StringIndexerModel¶

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

Model fitted by StringIndexer.

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.

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.

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’.

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.

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.

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.

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

Transforms the input dataset with optional parameters.

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.

It will be removed in future versions. Use labelsArray method instead.

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

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