OneHotEncoderModel¶

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

Model fitted by OneHotEncoder.

New in version 2.3.0.

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.
`getDropLast`()	Gets the value of dropLast or its default value.
`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.
`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.
`setDropLast`(value)	Sets the value of `dropLast`.
`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

`categorySizes`	Original number of categories for each feature being encoded.
`dropLast`
`handleInvalid`
`inputCol`
`inputCols`
`outputCol`
`outputCols`
`params`	Returns all params ordered by name.

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

getDropLast() → bool¶: Gets the value of dropLast or its default value.

New in version 2.3.0.

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.

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.

setDropLast(value: bool) → pyspark.ml.feature.OneHotEncoderModel [source]¶: Sets the value of dropLast.

New in version 3.0.0.

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

New in version 3.0.0.

setInputCol(value: str) → pyspark.ml.feature.OneHotEncoderModel [source]¶: Sets the value of inputCol.

New in version 3.0.0.

setInputCols(value: List[str]) → pyspark.ml.feature.OneHotEncoderModel [source]¶: Sets the value of inputCols.

New in version 3.0.0.

setOutputCol(value: str) → pyspark.ml.feature.OneHotEncoderModel [source]¶: Sets the value of outputCol.

New in version 3.0.0.

setOutputCols(value: List[str]) → pyspark.ml.feature.OneHotEncoderModel [source]¶: 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

categorySizes¶: Original number of categories for each feature being encoded. The array contains one value for each input column, in order.

New in version 2.3.0.

dropLast: pyspark.ml.param.Param[bool] = Param(parent='undefined', name='dropLast', doc='whether to drop the last category')¶

handleInvalid: pyspark.ml.param.Param[str] = Param(parent='undefined', name='handleInvalid', doc="How to handle invalid data during transform(). Options are 'keep' (invalid data presented as an extra categorical feature) or error (throw an error). Note that this Param is only used during transform; during fitting, invalid data will result in an error.")¶

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

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

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.

OneHotEncoder

PCA