PipelineModel#
- class pyspark.ml.PipelineModel(stages)[source]#
- Represents a compiled pipeline with transformers and fitted models. - New in version 1.3.0. - Methods - clear(param)- Clears a param from the param map if it has been explicitly set. - copy([extra])- Creates a copy of this instance. - 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. - 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. - 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. - 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. - New in version 1.4.0. - Parameters
- extra – extra parameters 
- Returns
- new 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 
 
 
 - 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. 
 - 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 - params#
- Returns all params ordered by name. The default implementation uses - dir()to get all attributes of type- Param.
 - uid#
- A unique id for the object.