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 java.util.Arrays;
  import java.util.List;
  
  import org.apache.spark.ml.classification.LogisticRegression;
  import org.apache.spark.ml.classification.LogisticRegressionModel;
  import org.apache.spark.ml.linalg.VectorUDT;
  import org.apache.spark.ml.linalg.Vectors;
  import org.apache.spark.ml.param.ParamMap;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import org.apache.spark.sql.RowFactory;
  import org.apache.spark.sql.types.DataTypes;
  import org.apache.spark.sql.types.Metadata;
  import org.apache.spark.sql.types.StructField;
  import org.apache.spark.sql.types.StructType;
  // $example off$
  import org.apache.spark.sql.SparkSession;
  
  /**
   * Java example for Estimator, Transformer, and Param.
   */
  public class JavaEstimatorTransformerParamExample {
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaEstimatorTransformerParamExample")
        .getOrCreate();
  
      // $example on$
      // Prepare training data.
      List<Row> dataTraining = Arrays.asList(
          RowFactory.create(1.0, Vectors.dense(0.0, 1.1, 0.1)),
          RowFactory.create(0.0, Vectors.dense(2.0, 1.0, -1.0)),
          RowFactory.create(0.0, Vectors.dense(2.0, 1.3, 1.0)),
          RowFactory.create(1.0, Vectors.dense(0.0, 1.2, -0.5))
      );
      StructType schema = new StructType(new StructField[]{
          new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
          new StructField("features", new VectorUDT(), false, Metadata.empty())
      });
      Dataset<Row> training = spark.createDataFrame(dataTraining, schema);
  
      // Create a LogisticRegression instance. This instance is an Estimator.
      LogisticRegression lr = new LogisticRegression();
      // Print out the parameters, documentation, and any default values.
      System.out.println("LogisticRegression parameters:\n" + lr.explainParams() + "\n");
  
      // We may set parameters using setter methods.
      lr.setMaxIter(10).setRegParam(0.01);
  
      // Learn a LogisticRegression model. This uses the parameters stored in lr.
      LogisticRegressionModel model1 = lr.fit(training);
      // Since model1 is a Model (i.e., a Transformer produced by an Estimator),
      // we can view the parameters it used during fit().
      // This prints the parameter (name: value) pairs, where names are unique IDs for this
      // LogisticRegression instance.
      System.out.println("Model 1 was fit using parameters: " + model1.parent().extractParamMap());
  
      // We may alternatively specify parameters using a ParamMap.
      ParamMap paramMap = new ParamMap()
        .put(lr.maxIter().w(20))  // Specify 1 Param.
        .put(lr.maxIter(), 30)  // This overwrites the original maxIter.
        .put(lr.regParam().w(0.1), lr.threshold().w(0.55));  // Specify multiple Params.
  
      // One can also combine ParamMaps.
      ParamMap paramMap2 = new ParamMap()
        .put(lr.probabilityCol().w("myProbability"));  // Change output column name
      ParamMap paramMapCombined = paramMap.$plus$plus(paramMap2);
  
      // Now learn a new model using the paramMapCombined parameters.
      // paramMapCombined overrides all parameters set earlier via lr.set* methods.
      LogisticRegressionModel model2 = lr.fit(training, paramMapCombined);
      System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap());
  
      // Prepare test documents.
      List<Row> dataTest = Arrays.asList(
          RowFactory.create(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
          RowFactory.create(0.0, Vectors.dense(3.0, 2.0, -0.1)),
          RowFactory.create(1.0, Vectors.dense(0.0, 2.2, -1.5))
      );
      Dataset<Row> test = spark.createDataFrame(dataTest, schema);
  
      // Make predictions on test documents using the Transformer.transform() method.
      // LogisticRegression.transform will only use the 'features' column.
      // Note that model2.transform() outputs a 'myProbability' column instead of the usual
      // 'probability' column since we renamed the lr.probabilityCol parameter previously.
      Dataset<Row> results = model2.transform(test);
      Dataset<Row> rows = results.select("features", "label", "myProbability", "prediction");
      for (Row r: rows.collectAsList()) {
        System.out.println("(" + r.get(0) + ", " + r.get(1) + ") -> prob=" + r.get(2)
          + ", prediction=" + r.get(3));
      }
      // $example off$
  
      spark.stop();
    }
  }
  