spark JavaChiSqSelectorExample 源码
spark JavaChiSqSelectorExample 代码
文件路径：/examples/src/main/java/org/apache/spark/examples/mllib/JavaChiSqSelectorExample.java
/*
* 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;
// $example on$
import org.apache.spark.api.java.JavaRDD;
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(lp -> {
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)
ChiSqSelectorModel transformer = selector.fit(discretizedData.rdd());
// Filter the top 50 features from each feature vector
JavaRDD<LabeledPoint> filteredData = discretizedData.map(lp ->
new LabeledPoint(lp.label(), transformer.transform(lp.features())));
// $example off$
System.out.println("filtered data: ");
filteredData.foreach(System.out::println);
jsc.stop();
}
}
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