Packages

•  package root

  Definition Classes

  root

•  package org

  Definition Classes

  root

•  package apache

  Definition Classes

  org

•  package spark

  Core Spark functionality.

  Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

  In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

  Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

  Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

  Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

  Definition Classes

  apache

•  package ml

  DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

  DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

  Definition Classes

  spark

•  package feature

  The ml.feature package provides common feature transformers that help convert raw data or features into more suitable forms for model fitting.

  Feature transformers

  The ml.feature package provides common feature transformers that help convert raw data or features into more suitable forms for model fitting. Most feature transformers are implemented as Transformers, which transform one DataFrame into another, e.g., HashingTF. Some feature transformers are implemented as Estimators, because the transformation requires some aggregated information of the dataset, e.g., document frequencies in IDF. For those feature transformers, calling Estimator.fit is required to obtain the model first, e.g., IDFModel, in order to apply transformation. The transformation is usually done by appending new columns to the input DataFrame, so all input columns are carried over.

  We try to make each transformer minimal, so it becomes flexible to assemble feature transformation pipelines. Pipeline can be used to chain feature transformers, and VectorAssembler can be used to combine multiple feature transformations, for example:

  import org.apache.spark.ml.feature._
  import org.apache.spark.ml.Pipeline
  
  // a DataFrame with three columns: id (integer), text (string), and rating (double).
  val df = spark.createDataFrame(Seq(
    (0, "Hi I heard about Spark", 3.0),
    (1, "I wish Java could use case classes", 4.0),
    (2, "Logistic regression models are neat", 4.0)
  )).toDF("id", "text", "rating")
  
  // define feature transformers
  val tok = new RegexTokenizer()
    .setInputCol("text")
    .setOutputCol("words")
  val sw = new StopWordsRemover()
    .setInputCol("words")
    .setOutputCol("filtered_words")
  val tf = new HashingTF()
    .setInputCol("filtered_words")
    .setOutputCol("tf")
    .setNumFeatures(10000)
  val idf = new IDF()
    .setInputCol("tf")
    .setOutputCol("tf_idf")
  val assembler = new VectorAssembler()
    .setInputCols(Array("tf_idf", "rating"))
    .setOutputCol("features")
  
  // assemble and fit the feature transformation pipeline
  val pipeline = new Pipeline()
    .setStages(Array(tok, sw, tf, idf, assembler))
  val model = pipeline.fit(df)
  
  // save transformed features with raw data
  model.transform(df)
    .select("id", "text", "rating", "features")
    .write.format("parquet").save("/output/path")

  Some feature transformers implemented in MLlib are inspired by those implemented in scikit-learn. The major difference is that most scikit-learn feature transformers operate eagerly on the entire input dataset, while MLlib's feature transformers operate lazily on individual columns, which is more efficient and flexible to handle large and complex datasets.

  Definition Classes

  ml

  See also

  scikit-learn.preprocessing

• Binarizer
• BucketedRandomProjectionLSH
• BucketedRandomProjectionLSHModel
• Bucketizer
• ChiSqSelector
• ChiSqSelectorModel
• CountVectorizer
• CountVectorizerModel
• DCT
• ElementwiseProduct
• FeatureHasher
• HashingTF
• IDF
• IDFModel
• Imputer
• ImputerModel
• IndexToString
• Interaction
• LabeledPoint
• MaxAbsScaler
• MaxAbsScalerModel
• MinHashLSH
• MinHashLSHModel
• MinMaxScaler
• MinMaxScalerModel
• NGram
• Normalizer
• OneHotEncoder
• OneHotEncoderModel
• PCA
• PCAModel
• PolynomialExpansion
• QuantileDiscretizer
• RFormula
• RFormulaModel
• RegexTokenizer
• RobustScaler
• RobustScalerModel
• SQLTransformer
• StandardScaler
• StandardScalerModel
• StopWordsRemover
• StringIndexer
• StringIndexerModel
• TargetEncoder
• TargetEncoderModel
• Tokenizer
• UnivariateFeatureSelector
• UnivariateFeatureSelectorModel
• VarianceThresholdSelector
• VarianceThresholdSelectorModel
• VectorAssembler
• VectorIndexer
• VectorIndexerModel
• VectorSizeHint
• VectorSlicer
• Word2Vec
• Word2VecModel

org.apache.spark.ml.feature

UnivariateFeatureSelector

Companion object UnivariateFeatureSelector