class TrainValidationSplit extends Estimator[TrainValidationSplitModel] with TrainValidationSplitParams with HasParallelism with HasCollectSubModels with MLWritable with Logging
Validation for hyper-parameter tuning. Randomly splits the input dataset into train and validation sets, and uses evaluation metric on the validation set to select the best model. Similar to CrossValidator, but only splits the set once.
- Annotations
- @Since( "1.5.0" )
- Source
- TrainValidationSplit.scala
- Grouped
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- By Inheritance
- TrainValidationSplit
- MLWritable
- HasCollectSubModels
- HasParallelism
- TrainValidationSplitParams
- ValidatorParams
- HasSeed
- Estimator
- PipelineStage
- Logging
- Params
- Serializable
- Serializable
- Identifiable
- AnyRef
- Any
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Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
-
val
estimator: Param[Estimator[_]]
param for the estimator to be validated
param for the estimator to be validated
- Definition Classes
- ValidatorParams
-
val
estimatorParamMaps: Param[Array[ParamMap]]
param for estimator param maps
param for estimator param maps
- Definition Classes
- ValidatorParams
-
val
evaluator: Param[Evaluator]
param for the evaluator used to select hyper-parameters that maximize the validated metric
param for the evaluator used to select hyper-parameters that maximize the validated metric
- Definition Classes
- ValidatorParams
-
final
val
seed: LongParam
Param for random seed.
Param for random seed.
- Definition Classes
- HasSeed
-
val
trainRatio: DoubleParam
Param for ratio between train and validation data.
Param for ratio between train and validation data. Must be between 0 and 1. Default: 0.75
- Definition Classes
- TrainValidationSplitParams
Members
-
final
def
clear(param: Param[_]): TrainValidationSplit.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
- Definition Classes
- Params
-
def
copy(extra: ParamMap): TrainValidationSplit
Creates a copy of this instance with the same UID and some extra 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()
.- Definition Classes
- TrainValidationSplit → Estimator → PipelineStage → Params
- Annotations
- @Since( "1.5.0" )
-
def
explainParam(param: Param[_]): String
Explains a param.
Explains a param.
- param
input param, must belong to this instance.
- returns
a string that contains the input param name, doc, and optionally its default value and the user-supplied value
- Definition Classes
- Params
-
def
explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See
explainParam()
.- Definition Classes
- Params
-
final
def
extractParamMap(): ParamMap
extractParamMap
with no extra values.extractParamMap
with no extra values.- Definition Classes
- Params
-
final
def
extractParamMap(extra: ParamMap): ParamMap
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
- Definition Classes
- Params
-
def
fit(dataset: Dataset[_]): TrainValidationSplitModel
Fits a model to the input data.
Fits a model to the input data.
- Definition Classes
- TrainValidationSplit → Estimator
- Annotations
- @Since( "2.0.0" )
-
def
fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[TrainValidationSplitModel]
Fits multiple models to the input data with multiple sets of parameters.
Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.
- dataset
input dataset
- paramMaps
An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.
- returns
fitted models, matching the input parameter maps
- Definition Classes
- Estimator
- Annotations
- @Since( "2.0.0" )
-
def
fit(dataset: Dataset[_], paramMap: ParamMap): TrainValidationSplitModel
Fits a single model to the input data with provided parameter map.
Fits a single model to the input data with provided parameter map.
- dataset
input dataset
- paramMap
Parameter map. These values override any specified in this Estimator's embedded ParamMap.
- returns
fitted model
- Definition Classes
- Estimator
- Annotations
- @Since( "2.0.0" )
-
def
fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): TrainValidationSplitModel
Fits a single model to the input data with optional parameters.
Fits a single model to the input data with optional parameters.
- dataset
input dataset
- firstParamPair
the first param pair, overrides embedded params
- otherParamPairs
other param pairs. These values override any specified in this Estimator's embedded ParamMap.
- returns
fitted model
- Definition Classes
- Estimator
- Annotations
- @Since( "2.0.0" ) @varargs()
-
final
def
get[T](param: Param[T]): Option[T]
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.
