org.apache.spark.ml

Class Predictor<FeaturesType,Learner extends Predictor<FeaturesType,Learner,M>,M extends PredictionModel<FeaturesType,M>>

java.lang.Object

org.apache.spark.ml.PipelineStage

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

org.apache.spark.ml.Predictor<FeaturesType,Learner,M>

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable

Direct Known Subclasses:

Classifier, DecisionTreeRegressor, GBTClassifier, GBTRegressor, LinearRegression, MultilayerPerceptronClassifier, RandomForestRegressor

public abstract class Predictor<FeaturesType,Learner extends Predictor<FeaturesType,Learner,M>,M extends PredictionModel<FeaturesType,M>>
extends Estimator<M>

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

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Predictor()

Method Summary

Methods

Modifier and Type	Method and Description
abstract Learner	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
protected RDD<LabeledPoint>	extractLabeledPoints(DataFrame dataset) Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.
Param<java.lang.String>	featuresCol() Param for features column name.
M	fit(DataFrame dataset) Fits a model to the input data.
java.lang.String	getFeaturesCol()
java.lang.String	getLabelCol()
java.lang.String	getPredictionCol()
Param<java.lang.String>	labelCol() Param for label column name.
Param<java.lang.String>	predictionCol() Param for prediction column name.
Learner	setFeaturesCol(java.lang.String value)
Learner	setLabelCol(java.lang.String value)
Learner	setPredictionCol(java.lang.String value)
protected abstract M	train(DataFrame dataset) Train a model using the given dataset and parameters.
StructType	transformSchema(StructType schema) :: DeveloperApi ::
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType) Validates and transforms the input schema with the provided param map.

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

fit, fit, fit, fit

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

transformSchema

Methods inherited from class java.lang.Object

clone, equals, finalize, 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, validateParams

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

toString, 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

Predictor

public Predictor()

Method Detail

setLabelCol

public Learner setLabelCol(java.lang.String value)

setFeaturesCol

public Learner setFeaturesCol(java.lang.String value)

setPredictionCol

public Learner setPredictionCol(java.lang.String value)

fit

public M fit(DataFrame dataset)

Description copied from class: Estimator

Fits a model to the input data.

Specified by:

fit in class Estimator<M extends PredictionModel<FeaturesType,M>>

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

copy

public abstract Learner 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.

Specified by:

copy in interface Params

Specified by:

copy in class Estimator<M extends PredictionModel<FeaturesType,M>>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

train

protected abstract M train(DataFrame dataset)

Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.

Parameters:

dataset - Training dataset

Returns:

Fitted model

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

:: DeveloperApi ::

Derives the output schema from the input schema.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)

extractLabeledPoints

protected RDD<LabeledPoint> extractLabeledPoints(DataFrame dataset)

Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                    boolean fitting,
                                    DataType featuresDataType)

Validates and transforms the input schema with the provided param map.

Parameters:

schema - input schema

fitting - whether this is in fitting

featuresDataType - SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

Returns:

output schema

labelCol

public Param<java.lang.String> labelCol()

Param for label column name.

Returns:

(undocumented)

getLabelCol

public java.lang.String getLabelCol()

featuresCol

public Param<java.lang.String> featuresCol()

Param for features column name.

Returns:

(undocumented)

getFeaturesCol

public java.lang.String getFeaturesCol()

predictionCol

public Param<java.lang.String> predictionCol()

Param for prediction column name.

Returns:

(undocumented)

getPredictionCol

public java.lang.String getPredictionCol()