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.

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.

Refer to the HashingTF Scala docs and the IDF Scala docs for more details on the API.

{% include_example scala/org/apache/spark/examples/ml/TfIdfExample.scala %}

Refer to the HashingTF Java docs and the IDF Java docs for more details on the API.

{% include_example java/org/apache/spark/examples/ml/JavaTfIdfExample.java %}

Refer to the HashingTF Python docs and the IDF Python docs for more details on the API.

{% include_example python/ml/tf_idf_example.py %}

Word2Vec

Word2Vec is an Estimator which takes sequences of words representing documents and trains a Word2VecModel. The model maps each word to a unique fixed-size vector. The Word2VecModel transforms each document into a vector using the average of all words in the document; this vector can then be used for as features for prediction, document similarity calculations, etc. Please refer to the MLlib user guide on Word2Vec for more details.

In the following code segment, we start with a set of documents, each of which 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.

Refer to the Word2Vec Scala docs for more details on the API.

{% include_example scala/org/apache/spark/examples/ml/Word2VecExample.scala %}

Refer to the Word2Vec Java docs for more details on the API.

{% include_example java/org/apache/spark/examples/ml/JavaWord2VecExample.java %}

Refer to the Word2Vec Python docs for more details on the API.

{% include_example python/ml/word2vec_example.py %}

CountVectorizer

CountVectorizer and CountVectorizerModel aim to help convert a collection of text documents to vectors of token counts. When an a-priori dictionary is not available, CountVectorizer can be used as an Estimator to extract the vocabulary and generates a CountVectorizerModel. The model produces sparse representations for the documents over the vocabulary, which can then be passed to other algorithms like LDA.

During the fitting process, CountVectorizer will select the top vocabSize words ordered by term frequency across the corpus. An optional parameter "minDF" also affect the fitting process by specifying the minimum number (or fraction if < 1.0) of documents a term must appear in to be included in the vocabulary.

Examples

Assume that we have the following DataFrame with columns id and texts:

 id | texts
----|----------
 0  | Array("a", "b", "c")
 1  | Array("a", "b", "b", "c", "a")

each row intexts is a document of type Array[String]. Invoking fit of CountVectorizer produces a CountVectorizerModel with vocabulary (a, b, c), then the output column "vector" after transformation contains:

 id | texts                           | vector
----|---------------------------------|---------------
 0  | Array("a", "b", "c")            | (3,[0,1,2],[1.0,1.0,1.0])
 1  | Array("a", "b", "b", "c", "a")  | (3,[0,1,2],[2.0,2.0,1.0])

each vector represents the token counts of the document over the vocabulary.

Refer to the CountVectorizer Scala docs and the CountVectorizerModel Scala docs for more details on the API.

{% include_example scala/org/apache/spark/examples/ml/CountVectorizerExample.scala %}

Refer to the CountVectorizer Java docs and the CountVectorizerModel Java docs for more details on the API.

{% include_example java/org/apache/spark/examples/ml/JavaCountVectorizerExample.java %}

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.

RegexTokenizer allows more advanced tokenization based on regular expression (regex) matching. By default, the parameter "pattern" (regex, default: \s+) is used as delimiters to split the input text. Alternatively, users can set parameter "gaps" to false indicating the regex "pattern" denotes "tokens" rather than splitting gaps, and find all matching occurrences as the tokenization result.

Refer to the Tokenizer Scala docs and the RegexTokenizer Scala docs for more details on the API.

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

val sentenceDataFrame = sqlContext.createDataFrame(Seq( (0, "Hi I heard about Spark"), (1, "I wish Java could use case classes"), (2, "Logistic,regression,models,are,neat") )).toDF("label", "sentence") val tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words") val regexTokenizer = new RegexTokenizer() .setInputCol("sentence") .setOutputCol("words") .setPattern("\W") // alternatively .setPattern("\w+").setGaps(false)

val tokenized = tokenizer.transform(sentenceDataFrame) tokenized.select("words", "label").take(3).foreach(println) val regexTokenized = regexTokenizer.transform(sentenceDataFrame) regexTokenized.select("words", "label").take(3).foreach(println) {% endhighlight %}

Refer to the Tokenizer Java docs and the RegexTokenizer Java docs for more details on the API.

