# Extracting, transforming and selecting 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

# Feature Extractors

## TF-IDF

Term frequency-inverse document frequency (TF-IDF) is a feature vectorization method widely used in text mining to reflect the importance of a term to a document in the corpus. Denote a term by $t$, a document by $d$, and the corpus by $D$. Term frequency $TF(t, d)$ is the number of times that term $t$ appears in document $d$, while document frequency $DF(t, D)$ is the number of documents that contains term $t$. If we only use term frequency to measure the importance, it is very easy to over-emphasize terms that appear very often but carry little information about the document, e.g. “a”, “the”, and “of”. If a term appears very often across the corpus, it means it doesn’t carry special information about a particular document. Inverse document frequency is a numerical measure of how much information a term provides: $IDF(t, D) = \log \frac{|D| + 1}{DF(t, D) + 1},$ where $|D|$ is the total number of documents in the corpus. Since logarithm is used, if a term appears in all documents, its IDF value becomes 0. Note that a smoothing term is applied to avoid dividing by zero for terms outside the corpus. The TF-IDF measure is simply the product of TF and IDF: $TFIDF(t, d, D) = TF(t, d) \cdot IDF(t, D).$ There are several variants on the definition of term frequency and document frequency. In MLlib, we separate TF and IDF to make them flexible.

TF: Both HashingTF and CountVectorizer can be used to generate the term frequency vectors.

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. HashingTF utilizes the hashing trick. A raw feature is mapped into an index (term) by applying a hash function. The hash function used here is MurmurHash 3. Then term frequencies are calculated based on the mapped indices. This approach avoids the need to compute a global term-to-index map, which can be expensive for a large corpus, but it suffers from potential hash collisions, where different raw features may become the same term after hashing. To reduce the chance of collision, we can increase the target feature dimension, i.e. the number of buckets of the hash table. Since a simple modulo is used to transform the hash function to a column index, it is advisable to use a power of two as the feature dimension, otherwise the features will not be mapped evenly to the columns. The default feature dimension is $2^{18} = 262,144$. An optional binary toggle parameter controls term frequency counts. When set to true all nonzero frequency counts are set to 1. This is especially useful for discrete probabilistic models that model binary, rather than integer, counts.

CountVectorizer converts text documents to vectors of term counts. Refer to CountVectorizer for more details.

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

Note: spark.ml doesn’t provide tools for text segmentation. We refer users to the Stanford NLP Group and scalanlp/chalk.

Examples

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.

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

val sentenceData = spark.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)
// alternatively, CountVectorizer can also be used to get term frequency vectors

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)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/TfIdfExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.HashingTF;
import org.apache.spark.ml.feature.IDF;
import org.apache.spark.ml.feature.IDFModel;
import org.apache.spark.ml.feature.Tokenizer;
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;

List<Row> data = Arrays.asList(
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[]{
});
Dataset<Row> sentenceData = spark.createDataFrame(data, schema);
Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words");
Dataset<Row> wordsData = tokenizer.transform(sentenceData);
int numFeatures = 20;
HashingTF hashingTF = new HashingTF()
.setInputCol("words")
.setOutputCol("rawFeatures")
.setNumFeatures(numFeatures);
Dataset<Row> featurizedData = hashingTF.transform(wordsData);
// alternatively, CountVectorizer can also be used to get term frequency vectors

IDF idf = new IDF().setInputCol("rawFeatures").setOutputCol("features");
IDFModel idfModel = idf.fit(featurizedData);
Dataset<Row> rescaledData = idfModel.transform(featurizedData);
for (Row r : rescaledData.select("features", "label").takeAsList(3)) {
Vector features = r.getAs(0);
Double label = r.getDouble(1);
System.out.println(features);
System.out.println(label);
}

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaTfIdfExample.java" in the Spark repo.

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

from pyspark.ml.feature import HashingTF, IDF, Tokenizer

sentenceData = spark.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)
# alternatively, CountVectorizer can also be used to get term frequency vectors

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)

Find full example code at "examples/src/main/python/ml/tf_idf_example.py" in the Spark repo.

## 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 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.

import org.apache.spark.ml.feature.Word2Vec

// Input data: Each row is a bag of words from a sentence or document.
val documentDF = spark.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)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/Word2VecExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.Word2Vec;
import org.apache.spark.ml.feature.Word2VecModel;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.*;

// Input data: Each row is a bag of words from a sentence or document.
List<Row> data = Arrays.asList(
RowFactory.create(Arrays.asList("Hi I heard about Spark".split(" "))),
RowFactory.create(Arrays.asList("I wish Java could use case classes".split(" "))),
RowFactory.create(Arrays.asList("Logistic regression models are neat".split(" ")))
);
StructType schema = new StructType(new StructField[]{
new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty())
});
Dataset<Row> documentDF = spark.createDataFrame(data, 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);
Dataset<Row> result = model.transform(documentDF);
for (Row r : result.select("result").takeAsList(3)) {
System.out.println(r);
}

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaWord2VecExample.java" in the Spark repo.

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

from pyspark.ml.feature import Word2Vec

# Input data: Each row is a bag of words from a sentence or document.
documentDF = spark.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)

Find full example code at "examples/src/main/python/ml/word2vec_example.py" in the Spark repo.

