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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.regression;
import java.io.Serializable;
import java.util.HashMap;
import java.util.Map;
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 org.apache.spark.mllib.classification.LogisticRegressionSuite;
import org.apache.spark.ml.tree.impl.TreeTests;
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;
public class JavaRandomForestRegressorSuite implements Serializable {
private transient JavaSparkContext sc;
@Before
public void setUp() {
sc = new JavaSparkContext("local", "JavaRandomForestRegressorSuite");
}
@After
public void tearDown() {
sc.stop();
sc = null;
}
@Test
public void runDT() {
int nPoints = 20;
double A = 2.0;
double B = -1.5;
JavaRDD<LabeledPoint> data = sc.parallelize(
LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache();
Map<Integer, Integer> categoricalFeatures = new HashMap<>();
Dataset<Row> dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 0);
// This tests setters. Training with various options is tested in Scala.
RandomForestRegressor rf = new RandomForestRegressor()
.setMaxDepth(2)
.setMaxBins(10)
.setMinInstancesPerNode(5)
.setMinInfoGain(0.0)
.setMaxMemoryInMB(256)
.setCacheNodeIds(false)
.setCheckpointInterval(10)
.setSubsamplingRate(1.0)
.setSeed(1234)
.setNumTrees(3)
.setMaxDepth(2); // duplicate setMaxDepth to check builder pattern
for (String impurity: RandomForestRegressor.supportedImpurities()) {
rf.setImpurity(impurity);
}
for (String featureSubsetStrategy: RandomForestRegressor.supportedFeatureSubsetStrategies()) {
rf.setFeatureSubsetStrategy(featureSubsetStrategy);
}
String realStrategies[] = {".1", ".10", "0.10", "0.1", "0.9", "1.0"};
for (String strategy: realStrategies) {
rf.setFeatureSubsetStrategy(strategy);
}
String integerStrategies[] = {"1", "10", "100", "1000", "10000"};
for (String strategy: integerStrategies) {
rf.setFeatureSubsetStrategy(strategy);
}
String invalidStrategies[] = {"-.1", "-.10", "-0.10", ".0", "0.0", "1.1", "0"};
for (String strategy: invalidStrategies) {
try {
rf.setFeatureSubsetStrategy(strategy);
Assert.fail("Expected exception to be thrown for invalid strategies");
} catch (Exception e) {
Assert.assertTrue(e instanceof IllegalArgumentException);
}
}
RandomForestRegressionModel model = rf.fit(dataFrame);
model.transform(dataFrame);
model.totalNumNodes();
model.toDebugString();
model.trees();
model.treeWeights();
Vector importances = model.featureImportances();
/*
// TODO: Add test once save/load are implemented. SPARK-6725
File tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "spark");
String path = tempDir.toURI().toString();
try {
model2.save(sc.sc(), path);
RandomForestRegressionModel sameModel = RandomForestRegressionModel.load(sc.sc(), path);
TreeTests.checkEqual(model2, sameModel);
} finally {
Utils.deleteRecursively(tempDir);
}
*/
}
}
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