IndexToString

class pyspark.ml.feature.IndexToString(*, inputCol=None, outputCol=None, labels=None)

A pyspark.ml.base.Transformer that maps a column of indices back to a new column of corresponding string values. The index-string mapping is either from the ML attributes of the input column, or from user-supplied labels (which take precedence over ML attributes).

New in version 1.6.0.

See also

StringIndexer

for converting categorical values into category indices

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.
getInputCol()	Gets the value of inputCol or its default value.
getLabels()	Gets the value of labels or its default value.
getOrDefault(param)	Gets the value of a param in the user-supplied param map or its default value.
getOutputCol()	Gets the value of outputCol or its default value.
getParam(paramName)	Gets a param by its name.
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.
setInputCol(value)	Sets the value of inputCol.
setLabels(value)	Sets the value of labels.
setOutputCol(value)	Sets the value of outputCol.
setParams(self, \*[, inputCol, outputCol, …])	Sets params for this IndexToString.
transform(dataset[, params])	Transforms the input dataset with optional parameters.
write()	Returns an MLWriter instance for this ML instance.

Attributes

inputCol
labels
outputCol
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)

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

getInputCol()

Gets the value of inputCol or its default value.

getLabels()

Gets the value of labels or its default value.

New in version 1.6.0.

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.

getOutputCol()

Gets the value of outputCol or its default value.

getParam(paramName)

Gets a param by its name.

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.

setInputCol(value)

Sets the value of inputCol.

setLabels(value)

Sets the value of labels.

New in version 1.6.0.

setOutputCol(value)

Sets the value of outputCol.

setParams(self, \*, inputCol=None, outputCol=None, labels=None)

Sets params for this IndexToString.

New in version 1.6.0.

transform(dataset, params=None)

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

datasetpyspark.sql.DataFrame

input dataset

paramsdict, optional

an optional param map that overrides embedded params.

Returns:

pyspark.sql.DataFrame

transformed dataset

write()

Returns an MLWriter instance for this ML instance.

Attributes Documentation

inputCol = Param(parent='undefined', name='inputCol', doc='input column name.')

labels = Param(parent='undefined', name='labels', doc='Optional array of labels specifying index-string mapping. If not provided or if empty, then metadata from inputCol is used instead.')

outputCol = Param(parent='undefined', name='outputCol', doc='output column name.')

params

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

ImputerModel Interaction