## 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 affects 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. Another optional binary toggle parameter controls the output vector. If set to true all nonzero counts are set to 1. This is especially useful for discrete probabilistic models that model binary, rather than integer, counts.

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 in texts 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.

import org.apache.spark.ml.feature.{CountVectorizer, CountVectorizerModel}

val df = spark.createDataFrame(Seq(
(0, Array("a", "b", "c")),
(1, Array("a", "b", "b", "c", "a"))
)).toDF("id", "words")

// fit a CountVectorizerModel from the corpus
val cvModel: CountVectorizerModel = new CountVectorizer()
.setInputCol("words")
.setOutputCol("features")
.setVocabSize(3)
.setMinDF(2)
.fit(df)

// alternatively, define CountVectorizerModel with a-priori vocabulary
val cvm = new CountVectorizerModel(Array("a", "b", "c"))
.setInputCol("words")
.setOutputCol("features")

cvModel.transform(df).select("features").show()

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/CountVectorizerExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.CountVectorizer;
import org.apache.spark.ml.feature.CountVectorizerModel;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.*;

// Input data: Each row is a bag of words from a sentence or document.
List<Row> data = Arrays.asList(
RowFactory.create(Arrays.asList("a", "b", "c")),
RowFactory.create(Arrays.asList("a", "b", "b", "c", "a"))
);
StructType schema = new StructType(new StructField [] {
new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty())
});
Dataset<Row> df = spark.createDataFrame(data, schema);

// fit a CountVectorizerModel from the corpus
CountVectorizerModel cvModel = new CountVectorizer()
.setInputCol("text")
.setOutputCol("feature")
.setVocabSize(3)
.setMinDF(2)
.fit(df);

// alternatively, define CountVectorizerModel with a-priori vocabulary
CountVectorizerModel cvm = new CountVectorizerModel(new String[]{"a", "b", "c"})
.setInputCol("text")
.setOutputCol("feature");

cvModel.transform(df).show();

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaCountVectorizerExample.java" in the Spark repo.

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

from pyspark.ml.feature import CountVectorizer

# Input data: Each row is a bag of words with a ID.
df = spark.createDataFrame([
(0, "a b c".split(" ")),
(1, "a b b c a".split(" "))
], ["id", "words"])

# fit a CountVectorizerModel from the corpus.
cv = CountVectorizer(inputCol="words", outputCol="features", vocabSize=3, minDF=2.0)
model = cv.fit(df)
result = model.transform(df)
result.show()

Find full example code at "examples/src/main/python/ml/count_vectorizer_example.py" in the Spark repo.

# 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.

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

val sentenceDataFrame = spark.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)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/TokenizerExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.RegexTokenizer;
import org.apache.spark.ml.feature.Tokenizer;
import org.apache.spark.sql.Dataset;
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.StructField;
import org.apache.spark.sql.types.StructType;

List<Row> data = 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[]{
});

Dataset<Row> sentenceDataFrame = spark.createDataFrame(data, schema);

Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words");

Dataset<Row> wordsDataFrame = tokenizer.transform(sentenceDataFrame);
for (Row r : wordsDataFrame.select("words", "label").takeAsList(3)) {
java.util.List<String> 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);

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java" in the Spark repo.

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

from pyspark.ml.feature import Tokenizer, RegexTokenizer

sentenceDataFrame = spark.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)

Find full example code at "examples/src/main/python/ml/tokenizer_example.py" in the Spark repo.

## 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. Default stop words for some languages are accessible by calling StopWordsRemover.loadDefaultStopWords(language), for which available options are “danish”, “dutch”, “english”, “finnish”, “french”, “german”, “hungarian”, “italian”, “norwegian”, “portuguese”, “russian”, “spanish”, “swedish” and “turkish”. 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.

import org.apache.spark.ml.feature.StopWordsRemover

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

val dataSet = spark.createDataFrame(Seq(
(0, Seq("I", "saw", "the", "red", "baloon")),
(1, Seq("Mary", "had", "a", "little", "lamb"))
)).toDF("id", "raw")

remover.transform(dataSet).show()

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/StopWordsRemoverExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.StopWordsRemover;
import org.apache.spark.sql.Dataset;
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.StructField;
import org.apache.spark.sql.types.StructType;

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

List<Row> data = Arrays.asList(
RowFactory.create(Arrays.asList("I", "saw", "the", "red", "baloon")),
);

StructType schema = new StructType(new StructField[]{
new StructField(
});

Dataset<Row> dataset = spark.createDataFrame(data, schema);
remover.transform(dataset).show();

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaStopWordsRemoverExample.java" in the Spark repo.

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

from pyspark.ml.feature import StopWordsRemover

sentenceData = spark.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)

Find full example code at "examples/src/main/python/ml/stopwords_remover_example.py" in the Spark repo.

## $n$-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.

import org.apache.spark.ml.feature.NGram

val wordDataFrame = spark.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(_.getAs[Stream[String]]("ngrams").toList).foreach(println)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/NGramExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.NGram;
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.StructField;
import org.apache.spark.sql.types.StructType;

List<Row> data = 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(
});

Dataset<Row> wordDataFrame = spark.createDataFrame(data, schema);

NGram ngramTransformer = new NGram().setInputCol("words").setOutputCol("ngrams");

Dataset<Row> ngramDataFrame = ngramTransformer.transform(wordDataFrame);

for (Row r : ngramDataFrame.select("ngrams", "label").takeAsList(3)) {
java.util.List<String> ngrams = r.getList(0);
for (String ngram : ngrams) System.out.print(ngram + " --- ");
System.out.println();
}

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaNGramExample.java" in the Spark repo.

