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, Identifiable, Params

Direct Known Subclasses:

ALS, CrossValidator, Pipeline, Predictor, StandardScaler

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

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

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Estimator()

Method Summary

Methods

Modifier and Type	Method and Description
abstract M	fit(DataFrame dataset, ParamMap paramMap) Fits a single model to the input data with provided parameter map.
scala.collection.Seq<M>	fit(DataFrame dataset, ParamMap[] paramMaps) Fits multiple models to the input data with multiple sets of parameters.
M	fit(DataFrame dataset, ParamPair<?>... paramPairs) Fits a single model to the input data with optional parameters.
M	fit(DataFrame dataset, scala.collection.Seq<ParamPair<?>> paramPairs) 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

addOutputColumn, checkInputColumn, explainParams, get, getParam, isSet, paramMap, params, set, set, validate, validate

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

uid

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, 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(DataFrame dataset,
    ParamPair<?>... paramPairs)

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

Parameters:

dataset - input dataset

paramPairs - Optional list of param pairs. These values override any specified in this Estimator's embedded ParamMap.

Returns:

fitted model

fit

public M fit(DataFrame dataset,
    scala.collection.Seq<ParamPair<?>> paramPairs)

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

Parameters:

dataset - input dataset

paramPairs - Optional list of param pairs. These values override any specified in this Estimator's embedded ParamMap.

Returns:

fitted model

fit

public abstract M fit(DataFrame 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 scala.collection.Seq<M> fit(DataFrame 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 overwrite 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