Package

org.apache.spark

ml

Permalink

package ml

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

Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. ml
  2. AnyRef
  3. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Type Members

  1. abstract class Estimator[M <: Model[M]] extends PipelineStage

    Permalink

    :: DeveloperApi :: Abstract class for estimators that fit models to data.

    :: DeveloperApi :: Abstract class for estimators that fit models to data.

    Annotations
    @DeveloperApi()
  2. abstract class Model[M <: Model[M]] extends Transformer

    Permalink

    :: DeveloperApi :: A fitted model, i.e., a Transformer produced by an Estimator.

    :: DeveloperApi :: A fitted model, i.e., a Transformer produced by an Estimator.

    M

    model type

    Annotations
    @DeveloperApi()
  3. class Pipeline extends Estimator[PipelineModel] with MLWritable

    Permalink

    A simple pipeline, which acts as an estimator.

    A simple pipeline, which acts as an estimator. A Pipeline consists of a sequence of stages, each of which is either an Estimator or a Transformer. When Pipeline.fit is called, the stages are executed in order. If a stage is an Estimator, its Estimator.fit method will be called on the input dataset to fit a model. Then the model, which is a transformer, will be used to transform the dataset as the input to the next stage. If a stage is a Transformer, its Transformer.transform method will be called to produce the dataset for the next stage. The fitted model from a Pipeline is a PipelineModel, which consists of fitted models and transformers, corresponding to the pipeline stages. If there are no stages, the pipeline acts as an identity transformer.

    Annotations
    @Since( "1.2.0" )
  4. class PipelineModel extends Model[PipelineModel] with MLWritable with Logging

    Permalink

    Represents a fitted pipeline.

    Represents a fitted pipeline.

    Annotations
    @Since( "1.2.0" )
  5. abstract class PipelineStage extends Params with Logging

    Permalink

    :: DeveloperApi :: A stage in a pipeline, either an Estimator or a Transformer.

    :: DeveloperApi :: A stage in a pipeline, either an Estimator or a Transformer.

    Annotations
    @DeveloperApi()
  6. abstract class PredictionModel[FeaturesType, M <: PredictionModel[FeaturesType, M]] extends Model[M] with PredictorParams

    Permalink

    :: DeveloperApi :: Abstraction for a model for prediction tasks (regression and classification).

    :: DeveloperApi :: Abstraction for a model for prediction tasks (regression and classification).

    FeaturesType

    Type of features. E.g., VectorUDT for vector features.

    M

    Specialization of PredictionModel. If you subclass this type, use this type parameter to specify the concrete type for the corresponding model.

    Annotations
    @DeveloperApi()
  7. abstract class Predictor[FeaturesType, Learner <: Predictor[FeaturesType, Learner, M], M <: PredictionModel[FeaturesType, M]] extends Estimator[M] with PredictorParams

    Permalink

    :: DeveloperApi :: Abstraction for prediction problems (regression and classification).

    :: DeveloperApi :: Abstraction for prediction problems (regression and classification). It accepts all NumericType labels and will automatically cast it to DoubleType in fit(). If this predictor supports weights, it accepts all NumericType weights, which will be automatically casted to DoubleType in fit().

    FeaturesType

    Type of features. E.g., VectorUDT for vector features.

    Learner

    Specialization of this class. If you subclass this type, use this type parameter to specify the concrete type.

    M

    Specialization of PredictionModel. If you subclass this type, use this type parameter to specify the concrete type for the corresponding model.

    Annotations
    @DeveloperApi()
  8. abstract class Transformer extends PipelineStage

    Permalink

    :: DeveloperApi :: Abstract class for transformers that transform one dataset into another.

    :: DeveloperApi :: Abstract class for transformers that transform one dataset into another.

    Annotations
    @DeveloperApi()
  9. abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, OUT, T]] extends Transformer with HasInputCol with HasOutputCol with Logging

    Permalink

    :: DeveloperApi :: Abstract class for transformers that take one input column, apply transformation, and output the result as a new column.

    :: DeveloperApi :: Abstract class for transformers that take one input column, apply transformation, and output the result as a new column.

    Annotations
    @DeveloperApi()

Value Members

  1. object Pipeline extends MLReadable[Pipeline] with Serializable

    Permalink
    Annotations
    @Since( "1.6.0" )
  2. object PipelineModel extends MLReadable[PipelineModel] with Serializable

    Permalink
    Annotations
    @Since( "1.6.0" )
  3. package attribute

    Permalink

    The ML pipeline API uses DataFrames as ML datasets.

    ML attributes

    The ML pipeline API uses DataFrames as ML datasets. Each dataset consists of typed columns, e.g., string, double, vector, etc. However, knowing only the column type may not be sufficient to handle the data properly. For instance, a double column with values 0.0, 1.0, 2.0, ... may represent some label indices, which cannot be treated as numeric values in ML algorithms, and, for another instance, we may want to know the names and types of features stored in a vector column. ML attributes are used to provide additional information to describe columns in a dataset.

    ML columns

    A column with ML attributes attached is called an ML column. The data in ML columns are stored as double values, i.e., an ML column is either a scalar column of double values or a vector column. Columns of other types must be encoded into ML columns using transformers. We use Attribute to describe a scalar ML column, and AttributeGroup to describe a vector ML column. ML attributes are stored in the metadata field of the column schema.

  4. package classification

    Permalink
  5. package clustering

    Permalink
  6. package evaluation

    Permalink
  7. package feature

    Permalink

    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.

    See also

    scikit-learn.preprocessing

  8. package fpm

    Permalink
  9. package image

    Permalink
  10. package linalg

    Permalink
  11. package param

    Permalink
  12. package recommendation

    Permalink
  13. package regression

    Permalink
  14. package source

    Permalink
  15. package stat

    Permalink
  16. package tree

    Permalink
  17. package tuning

    Permalink
  18. package util

    Permalink

Inherited from AnyRef

Inherited from Any

Members