spark-instrumented-optimizer/docs/ml-features.md

layout	title	displayTitle
global	Feature Extraction, Transformation, and Selection - SparkML	<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

TF-IDF (HashingTF and IDF)

Term Frequency-Inverse Document Frequency (TF-IDF) is a common text pre-processing step. In Spark ML, TF-IDF is separate into two parts: TF (+hashing) and IDF.

TF: HashingTF is a Transformer which takes sets of terms and converts those sets into fixed-length feature vectors. In text processing, a "set of terms" might be a bag of words. The algorithm combines Term Frequency (TF) counts with the hashing trick for dimensionality reduction.

IDF: IDF is an Estimator which fits on a dataset and produces an IDFModel. The IDFModel takes feature vectors (generally created from HashingTF) and scales each column. Intuitively, it down-weights columns which appear frequently in a corpus.

Please refer to the MLlib user guide on TF-IDF for more details on Term Frequency and Inverse Document Frequency. For API details, refer to the HashingTF API docs and the IDF API docs.

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 use HashingTF to hash the sentence into a feature vector. We use IDF to rescale the feature vectors; this generally improves performance when using text as features. Our feature vectors could then be passed to a learning algorithm.

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

val sentenceData = 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 wordsData = tokenizer.transform(sentenceData) val hashingTF = new HashingTF().setInputCol("words").setOutputCol("rawFeatures").setNumFeatures(20) val featurizedData = hashingTF.transform(wordsData) val idf = new IDF().setInputCol("rawFeatures").setOutputCol("features") val idfModel = idf.fit(featurizedData) val rescaledData = idfModel.transform(featurizedData) rescaledData.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.IDF; 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 sentenceData = sqlContext.createDataFrame(jrdd, schema); Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words"); DataFrame wordsData = tokenizer.transform(sentenceData); int numFeatures = 20; HashingTF hashingTF = new HashingTF() .setInputCol("words") .setOutputCol("rawFeatures") .setNumFeatures(numFeatures); DataFrame featurizedData = hashingTF.transform(wordsData); IDF idf = new IDF().setInputCol("rawFeatures").setOutputCol("features"); IDFModel idfModel = idf.fit(featurizedData); DataFrame rescaledData = idfModel.transform(featurizedData); for (Row r : rescaledData.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, IDF, Tokenizer

sentenceData = 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") wordsData = tokenizer.transform(sentenceData) hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=20) featurizedData = hashingTF.transform(wordsData) idf = IDF(inputCol="rawFeatures", outputCol="features") idfModel = idf.fit(featurizedData) rescaledData = idfModel.transform(featurizedData) for features_label in rescaledData.select("features", "label").take(3): print(features_label) {% endhighlight %}

Word2Vec

Word2Vec is an Estimator which takes sequences of words that represents documents and trains a Word2VecModel. The model is a Map(String, Vector) essentially, which maps each word to an unique fix-sized vector. The Word2VecModel transforms each documents into a vector using the average of all words in the document, which aims to other computations of documents such as similarity calculation consequencely. Please refer to the MLlib user guide on Word2Vec for more details on Word2Vec.

Word2Vec is implemented in Word2Vec. In the following code segment, we start with a set of documents, each of them is represented as a sequence of words. For each document, we transform it into a feature vector. This feature vector could then be passed to a learning algorithm.

{% highlight scala %} import org.apache.spark.ml.feature.Word2Vec

// Input data: Each row is a bag of words from a sentence or document. val documentDF = sqlContext.createDataFrame(Seq( "Hi I heard about Spark".split(" "), "I wish Java could use case classes".split(" "), "Logistic regression models are neat".split(" ") ).map(Tuple1.apply)).toDF("text")

// Learn a mapping from words to Vectors. val word2Vec = new Word2Vec() .setInputCol("text") .setOutputCol("result") .setVectorSize(3) .setMinCount(0) val model = word2Vec.fit(documentDF) val result = model.transform(documentDF) result.select("result").take(3).foreach(println) {% endhighlight %}

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

import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.sql.DataFrame; import org.apache.spark.sql.Row; import org.apache.spark.sql.RowFactory; import org.apache.spark.sql.SQLContext; import org.apache.spark.sql.types.*;

JavaSparkContext jsc = ... SQLContext sqlContext = ...

