RFormula¶

class pyspark.ml.feature.RFormula(*, formula: Optional[str] = None, featuresCol: str = 'features', labelCol: str = 'label', forceIndexLabel: bool = False, stringIndexerOrderType: str = 'frequencyDesc', handleInvalid: str = 'error')[source]¶

Implements the transforms required for fitting a dataset against an R model formula. Currently we support a limited subset of the R operators, including ‘~’, ‘.’, ‘:’, ‘+’, ‘-’, ‘*’, and ‘^’.

New in version 1.5.0.

Notes

Also see the R formula docs.

Examples

>>> df = spark.createDataFrame([
...     (1.0, 1.0, "a"),
...     (0.0, 2.0, "b"),
...     (0.0, 0.0, "a")
... ], ["y", "x", "s"])
>>> rf = RFormula(formula="y ~ x + s")
>>> model = rf.fit(df)
>>> model.getLabelCol()
'label'
>>> model.transform(df).show()
+---+---+---+---------+-----+
|  y|  x|  s| features|label|
+---+---+---+---------+-----+
|1.0|1.0|  a|[1.0,1.0]|  1.0|
|0.0|2.0|  b|[2.0,0.0]|  0.0|
|0.0|0.0|  a|[0.0,1.0]|  0.0|
+---+---+---+---------+-----+
...
>>> rf.fit(df, {rf.formula: "y ~ . - s"}).transform(df).show()
+---+---+---+--------+-----+
|  y|  x|  s|features|label|
+---+---+---+--------+-----+
|1.0|1.0|  a|   [1.0]|  1.0|
|0.0|2.0|  b|   [2.0]|  0.0|
|0.0|0.0|  a|   [0.0]|  0.0|
+---+---+---+--------+-----+
...
>>> rFormulaPath = temp_path + "/rFormula"
>>> rf.save(rFormulaPath)
>>> loadedRF = RFormula.load(rFormulaPath)
>>> loadedRF.getFormula() == rf.getFormula()
True
>>> loadedRF.getFeaturesCol() == rf.getFeaturesCol()
True
>>> loadedRF.getLabelCol() == rf.getLabelCol()
True
>>> loadedRF.getHandleInvalid() == rf.getHandleInvalid()
True
>>> str(loadedRF)
'RFormula(y ~ x + s) (uid=...)'
>>> modelPath = temp_path + "/rFormulaModel"
>>> model.save(modelPath)
>>> loadedModel = RFormulaModel.load(modelPath)
>>> loadedModel.uid == model.uid
True
>>> loadedModel.transform(df).show()
+---+---+---+---------+-----+
|  y|  x|  s| features|label|
+---+---+---+---------+-----+
|1.0|1.0|  a|[1.0,1.0]|  1.0|
|0.0|2.0|  b|[2.0,0.0]|  0.0|
|0.0|0.0|  a|[0.0,1.0]|  0.0|
+---+---+---+---------+-----+
...
>>> str(loadedModel)
'RFormulaModel(ResolvedRFormula(label=y, terms=[x,s], hasIntercept=true)) (uid=...)'

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.
`getForceIndexLabel`()	Gets the value of `forceIndexLabel`.
`getFormula`()	Gets the value of `formula`.
`getHandleInvalid`()	Gets the value of handleInvalid 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.
`getStringIndexerOrderType`()	Gets the value of `stringIndexerOrderType` or its default value ‘frequencyDesc’.
`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.
`setFeaturesCol`(value)	Sets the value of `featuresCol`.
`setForceIndexLabel`(value)	Sets the value of `forceIndexLabel`.
`setFormula`(value)	Sets the value of `formula`.
`setHandleInvalid`(value)	Sets the value of `handleInvalid`.
`setLabelCol`(value)	Sets the value of `labelCol`.
`setParams`(self, \*[, formula, featuresCol, …])	Sets params for RFormula.
`setStringIndexerOrderType`(value)	Sets the value of `stringIndexerOrderType`.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`featuresCol`
`forceIndexLabel`
`formula`
`handleInvalid`
`labelCol`
`params`	Returns all params ordered by name.
`stringIndexerOrderType`

