PipelineModel#

class pyspark.ml.connect.PipelineModel(stages=None)[source]#

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)[source]#

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.