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

from pyspark.ml.feature import NGram

wordDataFrame = spark.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)

Find full example code at "examples/src/main/python/ml/n_gram_example.py" in the Spark repo.

## 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. Both Vector and Double types are supported for inputCol.

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

import org.apache.spark.ml.feature.Binarizer

val data = Array((0, 0.1), (1, 0.8), (2, 0.2))
val dataFrame = spark.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)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/BinarizerExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.Binarizer;
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.StructField;
import org.apache.spark.sql.types.StructType;

List<Row> data = Arrays.asList(
RowFactory.create(0, 0.1),
RowFactory.create(1, 0.8),
RowFactory.create(2, 0.2)
);
StructType schema = new StructType(new StructField[]{
});
Dataset<Row> continuousDataFrame = spark.createDataFrame(data, schema);
Binarizer binarizer = new Binarizer()
.setInputCol("feature")
.setOutputCol("binarized_feature")
.setThreshold(0.5);
Dataset<Row> binarizedDataFrame = binarizer.transform(continuousDataFrame);
Dataset<Row> binarizedFeatures = binarizedDataFrame.select("binarized_feature");
for (Row r : binarizedFeatures.collectAsList()) {
Double binarized_value = r.getDouble(0);
System.out.println(binarized_value);
}

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java" in the Spark repo.

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

from pyspark.ml.feature import Binarizer

continuousDataFrame = spark.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)

Find full example code at "examples/src/main/python/ml/binarizer_example.py" in the Spark repo.

## 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.

import org.apache.spark.ml.feature.PCA
import org.apache.spark.ml.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 = spark.createDataFrame(data.map(Tuple1.apply)).toDF("features")
val pca = new PCA()
.setInputCol("features")
.setOutputCol("pcaFeatures")
.setK(3)
.fit(df)
result.show()

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/PCAExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

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.Dataset;
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;

List<Row> data = 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()),
});

Dataset<Row> df = spark.createDataFrame(data, schema);

PCAModel pca = new PCA()
.setInputCol("features")
.setOutputCol("pcaFeatures")
.setK(3)
.fit(df);

Dataset<Row> result = pca.transform(df).select("pcaFeatures");
result.show();

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaPCAExample.java" in the Spark repo.

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

from pyspark.ml.feature import PCA
from pyspark.ml.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 = spark.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)

Find full example code at "examples/src/main/python/ml/pca_example.py" in the Spark repo.

## 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.

import org.apache.spark.ml.feature.PolynomialExpansion
import org.apache.spark.ml.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 = spark.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)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/PolynomialExpansionExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.PolynomialExpansion;
import org.apache.spark.mllib.linalg.VectorUDT;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.sql.Dataset;
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;

PolynomialExpansion polyExpansion = new PolynomialExpansion()
.setInputCol("features")
.setOutputCol("polyFeatures")
.setDegree(3);

List<Row> data = 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()),
});

Dataset<Row> df = spark.createDataFrame(data, schema);
Dataset<Row> polyDF = polyExpansion.transform(df);

List<Row> rows = polyDF.select("polyFeatures").takeAsList(3);
for (Row r : rows) {
System.out.println(r.get(0));
}

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaPolynomialExpansionExample.java" in the Spark repo.

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

from pyspark.ml.feature import PolynomialExpansion
from pyspark.ml.linalg import Vectors

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

Find full example code at "examples/src/main/python/ml/polynomial_expansion_example.py" in the Spark repo.

## 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.

import org.apache.spark.ml.feature.DCT
import org.apache.spark.ml.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 = spark.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)

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/DCTExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.DCT;
import org.apache.spark.mllib.linalg.VectorUDT;
import org.apache.spark.mllib.linalg.Vectors;
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;

List<Row> data = 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()),
});
Dataset<Row> df = spark.createDataFrame(data, schema);
DCT dct = new DCT()
.setInputCol("features")
.setOutputCol("featuresDCT")
.setInverse(false);
Dataset<Row> dctDf = dct.transform(df);
dctDf.select("featuresDCT").show(3);

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaDCTExample.java" in the Spark repo.

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

from pyspark.ml.feature import DCT
from pyspark.ml.linalg import Vectors

df = spark.createDataFrame([
(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]),)], ["features"])

dct = DCT(inverse=False, inputCol="features", outputCol="featuresDCT")

dctDf = dct.transform(df)

for dcts in dctDf.select("featuresDCT").take(3):
print(dcts)

Find full example code at "examples/src/main/python/ml/dct_example.py" in the Spark repo.

## 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.

Additionally, there are two strategies regarding how StringIndexer will handle unseen labels when you have fit a StringIndexer on one dataset and then use it to transform another:

• throw an exception (which is the default)
• skip the row containing the unseen label entirely

Examples

Let’s go back to our previous example but this time reuse our previously defined StringIndexer on the following dataset:

 id | category
----|----------
0  | a
1  | b
2  | c
3  | d


If you’ve not set how StringIndexer handles unseen labels or set it to “error”, an exception will be thrown. However, if you had called setHandleInvalid("skip"), the following dataset will be generated:

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


Notice that the row containing “d” does not appear.

