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)

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.ml.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.Metadata;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;

List<Row> data = Arrays.asList(
  RowFactory.create(0.0, "Hi I heard about Spark"),
  RowFactory.create(0.0, "I wish Java could use case classes"),
  RowFactory.create(1.0, "Logistic regression models are neat")
);
StructType schema = new StructType(new StructField[]{
  new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
  new StructField("sentence", DataTypes.StringType, false, Metadata.empty())
});
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);
}

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)

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)

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);
}

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)

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()

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();

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()

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)

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.Metadata;
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[]{
  new StructField("label", DataTypes.IntegerType, false, Metadata.empty()),
  new StructField("sentence", DataTypes.StringType, false, Metadata.empty())
});

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);

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)

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()

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.Metadata;
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")),
  RowFactory.create(Arrays.asList("Mary", "had", "a", "little", "lamb"))
);

StructType schema = new StructType(new StructField[]{
  new StructField(
    "raw", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty())
});

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

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)

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)

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.Metadata;
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("label", DataTypes.DoubleType, false, Metadata.empty()),
  new StructField(
    "words", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty())
});

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();
}

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)

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)

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.Metadata;
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[]{
  new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
  new StructField("feature", DataTypes.DoubleType, false, Metadata.empty())
});
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);
}

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)

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)
val pcaDF = pca.transform(df)
val result = pcaDF.select("pcaFeatures")
result.show()

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.ml.linalg.VectorUDT;
import org.apache.spark.ml.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.Metadata;
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();

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)

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)

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

import org.apache.spark.ml.feature.PolynomialExpansion;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.ml.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.Metadata;
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));
}

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)

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)

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

import org.apache.spark.ml.feature.DCT;
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.Metadata;
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);

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)

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()

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();

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()

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()

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.Metadata;
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[]{
  new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
  new StructField("category", DataTypes.StringType, false, Metadata.empty())
});
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();

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()

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()

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.Metadata;
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[]{
  new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
  new StructField("category", DataTypes.StringType, false, Metadata.empty())
});

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();

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()

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

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

val indexer = new VectorIndexer()
  .setInputCol("features")
  .setOutputCol("indexed")
  .setMaxCategories(10)

val indexerModel = indexer.fit(data)

val categoricalFeatures: Set[Int] = indexerModel.categoryMaps.keys.toSet
println(s"Chose ${categoricalFeatures.size} categorical features: " +
  categoricalFeatures.mkString(", "))

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

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();

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()

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()

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();

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()

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

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

val scaler = new StandardScaler()
  .setInputCol("features")
  .setOutputCol("scaledFeatures")
  .setWithStd(true)
  .setWithMean(false)

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

// Normalize each feature to have unit standard deviation.
val scaledData = scalerModel.transform(dataFrame)
scaledData.show()

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

StandardScaler scaler = new StandardScaler()
  .setInputCol("features")
  .setOutputCol("scaledFeatures")
  .setWithStd(true)
  .setWithMean(false);

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

// Normalize each feature to have unit standard deviation.
Dataset<Row> scaledData = scalerModel.transform(dataFrame);
scaledData.show();

from pyspark.ml.feature import StandardScaler

dataFrame = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
scaler = StandardScaler(inputCol="features", outputCol="scaledFeatures",
                        withStd=True, withMean=False)

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

# Normalize each feature to have unit standard deviation.
scaledData = scalerModel.transform(dataFrame)
scaledData.show()

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

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

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

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

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

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

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

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

from pyspark.ml.feature import MinMaxScaler

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

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()

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

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

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

from pyspark.ml.feature import MaxAbsScaler

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

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()

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()

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

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

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[]{
  new StructField("features", DataTypes.DoubleType, false, Metadata.empty())
});
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();

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()

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()

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);
fields.add(DataTypes.createStructField("id", DataTypes.StringType, false));
fields.add(DataTypes.createStructField("vector", new VectorUDT(), false));

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();

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()