// Input data: Each row is a bag of words from a sentence or document. JavaRDD jrdd = jsc.parallelize(Lists.newArrayList( RowFactory.create(Lists.newArrayList("Hi I heard about Spark".split(" "))), RowFactory.create(Lists.newArrayList("I wish Java could use case classes".split(" "))), RowFactory.create(Lists.newArrayList("Logistic regression models are neat".split(" "))) )); StructType schema = new StructType(new StructField[]{ new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty()) }); DataFrame documentDF = sqlContext.createDataFrame(jrdd, schema);

// Learn a mapping from words to Vectors. Word2Vec word2Vec = new Word2Vec() .setInputCol("text") .setOutputCol("result") .setVectorSize(3) .setMinCount(0); Word2VecModel model = word2Vec.fit(documentDF); DataFrame result = model.transform(documentDF); for (Row r: result.select("result").take(3)) { System.out.println(r); } {% endhighlight %}

{% highlight python %} from pyspark.ml.feature import Word2Vec

Input data: Each row is a bag of words from a sentence or document.

documentDF = sqlContext.createDataFrame([ ("Hi I heard about Spark".split(" "), ), ("I wish Java could use case classes".split(" "), ), ("Logistic regression models are neat".split(" "), ) ], ["text"])

Learn a mapping from words to Vectors.

word2Vec = Word2Vec(vectorSize=3, minCount=0, inputCol="text", outputCol="result") model = word2Vec.fit(documentDF) result = model.transform(documentDF) for feature in result.select("result").take(3): print(feature) {% 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 %}

Binarizer

Binarization is the process of thresholding numerical features to binary features. As some probabilistic estimators make assumption that the input data is distributed according to Bernoulli distribution, a binarizer is useful for pre-processing the input data with continuous numerical features.

A simple Binarizer class provides this functionality. Besides the common parameters of inputCol and outputCol, Binarizer has the parameter threshold used for binarizing continuous numerical features. The features greater than the threshold, will be binarized to 1.0. The features equal to or less than the threshold, will be binarized to 0.0. The example below shows how to binarize numerical features.

{% highlight scala %} import org.apache.spark.ml.feature.Binarizer import org.apache.spark.sql.DataFrame

val data = Array( (0, 0.1), (1, 0.8), (2, 0.2) ) val dataFrame: DataFrame = sqlContext.createDataFrame(data).toDF("label", "feature")

val binarizer: Binarizer = new Binarizer() .setInputCol("feature") .setOutputCol("binarized_feature") .setThreshold(0.5)

val binarizedDataFrame = binarizer.transform(dataFrame) val binarizedFeatures = binarizedDataFrame.select("binarized_feature") binarizedFeatures.collect().foreach(println) {% endhighlight %}

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

import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.Binarizer; 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, 0.1), RowFactory.create(1, 0.8), RowFactory.create(2, 0.2) )); StructType schema = new StructType(new StructField[]{ new StructField("label", DataTypes.DoubleType, false, Metadata.empty()), new StructField("feature", DataTypes.DoubleType, false, Metadata.empty()) }); DataFrame continuousDataFrame = jsql.createDataFrame(jrdd, schema); Binarizer binarizer = new Binarizer() .setInputCol("feature") .setOutputCol("binarized_feature") .setThreshold(0.5); DataFrame binarizedDataFrame = binarizer.transform(continuousDataFrame); DataFrame binarizedFeatures = binarizedDataFrame.select("binarized_feature"); for (Row r : binarizedFeatures.collect()) { Double binarized_value = r.getDouble(0); System.out.println(binarized_value); } {% endhighlight %}

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

continuousDataFrame = sqlContext.createDataFrame([ (0, 0.1), (1, 0.8), (2, 0.2) ], ["label", "feature"]) binarizer = Binarizer(threshold=0.5, inputCol="feature", outputCol="binarized_feature") binarizedDataFrame = binarizer.transform(continuousDataFrame) binarizedFeatures = binarizedDataFrame.select("binarized_feature") for binarized_feature, in binarizedFeatures.collect(): print(binarized_feature) {% endhighlight %}

PolynomialExpansion

Polynomial expansion is the process of expanding your features into a polynomial space, which is formulated by an n-degree combination of original dimensions. A PolynomialExpansion class provides this functionality. The example below shows how to expand your features into a 3-degree polynomial space.