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

import org.apache.spark.ml.feature.StringIndexer

val df = spark.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()

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/StringIndexerExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.StringIndexer;
import org.apache.spark.sql.Dataset;
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.*;

List<Row> data = 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", IntegerType, false),
createStructField("category", StringType, false)
});
Dataset<Row> df = spark.createDataFrame(data, schema);
StringIndexer indexer = new StringIndexer()
.setInputCol("category")
.setOutputCol("categoryIndex");
Dataset<Row> indexed = indexer.fit(df).transform(df);
indexed.show();

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaStringIndexerExample.java" in the Spark repo.

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

from pyspark.ml.feature import StringIndexer

df = spark.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()

Find full example code at "examples/src/main/python/ml/string_indexer_example.py" in the Spark repo.

## IndexToString

Symmetrically to StringIndexer, IndexToString maps a column of label indices back to a column containing the original labels as strings. A common use case is to produce indices from labels with StringIndexer, train a model with those indices and retrieve the original labels from the column of predicted indices with IndexToString. However, you are free to supply your own labels.

Examples

Building on the StringIndexer example, let’s assume we have the following DataFrame with columns id and categoryIndex:

 id | categoryIndex
----|---------------
0  | 0.0
1  | 2.0
2  | 1.0
3  | 0.0
4  | 0.0
5  | 1.0


Applying IndexToString with categoryIndex as the input column, originalCategory as the output column, we are able to retrieve our original labels (they will be inferred from the columns’ metadata):

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


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

import org.apache.spark.ml.feature.{IndexToString, StringIndexer}

val df = spark.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 converter = new IndexToString()
.setInputCol("categoryIndex")
.setOutputCol("originalCategory")

val converted = converter.transform(indexed)
converted.select("id", "originalCategory").show()

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/IndexToStringExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.IndexToString;
import org.apache.spark.ml.feature.StringIndexer;
import org.apache.spark.ml.feature.StringIndexerModel;
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.StructField;
import org.apache.spark.sql.types.StructType;

List<Row> data = 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[]{
});
Dataset<Row> df = spark.createDataFrame(data, schema);

StringIndexerModel indexer = new StringIndexer()
.setInputCol("category")
.setOutputCol("categoryIndex")
.fit(df);
Dataset<Row> indexed = indexer.transform(df);

IndexToString converter = new IndexToString()
.setInputCol("categoryIndex")
.setOutputCol("originalCategory");
Dataset<Row> converted = converter.transform(indexed);
converted.select("id", "originalCategory").show();

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaIndexToStringExample.java" in the Spark repo.

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

from pyspark.ml.feature import IndexToString, StringIndexer

df = spark.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)

converter = IndexToString(inputCol="categoryIndex", outputCol="originalCategory")
converted = converter.transform(indexed)

converted.select("id", "originalCategory").show()

Find full example code at "examples/src/main/python/ml/index_to_string_example.py" in the Spark repo.

## 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.

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

val df = spark.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").show()

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/OneHotEncoderExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

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.Dataset;
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.StructField;
import org.apache.spark.sql.types.StructType;

List<Row> data = 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[]{
});

Dataset<Row> df = spark.createDataFrame(data, schema);

StringIndexerModel indexer = new StringIndexer()
.setInputCol("category")
.setOutputCol("categoryIndex")
.fit(df);
Dataset<Row> indexed = indexer.transform(df);

OneHotEncoder encoder = new OneHotEncoder()
.setInputCol("categoryIndex")
.setOutputCol("categoryVec");
Dataset<Row> encoded = encoder.transform(indexed);
encoded.select("id", "categoryVec").show();

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaOneHotEncoderExample.java" in the Spark repo.

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

from pyspark.ml.feature import OneHotEncoder, StringIndexer

df = spark.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(dropLast=False, inputCol="categoryIndex", outputCol="categoryVec")
encoded = encoder.transform(indexed)
encoded.select("id", "categoryVec").show()

Find full example code at "examples/src/main/python/ml/onehot_encoder_example.py" in the Spark repo.