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

import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.RegexTokenizer; 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(Arrays.asList( RowFactory.create(0, "Hi I heard about Spark"), RowFactory.create(1, "I wish Java could use case classes"), RowFactory.create(2, "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(); }

RegexTokenizer regexTokenizer = new RegexTokenizer() .setInputCol("sentence") .setOutputCol("words") .setPattern("\W"); // alternatively .setPattern("\w+").setGaps(false); {% endhighlight %}

Refer to the Tokenizer Python docs and the the RegexTokenizer Python docs for more details on the API.

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

sentenceDataFrame = sqlContext.createDataFrame([ (0, "Hi I heard about Spark"), (1, "I wish Java could use case classes"), (2, "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) regexTokenizer = RegexTokenizer(inputCol="sentence", outputCol="words", pattern="\W")

alternatively, pattern="\w+", gaps(False)

{% endhighlight %}

StopWordsRemover

Stop words are words which should be excluded from the input, typically because the words appear frequently and don't carry as much meaning.

StopWordsRemover takes as input a sequence of strings (e.g. the output of a Tokenizer) and drops all the stop words from the input sequences. The list of stopwords is specified by the stopWords parameter. We provide a list of stop words by default, accessible by calling getStopWords on a newly instantiated StopWordsRemover instance. A boolean parameter caseSensitive indicates if the matches should be case sensitive (false by default).

Examples

Assume that we have the following DataFrame with columns id and raw:

 id | raw
----|----------
 0  | [I, saw, the, red, baloon]
 1  | [Mary, had, a, little, lamb]

Applying StopWordsRemover with raw as the input column and filtered as the output column, we should get the following:

 id | raw                         | filtered
----|-----------------------------|--------------------
 0  | [I, saw, the, red, baloon]  |  [saw, red, baloon]
 1  | [Mary, had, a, little, lamb]|[Mary, little, lamb]

In filtered, the stop words "I", "the", "had", and "a" have been filtered out.

Refer to the StopWordsRemover Scala docs for more details on the API.

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

val remover = new StopWordsRemover() .setInputCol("raw") .setOutputCol("filtered") val dataSet = sqlContext.createDataFrame(Seq( (0, Seq("I", "saw", "the", "red", "baloon")), (1, Seq("Mary", "had", "a", "little", "lamb")) )).toDF("id", "raw")

remover.transform(dataSet).show() {% endhighlight %}

Refer to the StopWordsRemover Java docs for more details on the API.

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

import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.StopWordsRemover; 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;

StopWordsRemover remover = new StopWordsRemover() .setInputCol("raw") .setOutputCol("filtered");

JavaRDD rdd = jsc.parallelize(Arrays.asList( RowFactory.create(Arrays.asList("I", "saw", "the", "red", "baloon")), RowFactory.create(Arrays.asList("Mary", "had", "a", "little", "lamb")) )); StructType schema = new StructType(new StructField[] { new StructField("raw", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty()) }); DataFrame dataset = jsql.createDataFrame(rdd, schema);

remover.transform(dataset).show(); {% endhighlight %}

Refer to the StopWordsRemover Python docs for more details on the API.

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

sentenceData = sqlContext.createDataFrame([ (0, ["I", "saw", "the", "red", "baloon"]), (1, ["Mary", "had", "a", "little", "lamb"]) ], ["label", "raw"])

remover = StopWordsRemover(inputCol="raw", outputCol="filtered") remover.transform(sentenceData).show(truncate=False) {% endhighlight %}

$n$-gram

An n-gram is a sequence of n tokens (typically words) for some integer n. The NGram class can be used to transform input features into $n$-grams.

NGram takes as input a sequence of strings (e.g. the output of a Tokenizer). The parameter n is used to determine the number of terms in each $n$-gram. The output will consist of a sequence of $n$-grams where each $n$-gram is represented by a space-delimited string of n consecutive words. If the input sequence contains fewer than n strings, no output is produced.

Refer to the NGram Scala docs for more details on the API.

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

val wordDataFrame = sqlContext.createDataFrame(Seq( (0, Array("Hi", "I", "heard", "about", "Spark")), (1, Array("I", "wish", "Java", "could", "use", "case", "classes")), (2, Array("Logistic", "regression", "models", "are", "neat")) )).toDF("label", "words")

val ngram = new NGram().setInputCol("words").setOutputCol("ngrams") val ngramDataFrame = ngram.transform(wordDataFrame) ngramDataFrame.take(3).map(_.getAsStream[String].toList).foreach(println) {% endhighlight %}

Refer to the NGram Java docs for more details on the API.

