Package org.apache.spark.ml.tuning
Class CrossValidator
- All Implemented Interfaces:
Serializable
,org.apache.spark.internal.Logging
,Params
,HasCollectSubModels
,HasParallelism
,HasSeed
,CrossValidatorParams
,ValidatorParams
,Identifiable
,MLWritable
public class CrossValidator
extends Estimator<CrossValidatorModel>
implements CrossValidatorParams, HasParallelism, HasCollectSubModels, MLWritable, org.apache.spark.internal.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:
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Nested Class Summary
Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging
org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter
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Constructor Summary
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Method Summary
Modifier and TypeMethodDescriptionfinal BooleanParam
Param for whether to collect a list of sub-models trained during tuning.Creates a copy of this instance with the same UID and some extra params.param for the estimator to be validatedparam for estimator param mapsparam for the evaluator used to select hyper-parameters that maximize the validated metricFits a model to the input data.foldCol()
Param for the column name of user specified fold number.static CrossValidator
numFolds()
Param for number of folds for cross validation.The number of threads to use when running parallel algorithms.static MLReader<CrossValidator>
read()
final LongParam
seed()
Param for random seed.setCollectSubModels
(boolean value) Whether to collect submodels when fitting.setEstimator
(Estimator<?> value) setEstimatorParamMaps
(ParamMap[] value) setEvaluator
(Evaluator value) setFoldCol
(String value) setNumFolds
(int value) setParallelism
(int value) Set the maximum level of parallelism to evaluate models in parallel.setSeed
(long value) transformSchema
(StructType schema) Check transform validity and derive the output schema from the input schema.uid()
An immutable unique ID for the object and its derivatives.write()
Returns anMLWriter
instance for this ML instance.Methods inherited from class org.apache.spark.ml.PipelineStage
params
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
Methods inherited from interface org.apache.spark.ml.tuning.CrossValidatorParams
getFoldCol, getNumFolds
Methods inherited from interface org.apache.spark.ml.param.shared.HasCollectSubModels
getCollectSubModels
Methods inherited from interface org.apache.spark.ml.param.shared.HasParallelism
getExecutionContext, getParallelism
Methods inherited from interface org.apache.spark.ml.util.Identifiable
toString
Methods inherited from interface org.apache.spark.internal.Logging
initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext
Methods inherited from interface org.apache.spark.ml.util.MLWritable
save
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, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
Methods inherited from interface org.apache.spark.ml.tuning.ValidatorParams
getEstimator, getEstimatorParamMaps, getEvaluator, logTuningParams, transformSchemaImpl
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Constructor Details
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CrossValidator
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CrossValidator
public CrossValidator()
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Method Details
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read
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load
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collectSubModels
Description copied from interface:HasCollectSubModels
Param for whether to collect a list of sub-models trained during tuning. If set to false, then only the single best sub-model will be available after fitting. If set to true, then all sub-models will be available. Warning: For large models, collecting all sub-models can cause OOMs on the Spark driver.- Specified by:
collectSubModels
in interfaceHasCollectSubModels
- Returns:
- (undocumented)
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parallelism
Description copied from interface:HasParallelism
The number of threads to use when running parallel algorithms. Default is 1 for serial execution- Specified by:
parallelism
in interfaceHasParallelism
- Returns:
- (undocumented)
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numFolds
Description copied from interface:CrossValidatorParams
Param for number of folds for cross validation. Must be >= 2. Default: 3- Specified by:
numFolds
in interfaceCrossValidatorParams
- Returns:
- (undocumented)
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foldCol
Description copied from interface:CrossValidatorParams
Param for the column name of user specified fold number. Once this is specified,CrossValidator
won't do random k-fold split. Note that this column should be integer type with range [0, numFolds) and Spark will throw exception on out-of-range fold numbers.- Specified by:
foldCol
in interfaceCrossValidatorParams
- Returns:
- (undocumented)
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estimator
Description copied from interface:ValidatorParams
param for the estimator to be validated- Specified by:
estimator
in interfaceValidatorParams
- Returns:
- (undocumented)
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estimatorParamMaps
Description copied from interface:ValidatorParams
param for estimator param maps- Specified by:
estimatorParamMaps
in interfaceValidatorParams
- Returns:
- (undocumented)
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evaluator
Description copied from interface:ValidatorParams
param for the evaluator used to select hyper-parameters that maximize the validated metric- Specified by:
evaluator
in interfaceValidatorParams
- Returns:
- (undocumented)
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seed
Description copied from interface:HasSeed
Param for random seed. -
uid
Description copied from interface:Identifiable
An immutable unique ID for the object and its derivatives.- Specified by:
uid
in interfaceIdentifiable
- Returns:
- (undocumented)
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setEstimator
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setEstimatorParamMaps
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setEvaluator
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setNumFolds
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setSeed
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setFoldCol
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setParallelism
Set the maximum level of parallelism to evaluate models in parallel. Default is 1 for serial evaluation- Parameters:
value
- (undocumented)- Returns:
- (undocumented)
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setCollectSubModels
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)
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fit
Description copied from class:Estimator
Fits a model to the input data.- Specified by:
fit
in classEstimator<CrossValidatorModel>
- Parameters:
dataset
- (undocumented)- Returns:
- (undocumented)
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transformSchema
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 byParam.validate()
.Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
- Specified by:
transformSchema
in classPipelineStage
- Parameters:
schema
- (undocumented)- Returns:
- (undocumented)
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copy
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. SeedefaultCopy()
.- Specified by:
copy
in interfaceParams
- Specified by:
copy
in classEstimator<CrossValidatorModel>
- Parameters:
extra
- (undocumented)- Returns:
- (undocumented)
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write
Description copied from interface:MLWritable
Returns anMLWriter
instance for this ML instance.- Specified by:
write
in interfaceMLWritable
- Returns:
- (undocumented)
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