## 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.

import org.apache.spark.ml.feature.VectorIndexer

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) indexedData.show()  Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/VectorIndexerExample.scala" in the Spark repo. Refer to the VectorIndexer Java docs for more details on the API. import java.util.Map; import org.apache.spark.ml.feature.VectorIndexer; import org.apache.spark.ml.feature.VectorIndexerModel; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; Dataset<Row> data = spark.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 Dataset<Row> indexedData = indexerModel.transform(data); indexedData.show();  Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaVectorIndexerExample.java" in the Spark repo. Refer to the VectorIndexer Python docs for more details on the API. from pyspark.ml.feature import VectorIndexer data = spark.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) indexedData.show()  Find full example code at "examples/src/main/python/ml/vector_indexer_example.py" in the Spark repo. ## 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$norm. Refer to the Normalizer Scala docs for more details on the API. import org.apache.spark.ml.feature.Normalizer val dataFrame = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") // Normalize each Vector using$L^1$norm. val normalizer = new Normalizer() .setInputCol("features") .setOutputCol("normFeatures") .setP(1.0) val l1NormData = normalizer.transform(dataFrame) l1NormData.show() // Normalize each Vector using$L^\infty$norm. val lInfNormData = normalizer.transform(dataFrame, normalizer.p -> Double.PositiveInfinity) lInfNormData.show()  Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/NormalizerExample.scala" in the Spark repo. Refer to the Normalizer Java docs for more details on the API. import org.apache.spark.ml.feature.Normalizer; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; Dataset<Row> dataFrame = spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); // Normalize each Vector using$L^1$norm. Normalizer normalizer = new Normalizer() .setInputCol("features") .setOutputCol("normFeatures") .setP(1.0); Dataset<Row> l1NormData = normalizer.transform(dataFrame); l1NormData.show(); // Normalize each Vector using$L^\infty$norm. Dataset<Row> lInfNormData = normalizer.transform(dataFrame, normalizer.p().w(Double.POSITIVE_INFINITY)); lInfNormData.show();  Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaNormalizerExample.java" in the Spark repo. Refer to the Normalizer Python docs for more details on the API. from pyspark.ml.feature import Normalizer dataFrame = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") # Normalize each Vector using$L^1$norm. normalizer = Normalizer(inputCol="features", outputCol="normFeatures", p=1.0) l1NormData = normalizer.transform(dataFrame) l1NormData.show() # Normalize each Vector using$L^\infty\$ norm.
lInfNormData = normalizer.transform(dataFrame, {normalizer.p: float("inf")})
lInfNormData.show()

Find full example code at "examples/src/main/python/ml/normalizer_example.py" in the Spark repo.

## 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 an Estimator 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.

import org.apache.spark.ml.feature.StandardScaler

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)
scaledData.show()

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/StandardScalerExample.scala" in the Spark repo.

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

import org.apache.spark.ml.feature.StandardScaler;
import org.apache.spark.ml.feature.StandardScalerModel;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;

Dataset<Row> dataFrame =

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.
Dataset<Row> scaledData = scalerModel.transform(dataFrame);
scaledData.show();

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaStandardScalerExample.java" in the Spark repo.

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

from pyspark.ml.feature import StandardScaler

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)
scaledData.show()

Find full example code at "examples/src/main/python/ml/standard_scaler_example.py" in the Spark repo.

## 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.

import org.apache.spark.ml.feature.MinMaxScaler

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)
scaledData.show()

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/MinMaxScalerExample.scala" in the Spark repo.

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

import org.apache.spark.ml.feature.MinMaxScaler;
import org.apache.spark.ml.feature.MinMaxScalerModel;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;

Dataset<Row> dataFrame = spark
.format("libsvm")
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].
Dataset<Row> scaledData = scalerModel.transform(dataFrame);
scaledData.show();

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaMinMaxScalerExample.java" in the Spark repo.

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

from pyspark.ml.feature import MinMaxScaler

scaler = MinMaxScaler(inputCol="features", outputCol="scaledFeatures")

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

# rescale each feature to range [min, max].
scaledData = scalerModel.transform(dataFrame)
scaledData.show()

Find full example code at "examples/src/main/python/ml/min_max_scaler_example.py" in the Spark repo.

## MaxAbsScaler

MaxAbsScaler transforms a dataset of Vector rows, rescaling each feature to range [-1, 1] by dividing through the maximum absolute value in each feature. It does not shift/center the data, and thus does not destroy any sparsity.

MaxAbsScaler computes summary statistics on a data set and produces a MaxAbsScalerModel. The model can then transform each feature individually to range [-1, 1].

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

Refer to the MaxAbsScaler Scala docs and the MaxAbsScalerModel Scala docs for more details on the API.

import org.apache.spark.ml.feature.MaxAbsScaler

val scaler = new MaxAbsScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures")

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

// rescale each feature to range [-1, 1]
val scaledData = scalerModel.transform(dataFrame)
scaledData.show()

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/MaxAbsScalerExample.scala" in the Spark repo.

Refer to the MaxAbsScaler Java docs and the MaxAbsScalerModel Java docs for more details on the API.

import org.apache.spark.ml.feature.MaxAbsScaler;
import org.apache.spark.ml.feature.MaxAbsScalerModel;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;

Dataset<Row> dataFrame = spark
.format("libsvm")
MaxAbsScaler scaler = new MaxAbsScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures");

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

// rescale each feature to range [-1, 1].
Dataset<Row> scaledData = scalerModel.transform(dataFrame);
scaledData.show();

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaMaxAbsScalerExample.java" in the Spark repo.

Refer to the MaxAbsScaler Python docs and the MaxAbsScalerModel Python docs for more details on the API.

from pyspark.ml.feature import MaxAbsScaler

scaler = MaxAbsScaler(inputCol="features", outputCol="scaledFeatures")

# Compute summary statistics and generate MaxAbsScalerModel
scalerModel = scaler.fit(dataFrame)

# rescale each feature to range [-1, 1].
scaledData = scalerModel.transform(dataFrame)
scaledData.show()

Find full example code at "examples/src/main/python/ml/max_abs_scaler_example.py" in the Spark repo.

