path: root/docs/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

## TF-IDF (HashingTF and IDF)

[Term Frequency-Inverse Document Frequency (TF-IDF)](http://en.wikipedia.org/wiki/Tf%E2%80%93idf) 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](http://en.wikipedia.org/wiki/Feature_hashing) 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](mllib-feature-extraction.html#tf-idf) for more details on Term Frequency and Inverse Document Frequency.
For API details, refer to the [HashingTF API docs](api/scala/index.html#org.apache.spark.ml.feature.HashingTF) and the [IDF API docs](api/scala/index.html#org.apache.spark.ml.feature.IDF).

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.

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% 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 %}
</div>

<div data-lang="java" markdown="1">
{% 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<Row> 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 %}
</div>

<div data-lang="python" markdown="1">
{% 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 %}
</div>
</div>

## 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](mllib-feature-extraction.html#Word2Vec) for more details on Word2Vec.

Word2Vec is implemented in [Word2Vec](api/scala/index.html#org.apache.spark.ml.feature.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.

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% 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 %}
</div>

<div data-lang="java" markdown="1">
{% 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<Row> 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 %}
</div>

<div data-lang="python" markdown="1">
{% 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 %}
</div>
</div>

# Feature Transformers

## Tokenizer

[Tokenization](http://en.wikipedia.org/wiki/Lexical_analysis#Tokenization) is the process of taking text (such as a sentence) and breaking it into individual terms (usually words).  A simple [Tokenizer](api/scala/index.html#org.apache.spark.ml.feature.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](api/scala/index.html#org.apache.spark.ml.feature.RegexTokenizer).

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% 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 %}
</div>

<div data-lang="java" markdown="1">
{% 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<Row> 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<String> words = r.getList(0);
  for (String word : words) System.out.print(word + " ");
  System.out.println();
}
{% endhighlight %}
</div>

<div data-lang="python" markdown="1">
{% 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 %}
</div>
</div>


## 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](http://en.wikipedia.org/wiki/Bernoulli_distribution), a binarizer is useful for pre-processing the input data with continuous numerical features.

A simple [Binarizer](api/scala/index.html#org.apache.spark.ml.feature.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.

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% 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 %}
</div>

<div data-lang="java" markdown="1">
{% 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<Row> 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 %}
</div>

<div data-lang="python" markdown="1">
{% 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 %}
</div>
</div>

## PolynomialExpansion

[Polynomial expansion](http://en.wikipedia.org/wiki/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](api/scala/index.html#org.apache.spark.ml.feature.PolynomialExpansion) class provides this functionality.  The example below shows how to expand your features into a 3-degree polynomial space.

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% 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 %}
</div>

<div data-lang="java" markdown="1">
{% 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<Row> 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 %}
</div>

<div data-lang="python" markdown="1">
{% 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 %}
</div>
</div>

## OneHotEncoder

[One-hot encoding](http://en.wikipedia.org/wiki/One-hot) 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 

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% 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 %}
</div>

<div data-lang="java" markdown="1">
{% 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<Row> 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 %}
</div>

<div data-lang="python" markdown="1">
{% 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 %}
</div>
</div>

## VectorIndexer

`VectorIndexer` helps index categorical features in datasets of `Vector`s.
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](api/scala/index.html#org.apache.spark.mllib.linalg.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](api/scala/index.html#org.apache.spark.ml.feature.VectorIndexer) 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.

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% 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 %}
</div>

<div data-lang="java" markdown="1">
{% 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<LabeledPoint> 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 %}
</div>

<div data-lang="python" markdown="1">
{% 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 %}
</div>
</div>


## 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](http://en.wikipedia.org/wiki/Norm_%28mathematics%29#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.

