org.apache.spark.ml

Class Estimator<M extends Model<M>>

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Estimator<M>

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable

Direct Known Subclasses:

AFTSurvivalRegression, ALS, BisectingKMeans, BucketedRandomProjectionLSH, ChiSqSelector, CountVectorizer, CrossValidator, FPGrowth, GaussianMixture, IDF, Imputer, IsotonicRegression, KMeans, LDA, MaxAbsScaler, MinHashLSH, MinMaxScaler, OneVsRest, PCA, Pipeline, Predictor, QuantileDiscretizer, RFormula, StandardScaler, StringIndexer, TrainValidationSplit, VectorIndexer, Word2Vec

public abstract class Estimator<M extends Model<M>>
extends PipelineStage

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

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Estimator()

Method Summary

Modifier and Type	Method and Description
abstract Estimator<M>	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
abstract M	fit(Dataset<?> dataset) Fits a model to the input data.
M	fit(Dataset<?> dataset, ParamMap paramMap) Fits a single model to the input data with provided parameter map.
scala.collection.Seq<M>	fit(Dataset<?> dataset, ParamMap[] paramMaps) Fits multiple models to the input data with multiple sets of parameters.
M	fit(Dataset<?> dataset, ParamPair<?> firstParamPair, ParamPair<?>... otherParamPairs) Fits a single model to the input data with optional parameters.
M	fit(Dataset<?> dataset, ParamPair<?> firstParamPair, scala.collection.Seq<ParamPair<?>> otherParamPairs) Fits a single model to the input data with optional parameters.

Methods inherited from class org.apache.spark.ml.PipelineStage

transformSchema

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString, uid

Methods inherited from interface org.apache.spark.internal.Logging

initializeLogging, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

Estimator

public Estimator()

Method Detail

fit

public M fit(Dataset<?> dataset,
             ParamPair<?> firstParamPair,
             ParamPair<?>... otherParamPairs)

Fits a single model to the input data with optional parameters.

Parameters:

dataset - input dataset

firstParamPair - the first param pair, overrides embedded params

otherParamPairs - other param pairs. These values override any specified in this Estimator's embedded ParamMap.

Returns:

fitted model

fit

public M fit(Dataset<?> dataset,
             ParamPair<?> firstParamPair,
             scala.collection.Seq<ParamPair<?>> otherParamPairs)

Fits a single model to the input data with optional parameters.

Parameters:

dataset - input dataset

firstParamPair - the first param pair, overrides embedded params

otherParamPairs - other param pairs. These values override any specified in this Estimator's embedded ParamMap.

Returns:

fitted model

fit

public M fit(Dataset<?> dataset,
             ParamMap paramMap)

Fits a single model to the input data with provided parameter map.

Parameters:

dataset - input dataset

paramMap - Parameter map. These values override any specified in this Estimator's embedded ParamMap.

Returns:

fitted model

fit

public abstract M fit(Dataset<?> dataset)

Fits a model to the input data.

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

fit

public scala.collection.Seq<M> fit(Dataset<?> dataset,
                                   ParamMap[] paramMaps)

Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

Parameters:

dataset - input dataset

paramMaps - An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

Returns:

fitted models, matching the input parameter maps

copy

public abstract Estimator<M> copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

Specified by:

copy in interface Params

Specified by:

copy in class PipelineStage

Parameters:

extra - (undocumented)

Returns:

(undocumented)