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

import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.NGram; 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(Arrays.asList( RowFactory.create(0.0, Arrays.asList("Hi", "I", "heard", "about", "Spark")), RowFactory.create(1.0, Arrays.asList("I", "wish", "Java", "could", "use", "case", "classes")), RowFactory.create(2.0, Arrays.asList("Logistic", "regression", "models", "are", "neat")) )); StructType schema = new StructType(new StructField[]{ new StructField("label", DataTypes.DoubleType, false, Metadata.empty()), new StructField("words", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty()) }); DataFrame wordDataFrame = sqlContext.createDataFrame(jrdd, schema); NGram ngramTransformer = new NGram().setInputCol("words").setOutputCol("ngrams"); DataFrame ngramDataFrame = ngramTransformer.transform(wordDataFrame); for (Row r : ngramDataFrame.select("ngrams", "label").take(3)) { java.util.List ngrams = r.getList(0); for (String ngram : ngrams) System.out.print(ngram + " --- "); System.out.println(); } {% endhighlight %}

Refer to the NGram Python docs for more details on the API.

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

wordDataFrame = sqlContext.createDataFrame([ (0, ["Hi", "I", "heard", "about", "Spark"]), (1, ["I", "wish", "Java", "could", "use", "case", "classes"]), (2, ["Logistic", "regression", "models", "are", "neat"]) ], ["label", "words"]) ngram = NGram(inputCol="words", outputCol="ngrams") ngramDataFrame = ngram.transform(wordDataFrame) for ngrams_label in ngramDataFrame.select("ngrams", "label").take(3): print(ngrams_label) {% endhighlight %}

Binarizer

Binarization is the process of thresholding numerical features to binary (0/1) features.

Binarizer takes the common parameters inputCol and outputCol, as well as the threshold for binarization. Feature values greater than the threshold are binarized to 1.0; values equal to or less than the threshold are binarized to 0.0.

Refer to the Binarizer Scala docs for more details on the API.

{% 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 %}

Refer to the Binarizer Java docs for more details on the API.

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

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(Arrays.asList( 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 %}

Refer to the Binarizer Python docs for more details on the API.

{% 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 %}

PCA

PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. A PCA class trains a model to project vectors to a low-dimensional space using PCA. The example below shows how to project 5-dimensional feature vectors into 3-dimensional principal components.

Refer to the PCA Scala docs for more details on the API.

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

val data = Array( Vectors.sparse(5, Seq((1, 1.0), (3, 7.0))), Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0), Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0) ) val df = sqlContext.createDataFrame(data.map(Tuple1.apply)).toDF("features") val pca = new PCA() .setInputCol("features") .setOutputCol("pcaFeatures") .setK(3) .fit(df) val pcaDF = pca.transform(df) val result = pcaDF.select("pcaFeatures") result.show() {% endhighlight %}

Refer to the PCA Java docs for more details on the API.

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

import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.ml.feature.PCA import org.apache.spark.ml.feature.PCAModel 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 = ... JavaRDD data = jsc.parallelize(Arrays.asList( RowFactory.create(Vectors.sparse(5, new int[]{1, 3}, new double[]{1.0, 7.0})), RowFactory.create(Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0)), RowFactory.create(Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0)) )); StructType schema = new StructType(new StructField[] { new StructField("features", new VectorUDT(), false, Metadata.empty()), }); DataFrame df = jsql.createDataFrame(data, schema); PCAModel pca = new PCA() .setInputCol("features") .setOutputCol("pcaFeatures") .setK(3) .fit(df); DataFrame result = pca.transform(df).select("pcaFeatures"); result.show(); {% endhighlight %}

Refer to the PCA Python docs for more details on the API.

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

data = [(Vectors.sparse(5, [(1, 1.0), (3, 7.0)]),), (Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]),), (Vectors.dense([4.0, 0.0, 0.0, 6.0, 7.0]),)] df = sqlContext.createDataFrame(data,["features"]) pca = PCA(k=3, inputCol="features", outputCol="pcaFeatures") model = pca.fit(df) result = model.transform(df).select("pcaFeatures") result.show(truncate=False) {% 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.

Refer to the PolynomialExpansion Scala docs for more details on the API.

{% 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 %}

Refer to the PolynomialExpansion Java docs for more details on the API.

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

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(Arrays.asList( 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 %}

Refer to the PolynomialExpansion Python docs for more details on the API.

{% 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 %}

Discrete Cosine Transform (DCT)

The Discrete Cosine Transform transforms a length N real-valued sequence in the time domain into another length N real-valued sequence in the frequency domain. A DCT class provides this functionality, implementing the DCT-II and scaling the result by 1/\sqrt{2} such that the representing matrix for the transform is unitary. No shift is applied to the transformed sequence (e.g. the $0$th element of the transformed sequence is the $0$th DCT coefficient and not the $N/2$th).

