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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.lang.Math;
import java.util.List;
import org.junit.After;
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.DataFrame;
import org.apache.spark.sql.SQLContext;
import org.apache.spark.sql.Row;
public class JavaLogisticRegressionSuite implements Serializable {
private transient JavaSparkContext jsc;
private transient SQLContext jsql;
private transient DataFrame 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(lr.getLabelCol().equals("label"));
LogisticRegressionModel model = lr.fit(dataset);
model.transform(dataset).registerTempTable("prediction");
DataFrame predictions = jsql.sql("SELECT label, probability, prediction FROM prediction");
predictions.collectAsList();
// Check defaults
assert(model.getThreshold() == 0.5);
assert(model.getFeaturesCol().equals("features"));
assert(model.getPredictionCol().equals("prediction"));
assert(model.getProbabilityCol().equals("probability"));
}
@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);
assert(model.fittingParamMap().apply(lr.maxIter()).equals(10));
assert(model.fittingParamMap().apply(lr.regParam()).equals(1.0));
assert(model.fittingParamMap().apply(lr.threshold()).equals(0.6));
assert(model.getThreshold() == 0.6);
// Modify model params, and check that the params worked.
model.setThreshold(1.0);
model.transform(dataset).registerTempTable("predAllZero");
DataFrame predAllZero = jsql.sql("SELECT prediction, myProbability FROM predAllZero");
for (Row r: predAllZero.collectAsList()) {
assert(r.getDouble(0) == 0.0);
}
// Call transform with params, and check that the params worked.
model.transform(dataset, model.threshold().w(0.0), model.probabilityCol().w("myProb"))
.registerTempTable("predNotAllZero");
DataFrame 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(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"));
assert(model2.fittingParamMap().apply(lr.maxIter()).equals(5));
assert(model2.fittingParamMap().apply(lr.regParam()).equals(0.1));
assert(model2.fittingParamMap().apply(lr.threshold()).equals(0.4));
assert(model2.getThreshold() == 0.4);
assert(model2.getProbabilityCol().equals("theProb"));
}
@SuppressWarnings("unchecked")
@Test
public void logisticRegressionPredictorClassifierMethods() {
LogisticRegression lr = new LogisticRegression();
LogisticRegressionModel model = lr.fit(dataset);
assert(model.numClasses() == 2);
model.transform(dataset).registerTempTable("transformed");
DataFrame trans1 = jsql.sql("SELECT rawPrediction, probability FROM transformed");
for (Row row: trans1.collect()) {
Vector raw = (Vector)row.get(0);
Vector prob = (Vector)row.get(1);
assert(raw.size() == 2);
assert(prob.size() == 2);
double probFromRaw1 = 1.0 / (1.0 + Math.exp(-raw.apply(1)));
assert(Math.abs(prob.apply(1) - probFromRaw1) < eps);
assert(Math.abs(prob.apply(0) - (1.0 - probFromRaw1)) < eps);
}
DataFrame trans2 = jsql.sql("SELECT prediction, probability FROM transformed");
for (Row row: trans2.collect()) {
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(probOfPred >= prob.apply(i));
}
}
}
}
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