Github user yinxusen commented on a diff in the pull request:
https://github.com/apache/spark/pull/11142#discussion_r53559180
--- Diff:
examples/src/main/java/org/apache/spark/examples/mllib/JavaChiSqSelectorExample.java
---
@@ -0,0 +1,74 @@
+/*
+ * 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;
+// $example on$
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+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()));
+ }
+ }
+ );
+ // $example off$
+
--- End diff --
It's better to add an output of `filteredData` to make it a complete
example code. Refer to
https://github.com/apache/spark/blob/master/examples/src/main/java/org/apache/spark/examples/mllib/JavaLDAExample.java#L68
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