From 4abd9d290bba24f75e09c516e9e06657b03bcfbd Mon Sep 17 00:00:00 2001 From: Xusen Yin Date: Wed, 25 Nov 2015 15:10:04 +0800 Subject: [PATCH 1/2] add docs of ChiSqSelector --- docs/ml-features.md | 49 +++++++++++++ .../examples/ml/JavaChiSqSelectorExample.java | 71 +++++++++++++++++++ .../examples/ml/ChiSqSelectorExample.scala | 57 +++++++++++++++ 3 files changed, 177 insertions(+) create mode 100644 examples/src/main/java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java create mode 100644 examples/src/main/scala/org/apache/spark/examples/ml/ChiSqSelectorExample.scala diff --git a/docs/ml-features.md b/docs/ml-features.md index cd1838d6d2882..233825c5632ef 100644 --- a/docs/ml-features.md +++ b/docs/ml-features.md @@ -1949,3 +1949,52 @@ output.select("features", "label").show() {% endhighlight %} + +## ChiSqSelector + +`ChiSqSelector` stands for Chi-Squared feature selection. It operates on labeled data with +categorical features. ChiSqSelector orders features based on a +[Chi-Squared test of independence](https://en.wikipedia.org/wiki/Chi-squared_test) +from the class, and then filters (selects) the top features which the class label depends on the +most. This is akin to yielding the features with the most predictive power. + +**Examples** + +Assume that we have a DataFrame with the columns `id`, `features`, and `clicked`: + +~~~ +id | features | clicked +---|-----------------------|--------- + 7 | [0.0, 0.0, 18.0, 1.0] | 1.0 + 8 | [0.0, 1.0, 12.0, 0.0] | 0.0 + 9 | [1.0, 0.0, 15.0, 0.1] | 0.0 +~~~ + +If we use `ChiSqSelector` with a `numTopFeatures = 1`, then according to our label `clicked` the +last column of our `features` is the result: + +~~~ +id | features | clicked | result +---|-----------------------|---------|------- + 7 | [0.0, 0.0, 18.0, 1.0] | 1.0 | [1.0] + 8 | [0.0, 1.0, 12.0, 0.0] | 0.0 | [0.0] + 9 | [1.0, 0.0, 15.0, 0.1] | 0.0 | [0.1] +~~~ + +
+
+ +Refer to the [ChiSqSelector Scala docs](api/scala/index.html#org.apache.spark.ml.feature.ChiSqSelector) +for more details on the API. + +{% include_example scala/org/apache/spark/examples/ml/ChiSqSelectorExample.scala %} +
+ +
+ +Refer to the [ChiSqSelector Java docs](api/java/org/apache/spark/ml/feature/ChiSqSelector.html) +for more details on the API. + +{% include_example java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java %} +
+
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java new file mode 100644 index 0000000000000..704e517c32edd --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java @@ -0,0 +1,71 @@ +/* + * 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; + +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.sql.SQLContext; + +// $example on$ +import java.util.Arrays; + +import org.apache.spark.ml.feature.ChiSqSelector; +import org.apache.spark.mllib.linalg.VectorUDT; +import org.apache.spark.mllib.linalg.Vectors; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.RowFactory; +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; +// $example off$ + +public class JavaChiSqSelectorExample { + public static void main(String[] args) { + SparkConf conf = new SparkConf().setAppName("JavaChiSqSelectorExample"); + JavaSparkContext jsc = new JavaSparkContext(conf); + SQLContext sqlContext = new SQLContext(jsc); + + // $example on$ + JavaRDD jrdd = jsc.parallelize(Arrays.asList( + RowFactory.create(7, Vectors.dense(0.0, 0.0, 18.0, 1.0), 1.0), + RowFactory.create(8, Vectors.dense(0.0, 1.0, 12.0, 0.0), 0.0), + RowFactory.create(9, Vectors.dense(1.0, 0.0, 15.0, 0.1), 0.0) + )); + StructType schema = new StructType(new StructField[]{ + new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), + new StructField("features", new VectorUDT(), false, Metadata.empty()), + new StructField("clicked", DataTypes.DoubleType, false, Metadata.empty()) + }); + + DataFrame df = sqlContext.createDataFrame(jrdd, schema); + + ChiSqSelector selector = new