org.apache.spark.ml

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

Object

org.apache.spark.ml.PipelineStage

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

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

Type Parameters:

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.

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging, Params, HasFeaturesCol, HasLabelCol, HasPredictionCol, PredictorParams, Identifiable

Direct Known Subclasses:

Classifier, Regressor

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

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().

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Predictor()

Method Summary

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.
Param<String>	featuresCol() Param for features column name.
M	fit(Dataset<?> dataset) Fits a model to the input data.
Param<String>	labelCol() Param for label column name.
Param<String>	predictionCol() Param for prediction column name.
Learner	setFeaturesCol(String value)
Learner	setLabelCol(String value)
Learner	setPredictionCol(String value)
StructType	transformSchema(StructType schema) Check transform validity and derive the output schema from the input schema.

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

fit, fit, fit, fit

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

params

Methods inherited from class Object

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

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

extractInstances, extractInstances, validateAndTransformSchema

Methods inherited from interface org.apache.spark.ml.param.shared.HasLabelCol

getLabelCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasFeaturesCol

getFeaturesCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol

getPredictionCol

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

$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize

Constructor Detail

Predictor

public Predictor()

Method Detail

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. See defaultCopy().

Specified by:

copy in interface Params

Specified by:

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

Parameters:

extra - (undocumented)

Returns:

(undocumented)

featuresCol

public final Param<String> featuresCol()

Description copied from interface: HasFeaturesCol

Param for features column name.

Specified by:

featuresCol in interface HasFeaturesCol

Returns:

(undocumented)

fit

public M fit(Dataset<?> 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)

labelCol

public final Param<String> labelCol()

Description copied from interface: HasLabelCol

Param for label column name.

Specified by:

labelCol in interface HasLabelCol

Returns:

(undocumented)

predictionCol

public final Param<String> predictionCol()

Description copied from interface: HasPredictionCol

Param for prediction column name.

Specified by:

predictionCol in interface HasPredictionCol

Returns:

(undocumented)

setFeaturesCol

public Learner setFeaturesCol(String value)

setLabelCol

public Learner setLabelCol(String value)

setPredictionCol

public Learner setPredictionCol(String value)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

Check transform validity and derive the output schema from the input schema.

We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)