Refer to the DCT Scala docs for more details on the API.

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

val data = Seq( Vectors.dense(0.0, 1.0, -2.0, 3.0), Vectors.dense(-1.0, 2.0, 4.0, -7.0), Vectors.dense(14.0, -2.0, -5.0, 1.0)) val df = sqlContext.createDataFrame(data.map(Tuple1.apply)).toDF("features") val dct = new DCT() .setInputCol("features") .setOutputCol("featuresDCT") .setInverse(false) val dctDf = dct.transform(df) dctDf.select("featuresDCT").show(3) {% endhighlight %}

Refer to the DCT Java docs for more details on the API.

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

import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.ml.feature.DCT; 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;

JavaRDD data = jsc.parallelize(Arrays.asList( RowFactory.create(Vectors.dense(0.0, 1.0, -2.0, 3.0)), RowFactory.create(Vectors.dense(-1.0, 2.0, 4.0, -7.0)), RowFactory.create(Vectors.dense(14.0, -2.0, -5.0, 1.0)) )); StructType schema = new StructType(new StructField[] { new StructField("features", new VectorUDT(), false, Metadata.empty()), }); DataFrame df = jsql.createDataFrame(data, schema); DCT dct = new DCT() .setInputCol("features") .setOutputCol("featuresDCT") .setInverse(false); DataFrame dctDf = dct.transform(df); dctDf.select("featuresDCT").show(3); {% endhighlight %}

StringIndexer

StringIndexer encodes a string column of labels to a column of label indices. The indices are in [0, numLabels), ordered by label frequencies. So the most frequent label gets index 0. If the input column is numeric, we cast it to string and index the string values. When downstream pipeline components such as Estimator or Transformer make use of this string-indexed label, you must set the input column of the component to this string-indexed column name. In many cases, you can set the input column with setInputCol.

Examples

Assume that we have the following DataFrame with columns id and category:

 id | category
----|----------
 0  | a
 1  | b
 2  | c
 3  | a
 4  | a
 5  | c

category is a string column with three labels: "a", "b", and "c". Applying StringIndexer with category as the input column and categoryIndex as the output column, we should get the following:

 id | category | categoryIndex
----|----------|---------------
 0  | a        | 0.0
 1  | b        | 2.0
 2  | c        | 1.0
 3  | a        | 0.0
 4  | a        | 0.0
 5  | c        | 1.0

"a" gets index 0 because it is the most frequent, followed by "c" with index 1 and "b" with index 2.

Refer to the StringIndexer Scala docs for more details on the API.

{% highlight scala %} import org.apache.spark.ml.feature.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") val indexed = indexer.fit(df).transform(df) indexed.show() {% endhighlight %}

Refer to the StringIndexer Java docs for more details on the API.

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

import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.StringIndexer; import org.apache.spark.sql.DataFrame; import org.apache.spark.sql.Row; import org.apache.spark.sql.RowFactory; import org.apache.spark.sql.types.StructField; import org.apache.spark.sql.types.StructType; import static org.apache.spark.sql.types.DataTypes.*;

JavaRDD jrdd = jsc.parallelize(Arrays.asList( 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[] { createStructField("id", DoubleType, false), createStructField("category", StringType, false) }); DataFrame df = sqlContext.createDataFrame(jrdd, schema); StringIndexer indexer = new StringIndexer() .setInputCol("category") .setOutputCol("categoryIndex"); DataFrame indexed = indexer.fit(df).transform(df); indexed.show(); {% endhighlight %}

Refer to the StringIndexer Python docs for more details on the API.

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

df = sqlContext.createDataFrame( [(0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c")], ["id", "category"]) indexer = StringIndexer(inputCol="category", outputCol="categoryIndex") indexed = indexer.fit(df).transform(df) indexed.show() {% 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

Refer to the OneHotEncoder Scala docs for more details on the API.

{% 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 %}

Refer to the OneHotEncoder Java docs for more details on the API.

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

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(Arrays.asList( 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 %}

Refer to the OneHotEncoder Python docs for more details on the API.

{% 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.

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.

Refer to the VectorIndexer Scala docs for more details on the API.

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

val data = sqlContext.read.format("libsvm") .load("data/mllib/sample_libsvm_data.txt") 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 %}

Refer to the VectorIndexer Java docs for more details on the API.