## 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 and lower bounds of the targeted column, you should add Double.NegativeInfinity and Double.PositiveInfinity as the bounds of your splits to prevent a potential 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.

import org.apache.spark.ml.feature.Bucketizer

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 = spark.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)
bucketedData.show()

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/BucketizerExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.Bucketizer;
import org.apache.spark.sql.Dataset;
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.StructField;
import org.apache.spark.sql.types.StructType;

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

List<Row> data = Arrays.asList(
RowFactory.create(-0.5),
RowFactory.create(-0.3),
RowFactory.create(0.0),
RowFactory.create(0.2)
);
StructType schema = new StructType(new StructField[]{
});
Dataset<Row> dataFrame = spark.createDataFrame(data, schema);

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

// Transform original data into its bucket index.
Dataset<Row> bucketedData = bucketizer.transform(dataFrame);
bucketedData.show();

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaBucketizerExample.java" in the Spark repo.

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

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 = spark.createDataFrame(data, ["features"])

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

# Transform original data into its bucket index.
bucketedData = bucketizer.transform(dataFrame)
bucketedData.show()

Find full example code at "examples/src/main/python/ml/bucketizer_example.py" in the Spark repo.

## 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.

import org.apache.spark.ml.feature.ElementwiseProduct
import org.apache.spark.ml.linalg.Vectors

// Create some vector data; also works for sparse vectors
val dataFrame = spark.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()

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/ElementwiseProductExample.scala" in the Spark repo.

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

import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.ElementwiseProduct;
import org.apache.spark.ml.linalg.Vector;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.ml.linalg.Vectors;
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.StructField;
import org.apache.spark.sql.types.StructType;

// Create some vector data; also works for sparse vectors
List<Row> data = 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<StructField> fields = new ArrayList<>(2);

StructType schema = DataTypes.createStructType(fields);

Dataset<Row> dataFrame = spark.createDataFrame(data, 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();

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaElementwiseProductExample.java" in the Spark repo.

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

from pyspark.ml.feature import ElementwiseProduct
from pyspark.ml.linalg import Vectors

# Create some vector data; also works for sparse vectors
data = [(Vectors.dense([1.0, 2.0, 3.0]),), (Vectors.dense([4.0, 5.0, 6.0]),)]
df = spark.createDataFrame(data, ["vector"])
transformer = ElementwiseProduct(scalingVec=Vectors.dense([0.0, 1.0, 2.0]),
inputCol="vector", outputCol="transformedVector")
# Batch transform the vectors to create new column:
transformer.transform(df).show()

Find full example code at "examples/src/main/python/ml/elementwise_product_example.py" in the Spark repo.

## SQLTransformer

SQLTransformer implements the transformations which are defined by SQL statement. Currently we only support SQL syntax like "SELECT ... FROM __THIS__ ..." where "__THIS__" represents the underlying table of the input dataset. The select clause specifies the fields, constants, and expressions to display in the output, and can be any select clause that Spark SQL supports. Users can also use Spark SQL built-in function and UDFs to operate on these selected columns. For example, SQLTransformer supports statements like:

• SELECT a, a + b AS a_b FROM __THIS__
• SELECT a, SQRT(b) AS b_sqrt FROM __THIS__ where a > 5
• SELECT a, b, SUM(c) AS c_sum FROM __THIS__ GROUP BY a, b

Examples

Assume that we have the following DataFrame with columns id, v1 and v2:

 id |  v1 |  v2
----|-----|-----
0  | 1.0 | 3.0
2  | 2.0 | 5.0


This is the output of the SQLTransformer with statement "SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__":

 id |  v1 |  v2 |  v3 |  v4
----|-----|-----|-----|-----
0  | 1.0 | 3.0 | 4.0 | 3.0
2  | 2.0 | 5.0 | 7.0 |10.0


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

import org.apache.spark.ml.feature.SQLTransformer

val df = spark.createDataFrame(
Seq((0, 1.0, 3.0), (2, 2.0, 5.0))).toDF("id", "v1", "v2")

val sqlTrans = new SQLTransformer().setStatement(
"SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__")

sqlTrans.transform(df).show()

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/SQLTransformerExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.SQLTransformer;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.*;

List<Row> data = Arrays.asList(
RowFactory.create(0, 1.0, 3.0),
RowFactory.create(2, 2.0, 5.0)
);
StructType schema = new StructType(new StructField [] {
});
Dataset<Row> df = spark.createDataFrame(data, schema);

SQLTransformer sqlTrans = new SQLTransformer().setStatement(
"SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__");

sqlTrans.transform(df).show();

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaSQLTransformerExample.java" in the Spark repo.

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

from pyspark.ml.feature import SQLTransformer

df = spark.createDataFrame([
(0, 1.0, 3.0),
(2, 2.0, 5.0)
], ["id", "v1", "v2"])
sqlTrans = SQLTransformer(
statement="SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__")
sqlTrans.transform(df).show()

Find full example code at "examples/src/main/python/ml/sql_transformer.py" in the Spark repo.

## 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.

import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.linalg.Vectors

val dataset = spark.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())

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/VectorAssemblerExample.scala" in the Spark repo.

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

import java.util.Arrays;

import org.apache.spark.ml.feature.VectorAssembler;
import org.apache.spark.mllib.linalg.VectorUDT;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.sql.Dataset;
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);
Dataset<Row> dataset = spark.createDataFrame(Arrays.asList(row), schema);

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

Dataset<Row> output = assembler.transform(dataset);
System.out.println(output.select("features", "clicked").first());

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaVectorAssemblerExample.java" in the Spark repo.