Methods Documentation

clear(param: pyspark.ml.param.Param) → None¶: Clears a param from the param map if it has been explicitly set.

copy(extra: Optional[ParamMap] = None) → JP¶

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters

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

Returns

JavaParams: Copy of this instance

explainParam(param: Union[str, pyspark.ml.param.Param]) → str¶: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str¶: Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: Optional[ParamMap] = None) → ParamMap¶

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: pyspark.sql.dataframe.DataFrame, params: Union[ParamMap, List[ParamMap], Tuple[ParamMap], None] = None) → Union[M, List[M]]¶

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

:py:class:`Transformer` or a list ofpy:class:Transformer: fitted model(s)

fitMultiple(dataset: pyspark.sql.dataframe.DataFrame, paramMaps: Sequence[ParamMap]) → Iterator[Tuple[int, M]]¶

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

getForceIndexLabel() → bool¶: Gets the value of forceIndexLabel.

New in version 2.1.0.

getFormula() → str¶: Gets the value of formula.

New in version 1.5.0.

getHandleInvalid() → str¶: Gets the value of handleInvalid or its default value.

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

getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]¶: 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: str) → pyspark.ml.param.Param ¶: Gets a param by its name.

getStringIndexerOrderType() → str¶: Gets the value of stringIndexerOrderType or its default value ‘frequencyDesc’.

New in version 2.3.0.

hasDefault(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param has a default value.

hasParam(paramName: str) → bool¶: Tests whether this instance contains a param with a given (string) name.

isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param is explicitly set by user or has a default value.

isSet(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL¶: Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read() → pyspark.ml.util.JavaMLReader[RL]¶: Returns an MLReader instance for this class.

save(path: str) → None¶: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: pyspark.ml.param.Param, value: Any) → None¶: Sets a parameter in the embedded param map.

setFeaturesCol(value: str) → pyspark.ml.feature.RFormula [source]¶: Sets the value of featuresCol.

setForceIndexLabel(value: bool) → pyspark.ml.feature.RFormula [source]¶: Sets the value of forceIndexLabel.

New in version 2.1.0.

setFormula(value: str) → pyspark.ml.feature.RFormula [source]¶: Sets the value of formula.

New in version 1.5.0.

setHandleInvalid(value: str) → pyspark.ml.feature.RFormula [source]¶: Sets the value of handleInvalid.

setLabelCol(value: str) → pyspark.ml.feature.RFormula [source]¶: Sets the value of labelCol.

setParams(self, \*, formula=None, featuresCol="features", labelCol="label", forceIndexLabel=False, stringIndexerOrderType="frequencyDesc", handleInvalid="error")[source]¶: Sets params for RFormula.

New in version 1.5.0.

setStringIndexerOrderType(value: str) → pyspark.ml.feature.RFormula [source]¶: Sets the value of stringIndexerOrderType.

New in version 2.3.0.

write() → pyspark.ml.util.JavaMLWriter¶: Returns an MLWriter instance for this ML instance.

Attributes Documentation

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

forceIndexLabel = Param(parent='undefined', name='forceIndexLabel', doc='Force to index label whether it is numeric or string')¶

formula = Param(parent='undefined', name='formula', doc='R model formula')¶

handleInvalid = Param(parent='undefined', name='handleInvalid', doc="how to handle invalid entries. Options are 'skip' (filter out rows with invalid values), 'error' (throw an error), or 'keep' (put invalid data in a special additional bucket, at index numLabels).")¶

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.

stringIndexerOrderType = Param(parent='undefined', name='stringIndexerOrderType', doc='How to order categories of a string feature column used by StringIndexer. The last category after ordering is dropped when encoding strings. Supported options: frequencyDesc, frequencyAsc, alphabetDesc, alphabetAsc. The default value is frequencyDesc. When the ordering is set to alphabetDesc, RFormula drops the same category as R when encoding strings.')¶

RegexTokenizer

RFormulaModel