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

import org.apache.spark.ml.feature.VectorIndexer; import org.apache.spark.ml.feature.VectorIndexerModel; import org.apache.spark.sql.DataFrame;

DataFrame data = sqlContext.read.format("libsvm") .load("data/mllib/sample_libsvm_data.txt"); 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 %}

Refer to the VectorIndexer Python docs for more details on the API.

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

data = sqlContext.read.format("libsvm") .load("data/mllib/sample_libsvm_data.txt") 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.

Refer to the Normalizer Scala docs for more details on the API.

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

val dataFrame = sqlContext.read.format("libsvm") .load("data/mllib/sample_libsvm_data.txt")

// 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 %}

Refer to the Normalizer Java docs for more details on the API.

{% highlight java %} import org.apache.spark.ml.feature.Normalizer; import org.apache.spark.sql.DataFrame;

DataFrame dataFrame = sqlContext.read.format("libsvm") .load("data/mllib/sample_libsvm_data.txt");

// 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 %}

Refer to the Normalizer Python docs for more details on the API.

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

dataFrame = sqlContext.read.format("libsvm") .load("data/mllib/sample_libsvm_data.txt")

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.

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

Refer to the StandardScaler Scala docs for more details on the API.

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

val dataFrame = sqlContext.read.format("libsvm") .load("data/mllib/sample_libsvm_data.txt") 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 %}

Refer to the StandardScaler Java docs for more details on the API.

{% highlight java %} import org.apache.spark.ml.feature.StandardScaler; import org.apache.spark.ml.feature.StandardScalerModel; import org.apache.spark.sql.DataFrame;

DataFrame dataFrame = sqlContext.read.format("libsvm") .load("data/mllib/sample_libsvm_data.txt"); 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 %}

Refer to the StandardScaler Python docs for more details on the API.

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

dataFrame = sqlContext.read.format("libsvm") .load("data/mllib/sample_libsvm_data.txt") 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 %}

MinMaxScaler

MinMaxScaler transforms a dataset of Vector rows, rescaling each feature to a specific range (often [0, 1]). It takes parameters:

• min: 0.0 by default. Lower bound after transformation, shared by all features.
• max: 1.0 by default. Upper bound after transformation, shared by all features.

MinMaxScaler computes summary statistics on a data set and produces a MinMaxScalerModel. The model can then transform each feature individually such that it is in the given range.

The rescaled value for a feature E is calculated as, \begin{equation} Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) + min \end{equation} For the case E_{max} == E_{min}, Rescaled(e_i) = 0.5 * (max + min)

Note that since zero values will probably be transformed to non-zero values, output of the transformer will be DenseVector even for sparse input.

The following example demonstrates how to load a dataset in libsvm format and then rescale each feature to [0, 1].

Refer to the MinMaxScaler Scala docs and the MinMaxScalerModel Scala docs for more details on the API.

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

val dataFrame = sqlContext.read.format("libsvm") .load("data/mllib/sample_libsvm_data.txt") val scaler = new MinMaxScaler() .setInputCol("features") .setOutputCol("scaledFeatures")

// Compute summary statistics and generate MinMaxScalerModel val scalerModel = scaler.fit(dataFrame)

// rescale each feature to range [min, max]. val scaledData = scalerModel.transform(dataFrame) {% endhighlight %}

Refer to the MinMaxScaler Java docs and the MinMaxScalerModel Java docs for more details on the API.

{% highlight java %} import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.MinMaxScaler; import org.apache.spark.ml.feature.MinMaxScalerModel; import org.apache.spark.sql.DataFrame;

DataFrame dataFrame = sqlContext.read.format("libsvm") .load("data/mllib/sample_libsvm_data.txt"); MinMaxScaler scaler = new MinMaxScaler() .setInputCol("features") .setOutputCol("scaledFeatures");

// Compute summary statistics and generate MinMaxScalerModel MinMaxScalerModel scalerModel = scaler.fit(dataFrame);

// rescale each feature to range [min, max]. DataFrame scaledData = scalerModel.transform(dataFrame); {% endhighlight %}

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.

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

Refer to the Bucketizer Scala docs for more details on the API.

{% 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 %}

Refer to the Bucketizer Java docs for more details on the API.

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

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 data = jsc.parallelize(Arrays.asList( 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 %}

Refer to the Bucketizer Python docs for more details on the API.

