Pipeline#

class pyspark.ml.connect.Pipeline(*, stages=None)[source]#

A simple pipeline, which acts as an estimator. A Pipeline consists of a sequence of stages, each of which is either an Estimator or a Transformer. When Pipeline.fit() is called, the stages are executed in order. If a stage is an Estimator, its Estimator.fit() method will be called on the input dataset to fit a model. Then the model, which is a transformer, will be used to transform the dataset as the input to the next stage. If a stage is a Transformer, its Transformer.transform() method will be called to produce the dataset for the next stage. The fitted model from a Pipeline is a PipelineModel, which consists of fitted models and transformers, corresponding to the pipeline stages. If stages is an empty list, the pipeline acts as an identity transformer.

New in version 3.5.0.

Examples

>>> from pyspark.ml.connect import Pipeline
>>> from pyspark.ml.connect.classification import LogisticRegression
>>> from pyspark.ml.connect.feature import StandardScaler
>>> scaler = StandardScaler(inputCol='features', outputCol='scaled_features')
>>> lor = LogisticRegression(maxIter=20, learningRate=0.01)
>>> pipeline=Pipeline(stages=[scaler, lor])
>>> dataset = spark.createDataFrame([
...     ([1.0, 2.0], 1),
...     ([2.0, -1.0], 1),
...     ([-3.0, -2.0], 0),
...     ([-1.0, -2.0], 0),
... ], schema=['features', 'label'])
>>> pipeline_model = pipeline.fit(dataset)
>>> transformed_dataset = pipeline_model.transform(dataset)
>>> transformed_dataset.show()
+------------+-----+--------------------+----------+--------------------+
|    features|label|     scaled_features|prediction|         probability|
+------------+-----+--------------------+----------+--------------------+
|  [1.0, 2.0]|    1|[0.56373452100212...|         1|[0.02423273026943...|
| [2.0, -1.0]|    1|[1.01472213780381...|         1|[0.09334788471460...|
|[-3.0, -2.0]|    0|[-1.2402159462046...|         0|[0.99808156490325...|
|[-1.0, -2.0]|    0|[-0.3382407126012...|         0|[0.96210002899169...|
+------------+-----+--------------------+----------+--------------------+
>>> pipeline_model.saveToLocal("/tmp/pipeline")
>>> loaded_pipeline_model = PipelineModel.loadFromLocal("/tmp/pipeline")

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.
`fit`(dataset[, params])	Fits a model to the input dataset with optional parameters.
`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.
`getStages`()	Get pipeline stages.
`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.
`setParams`(self, \*[, stages])	Sets params for Pipeline.
`setStages`(value)	Set pipeline stages.

Attributes

`params`	Returns all params ordered by name.
`stages`

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

extradict, optional: extra parameters

Returns

Pipeline: 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

fit(dataset, params=None)#

Fits a model to the input dataset with optional parameters.

New in version 3.5.0.

Parameters

datasetpyspark.sql.DataFrame or py:class:pandas.DataFrame: input dataset, it can be either pandas dataframe or spark dataframe.
paramsa dict of param values, optional: an optional param map that overrides embedded params.

Returns

Transformer: fitted model

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.

getStages()[source]#: Get pipeline stages.

New in version 3.5.0.

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.

setParams(self, \*, stages=None)[source]#: Sets params for Pipeline.

New in version 3.5.0.

setStages(value)[source]#

Set pipeline stages.

New in version 3.5.0.

Parameters

valuelist: of pyspark.ml.connect.Transformer or pyspark.ml.connect.Estimator

Returns

Pipeline: the pipeline instance

Attributes Documentation

params#: Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

stages = Param(parent='undefined', name='stages', doc='a list of pipeline stages')#

uid#: A unique id for the object.