Predictor¶

class pyspark.ml.Predictor[source]¶

Estimator for prediction tasks (regression and classification).

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 the same 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.
`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.
`fitMultiple`(dataset, paramMaps)	Fits a model to the input dataset for each param map in paramMaps.
`getFeaturesCol`()	Gets the value of featuresCol or its default value.
`getLabelCol`()	Gets the value of labelCol 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.
`getPredictionCol`()	Gets the value of predictionCol 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.
`set`(param, value)	Sets a parameter in the embedded param map.
`setFeaturesCol`(value)	Sets the value of `featuresCol`.
`setLabelCol`(value)	Sets the value of `labelCol`.
`setPredictionCol`(value)	Sets the value of `predictionCol`.

Attributes

`featuresCol`
`labelCol`
`params`	Returns all params ordered by name.
`predictionCol`

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 with the same uid and some extra params. The default implementation creates a shallow copy using copy.copy(), and then copies the embedded and extra parameters over and returns the copy. Subclasses should override this method if the default approach is not sufficient.

Parameters:

extradict, optional: Extra parameters to copy to the new instance

Returns:

Params: 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

fit(dataset, params=None)¶

Fits a model to the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

datasetpyspark.sql.DataFrame: input dataset.
paramsdict or list or tuple, optional: an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.

Returns:

Transformer or a list of Transformer: fitted model(s)

fitMultiple(dataset, paramMaps)¶

Fits a model to the input dataset for each param map in paramMaps.

New in version 2.3.0.

Parameters:

datasetpyspark.sql.DataFrame: input dataset.
paramMapscollections.abc.Sequence: A Sequence of param maps.

Returns:

_FitMultipleIterator: A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.

getFeaturesCol()¶: Gets the value of featuresCol or its default value.

getLabelCol()¶: Gets the value of labelCol or its default value.

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.

getPredictionCol()¶: Gets the value of predictionCol 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.

set(param, value)¶: Sets a parameter in the embedded param map.

setFeaturesCol(value)[source]¶: Sets the value of featuresCol.

New in version 3.0.0.

setLabelCol(value)[source]¶: Sets the value of labelCol.

New in version 3.0.0.

setPredictionCol(value)[source]¶: Sets the value of predictionCol.

New in version 3.0.0.

Attributes Documentation

featuresCol = Param(parent='undefined', name='featuresCol', doc='features column name.')¶

labelCol = Param(parent='undefined', name='labelCol', doc='label column name.')¶

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

predictionCol = Param(parent='undefined', name='predictionCol', doc='prediction column name.')¶

Model PredictionModel