- Definition Classes
- Params
-
final
def
getDefault[T](param: Param[T]): Option[T]
Gets the default value of a parameter.
Gets the default value of a parameter.
- Definition Classes
- Params
-
final
def
getOrDefault[T](param: Param[T]): T
Gets the value of a param in the embedded param map or its default value.
Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.
- Definition Classes
- Params
-
def
getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.
- Definition Classes
- Params
-
final
def
hasDefault[T](param: Param[T]): Boolean
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.
- Definition Classes
- Params
-
def
hasParam(paramName: String): Boolean
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.
- Definition Classes
- Params
-
final
def
isDefined(param: Param[_]): Boolean
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.
- Definition Classes
- Params
-
final
def
isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
-
lazy val
params: Array[Param[_]]
Returns all params sorted by their names.
Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.
- Definition Classes
- Params
- Note
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
-
def
save(path: String): Unit
Saves this ML instance to the input path, a shortcut of
write.save(path)
.Saves this ML instance to the input path, a shortcut of
write.save(path)
.- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
-
final
def
set[T](param: Param[T], value: T): TrainValidationSplit.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
-
def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
-
def
transformSchema(schema: StructType): StructType
Check transform validity and derive the output schema from the input schema.
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.
- Definition Classes
- TrainValidationSplit → PipelineStage
- Annotations
- @Since( "1.5.0" )
-
val
uid: String
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.
- Definition Classes
- TrainValidationSplit → Identifiable
- Annotations
- @Since( "1.5.0" )
-
def
write: MLWriter
Returns an
MLWriter
instance for this ML instance.Returns an
MLWriter
instance for this ML instance.- Definition Classes
- TrainValidationSplit → MLWritable
- Annotations
- @Since( "2.0.0" )
Parameter setters
-
def
setEstimator(value: Estimator[_]): TrainValidationSplit.this.type
- Annotations
- @Since( "1.5.0" )
-
def
setEstimatorParamMaps(value: Array[ParamMap]): TrainValidationSplit.this.type
- Annotations
- @Since( "1.5.0" )
-
def
setEvaluator(value: Evaluator): TrainValidationSplit.this.type
- Annotations
- @Since( "1.5.0" )
-
def
setSeed(value: Long): TrainValidationSplit.this.type
- Annotations
- @Since( "2.0.0" )
-
def
setTrainRatio(value: Double): TrainValidationSplit.this.type
- Annotations
- @Since( "1.5.0" )
Parameter getters
-
def
getEstimator: Estimator[_]
- Definition Classes
- ValidatorParams
-
def
getEstimatorParamMaps: Array[ParamMap]
- Definition Classes
- ValidatorParams
-
def
getEvaluator: Evaluator
- Definition Classes
- ValidatorParams
-
final
def
getSeed: Long
- Definition Classes
- HasSeed
-
def
getTrainRatio: Double
- Definition Classes
- TrainValidationSplitParams
(expert-only) Parameters
A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
-
final
val
collectSubModels: BooleanParam
Param for whether to collect a list of sub-models trained during tuning.
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.
- Definition Classes
- HasCollectSubModels
-
val
parallelism: IntParam
The number of threads to use when running parallel algorithms.
The number of threads to use when running parallel algorithms. Default is 1 for serial execution
- Definition Classes
- HasParallelism
(expert-only) Parameter setters
-
def
setCollectSubModels(value: Boolean): TrainValidationSplit.this.type
Whether to collect submodels when fitting.
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
org.apache.spark.ml.tuning.TrainValidationSplitModel.TrainValidationSplitModelWriter
for more information.- Annotations
- @Since( "2.3.0" )
-
def
setParallelism(value: Int): TrainValidationSplit.this.type
Set the maximum level of parallelism to evaluate models in parallel.
Set the maximum level of parallelism to evaluate models in parallel. Default is 1 for serial evaluation
- Annotations
- @Since( "2.3.0" )
(expert-only) Parameter getters
-
final
def
getCollectSubModels: Boolean
- Definition Classes
- HasCollectSubModels
-
def
getParallelism: Int
- Definition Classes
- HasParallelism