/* * 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.examples.ml; // $example on$ import java.util.Arrays; // $example off$ // $example on$ import org.apache.spark.ml.Pipeline; import org.apache.spark.ml.PipelineStage; import org.apache.spark.ml.classification.LogisticRegression; import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator; import org.apache.spark.ml.feature.HashingTF; import org.apache.spark.ml.feature.Tokenizer; import org.apache.spark.ml.param.ParamMap; import org.apache.spark.ml.tuning.CrossValidator; import org.apache.spark.ml.tuning.CrossValidatorModel; import org.apache.spark.ml.tuning.ParamGridBuilder; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; // $example off$ import org.apache.spark.sql.SparkSession; /** * Java example for Model Selection via Cross Validation. */ public class JavaModelSelectionViaCrossValidationExample { public static void main(String[] args) { SparkSession spark = SparkSession .builder() .appName("JavaModelSelectionViaCrossValidationExample") .getOrCreate(); // $example on$ // Prepare training documents, which are labeled. Dataset<Row> training = spark.createDataFrame(Arrays.asList( new JavaLabeledDocument(0L, "a b c d e spark", 1.0), new JavaLabeledDocument(1L, "b d", 0.0), new JavaLabeledDocument(2L,"spark f g h", 1.0), new JavaLabeledDocument(3L, "hadoop mapreduce", 0.0), new JavaLabeledDocument(4L, "b spark who", 1.0), new JavaLabeledDocument(5L, "g d a y", 0.0), new JavaLabeledDocument(6L, "spark fly", 1.0), new JavaLabeledDocument(7L, "was mapreduce", 0.0), new JavaLabeledDocument(8L, "e spark program", 1.0), new JavaLabeledDocument(9L, "a e c l", 0.0), new JavaLabeledDocument(10L, "spark compile", 1.0), new JavaLabeledDocument(11L, "hadoop software", 0.0) ), JavaLabeledDocument.class); // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr. Tokenizer tokenizer = new Tokenizer() .setInputCol("text") .setOutputCol("words"); HashingTF hashingTF = new HashingTF() .setNumFeatures(1000) .setInputCol(tokenizer.getOutputCol()) .setOutputCol("features"); LogisticRegression lr = new LogisticRegression() .setMaxIter(10) .setRegParam(0.01); Pipeline pipeline = new Pipeline() .setStages(new PipelineStage[] {tokenizer, hashingTF, lr}); // We use a ParamGridBuilder to construct a grid of parameters to search over. // With 3 values for hashingTF.numFeatures and 2 values for lr.regParam, // this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from. ParamMap[] paramGrid = new ParamGridBuilder() .addGrid(hashingTF.numFeatures(), new int[] {10, 100, 1000}) .addGrid(lr.regParam(), new double[] {0.1, 0.01}) .build(); // We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance. // This will allow us to jointly choose parameters for all Pipeline stages. // A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator. // Note that the evaluator here is a BinaryClassificationEvaluator and its default metric // is areaUnderROC. CrossValidator cv = new CrossValidator() .setEstimator(pipeline) .setEvaluator(new BinaryClassificationEvaluator()) .setEstimatorParamMaps(paramGrid).setNumFolds(2); // Use 3+ in practice // Run cross-validation, and choose the best set of parameters. CrossValidatorModel cvModel = cv.fit(training); // Prepare test documents, which are unlabeled. Dataset<Row> test = spark.createDataFrame(Arrays.asList( new JavaDocument(4L, "spark i j k"), new JavaDocument(5L, "l m n"), new JavaDocument(6L, "mapreduce spark"), new JavaDocument(7L, "apache hadoop") ), JavaDocument.class); // Make predictions on test documents. cvModel uses the best model found (lrModel). Dataset<Row> predictions = cvModel.transform(test); for (Row r : predictions.select("id", "text", "probability", "prediction").collectAsList()) { System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2) + ", prediction=" + r.get(3)); } // $example off$ spark.stop(); } }