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[SPARK-9910] [ML] User guide for train validation split
Author: martinzapletal <zapletal-martin@email.cz> Closes #8377 from zapletal-martin/SPARK-9910. (cherry picked from commit e8ea5ba) Signed-off-by: Xiangrui Meng <meng@databricks.com>
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examples/src/main/java/org/apache/spark/examples/ml/JavaTrainValidationSplitExample.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.examples.ml; | ||
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| import org.apache.spark.SparkConf; | ||
| import org.apache.spark.api.java.JavaSparkContext; | ||
| import org.apache.spark.ml.evaluation.RegressionEvaluator; | ||
| import org.apache.spark.ml.param.ParamMap; | ||
| import org.apache.spark.ml.regression.LinearRegression; | ||
| import org.apache.spark.ml.tuning.*; | ||
| import org.apache.spark.mllib.regression.LabeledPoint; | ||
| import org.apache.spark.mllib.util.MLUtils; | ||
| import org.apache.spark.sql.DataFrame; | ||
| import org.apache.spark.sql.SQLContext; | ||
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| /** | ||
| * A simple example demonstrating model selection using TrainValidationSplit. | ||
| * | ||
| * The example is based on {@link org.apache.spark.examples.ml.JavaSimpleParamsExample} | ||
| * using linear regression. | ||
| * | ||
| * Run with | ||
| * {{{ | ||
| * bin/run-example ml.JavaTrainValidationSplitExample | ||
| * }}} | ||
| */ | ||
| public class JavaTrainValidationSplitExample { | ||
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| public static void main(String[] args) { | ||
| SparkConf conf = new SparkConf().setAppName("JavaTrainValidationSplitExample"); | ||
| JavaSparkContext jsc = new JavaSparkContext(conf); | ||
| SQLContext jsql = new SQLContext(jsc); | ||
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| DataFrame data = jsql.createDataFrame( | ||
| MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt"), | ||
| LabeledPoint.class); | ||
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| // Prepare training and test data. | ||
| DataFrame[] splits = data.randomSplit(new double [] {0.9, 0.1}, 12345); | ||
| DataFrame training = splits[0]; | ||
| DataFrame test = splits[1]; | ||
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| LinearRegression lr = new LinearRegression(); | ||
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| // We use a ParamGridBuilder to construct a grid of parameters to search over. | ||
| // TrainValidationSplit will try all combinations of values and determine best model using | ||
| // the evaluator. | ||
| ParamMap[] paramGrid = new ParamGridBuilder() | ||
| .addGrid(lr.regParam(), new double[] {0.1, 0.01}) | ||
| .addGrid(lr.fitIntercept()) | ||
| .addGrid(lr.elasticNetParam(), new double[] {0.0, 0.5, 1.0}) | ||
| .build(); | ||
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| // In this case the estimator is simply the linear regression. | ||
| // A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator. | ||
| TrainValidationSplit trainValidationSplit = new TrainValidationSplit() | ||
| .setEstimator(lr) | ||
| .setEvaluator(new RegressionEvaluator()) | ||
| .setEstimatorParamMaps(paramGrid); | ||
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| // 80% of the data will be used for training and the remaining 20% for validation. | ||
| trainValidationSplit.setTrainRatio(0.8); | ||
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| // Run train validation split, and choose the best set of parameters. | ||
| TrainValidationSplitModel model = trainValidationSplit.fit(training); | ||
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| // Make predictions on test data. model is the model with combination of parameters | ||
| // that performed best. | ||
| model.transform(test) | ||
| .select("features", "label", "prediction") | ||
| .show(); | ||
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| jsc.stop(); | ||
| } | ||
| } |
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examples/src/main/scala/org/apache/spark/examples/ml/TrainValidationSplitExample.scala
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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.examples.ml | ||
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| import org.apache.spark.ml.evaluation.RegressionEvaluator | ||
| import org.apache.spark.ml.regression.LinearRegression | ||
| import org.apache.spark.ml.tuning.{ParamGridBuilder, TrainValidationSplit} | ||
| import org.apache.spark.mllib.util.MLUtils | ||
| import org.apache.spark.sql.SQLContext | ||
| import org.apache.spark.{SparkConf, SparkContext} | ||
|
|
||
| /** | ||
| * A simple example demonstrating model selection using TrainValidationSplit. | ||
| * | ||
| * The example is based on [[SimpleParamsExample]] using linear regression. | ||
| * Run with | ||
| * {{{ | ||
| * bin/run-example ml.TrainValidationSplitExample | ||
| * }}} | ||
| */ | ||
| object TrainValidationSplitExample { | ||
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| def main(args: Array[String]): Unit = { | ||
| val conf = new SparkConf().setAppName("TrainValidationSplitExample") | ||
| val sc = new SparkContext(conf) | ||
| val sqlContext = new SQLContext(sc) | ||
| import sqlContext.implicits._ | ||
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| // Prepare training and test data. | ||
| val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() | ||
| val Array(training, test) = data.randomSplit(Array(0.9, 0.1), seed = 12345) | ||
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| val lr = new LinearRegression() | ||
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| // We use a ParamGridBuilder to construct a grid of parameters to search over. | ||
| // TrainValidationSplit will try all combinations of values and determine best model using | ||
| // the evaluator. | ||
| val paramGrid = new ParamGridBuilder() | ||
| .addGrid(lr.regParam, Array(0.1, 0.01)) | ||
| .addGrid(lr.fitIntercept, Array(true, false)) | ||
| .addGrid(lr.elasticNetParam, Array(0.0, 0.5, 1.0)) | ||
| .build() | ||
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| // In this case the estimator is simply the linear regression. | ||
| // A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator. | ||
| val trainValidationSplit = new TrainValidationSplit() | ||
| .setEstimator(lr) | ||
| .setEvaluator(new RegressionEvaluator) | ||
| .setEstimatorParamMaps(paramGrid) | ||
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| // 80% of the data will be used for training and the remaining 20% for validation. | ||
| trainValidationSplit.setTrainRatio(0.8) | ||
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| // Run train validation split, and choose the best set of parameters. | ||
| val model = trainValidationSplit.fit(training) | ||
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| // Make predictions on test data. model is the model with combination of parameters | ||
| // that performed best. | ||
| model.transform(test) | ||
| .select("features", "label", "prediction") | ||
| .show() | ||
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| sc.stop() | ||
| } | ||
| } |