{% 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 %}

ElementwiseProduct

ElementwiseProduct multiplies each input vector by a provided "weight" vector, using element-wise multiplication. In other words, it scales each column of the dataset by a scalar multiplier. This represents the Hadamard product between the input vector, v and transforming vector, w, to yield a result vector.

\[ \begin{pmatrix} v_1 \\ \vdots \\ v_N \end{pmatrix} \circ \begin{pmatrix} w_1 \\ \vdots \\ w_N \end{pmatrix} = \begin{pmatrix} v_1 w_1 \\ \vdots \\ v_N w_N \end{pmatrix} \]

This example below demonstrates how to transform vectors using a transforming vector value.

Refer to the ElementwiseProduct Scala docs for more details on the API.

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

// Create some vector data; also works for sparse vectors val dataFrame = sqlContext.createDataFrame(Seq( ("a", Vectors.dense(1.0, 2.0, 3.0)), ("b", Vectors.dense(4.0, 5.0, 6.0)))).toDF("id", "vector")

val transformingVector = Vectors.dense(0.0, 1.0, 2.0) val transformer = new ElementwiseProduct() .setScalingVec(transformingVector) .setInputCol("vector") .setOutputCol("transformedVector")

// Batch transform the vectors to create new column: transformer.transform(dataFrame).show()

{% endhighlight %}

Refer to the ElementwiseProduct Java docs for more details on the API.

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

import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.ElementwiseProduct; import org.apache.spark.mllib.linalg.Vector; 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.DataTypes; import org.apache.spark.sql.types.Metadata; import org.apache.spark.sql.types.StructField; import org.apache.spark.sql.types.StructType;

// Create some vector data; also works for sparse vectors JavaRDD jrdd = jsc.parallelize(Arrays.asList( RowFactory.create("a", Vectors.dense(1.0, 2.0, 3.0)), RowFactory.create("b", Vectors.dense(4.0, 5.0, 6.0)) )); List fields = new ArrayList(2); fields.add(DataTypes.createStructField("id", DataTypes.StringType, false)); fields.add(DataTypes.createStructField("vector", DataTypes.StringType, false)); StructType schema = DataTypes.createStructType(fields); DataFrame dataFrame = sqlContext.createDataFrame(jrdd, schema); Vector transformingVector = Vectors.dense(0.0, 1.0, 2.0); ElementwiseProduct transformer = new ElementwiseProduct() .setScalingVec(transformingVector) .setInputCol("vector") .setOutputCol("transformedVector"); // Batch transform the vectors to create new column: transformer.transform(dataFrame).show();

{% endhighlight %}

Refer to the ElementwiseProduct Python docs for more details on the API.

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

data = [(Vectors.dense([1.0, 2.0, 3.0]),), (Vectors.dense([4.0, 5.0, 6.0]),)] df = sqlContext.createDataFrame(data, ["vector"]) transformer = ElementwiseProduct(scalingVec=Vectors.dense([0.0, 1.0, 2.0]), inputCol="vector", outputCol="transformedVector") transformer.transform(df).show()

{% endhighlight %}

VectorAssembler

VectorAssembler is a transformer that combines a given list of columns into a single vector column. It is useful for combining raw features and features generated by different feature transformers into a single feature vector, in order to train ML models like logistic regression and decision trees. VectorAssembler accepts the following input column types: all numeric types, boolean type, and vector type. In each row, the values of the input columns will be concatenated into a vector in the specified order.

Examples

Assume that we have a DataFrame with the columns id, hour, mobile, userFeatures, and clicked:

 id | hour | mobile | userFeatures     | clicked
----|------|--------|------------------|---------
 0  | 18   | 1.0    | [0.0, 10.0, 0.5] | 1.0

userFeatures is a vector column that contains three user features. We want to combine hour, mobile, and userFeatures into a single feature vector called features and use it to predict clicked or not. If we set VectorAssembler's input columns to hour, mobile, and userFeatures and output column to features, after transformation we should get the following DataFrame:

 id | hour | mobile | userFeatures     | clicked | features
----|------|--------|------------------|---------|-----------------------------
 0  | 18   | 1.0    | [0.0, 10.0, 0.5] | 1.0     | [18.0, 1.0, 0.0, 10.0, 0.5]

Refer to the VectorAssembler Scala docs for more details on the API.

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

val dataset = sqlContext.createDataFrame( Seq((0, 18, 1.0, Vectors.dense(0.0, 10.0, 0.5), 1.0)) ).toDF("id", "hour", "mobile", "userFeatures", "clicked") val assembler = new VectorAssembler() .setInputCols(Array("hour", "mobile", "userFeatures")) .setOutputCol("features") val output = assembler.transform(dataset) println(output.select("features", "clicked").first()) {% endhighlight %}

Refer to the VectorAssembler Java docs for more details on the API.

