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.mllib;
  
  import org.apache.spark.SparkConf;
  import org.apache.spark.api.java.JavaSparkContext;
  import org.apache.spark.api.java.function.VoidFunction;
  // $example on$
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.api.java.function.Function;
  import org.apache.spark.mllib.feature.ChiSqSelector;
  import org.apache.spark.mllib.feature.ChiSqSelectorModel;
  import org.apache.spark.mllib.linalg.Vectors;
  import org.apache.spark.mllib.regression.LabeledPoint;
  import org.apache.spark.mllib.util.MLUtils;
  // $example off$
  
  public class JavaChiSqSelectorExample {
    public static void main(String[] args) {
  
      SparkConf conf = new SparkConf().setAppName("JavaChiSqSelectorExample");
      JavaSparkContext jsc = new JavaSparkContext(conf);
  
      // $example on$
      JavaRDD<LabeledPoint> points = MLUtils.loadLibSVMFile(jsc.sc(),
        "data/mllib/sample_libsvm_data.txt").toJavaRDD().cache();
  
      // Discretize data in 16 equal bins since ChiSqSelector requires categorical features
      // Although features are doubles, the ChiSqSelector treats each unique value as a category
      JavaRDD<LabeledPoint> discretizedData = points.map(
        new Function<LabeledPoint, LabeledPoint>() {
          @Override
          public LabeledPoint call(LabeledPoint lp) {
            final double[] discretizedFeatures = new double[lp.features().size()];
            for (int i = 0; i < lp.features().size(); ++i) {
              discretizedFeatures[i] = Math.floor(lp.features().apply(i) / 16);
            }
            return new LabeledPoint(lp.label(), Vectors.dense(discretizedFeatures));
          }
        }
      );
  
      // Create ChiSqSelector that will select top 50 of 692 features
      ChiSqSelector selector = new ChiSqSelector(50);
      // Create ChiSqSelector model (selecting features)
      final ChiSqSelectorModel transformer = selector.fit(discretizedData.rdd());
      // Filter the top 50 features from each feature vector
      JavaRDD<LabeledPoint> filteredData = discretizedData.map(
        new Function<LabeledPoint, LabeledPoint>() {
          @Override
          public LabeledPoint call(LabeledPoint lp) {
            return new LabeledPoint(lp.label(), transformer.transform(lp.features()));
          }
        }
      );
      // $example off$
  
      System.out.println("filtered data: ");
      filteredData.foreach(new VoidFunction<LabeledPoint>() {
        @Override
        public void call(LabeledPoint labeledPoint) throws Exception {
          System.out.println(labeledPoint.toString());
        }
      });
  
      jsc.stop();
    }
  }
  