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

from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import VectorAssembler

dataset = spark.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())

Find full example code at "examples/src/main/python/ml/vector_assembler_example.py" in the Spark repo.

## QuantileDiscretizer

QuantileDiscretizer takes a column with continuous features and outputs a column with binned categorical features. The number of bins is set by the numBuckets parameter. The bin ranges are chosen using an approximate algorithm (see the documentation for approxQuantile for a detailed description). The precision of the approximation can be controlled with the relativeError parameter. When set to zero, exact quantiles are calculated (Note: Computing exact quantiles is an expensive operation). The lower and upper bin bounds will be -Infinity and +Infinity covering all real values.

Examples

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

 id | hour
----|------
0  | 18.0
----|------
1  | 19.0
----|------
2  | 8.0
----|------
3  | 5.0
----|------
4  | 2.2


hour is a continuous feature with Double type. We want to turn the continuous feature into a categorical one. Given numBuckets = 3, we should get the following DataFrame:

 id | hour | result
----|------|------
0  | 18.0 | 2.0
----|------|------
1  | 19.0 | 2.0
----|------|------
2  | 8.0  | 1.0
----|------|------
3  | 5.0  | 1.0
----|------|------
4  | 2.2  | 0.0


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

import org.apache.spark.ml.feature.QuantileDiscretizer

val data = Array((0, 18.0), (1, 19.0), (2, 8.0), (3, 5.0), (4, 2.2))
var df = spark.createDataFrame(data).toDF("id", "hour")

val discretizer = new QuantileDiscretizer()
.setInputCol("hour")
.setOutputCol("result")
.setNumBuckets(3)

val result = discretizer.fit(df).transform(df)
result.show()

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.QuantileDiscretizer;
import org.apache.spark.sql.Dataset;
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.StructField;
import org.apache.spark.sql.types.StructType;

List<Row> data = Arrays.asList(
RowFactory.create(0, 18.0),
RowFactory.create(1, 19.0),
RowFactory.create(2, 8.0),
RowFactory.create(3, 5.0),
RowFactory.create(4, 2.2)
);

StructType schema = new StructType(new StructField[]{
});

Dataset<Row> df = spark.createDataFrame(data, schema);

QuantileDiscretizer discretizer = new QuantileDiscretizer()
.setInputCol("hour")
.setOutputCol("result")
.setNumBuckets(3);

Dataset<Row> result = discretizer.fit(df).transform(df);
result.show();

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java" in the Spark repo.

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

from pyspark.ml.feature import QuantileDiscretizer

data = [(0, 18.0,), (1, 19.0,), (2, 8.0,), (3, 5.0,), (4, 2.2,)]
df = spark.createDataFrame(data, ["id", "hour"])

discretizer = QuantileDiscretizer(numBuckets=3, inputCol="hour", outputCol="result")

result = discretizer.fit(df).transform(df)
result.show()

Find full example code at "examples/src/main/python/ml/quantile_discretizer_example.py" in the Spark repo.

# 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 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 represent the indices into the vector, setIndices().

2. String indices that represent 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 thrown if empty input attributes are encountered.

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. Assume that the first column of userFeatures are all zeros, so we want to remove it and select only the last two columns. 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 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.

import java.util.Arrays

import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NumericAttribute}
import org.apache.spark.ml.feature.VectorSlicer
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.sql.Row
import org.apache.spark.sql.types.StructType

val data = Arrays.asList(Row(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 dataset = spark.createDataFrame(data, StructType(Array(attrGroup.toStructField())))

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

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

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

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/VectorSlicerExample.scala" in the Spark repo.

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

import java.util.List;

import org.apache.spark.ml.attribute.Attribute;
import org.apache.spark.ml.attribute.AttributeGroup;
import org.apache.spark.ml.attribute.NumericAttribute;
import org.apache.spark.ml.feature.VectorSlicer;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.types.*;

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

List<Row> data = 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))
);

Dataset<Row> dataset =

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"})

Dataset<Row> output = vectorSlicer.transform(dataset);

System.out.println(output.select("userFeatures", "features").first());

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaVectorSlicerExample.java" in the Spark repo.

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

from pyspark.ml.feature import VectorSlicer
from pyspark.ml.linalg import Vectors
from pyspark.sql.types import Row

df = spark.createDataFrame([
Row(userFeatures=Vectors.sparse(3, {0: -2.0, 1: 2.3}),),
Row(userFeatures=Vectors.dense([-2.0, 2.3, 0.0]),)])

slicer = VectorSlicer(inputCol="userFeatures", outputCol="features", indices=)

output = slicer.transform(df)

output.select("userFeatures", "features").show()

Find full example code at "examples/src/main/python/ml/vector_slicer_example.py" in the Spark repo.

## RFormula

RFormula selects columns specified by an R model formula. Currently we support a limited subset of the R operators, including ‘~’, ‘.’, ‘:’, ‘+’, and ‘-‘. The basic operators are:

• ~ separate target and terms
• + concat terms, “+ 0” means removing intercept
• - remove a term, “- 1” means removing intercept
• : interaction (multiplication for numeric values, or binarized categorical values)
• . all columns except target

Suppose a and b are double columns, we use the following simple examples to illustrate the effect of RFormula:

• y ~ a + b means model y ~ w0 + w1 * a + w2 * b where w0 is the intercept and w1, w2 are coefficients.
• y ~ a + b + a:b - 1 means model y ~ w1 * a + w2 * b + w3 * a * b where w1, w2, w3 are coefficients.

