RFormula

class pyspark.ml.feature.RFormula(*, formula=None, featuresCol='features', labelCol='label', forceIndexLabel=False, stringIndexerOrderType='frequencyDesc', handleInvalid='error')

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)

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. 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)

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.

getForceIndexLabel()

Gets the value of forceIndexLabel.

New in version 2.1.0.

getFormula()

Gets the value of formula.

New in version 1.5.0.

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. Raises an error if neither is set.

getParam(paramName)

Gets a param by its name.

getStringIndexerOrderType()

Gets the value of stringIndexerOrderType or its default value ‘frequencyDesc’.

New in version 2.3.0.

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)

Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod 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.

New in version 2.1.0.

setFormula(value)

Sets the value of formula.

New in version 1.5.0.

setHandleInvalid(value)

Sets the value of handleInvalid.

setLabelCol(value)

Sets the value of labelCol.

setParams(self, \*, formula=None, featuresCol="features", labelCol="label", forceIndexLabel=False, stringIndexerOrderType="frequencyDesc", handleInvalid="error")

Sets params for RFormula.

New in version 1.5.0.

setStringIndexerOrderType(value)

Sets the value of stringIndexerOrderType.

New in version 2.3.0.

write()

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