{% highlight scala %} import org.apache.spark.ml.feature.PolynomialExpansion import org.apache.spark.mllib.linalg.Vectors

val data = Array( Vectors.dense(-2.0, 2.3), Vectors.dense(0.0, 0.0), Vectors.dense(0.6, -1.1) ) val df = sqlContext.createDataFrame(data.map(Tuple1.apply)).toDF("features") val polynomialExpansion = new PolynomialExpansion() .setInputCol("features") .setOutputCol("polyFeatures") .setDegree(3) val polyDF = polynomialExpansion.transform(df) polyDF.select("polyFeatures").take(3).foreach(println) {% endhighlight %}

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

import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.mllib.linalg.Vector; 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.SQLContext; import org.apache.spark.sql.types.Metadata; import org.apache.spark.sql.types.StructField; import org.apache.spark.sql.types.StructType;

JavaSparkContext jsc = ... SQLContext jsql = ... PolynomialExpansion polyExpansion = new PolynomialExpansion() .setInputCol("features") .setOutputCol("polyFeatures") .setDegree(3); JavaRDD data = jsc.parallelize(Lists.newArrayList( RowFactory.create(Vectors.dense(-2.0, 2.3)), RowFactory.create(Vectors.dense(0.0, 0.0)), RowFactory.create(Vectors.dense(0.6, -1.1)) )); StructType schema = new StructType(new StructField[] { new StructField("features", new VectorUDT(), false, Metadata.empty()), }); DataFrame df = jsql.createDataFrame(data, schema); DataFrame polyDF = polyExpansion.transform(df); Row[] row = polyDF.select("polyFeatures").take(3); for (Row r : row) { System.out.println(r.get(0)); } {% endhighlight %}

{% highlight python %} from pyspark.ml.feature import PolynomialExpansion from pyspark.mllib.linalg import Vectors

df = sqlContext.createDataFrame( [(Vectors.dense([-2.0, 2.3]), ), (Vectors.dense([0.0, 0.0]), ), (Vectors.dense([0.6, -1.1]), )], ["features"]) px = PolynomialExpansion(degree=2, inputCol="features", outputCol="polyFeatures") polyDF = px.transform(df) for expanded in polyDF.select("polyFeatures").take(3): print(expanded) {% endhighlight %}

OneHotEncoder

One-hot encoding maps a column of label indices to a column of binary vectors, with at most a single one-value. This encoding allows algorithms which expect continuous features, such as Logistic Regression, to use categorical features

{% highlight scala %} import org.apache.spark.ml.feature.{OneHotEncoder, StringIndexer}

val df = sqlContext.createDataFrame(Seq( (0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c") )).toDF("id", "category")

val indexer = new StringIndexer() .setInputCol("category") .setOutputCol("categoryIndex") .fit(df) val indexed = indexer.transform(df)

val encoder = new OneHotEncoder().setInputCol("categoryIndex"). setOutputCol("categoryVec") val encoded = encoder.transform(indexed) encoded.select("id", "categoryVec").foreach(println) {% endhighlight %}

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

import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.OneHotEncoder; import org.apache.spark.ml.feature.StringIndexer; import org.apache.spark.ml.feature.StringIndexerModel; 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, "a"), RowFactory.create(1, "b"), RowFactory.create(2, "c"), RowFactory.create(3, "a"), RowFactory.create(4, "a"), RowFactory.create(5, "c") )); StructType schema = new StructType(new StructField[]{ new StructField("id", DataTypes.DoubleType, false, Metadata.empty()), new StructField("category", DataTypes.StringType, false, Metadata.empty()) }); DataFrame df = sqlContext.createDataFrame(jrdd, schema); StringIndexerModel indexer = new StringIndexer() .setInputCol("category") .setOutputCol("categoryIndex") .fit(df); DataFrame indexed = indexer.transform(df);

