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.regression.AFTSurvivalRegression;
  import org.apache.spark.ml.regression.AFTSurvivalRegressionModel;
  import org.apache.spark.ml.linalg.VectorUDT;
  import org.apache.spark.ml.linalg.Vectors;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import org.apache.spark.sql.RowFactory;
  import org.apache.spark.sql.SparkSession;
  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;
  // $example off$
  
  /**
   * An example demonstrating AFTSurvivalRegression.
   * Run with
   * <pre>
   * bin/run-example ml.JavaAFTSurvivalRegressionExample
   * </pre>
   */
  public class JavaAFTSurvivalRegressionExample {
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaAFTSurvivalRegressionExample")
        .getOrCreate();
  
      // $example on$
      List<Row> data = Arrays.asList(
        RowFactory.create(1.218, 1.0, Vectors.dense(1.560, -0.605)),
        RowFactory.create(2.949, 0.0, Vectors.dense(0.346, 2.158)),
        RowFactory.create(3.627, 0.0, Vectors.dense(1.380, 0.231)),
        RowFactory.create(0.273, 1.0, Vectors.dense(0.520, 1.151)),
        RowFactory.create(4.199, 0.0, Vectors.dense(0.795, -0.226))
      );
      StructType schema = new StructType(new StructField[]{
        new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
        new StructField("censor", DataTypes.DoubleType, false, Metadata.empty()),
        new StructField("features", new VectorUDT(), false, Metadata.empty())
      });
      Dataset<Row> training = spark.createDataFrame(data, schema);
      double[] quantileProbabilities = new double[]{0.3, 0.6};
      AFTSurvivalRegression aft = new AFTSurvivalRegression()
        .setQuantileProbabilities(quantileProbabilities)
        .setQuantilesCol("quantiles");
  
      AFTSurvivalRegressionModel model = aft.fit(training);
  
      // Print the coefficients, intercept and scale parameter for AFT survival regression
      System.out.println("Coefficients: " + model.coefficients());
      System.out.println("Intercept: " + model.intercept());
      System.out.println("Scale: " + model.scale());
      model.transform(training).show(false);
      // $example off$
  
      spark.stop();
    }
  }
  