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mllib/src/test/java/org/apache/spark/ml/example/JavaLogisticRegressionSuite.java
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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. | ||
| */ | ||
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| package org.apache.spark.ml.example; | ||
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| import java.io.Serializable; | ||
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| import org.apache.spark.api.java.JavaRDD; | ||
| import org.apache.spark.api.java.JavaSparkContext; | ||
| import org.apache.spark.ml.Pipeline; | ||
| import org.apache.spark.ml.PipelineModel; | ||
| import org.apache.spark.ml.PipelineStage; | ||
| import org.apache.spark.ml.param.ParamGridBuilder; | ||
| import org.apache.spark.ml.param.ParamMap; | ||
| import org.apache.spark.mllib.regression.LabeledPoint; | ||
| import org.apache.spark.mllib.util.MLUtils; | ||
| import org.apache.spark.sql.api.java.JavaSQLContext; | ||
| import org.apache.spark.sql.api.java.JavaSchemaRDD; | ||
| import org.apache.spark.sql.api.java.Row; | ||
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| import org.junit.After; | ||
| import org.junit.Before; | ||
| import org.junit.Test; | ||
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| public class JavaLogisticRegressionSuite implements Serializable { | ||
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| private transient JavaSparkContext jsc; | ||
| private transient JavaSQLContext jsql; | ||
| private transient JavaSchemaRDD dataset; | ||
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| @Before | ||
| public void setUp() { | ||
| jsc = new JavaSparkContext("local", "JavaLogisticRegressionSuite"); | ||
| jsql = new JavaSQLContext(jsc); | ||
| JavaRDD<LabeledPoint> points = | ||
| MLUtils.loadLibSVMFile(jsc.sc(), "../data/mllib/sample_binary_classification_data.txt") | ||
| .toJavaRDD(); | ||
| dataset = jsql.applySchema(points, LabeledPoint.class); | ||
| } | ||
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| @After | ||
| public void tearDown() { | ||
| jsc.stop(); | ||
| jsc = null; | ||
| } | ||
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| @Test | ||
| public void logisticRegression() { | ||
| LogisticRegression lr = new LogisticRegression() | ||
| .setMaxIter(10) | ||
| .setRegParam(1.0); | ||
| lr.model().setThreshold(0.8); | ||
| // In Java we can access baseSchemaRDD, while in Scala we cannot. | ||
| LogisticRegressionModel model = lr.fit(dataset.baseSchemaRDD()); | ||
| model.transform(dataset.baseSchemaRDD()).registerTempTable("prediction"); | ||
| JavaSchemaRDD predictions = jsql.sql("SELECT label, score, prediction FROM prediction"); | ||
| for (Row r: predictions.collect()) { | ||
| System.out.println(r); | ||
| } | ||
| } | ||
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| @Test | ||
| public void logisticRegressionWithCrossValidation() { | ||
| LogisticRegression lr = new LogisticRegression(); | ||
| ParamMap[] lrParamMaps = new ParamGridBuilder() | ||
| .addMulti(lr.regParam(), new double[] {0.1, 100.0}) | ||
| .addMulti(lr.maxIter(), new int[] {0, 5}) | ||
| .build(); | ||
| BinaryClassificationEvaluator eval = new BinaryClassificationEvaluator(); | ||
| CrossValidator cv = new CrossValidator() | ||
| .setEstimator(lr) | ||
| .setEstimatorParamMaps(lrParamMaps) | ||
| .setEvaluator(eval) | ||
| .setNumFolds(3); | ||
| CrossValidatorModel bestModel = cv.fit(dataset.baseSchemaRDD()); | ||
| } | ||
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| @Test | ||
| public void logisticRegressionWithPipeline() { | ||
| StandardScaler scaler = new StandardScaler() | ||
| .setInputCol("features") | ||
| .setOutputCol("scaledFeatures"); | ||
| LogisticRegression lr = new LogisticRegression() | ||
| .setFeaturesCol("scaledFeatures"); | ||
| Pipeline pipeline = new Pipeline() | ||
| .setStages(new PipelineStage[] {scaler, lr}); | ||
| PipelineModel model = pipeline.fit(dataset.baseSchemaRDD()); | ||
| model.transform(dataset.baseSchemaRDD()).registerTempTable("prediction"); | ||
| JavaSchemaRDD predictions = jsql.sql("SELECT label, score, prediction FROM prediction"); | ||
| for (Row r: predictions.collect()) { | ||
| System.out.println(r); | ||
| } | ||
| } | ||
| } |