ml-features.md

layout: global
title: Feature Extraction, Transformation, and Selection - SparkML
displayTitle: <a href="ml-guide.html">ML</a> - Features

This section covers algorithms for working with features, roughly divided into these groups:

• Extraction: Extracting features from "raw" data
• Transformation: Scaling, converting, or modifying features
• Selection: Selecting a subset from a larger set of features

Table of Contents

• This will become a table of contents (this text will be scraped). {:toc}

Feature Extractors

Hashing Term-Frequency (HashingTF)

HashingTF is a Transformer which takes sets of terms (e.g., String terms can be sets of words) and converts those sets into fixed-length feature vectors. The algorithm combines Term Frequency (TF) counts with the hashing trick for dimensionality reduction. Please refer to the MLlib user guide on TF-IDF for more details on Term-Frequency.

HashingTF is implemented in HashingTF. In the following code segment, we start with a set of sentences. We split each sentence into words using Tokenizer. For each sentence (bag of words), we hash it into a feature vector. This feature vector could then be passed to a learning algorithm.

{% highlight scala %} import org.apache.spark.ml.feature.{HashingTF, Tokenizer}

val sentenceDataFrame = sqlContext.createDataFrame(Seq( (0, "Hi I heard about Spark"), (0, "I wish Java could use case classes"), (1, "Logistic regression models are neat") )).toDF("label", "sentence") val tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words") val wordsDataFrame = tokenizer.transform(sentenceDataFrame) val hashingTF = new HashingTF().setInputCol("words").setOutputCol("features").setNumFeatures(20) val featurized = hashingTF.transform(wordsDataFrame) featurized.select("features", "label").take(3).foreach(println) {% endhighlight %}

{% highlight java %} import com.google.common.collect.Lists;

import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.HashingTF; import org.apache.spark.ml.feature.Tokenizer; import org.apache.spark.mllib.linalg.Vector; 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;

JavaRDD jrdd = jsc.parallelize(Lists.newArrayList( RowFactory.create(0, "Hi I heard about Spark"), RowFactory.create(0, "I wish Java could use case classes"), RowFactory.create(1, "Logistic regression models are neat") )); StructType schema = new StructType(new StructField[]{ new StructField("label", DataTypes.DoubleType, false, Metadata.empty()), new StructField("sentence", DataTypes.StringType, false, Metadata.empty()) }); DataFrame sentenceDataFrame = sqlContext.createDataFrame(jrdd, schema); Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words"); DataFrame wordsDataFrame = tokenizer.transform(sentenceDataFrame); int numFeatures = 20; HashingTF hashingTF = new HashingTF() .setInputCol("words") .setOutputCol("features") .setNumFeatures(numFeatures); DataFrame featurized = hashingTF.transform(wordsDataFrame); for (Row r : featurized.select("features", "label").take(3)) { Vector features = r.getAs(0); Double label = r.getDouble(1); System.out.println(features); } {% endhighlight %}

{% highlight python %} from pyspark.ml.feature import HashingTF, Tokenizer

sentenceDataFrame = sqlContext.createDataFrame([ (0, "Hi I heard about Spark"), (0, "I wish Java could use case classes"), (1, "Logistic regression models are neat") ], ["label", "sentence"]) tokenizer = Tokenizer(inputCol="sentence", outputCol="words") wordsDataFrame = tokenizer.transform(sentenceDataFrame) hashingTF = HashingTF(inputCol="words", outputCol="features", numFeatures=20) featurized = hashingTF.transform(wordsDataFrame) for features_label in featurized.select("features", "label").take(3): print features_label {% endhighlight %}

Feature Transformers

Tokenizer

Tokenization is the process of taking text (such as a sentence) and breaking it into individual terms (usually words). A simple Tokenizer class provides this functionality. The example below shows how to split sentences into sequences of words.

Note: A more advanced tokenizer is provided via RegexTokenizer.

{% highlight scala %} import org.apache.spark.ml.feature.Tokenizer

val sentenceDataFrame = sqlContext.createDataFrame(Seq( (0, "Hi I heard about Spark"), (0, "I wish Java could use case classes"), (1, "Logistic regression models are neat") )).toDF("label", "sentence") val tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words") val wordsDataFrame = tokenizer.transform(sentenceDataFrame) wordsDataFrame.select("words", "label").take(3).foreach(println) {% endhighlight %}

{% highlight java %} import com.google.common.collect.Lists;

import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.Tokenizer; import org.apache.spark.mllib.linalg.Vector; 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;

JavaRDD jrdd = jsc.parallelize(Lists.newArrayList( RowFactory.create(0, "Hi I heard about Spark"), RowFactory.create(0, "I wish Java could use case classes"), RowFactory.create(1, "Logistic regression models are neat") )); StructType schema = new StructType(new StructField[]{ new StructField("label", DataTypes.DoubleType, false, Metadata.empty()), new StructField("sentence", DataTypes.StringType, false, Metadata.empty()) }); DataFrame sentenceDataFrame = sqlContext.createDataFrame(jrdd, schema); Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words"); DataFrame wordsDataFrame = tokenizer.transform(sentenceDataFrame); for (Row r : wordsDataFrame.select("words", "label").take(3)) { java.util.List words = r.getList(0); for (String word : words) System.out.print(word + " "); System.out.println(); } {% endhighlight %}

{% highlight python %} from pyspark.ml.feature import Tokenizer

sentenceDataFrame = sqlContext.createDataFrame([ (0, "Hi I heard about Spark"), (0, "I wish Java could use case classes"), (1, "Logistic regression models are neat") ], ["label", "sentence"]) tokenizer = Tokenizer(inputCol="sentence", outputCol="words") wordsDataFrame = tokenizer.transform(sentenceDataFrame) for words_label in wordsDataFrame.select("words", "label").take(3): print words_label {% endhighlight %}

Feature Selectors