OneHotEncoder encoder = new OneHotEncoder() .setInputCol("categoryIndex") .setOutputCol("categoryVec"); DataFrame encoded = encoder.transform(indexed); {% endhighlight %}

{% highlight python %} from pyspark.ml.feature import OneHotEncoder, StringIndexer

df = sqlContext.createDataFrame([ (0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c") ], ["id", "category"])

stringIndexer = StringIndexer(inputCol="category", outputCol="categoryIndex") model = stringIndexer.fit(df) indexed = model.transform(df) encoder = OneHotEncoder(includeFirst=False, inputCol="categoryIndex", outputCol="categoryVec") encoded = encoder.transform(indexed) {% endhighlight %}

VectorIndexer

VectorIndexer helps index categorical features in datasets of Vectors. It can both automatically decide which features are categorical and convert original values to category indices. Specifically, it does the following:

1. Take an input column of type Vector and a parameter maxCategories.
2. Decide which features should be categorical based on the number of distinct values, where features with at most maxCategories are declared categorical.
3. Compute 0-based category indices for each categorical feature.
4. Index categorical features and transform original feature values to indices.

Indexing categorical features allows algorithms such as Decision Trees and Tree Ensembles to treat categorical features appropriately, improving performance.

Please refer to the VectorIndexer API docs for more details.

In the example below, we read in a dataset of labeled points and then use VectorIndexer to decide which features should be treated as categorical. We transform the categorical feature values to their indices. This transformed data could then be passed to algorithms such as DecisionTreeRegressor that handle categorical features.

{% highlight scala %} import org.apache.spark.ml.feature.VectorIndexer import org.apache.spark.mllib.util.MLUtils

val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() val indexer = new VectorIndexer() .setInputCol("features") .setOutputCol("indexed") .setMaxCategories(10) val indexerModel = indexer.fit(data) val categoricalFeatures: Set[Int] = indexerModel.categoryMaps.keys.toSet println(s"Chose ${categoricalFeatures.size} categorical features: " + categoricalFeatures.mkString(", "))

// Create new column "indexed" with categorical values transformed to indices val indexedData = indexerModel.transform(data) {% endhighlight %}

{% highlight java %} import java.util.Map;

import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.VectorIndexer; import org.apache.spark.ml.feature.VectorIndexerModel; import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.mllib.util.MLUtils; import org.apache.spark.sql.DataFrame;

JavaRDD rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD(); DataFrame data = sqlContext.createDataFrame(rdd, LabeledPoint.class); VectorIndexer indexer = new VectorIndexer() .setInputCol("features") .setOutputCol("indexed") .setMaxCategories(10); VectorIndexerModel indexerModel = indexer.fit(data); Map<Integer, Map<Double, Integer>> categoryMaps = indexerModel.javaCategoryMaps(); System.out.print("Chose " + categoryMaps.size() + "categorical features:"); for (Integer feature : categoryMaps.keySet()) { System.out.print(" " + feature); } System.out.println();

// Create new column "indexed" with categorical values transformed to indices DataFrame indexedData = indexerModel.transform(data); {% endhighlight %}

{% highlight python %} from pyspark.ml.feature import VectorIndexer from pyspark.mllib.util import MLUtils

data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() indexer = VectorIndexer(inputCol="features", outputCol="indexed", maxCategories=10) indexerModel = indexer.fit(data)

Create new column "indexed" with categorical values transformed to indices

indexedData = indexerModel.transform(data) {% endhighlight %}

Normalizer

Normalizer is a Transformer which transforms a dataset of Vector rows, normalizing each Vector to have unit norm. It takes parameter p, which specifies the p-norm used for normalization. (p = 2 by default.) This normalization can help standardize your input data and improve the behavior of learning algorithms.

The following example demonstrates how to load a dataset in libsvm format and then normalize each row to have unit L^2 norm and unit L^\infty norm.

