Package org.apache.spark.ml.tuning
Class TrainValidationSplitModel
Object
org.apache.spark.ml.PipelineStage
org.apache.spark.ml.Transformer
org.apache.spark.ml.Model<TrainValidationSplitModel>
org.apache.spark.ml.tuning.TrainValidationSplitModel
- All Implemented Interfaces:
Serializable
,org.apache.spark.internal.Logging
,Params
,HasSeed
,TrainValidationSplitParams
,ValidatorParams
,Identifiable
,MLWritable
,scala.Serializable
public class TrainValidationSplitModel
extends Model<TrainValidationSplitModel>
implements TrainValidationSplitParams, MLWritable
Model from train validation split.
param: uid Id. param: bestModel Estimator determined best model. param: validationMetrics Evaluated validation metrics.
- See Also:
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Nested Class Summary
Modifier and TypeClassDescriptionstatic final class
Writer for TrainValidationSplitModel.Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging
org.apache.spark.internal.Logging.SparkShellLoggingFilter
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Method Summary
Modifier and TypeMethodDescriptionModel<?>
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 metricboolean
static TrainValidationSplitModel
static MLReader<TrainValidationSplitModel>
read()
final LongParam
seed()
Param for random seed.Model<?>[]
toString()
Param for ratio between train and validation data.Transforms the input dataset.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.double[]
write()
Returns anMLWriter
instance for this ML instance.Methods inherited from class org.apache.spark.ml.Transformer
transform, transform, transform
Methods inherited from class org.apache.spark.ml.PipelineStage
params
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
Methods inherited from interface org.apache.spark.internal.Logging
initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq
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.TrainValidationSplitParams
getTrainRatio
Methods inherited from interface org.apache.spark.ml.tuning.ValidatorParams
getEstimator, getEstimatorParamMaps, getEvaluator, logTuningParams, transformSchemaImpl
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Method Details
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read
-
load
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trainRatio
Description copied from interface:TrainValidationSplitParams
Param for ratio between train and validation data. Must be between 0 and 1. Default: 0.75- Specified by:
trainRatio
in interfaceTrainValidationSplitParams
- Returns:
- (undocumented)
-
estimator
Description copied from interface:ValidatorParams
param for the estimator to be validated- Specified by:
estimator
in interfaceValidatorParams
- Returns:
- (undocumented)
-
estimatorParamMaps
Description copied from interface:ValidatorParams
param for estimator param maps- Specified by:
estimatorParamMaps
in interfaceValidatorParams
- Returns:
- (undocumented)
-
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)
-
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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bestModel
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validationMetrics
public double[] validationMetrics() -
subModels
- Returns:
- submodels represented in array. The index of array corresponds to the ordering of estimatorParamMaps
- Throws:
IllegalArgumentException
- if subModels are not available. To retrieve subModels, make sure to set collectSubModels to true before fitting.
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hasSubModels
public boolean hasSubModels() -
transform
Description copied from class:Transformer
Transforms the input dataset.- Specified by:
transform
in classTransformer
- Parameters:
dataset
- (undocumented)- Returns:
- (undocumented)
-
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 classModel<TrainValidationSplitModel>
- 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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toString
- Specified by:
toString
in interfaceIdentifiable
- Overrides:
toString
in classObject
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