDataFrame(datasetRDD, LabeledPoint.class);
dataset.registerTempTable("dataset");
}
@After
public void tearDown() {
jsc.stop();
jsc = null;
}
@Test
public void logisticRegressionDefaultParams() {
LogisticRegression lr = new LogisticRegression();
Assert.assertEquals(lr.getLabelCol(), "label");
LogisticRegressionModel model = lr.fit(dataset);
model.transform(dataset).registerTempTable("prediction");
Dataset<Row> predictions = jsql.sql("SELECT label, probability, prediction FROM prediction");
predictions.collectAsList();
// Check defaults
Assert.assertEquals(0.5, model.getThreshold(), eps);
Assert.assertEquals("features", model.getFeaturesCol());
Assert.assertEquals("prediction", model.getPredictionCol());
Assert.assertEquals("probability", model.getProbabilityCol());
}
@Test
public void logisticRegressionWithSetters() {
// Set params, train, and check as many params as we can.
LogisticRegression lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(1.0)
.setThreshold(0.6)
.setProbabilityCol("myProbability");
LogisticRegressionModel model = lr.fit(dataset);
LogisticRegression parent = (LogisticRegression) model.parent();
Assert.assertEquals(10, parent.getMaxIter());
Assert.assertEquals(1.0, parent.getRegParam(), eps);
Assert.assertEquals(0.4, parent.getThresholds()[0], eps);
Assert.assertEquals(0.6, parent.getThresholds()[1], eps);
Assert.assertEquals(0.6, parent.getThreshold(), eps);
Assert.assertEquals(0.6, model.getThreshold(), eps);
// Modify model params, and check that the params worked.
model.setThreshold(1.0);
model.transform(dataset).registerTempTable("predAllZero");
Dataset<Row> predAllZero = jsql.sql("SELECT prediction, myProbability FROM predAllZero");
for (Row r: predAllZero.collectAsList()) {
Assert.assertEquals(0.0, r.getDouble(0), eps);
}
// Call transform with params, and check that the params worked.
model.transform(dataset, model.threshold().w(0.0), model.probabilityCol().w("myProb"))
.registerTempTable("predNotAllZero");
Dataset<Row> predNotAllZero = jsql.sql("SELECT prediction, myProb FROM predNotAllZero");
boolean foundNonZero = false;
for (Row r: predNotAllZero.collectAsList()) {
if (r.getDouble(0) != 0.0) foundNonZero = true;
}
Assert.assertTrue(foundNonZero);
// Call fit() with new params, and check as many params as we can.
LogisticRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1),
lr.threshold().w(0.4), lr.probabilityCol().w("theProb"));
LogisticRegression parent2 = (LogisticRegression) model2.parent();
Assert.assertEquals(5, parent2.getMaxIter());
Assert.assertEquals(0.1, parent2.getRegParam(), eps);
Assert.assertEquals(0.4, parent2.getThreshold(), eps);
Assert.assertEquals(0.4, model2.getThreshold(), eps);
Assert.assertEquals("theProb", model2.getProbabilityCol());
}
@SuppressWarnings("unchecked")
@Test
public void logisticRegressionPredictorClassifierMethods() {
LogisticRegression lr = new LogisticRegression();
LogisticRegressionModel model = lr.fit(dataset);
Assert.assertEquals(2, model.numClasses());
model.transform(dataset).registerTempTable("transformed");
Dataset<Row> trans1 = jsql.sql("SELECT rawPrediction, probability FROM transformed");
for (Row row: trans1.collectAsList()) {
Vector raw = (Vector)row.get(0);
Vector prob = (Vector)row.get(1);
Assert.assertEquals(raw.size(), 2);
Assert.assertEquals(prob.size(), 2);
double probFromRaw1 = 1.0 / (1.0 + Math.exp(-raw.apply(1)));
Assert.assertEquals(0, Math.abs(prob.apply(1) - probFromRaw1), eps);
Assert.assertEquals(0, Math.abs(prob.apply(0) - (1.0 - probFromRaw1)), eps);
}
Dataset<Row> trans2 = jsql.sql("SELECT prediction, probability FROM transformed");
for (Row row: trans2.collectAsList()) {
double pred = row.getDouble(0);
Vector prob = (Vector)row.get(1);
double probOfPred = prob.apply((int)pred);
for (int i = 0; i < prob.size(); ++i) {
Assert.assertTrue(probOfPred >= prob.apply(i));
}
}
}
@Test
public void logisticRegressionTrainingSummary() {
LogisticRegression lr = new LogisticRegression();
LogisticRegressionModel model = lr.fit(dataset);
LogisticRegressionTrainingSummary summary = model.summary();
Assert.assertEquals(summary.totalIterations(), summary.objectiveHistory().length);
}
}
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