{% highlight scala %} import org.apache.spark.ml.feature.Normalizer import org.apache.spark.mllib.util.MLUtils

val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") val dataFrame = sqlContext.createDataFrame(data)

// Normalize each Vector using L^1 norm. val normalizer = new Normalizer() .setInputCol("features") .setOutputCol("normFeatures") .setP(1.0) val l1NormData = normalizer.transform(dataFrame)

// Normalize each Vector using L^\infty norm. val lInfNormData = normalizer.transform(dataFrame, normalizer.p -> Double.PositiveInfinity) {% endhighlight %}

{% highlight java %} import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.Normalizer; import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.mllib.util.MLUtils; import org.apache.spark.sql.DataFrame;

JavaRDD data = MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD(); DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);

// Normalize each Vector using L^1 norm. Normalizer normalizer = new Normalizer() .setInputCol("features") .setOutputCol("normFeatures") .setP(1.0); DataFrame l1NormData = normalizer.transform(dataFrame);

// Normalize each Vector using L^\infty norm. DataFrame lInfNormData = normalizer.transform(dataFrame, normalizer.p().w(Double.POSITIVE_INFINITY)); {% endhighlight %}

{% highlight python %} from pyspark.mllib.util import MLUtils from pyspark.ml.feature import Normalizer

data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") dataFrame = sqlContext.createDataFrame(data)

Normalize each Vector using L^1 norm.

normalizer = Normalizer(inputCol="features", outputCol="normFeatures", p=1.0) l1NormData = normalizer.transform(dataFrame)

Normalize each Vector using L^\infty norm.

lInfNormData = normalizer.transform(dataFrame, {normalizer.p: float("inf")}) {% endhighlight %}

StandardScaler

StandardScaler transforms a dataset of Vector rows, normalizing each feature to have unit standard deviation and/or zero mean. It takes parameters:

• withStd: True by default. Scales the data to unit standard deviation.
• withMean: False by default. Centers the data with mean before scaling. It will build a dense output, so this does not work on sparse input and will raise an exception.

StandardScaler is a Model which can be fit on a dataset to produce a StandardScalerModel; this amounts to computing summary statistics. The model can then transform a Vector column in a dataset to have unit standard deviation and/or zero mean features.

Note that if the standard deviation of a feature is zero, it will return default 0.0 value in the Vector for that feature.

More details can be found in the API docs for StandardScaler and StandardScalerModel.

The following example demonstrates how to load a dataset in libsvm format and then normalize each feature to have unit standard deviation.

{% highlight scala %} import org.apache.spark.ml.feature.StandardScaler import org.apache.spark.mllib.util.MLUtils

val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") val dataFrame = sqlContext.createDataFrame(data) val scaler = new StandardScaler() .setInputCol("features") .setOutputCol("scaledFeatures") .setWithStd(true) .setWithMean(false)

// Compute summary statistics by fitting the StandardScaler val scalerModel = scaler.fit(dataFrame)

// Normalize each feature to have unit standard deviation. val scaledData = scalerModel.transform(dataFrame) {% endhighlight %}

{% highlight java %} import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.StandardScaler; import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.mllib.util.MLUtils; import org.apache.spark.sql.DataFrame;

JavaRDD data = MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD(); DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class); StandardScaler scaler = new StandardScaler() .setInputCol("features") .setOutputCol("scaledFeatures") .setWithStd(true) .setWithMean(false);

// Compute summary statistics by fitting the StandardScaler StandardScalerModel scalerModel = scaler.fit(dataFrame);

// Normalize each feature to have unit standard deviation. DataFrame scaledData = scalerModel.transform(dataFrame); {% endhighlight %}

{% highlight python %} from pyspark.mllib.util import MLUtils from pyspark.ml.feature import StandardScaler

data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") dataFrame = sqlContext.createDataFrame(data) scaler = StandardScaler(inputCol="features", outputCol="scaledFeatures", withStd=True, withMean=False)

Compute summary statistics by fitting the StandardScaler

scalerModel = scaler.fit(dataFrame)

Normalize each feature to have unit standard deviation.

scaledData = scalerModel.transform(dataFrame) {% endhighlight %}

Feature Selectors