RFormula produces a vector column of features and a double or string column of label. 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 the label column is of type string, it will be first transformed to double with StringIndexer. If the label column does not exist 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.

import org.apache.spark.ml.feature.RFormula

val dataset = spark.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()

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/RFormulaExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.RFormula;
import org.apache.spark.sql.Dataset;
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.*;

StructType schema = createStructType(new StructField[]{
createStructField("id", IntegerType, false),
createStructField("country", StringType, false),
createStructField("hour", IntegerType, false),
createStructField("clicked", DoubleType, false)
});

List<Row> data = Arrays.asList(
RowFactory.create(7, "US", 18, 1.0),
RowFactory.create(8, "CA", 12, 0.0),
RowFactory.create(9, "NZ", 15, 0.0)
);

Dataset<Row> dataset = spark.createDataFrame(data, schema);
RFormula formula = new RFormula()
.setFormula("clicked ~ country + hour")
.setFeaturesCol("features")
.setLabelCol("label");
Dataset<Row> output = formula.fit(dataset).transform(dataset);
output.select("features", "label").show();

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaRFormulaExample.java" in the Spark repo.

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

from pyspark.ml.feature import RFormula

dataset = spark.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()

Find full example code at "examples/src/main/python/ml/rformula_example.py" in the Spark repo.

## ChiSqSelector

ChiSqSelector stands for Chi-Squared feature selection. It operates on labeled data with categorical features. ChiSqSelector orders features based on a Chi-Squared test of independence from the class, and then filters (selects) the top features which the class label depends on the most. This is akin to yielding the features with the most predictive power.

Examples

Assume that we have a DataFrame with the columns id, features, and clicked, which is used as our target to be predicted:

id | features              | clicked
---|-----------------------|---------
7 | [0.0, 0.0, 18.0, 1.0] | 1.0
8 | [0.0, 1.0, 12.0, 0.0] | 0.0
9 | [1.0, 0.0, 15.0, 0.1] | 0.0


If we use ChiSqSelector with numTopFeatures = 1, then according to our label clicked the last column in our features is chosen as the most useful feature:

id | features              | clicked | selectedFeatures
---|-----------------------|---------|------------------
7 | [0.0, 0.0, 18.0, 1.0] | 1.0     | [1.0]
8 | [0.0, 1.0, 12.0, 0.0] | 0.0     | [0.0]
9 | [1.0, 0.0, 15.0, 0.1] | 0.0     | [0.1]


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

import org.apache.spark.ml.feature.ChiSqSelector
import org.apache.spark.ml.linalg.Vectors

val data = Seq(
(7, Vectors.dense(0.0, 0.0, 18.0, 1.0), 1.0),
(8, Vectors.dense(0.0, 1.0, 12.0, 0.0), 0.0),
(9, Vectors.dense(1.0, 0.0, 15.0, 0.1), 0.0)
)

val df = spark.createDataset(data).toDF("id", "features", "clicked")

val selector = new ChiSqSelector()
.setNumTopFeatures(1)
.setFeaturesCol("features")
.setLabelCol("clicked")
.setOutputCol("selectedFeatures")

val result = selector.fit(df).transform(df)
result.show()

Find full example code at "examples/src/main/scala/org/apache/spark/examples/ml/ChiSqSelectorExample.scala" in the Spark repo.

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

import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.ChiSqSelector;
import org.apache.spark.mllib.linalg.VectorUDT;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;

List<Row> data = Arrays.asList(
RowFactory.create(7, Vectors.dense(0.0, 0.0, 18.0, 1.0), 1.0),
RowFactory.create(8, Vectors.dense(0.0, 1.0, 12.0, 0.0), 0.0),
RowFactory.create(9, Vectors.dense(1.0, 0.0, 15.0, 0.1), 0.0)
);
StructType schema = new StructType(new StructField[]{
new StructField("features", new VectorUDT(), false, Metadata.empty()),
});

Dataset<Row> df = spark.createDataFrame(data, schema);

ChiSqSelector selector = new ChiSqSelector()
.setNumTopFeatures(1)
.setFeaturesCol("features")
.setLabelCol("clicked")
.setOutputCol("selectedFeatures");

Dataset<Row> result = selector.fit(df).transform(df);
result.show();

Find full example code at "examples/src/main/java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java" in the Spark repo.

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

from pyspark.ml.feature import ChiSqSelector
from pyspark.ml.linalg import Vectors

df = spark.createDataFrame([
(7, Vectors.dense([0.0, 0.0, 18.0, 1.0]), 1.0,),
(8, Vectors.dense([0.0, 1.0, 12.0, 0.0]), 0.0,),
(9, Vectors.dense([1.0, 0.0, 15.0, 0.1]), 0.0,)], ["id", "features", "clicked"])

selector = ChiSqSelector(numTopFeatures=1, featuresCol="features",
outputCol="selectedFeatures", labelCol="clicked")

result = selector.fit(df).transform(df)
result.show()

Find full example code at "examples/src/main/python/ml/chisq_selector_example.py" in the Spark repo.