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

import org.apache.spark.api.java.JavaRDD; 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.; import static org.apache.spark.sql.types.DataTypes.;

StructType schema = createStructType(new StructField[] { createStructField("id", IntegerType, false), createStructField("hour", IntegerType, false), createStructField("mobile", DoubleType, false), createStructField("userFeatures", new VectorUDT(), false), createStructField("clicked", DoubleType, false) }); Row row = RowFactory.create(0, 18, 1.0, Vectors.dense(0.0, 10.0, 0.5), 1.0); JavaRDD rdd = jsc.parallelize(Arrays.asList(row)); DataFrame dataset = sqlContext.createDataFrame(rdd, schema);

VectorAssembler assembler = new VectorAssembler() .setInputCols(new String[] {"hour", "mobile", "userFeatures"}) .setOutputCol("features");

DataFrame output = assembler.transform(dataset); System.out.println(output.select("features", "clicked").first()); {% endhighlight %}

Refer to the VectorAssembler Python docs for more details on the API.

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

dataset = sqlContext.createDataFrame( [(0, 18, 1.0, Vectors.dense([0.0, 10.0, 0.5]), 1.0)], ["id", "hour", "mobile", "userFeatures", "clicked"]) assembler = VectorAssembler( inputCols=["hour", "mobile", "userFeatures"], outputCol="features") output = assembler.transform(dataset) print(output.select("features", "clicked").first()) {% endhighlight %}

Feature Selectors

VectorSlicer

VectorSlicer is a transformer that takes a feature vector and outputs a new feature vector with a sub-array of the original features. It is useful for extracting features from a vector column.

VectorSlicer accepts a vector column with a specified indices, then outputs a new vector column whose values are selected via those indices. There are two types of indices,

1. Integer indices that represents the indices into the vector, setIndices();

2. String indices that represents the names of features into the vector, setNames(). This requires the vector column to have an AttributeGroup since the implementation matches on the name field of an Attribute.

Specification by integer and string are both acceptable. Moreover, you can use integer index and string name simultaneously. At least one feature must be selected. Duplicate features are not allowed, so there can be no overlap between selected indices and names. Note that if names of features are selected, an exception will be threw out when encountering with empty input attributes.

The output vector will order features with the selected indices first (in the order given), followed by the selected names (in the order given).

Examples

Suppose that we have a DataFrame with the column userFeatures:

 userFeatures     
------------------
 [0.0, 10.0, 0.5] 

userFeatures is a vector column that contains three user features. Assuming that the first column of userFeatures are all zeros, so we want to remove it and only the last two columns are selected. The VectorSlicer selects the last two elements with setIndices(1, 2) then produces a new vector column named features:

 userFeatures     | features
------------------|-----------------------------
 [0.0, 10.0, 0.5] | [10.0, 0.5]

Suppose also that we have a potential input attributes for the userFeatures, i.e. ["f1", "f2", "f3"], then we can use setNames("f2", "f3") to select them.

 userFeatures     | features
------------------|-----------------------------
 [0.0, 10.0, 0.5] | [10.0, 0.5]
 ["f1", "f2", "f3"] | ["f2", "f3"]

Refer to the VectorSlicer Scala docs for more details on the API.

{% highlight scala %} import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NumericAttribute} import org.apache.spark.ml.feature.VectorSlicer import org.apache.spark.sql.types.StructType import org.apache.spark.sql.{DataFrame, Row, SQLContext}

val data = Array( Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))), Vectors.dense(-2.0, 2.3, 0.0) )

val defaultAttr = NumericAttribute.defaultAttr val attrs = Array("f1", "f2", "f3").map(defaultAttr.withName) val attrGroup = new AttributeGroup("userFeatures", attrs.asInstanceOf[Array[Attribute]])

val dataRDD = sc.parallelize(data).map(Row.apply) val dataset = sqlContext.createDataFrame(dataRDD, StructType(attrGroup.toStructField()))

val slicer = new VectorSlicer().setInputCol("userFeatures").setOutputCol("features")

slicer.setIndices(1).setNames("f3") // or slicer.setIndices(Array(1, 2)), or slicer.setNames(Array("f2", "f3"))

val output = slicer.transform(dataset) println(output.select("userFeatures", "features").first()) {% endhighlight %}

Refer to the VectorSlicer Java docs for more details on the API.