ChiSqSelector() + .setNumTopFeatures(1) + .setFeaturesCol("features") + .setLabelCol("clicked") + .setOutputCol("result"); + + DataFrame result = selector.fit(df).transform(df); + result.show(); + // $example off$ + jsc.stop(); + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/ChiSqSelectorExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/ChiSqSelectorExample.scala new file mode 100644 index 0000000000000..63401a94b8d2a --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/ChiSqSelectorExample.scala @@ -0,0 +1,57 @@ +/* + * 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.ml + +// $example on$ +import org.apache.spark.ml.feature.ChiSqSelector +import org.apache.spark.mllib.linalg.Vectors +// $example off$ +import org.apache.spark.sql.SQLContext +import org.apache.spark.{SparkConf, SparkContext} + +object ChiSqSelectorExample { + def main(args: Array[String]) { + val conf = new SparkConf().setAppName("ChiSqSelectorExample") + val sc = new SparkContext(conf) + + val sqlContext = SQLContext.getOrCreate(sc) + import sqlContext.implicits._ + + // $example on$ + val data = Seq( + (7, Vectors.dense(0.0, 0.0, 18.0, 1.0), 1.0), + (8, Vectors.dense(0.0, 1.0, 12.0, 0.0), 0.0), + (9, Vectors.dense(1.0, 0.0, 15.0, 0.1), 0.0) + ) + + val df = sc.parallelize(data).toDF("id", "features", "clicked") + + val selector = new ChiSqSelector() + .setNumTopFeatures(1) + .setFeaturesCol("features") + .setLabelCol("clicked") + .setOutputCol("result") + + val result = selector.fit(df).transform(df) + result.show() + // $example off$ + sc.stop() + } +} +// scalastyle:on println From a77743f9bd4b56d75764927e3070ca993fb10439 Mon Sep 17 00:00:00 2001 From: Xusen Yin Date: Thu, 26 Nov 2015 12:24:38 +0800 Subject: [PATCH 2/2] fix issues --- docs/ml-features.md | 9 +++++---- .../spark/examples/ml/JavaChiSqSelectorExample.java | 2 +- .../apache/spark/examples/ml/ChiSqSelectorExample.scala | 2 +- 3 files changed, 7 insertions(+), 6 deletions(-) diff --git a/docs/ml-features.md b/docs/ml-features.md index 233825c5632ef..5f888775553f2 100644 --- a/docs/ml-features.md +++ b/docs/ml-features.md @@ -1960,7 +1960,8 @@ most. This is akin to yielding the features with the most predictive power. **Examples** -Assume that we have a DataFrame with the columns `id`, `features`, and `clicked`: +Assume that we have a DataFrame with the columns `id`, `features`, and `clicked`, which is used as +our target to be predicted: ~~~ id | features | clicked @@ -1971,11 +1972,11 @@ id | features | clicked ~~~ If we use `ChiSqSelector` with a `numTopFeatures = 1`, then according to our label `clicked` the -last column of our `features` is the result: +last column in our `features` chosen as the most useful feature: ~~~ -id | features | clicked | result ----|-----------------------|---------|------- +id | features | clicked | selectedFeatures +---|-----------------------|---------|------------------ 7 | [0.0, 0.0, 18.0, 1.0] | 1.0 | [1.0] 8 | [0.0, 1.0, 12.0, 0.0] | 0.0 | [0.0] 9 | [1.0, 0.0, 15.0, 0.1] | 0.0 | [0.1] diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java index 704e517c32edd..ede05d6e20118 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java @@ -61,7 +61,7 @@ public static void main(String[] args) { .setNumTopFeatures(1) .setFeaturesCol("features") .setLabelCol("clicked") - .setOutputCol("result"); + .setOutputCol("selectedFeatures"); DataFrame result = selector.fit(df).transform(df); result.show(); diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/ChiSqSelectorExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/ChiSqSelectorExample.scala index 63401a94b8d2a..a8d2bc4907e80 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/ChiSqSelectorExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/ChiSqSelectorExample.scala @@ -46,7 +46,7 @@ object ChiSqSelectorExample { .setNumTopFeatures(1) .setFeaturesCol("features") .setLabelCol("clicked") - .setOutputCol("result") + .setOutputCol("selectedFeatures") val result = selector.fit(df).transform(df) result.show()