StringIndexerModel

class pyspark.ml.feature.StringIndexerModel(java_model: Optional[JavaObject] = None)

Model fitted by StringIndexer.

New in version 1.4.0.

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.
from_arrays_of_labels(arrayOfLabels, inputCols)	Construct the model directly from an array of array of label strings, requires an active SparkContext.
from_labels(labels, inputCol[, outputCol, …])	Construct the model directly from an array of label strings, requires an active SparkContext.
getHandleInvalid()	Gets the value of handleInvalid or its default value.
getInputCol()	Gets the value of inputCol or its default value.
getInputCols()	Gets the value of inputCols 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.
getOutputCols()	Gets the value of outputCols or its default value.
getParam(paramName)	Gets a param by its name.
getStringOrderType()	Gets the value of stringOrderType 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.
setHandleInvalid(value)	Sets the value of handleInvalid.
setInputCol(value)	Sets the value of inputCol.
setInputCols(value)	Sets the value of inputCols.
setOutputCol(value)	Sets the value of outputCol.
setOutputCols(value)	Sets the value of outputCols.
transform(dataset[, params])	Transforms the input dataset with optional parameters.
write()	Returns an MLWriter instance for this ML instance.

Attributes

handleInvalid
inputCol
inputCols
labels	Ordered list of labels, corresponding to indices to be assigned.
labelsArray	Array of ordered list of labels, corresponding to indices to be assigned for each input column.
outputCol
outputCols
params	Returns all params ordered by name.
stringOrderType

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

classmethod from_arrays_of_labels(arrayOfLabels: List[List[str]], inputCols: List[str], outputCols: Optional[List[str]] = None, handleInvalid: Optional[str] = None) → pyspark.ml.feature.StringIndexerModel

Construct the model directly from an array of array of label strings, requires an active SparkContext.

New in version 3.0.0.

classmethod from_labels(labels: List[str], inputCol: str, outputCol: Optional[str] = None, handleInvalid: Optional[str] = None) → pyspark.ml.feature.StringIndexerModel

Construct the model directly from an array of label strings, requires an active SparkContext.

New in version 2.4.0.

getHandleInvalid() → str

Gets the value of handleInvalid or its default value.

getInputCol() → str

Gets the value of inputCol or its default value.

getInputCols() → List[str]

Gets the value of inputCols 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.

getOutputCol() → str

Gets the value of outputCol or its default value.

getOutputCols() → List[str]

Gets the value of outputCols or its default value.

getParam(paramName: str) → pyspark.ml.param.Param

Gets a param by its name.

getStringOrderType() → str

Gets the value of stringOrderType 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.

setHandleInvalid(value: str) → pyspark.ml.feature.StringIndexerModel

Sets the value of handleInvalid.

New in version 2.4.0.

setInputCol(value: str) → pyspark.ml.feature.StringIndexerModel

Sets the value of inputCol.

setInputCols(value: List[str]) → pyspark.ml.feature.StringIndexerModel

Sets the value of inputCols.

New in version 3.0.0.

setOutputCol(value: str) → pyspark.ml.feature.StringIndexerModel

Sets the value of outputCol.

setOutputCols(value: List[str]) → pyspark.ml.feature.StringIndexerModel

Sets the value of outputCols.

New in version 3.0.0.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame

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() → pyspark.ml.util.JavaMLWriter

Returns an MLWriter instance for this ML instance.

Attributes Documentation

handleInvalid: pyspark.ml.param.Param[str] = Param(parent='undefined', name='handleInvalid', doc="how to handle invalid data (unseen or NULL values) in features and label column of string type. Options are 'skip' (filter out rows with invalid data), error (throw an error), or 'keep' (put invalid data in a special additional bucket, at index numLabels).")

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

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

labels

Ordered list of labels, corresponding to indices to be assigned.

Deprecated since version 3.1.0: It will be removed in future versions. Use labelsArray method instead.

New in version 1.5.0.

labelsArray

Array of ordered list of labels, corresponding to indices to be assigned for each input column.

New in version 3.0.2.

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

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

params

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

stringOrderType: pyspark.ml.param.Param[str] = Param(parent='undefined', name='stringOrderType', doc='How to order labels of string column. The first label after ordering is assigned an index of 0. Supported options: frequencyDesc, frequencyAsc, alphabetDesc, alphabetAsc. Default is frequencyDesc. In case of equal frequency when under frequencyDesc/Asc, the strings are further sorted alphabetically')

StringIndexer Tokenizer