content: /*
   * 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 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.ParamGridBuilder;
  import org.apache.spark.ml.tuning.TrainValidationSplit;
  import org.apache.spark.ml.tuning.TrainValidationSplitModel;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  // $example off$
  import org.apache.spark.sql.SparkSession;
  
  /**
   * Java 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.JavaModelSelectionViaTrainValidationSplitExample
   * }}}
   */
  public class JavaModelSelectionViaTrainValidationSplitExample {
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaModelSelectionViaTrainValidationSplitExample")
        .getOrCreate();
  
      // $example on$
      Dataset<Row> data = spark.read().format("libsvm")
        .load("data/mllib/sample_linear_regression_data.txt");
  
      // Prepare training and test data.
      Dataset<Row>[] splits = data.randomSplit(new double[] {0.9, 0.1}, 12345);
      Dataset<Row> training = splits[0];
      Dataset<Row> test = splits[1];
  
      LinearRegression lr = new LinearRegression();
  
      // 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();
  
      // 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)
        .setTrainRatio(0.8);  // 80% for training and the remaining 20% for validation
  
      // Run train validation split, and choose the best set of parameters.
      TrainValidationSplitModel model = trainValidationSplit.fit(training);
  
      // Make predictions on test data. model is the model with combination of parameters
      // that performed best.
      model.transform(test)
        .select("features", "label", "prediction")
        .show();
      // $example off$
  
      spark.stop();
    }
  }
  