org.apache.spark.ml.tuning

Class CrossValidator

Object

org.apache.spark.ml.PipelineStage

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

org.apache.spark.ml.tuning.CrossValidator

All Implemented Interfaces:

java.io.Serializable, Logging, Params, HasCollectSubModels, HasParallelism, HasSeed, CrossValidatorParams, ValidatorParams, Identifiable, MLWritable

public class CrossValidator
extends Estimator<CrossValidatorModel>
implements CrossValidatorParams, HasParallelism, HasCollectSubModels, MLWritable, Logging

K-fold cross validation performs model selection by splitting the dataset into a set of non-overlapping randomly partitioned folds which are used as separate training and test datasets e.g., with k=3 folds, K-fold cross validation will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing. Each fold is used as the test set exactly once.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
CrossValidator()
CrossValidator(String uid)

Method Summary

Modifier and Type	Method and Description
CrossValidator	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
CrossValidatorModel	fit(Dataset<?> dataset) Fits a model to the input data.
static CrossValidator	load(String path)
static MLReader<CrossValidator>	read()
CrossValidator	setCollectSubModels(boolean value) Whether to collect submodels when fitting.
CrossValidator	setEstimator(Estimator<?> value)
CrossValidator	setEstimatorParamMaps(ParamMap[] value)
CrossValidator	setEvaluator(Evaluator value)
CrossValidator	setNumFolds(int value)
CrossValidator	setParallelism(int value) Set the maximum level of parallelism to evaluate models in parallel.
CrossValidator	setSeed(long value)
StructType	transformSchema(StructType schema) :: DeveloperApi ::
String	uid() An immutable unique ID for the object and its derivatives.
MLWriter	write() Returns an MLWriter instance for this ML instance.

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

fit, fit, fit, fit

Methods inherited from class Object

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

Methods inherited from interface org.apache.spark.ml.tuning.CrossValidatorParams

getNumFolds, numFolds

Methods inherited from interface org.apache.spark.ml.tuning.ValidatorParams

estimator, estimatorParamMaps, evaluator, getEstimator, getEstimatorParamMaps, getEvaluator, logTuningParams, transformSchemaImpl

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

getSeed, seed

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

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

getExecutionContext, getParallelism, parallelism

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

collectSubModels, getCollectSubModels

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

save

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

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

Constructor Detail

CrossValidator

public CrossValidator(String uid)

CrossValidator

public CrossValidator()

Method Detail

read

public static MLReader<CrossValidator> read()

load

public static CrossValidator load(String path)

uid

public String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

setEstimator

public CrossValidator setEstimator(Estimator<?> value)

setEstimatorParamMaps

public CrossValidator setEstimatorParamMaps(ParamMap[] value)

setEvaluator

public CrossValidator setEvaluator(Evaluator value)

setNumFolds

public CrossValidator setNumFolds(int value)

setSeed

public CrossValidator setSeed(long value)

setParallelism

public CrossValidator setParallelism(int value)

Set the maximum level of parallelism to evaluate models in parallel. Default is 1 for serial evaluation

Parameters:

value - (undocumented)

Returns:

(undocumented)

setCollectSubModels

public CrossValidator setCollectSubModels(boolean value)

Whether to collect submodels when fitting. If set, we can get submodels from the returned model.

Note: If set this param, when you save the returned model, you can set an option "persistSubModels" to be "true" before saving, in order to save these submodels. You can check documents of CrossValidatorModel.CrossValidatorModelWriter for more information.

Parameters:

value - (undocumented)

Returns:

(undocumented)

fit

public CrossValidatorModel fit(Dataset<?> dataset)

Description copied from class: Estimator

Fits a model to the input data.

Specified by:

fit in class Estimator<CrossValidatorModel>

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

:: DeveloperApi ::

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)

copy

public CrossValidator 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<CrossValidatorModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

write

public MLWriter write()

Description copied from interface: MLWritable

Returns an MLWriter instance for this ML instance.

Specified by:

write in interface MLWritable

Returns:

(undocumented)