CrossValidatorModel#
- class pyspark.ml.tuning.CrossValidatorModel(bestModel, avgMetrics=None, subModels=None, stdMetrics=None)[source]#
- CrossValidatorModel contains the model with the highest average cross-validation metric across folds and uses this model to transform input data. CrossValidatorModel also tracks the metrics for each param map evaluated. - New in version 1.4.0. - Notes - Since version 3.3.0, CrossValidatorModel contains a new attribute “stdMetrics”, which represent standard deviation of metrics for each paramMap in CrossValidator.estimatorParamMaps. - 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. - Gets the value of foldCol or its default value. - Gets the value of numFolds 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. - 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 cross validation - Average cross-validation metrics for each paramMap in CrossValidator.estimatorParamMaps, in the corresponding order. - sub model list from cross validation - standard deviation of metrics for each paramMap in CrossValidator.estimatorParamMaps, in the corresponding order. - 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. It does not copy the extra Params into the subModels. - New in version 1.4.0. - Parameters
- extradict, optional
- Extra parameters to copy to the new instance 
 
- Returns
- CrossValidatorModel
- Copy 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. 
 - getFoldCol()#
- Gets the value of foldCol or its default value. - New in version 3.1.0. 
 - getNumFolds()#
- Gets the value of numFolds or its default value. - New in version 1.4.0. 
 - 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. 
 - 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
- datasetpyspark.sql.DataFrame
- input dataset 
- paramsdict, optional
- an optional param map that overrides embedded params. 
 
- dataset
- Returns
- pyspark.sql.DataFrame
- transformed 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')#
 - foldCol = Param(parent='undefined', name='foldCol', doc="Param for the column name of user specified fold number. Once this is specified, :py:class:`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.")#
 - numFolds = Param(parent='undefined', name='numFolds', doc='number of folds for cross validation')#
 - params#
- Returns all params ordered by name. The default implementation uses - dir()to get all attributes of type- Param.
 - seed = Param(parent='undefined', name='seed', doc='random seed.')#
 - bestModel#
- best model from cross validation 
 - avgMetrics#
- Average cross-validation metrics for each paramMap in CrossValidator.estimatorParamMaps, in the corresponding order. 
 - subModels#
- sub model list from cross validation 
 - stdMetrics#
- standard deviation of metrics for each paramMap in CrossValidator.estimatorParamMaps, in the corresponding order. 
 - uid#
- A unique id for the object.