<div class="codetabs">
<div data-lang="scala">
{% 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 %}
</div>

<div data-lang="java">
{% 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<LabeledPoint> 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 %}
</div>

<div data-lang="python">
{% 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 %}
</div>
</div>


## 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](api/scala/index.html#org.apache.spark.ml.feature.StandardScaler) and
[StandardScalerModel](api/scala/index.html#org.apache.spark.ml.feature.StandardScalerModel).

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

<div class="codetabs">
<div data-lang="scala">
{% 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 %}
</div>

<div data-lang="java">
{% 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<LabeledPoint> 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 %}
</div>

<div data-lang="python">
{% 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 %}
</div>
</div>

## Bucketizer

`Bucketizer` transforms a column of continuous features to a column of feature buckets, where the buckets are specified by users. It takes a parameter:

* `splits`: Parameter for mapping continuous features into buckets. With n+1 splits, there are n buckets. A bucket defined by splits x,y holds values in the range [x,y) except the last bucket, which also includes y. Splits should be strictly increasing. Values at -inf, inf must be explicitly provided to cover all Double values; Otherwise, values outside the splits specified will be treated as errors. Two examples of `splits` are `Array(Double.NegativeInfinity, 0.0, 1.0, Double.PositiveInfinity)` and `Array(0.0, 1.0, 2.0)`.

Note that if you have no idea of the upper bound and lower bound of the targeted column, you would better add the `Double.NegativeInfinity` and `Double.PositiveInfinity` as the bounds of your splits to prevent a potenial out of Bucketizer bounds exception.

Note also that the splits that you provided have to be in strictly increasing order, i.e. `s0 < s1 < s2 < ... < sn`.

More details can be found in the API docs for [Bucketizer](api/scala/index.html#org.apache.spark.ml.feature.Bucketizer).

The following example demonstrates how to bucketize a column of `Double`s into another index-wised column.

<div class="codetabs">
<div data-lang="scala">
{% highlight scala %}
import org.apache.spark.ml.feature.Bucketizer
import org.apache.spark.sql.DataFrame

val splits = Array(Double.NegativeInfinity, -0.5, 0.0, 0.5, Double.PositiveInfinity)

val data = Array(-0.5, -0.3, 0.0, 0.2)
val dataFrame = sqlContext.createDataFrame(data.map(Tuple1.apply)).toDF("features")

val bucketizer = new Bucketizer()
  .setInputCol("features")
  .setOutputCol("bucketedFeatures")
  .setSplits(splits)

// Transform original data into its bucket index.
val bucketedData = bucketizer.transform(dataFrame)
{% endhighlight %}
</div>

<div data-lang="java">
{% highlight java %}
import com.google.common.collect.Lists;

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;

double[] splits = {Double.NEGATIVE_INFINITY, -0.5, 0.0, 0.5, Double.POSITIVE_INFINITY};

JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
  RowFactory.create(-0.5),
  RowFactory.create(-0.3),
  RowFactory.create(0.0),
  RowFactory.create(0.2)
));
StructType schema = new StructType(new StructField[] {
  new StructField("features", DataTypes.DoubleType, false, Metadata.empty())
});
DataFrame dataFrame = jsql.createDataFrame(data, schema);

Bucketizer bucketizer = new Bucketizer()
  .setInputCol("features")
  .setOutputCol("bucketedFeatures")
  .setSplits(splits);

// Transform original data into its bucket index.
DataFrame bucketedData = bucketizer.transform(dataFrame);
{% endhighlight %}
</div>

<div data-lang="python">
{% highlight python %}
from pyspark.ml.feature import Bucketizer

splits = [-float("inf"), -0.5, 0.0, 0.5, float("inf")]

data = [(-0.5,), (-0.3,), (0.0,), (0.2,)]
dataFrame = sqlContext.createDataFrame(data, ["features"])

bucketizer = Bucketizer(splits=splits, inputCol="features", outputCol="bucketedFeatures")

# Transform original data into its bucket index.
bucketedData = bucketizer.transform(dataFrame)
{% endhighlight %}
</div>
</div>

# Feature Selectors