PipelineModel

class pyspark.ml.connect.PipelineModel(stages=None)

Represents a compiled pipeline with transformers and fitted models.

New in version 3.5.0.

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.
explainParams()	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.
get_uid_map(instance)
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)	Load Estimator / Transformer / Model / Evaluator from provided cloud storage path.
loadFromLocal(path)	Load Estimator / Transformer / Model / Evaluator from provided local path.
save(path, *[, overwrite])	Save Estimator / Transformer / Model / Evaluator to provided cloud storage path.
saveToLocal(path, *[, overwrite])	Save Estimator / Transformer / Model / Evaluator to provided local path.
set(param, value)	Sets a parameter in the embedded param map.
transform(dataset[, params])	Transforms the input dataset.

Attributes

params

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)

Creates a copy of this instance.

New in version 3.5.0.

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.

static get_uid_map(instance)

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)

Load Estimator / Transformer / Model / Evaluator from provided cloud storage path.

New in version 3.5.0.

classmethod loadFromLocal(path)

Load Estimator / Transformer / Model / Evaluator from provided local path.

New in version 3.5.0.

save(path, *, overwrite=False)

Save Estimator / Transformer / Model / Evaluator to provided cloud storage path.

New in version 3.5.0.

saveToLocal(path, *, overwrite=False)

Save Estimator / Transformer / Model / Evaluator to provided local path.

New in version 3.5.0.

set(param, value)

Sets a parameter in the embedded param map.

transform(dataset, params=None)

Transforms the input dataset. The dataset can be either pandas dataframe or spark dataframe， if it is a spark DataFrame, the result of transformation is a new spark DataFrame that contains all existing columns and output columns with names, If it is a pandas DataFrame, the result of transformation is a shallow copy of the input pandas dataframe with output columns with names.

Note: Transformers does not allow output column having the same name with existing columns.

Parameters

datasetpyspark.sql.DataFrame or py:class:pandas.DataFrame

input dataset.

paramsdict, optional

an optional param map that overrides embedded params.

Returns

pyspark.sql.DataFrame or py:class:pandas.DataFrame

transformed dataset, the type of output dataframe is consistent with input dataframe.

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.