TrainValidationSplitModel#
- class pyspark.ml.tuning.TrainValidationSplitModel(bestModel, validationMetrics=None, subModels=None)[source]#
Model from train validation split.
New in version 2.0.0.
Methods
clear(param)Clears a param from the param map if it has been explicitly set.
copy([extra])Creates a copy of this instance with a randomly generated uid and some extra params.
explainParam(param)Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap([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 < user-supplied values < extra.
Gets the value of estimator or its default value.
Gets the value of estimatorParamMaps or its default value.
Gets the value of evaluator or its default value.
getOrDefault(param)Gets the value of a param in the user-supplied param map or its default value.
getParam(paramName)Gets a param by its name.
getSeed()Gets the value of seed or its default value.
Gets the value of trainRatio or its default value.
hasDefault(param)Checks whether a param has a default value.
hasParam(paramName)Tests whether this instance contains a param with a given (string) name.
isDefined(param)Checks whether a param is explicitly set by user or has a default value.
isSet(param)Checks whether a param is explicitly set by user.
load(path)Reads an ML instance from the input path, a shortcut of read().load(path).
read()Returns an MLReader instance for this class.
save(path)Save this ML instance to the given path, a shortcut of 'write().save(path)'.
set(param, value)Sets a parameter in the embedded param map.
transform(dataset[, params])Transforms the input dataset with optional parameters.
write()Returns an MLWriter instance for this ML instance.
Attributes
Returns all params ordered by name.
best model from train validation split
evaluated validation metrics
sub models from train validation split
Methods Documentation
- clear(param)#
Clears a param from the param map if it has been explicitly set.
- copy(extra=None)[source]#
Creates a copy of this instance with a randomly generated uid and some extra params. This copies the underlying bestModel, creates a deep copy of the embedded paramMap, and copies the embedded and extra parameters over. And, this creates a shallow copy of the validationMetrics. It does not copy the extra Params into the subModels.
New in version 2.0.0.
- Parameters
- extradict, optional
Extra parameters to copy to the new instance
- Returns
TrainValidationSplitModelCopy of this instance
- explainParam(param)#
Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
- explainParams()#
Returns the documentation of all params with their optionally default values and user-supplied values.
- extractParamMap(extra=None)#
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 < user-supplied values < extra.
- Parameters
- extradict, optional
extra param values
- Returns
- dict
merged param map
- getEstimator()#
Gets the value of estimator or its default value.
New in version 2.0.0.
- getEstimatorParamMaps()#
Gets the value of estimatorParamMaps or its default value.
New in version 2.0.0.
- getEvaluator()#
Gets the value of evaluator or its default value.
New in version 2.0.0.
- getOrDefault(param)#
Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
- getParam(paramName)#
Gets a param by its name.
- getSeed()#
Gets the value of seed or its default value.
- getTrainRatio()#
Gets the value of trainRatio or its default value.
New in version 2.0.0.
- hasDefault(param)#
Checks whether a param has a default value.
- hasParam(paramName)#
Tests whether this instance contains a param with a given (string) name.
- isDefined(param)#
Checks whether a param is explicitly set by user or has a default value.
- isSet(param)#
Checks whether a param is explicitly set by user.
- classmethod load(path)#
Reads an ML instance from the input path, a shortcut of read().load(path).
- save(path)#
Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
- set(param, value)#
Sets a parameter in the embedded param map.
- transform(dataset, params=None)#
Transforms the input dataset with optional parameters.
New in version 1.3.0.
- Parameters
- dataset
pyspark.sql.DataFrame input dataset
- paramsdict, optional
an optional param map that overrides embedded params.
- dataset
- Returns
pyspark.sql.DataFrametransformed dataset
Attributes Documentation
- estimator = Param(parent='undefined', name='estimator', doc='estimator to be cross-validated')#
- estimatorParamMaps = Param(parent='undefined', name='estimatorParamMaps', doc='estimator param maps')#
- evaluator = Param(parent='undefined', name='evaluator', doc='evaluator used to select hyper-parameters that maximize the validator metric')#
- params#
Returns all params ordered by name. The default implementation uses
dir()to get all attributes of typeParam.
- seed = Param(parent='undefined', name='seed', doc='random seed.')#
- trainRatio = Param(parent='undefined', name='trainRatio', doc='Param for ratio between train and validation data. Must be between 0 and 1.')#
- bestModel#
best model from train validation split
- validationMetrics#
evaluated validation metrics
- subModels#
sub models from train validation split
- uid#
A unique id for the object.