Github user yinxusen commented on a diff in the pull request:
https://github.com/apache/spark/pull/11142#discussion_r53560786
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/ChiSqSelectorExample.scala
---
@@ -0,0 +1,58 @@
+/*
+ * 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.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.mllib
+
+import org.apache.spark.SparkConf
+// $example on$
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.feature.ChiSqSelector
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.util.MLUtils
+// $example off$
+
+object ChiSqSelectorExample {
+
+ def main(args: Array[String]) {
+
+ val conf = new SparkConf().setAppName("ChiSqSelectorExample")
+ val sc = new SparkContext(conf)
+
+ // $example on$
+ // Load some data in libsvm format
+ val data = MLUtils.loadLibSVMFile(sc,
"data/mllib/sample_libsvm_data.txt")
+ // Discretize data in 16 equal bins since ChiSqSelector requires
categorical features
+ // Even though features are doubles, the ChiSqSelector treats each
unique value as a category
+ val discretizedData = data.map { lp =>
+ LabeledPoint(lp.label, Vectors.dense(lp.features.toArray.map { x =>
(x / 16).floor } ) )
+ }
+ // Create ChiSqSelector that will select top 50 of 692 features
+ val selector = new ChiSqSelector(50)
+ // Create ChiSqSelector model (selecting features)
+ val transformer = selector.fit(discretizedData)
+ // Filter the top 50 features from each feature vector
+ val filteredData = discretizedData.map { lp =>
+ LabeledPoint(lp.label, transformer.transform(lp.features))
+ }
+ // $example off$
+
--- End diff --
add an output of `filteredData`
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