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

import org.apache.spark.api.java.JavaRDD; 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.; import static org.apache.spark.sql.types.DataTypes.;

Attribute[] attrs = new Attribute[]{ NumericAttribute.defaultAttr().withName("f1"), NumericAttribute.defaultAttr().withName("f2"), NumericAttribute.defaultAttr().withName("f3") }; AttributeGroup group = new AttributeGroup("userFeatures", attrs);

JavaRDD jrdd = jsc.parallelize(Lists.newArrayList( RowFactory.create(Vectors.sparse(3, new int[]{0, 1}, new double[]{-2.0, 2.3})), RowFactory.create(Vectors.dense(-2.0, 2.3, 0.0)) ));

DataFrame dataset = jsql.createDataFrame(jrdd, (new StructType()).add(group.toStructField()));

VectorSlicer vectorSlicer = new VectorSlicer() .setInputCol("userFeatures").setOutputCol("features");

vectorSlicer.setIndices(new int[]{1}).setNames(new String[]{"f3"}); // or slicer.setIndices(new int[]{1, 2}), or slicer.setNames(new String[]{"f2", "f3"})

DataFrame output = vectorSlicer.transform(dataset);

System.out.println(output.select("userFeatures", "features").first()); {% endhighlight %}

RFormula

RFormula selects columns specified by an R model formula. It produces a vector column of features and a double column of labels. Like when formulas are used in R for linear regression, string input columns will be one-hot encoded, and numeric columns will be cast to doubles. If not already present in the DataFrame, the output label column will be created from the specified response variable in the formula.

Examples

Assume that we have a DataFrame with the columns id, country, hour, and clicked:

id | country | hour | clicked
---|---------|------|---------
 7 | "US"    | 18   | 1.0
 8 | "CA"    | 12   | 0.0
 9 | "NZ"    | 15   | 0.0

If we use RFormula with a formula string of clicked ~ country + hour, which indicates that we want to predict clicked based on country and hour, after transformation we should get the following DataFrame:

id | country | hour | clicked | features         | label
---|---------|------|---------|------------------|-------
 7 | "US"    | 18   | 1.0     | [0.0, 0.0, 18.0] | 1.0
 8 | "CA"    | 12   | 0.0     | [0.0, 1.0, 12.0] | 0.0
 9 | "NZ"    | 15   | 0.0     | [1.0, 0.0, 15.0] | 0.0

Refer to the RFormula Scala docs for more details on the API.

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

val dataset = sqlContext.createDataFrame(Seq( (7, "US", 18, 1.0), (8, "CA", 12, 0.0), (9, "NZ", 15, 0.0) )).toDF("id", "country", "hour", "clicked") val formula = new RFormula() .setFormula("clicked ~ country + hour") .setFeaturesCol("features") .setLabelCol("label") val output = formula.fit(dataset).transform(dataset) output.select("features", "label").show() {% endhighlight %}

Refer to the RFormula Java docs for more details on the API.

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

import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.RFormula; import org.apache.spark.sql.DataFrame; import org.apache.spark.sql.Row; import org.apache.spark.sql.RowFactory; import org.apache.spark.sql.types.; import static org.apache.spark.sql.types.DataTypes.;

StructType schema = createStructType(new StructField[] { createStructField("id", IntegerType, false), createStructField("country", StringType, false), createStructField("hour", IntegerType, false), createStructField("clicked", DoubleType, false) }); JavaRDD rdd = jsc.parallelize(Arrays.asList( RowFactory.create(7, "US", 18, 1.0), RowFactory.create(8, "CA", 12, 0.0), RowFactory.create(9, "NZ", 15, 0.0) )); DataFrame dataset = sqlContext.createDataFrame(rdd, schema);

RFormula formula = new RFormula() .setFormula("clicked ~ country + hour") .setFeaturesCol("features") .setLabelCol("label");

DataFrame output = formula.fit(dataset).transform(dataset); output.select("features", "label").show(); {% endhighlight %}

Refer to the RFormula Python docs for more details on the API.

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

dataset = sqlContext.createDataFrame( [(7, "US", 18, 1.0), (8, "CA", 12, 0.0), (9, "NZ", 15, 0.0)], ["id", "country", "hour", "clicked"]) formula = RFormula( formula="clicked ~ country + hour", featuresCol="features", labelCol="label") output = formula.fit(dataset).transform(dataset) output.select("features", "label").show() {% endhighlight %}