VectorIndexerModel¶

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

Model fitted by VectorIndexer.

Transform categorical features to use 0-based indices instead of their original values.

Categorical features are mapped to indices.
Continuous features (columns) are left unchanged.

This also appends metadata to the output column, marking features as Numeric (continuous), Nominal (categorical), or Binary (either continuous or categorical). Non-ML metadata is not carried over from the input to the output column.

This maintains vector sparsity.

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.
`getHandleInvalid`()	Gets the value of handleInvalid or its default value.
`getInputCol`()	Gets the value of inputCol or its default value.
`getMaxCategories`()	Gets the value of maxCategories 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`.
`setOutputCol`(value)	Sets the value of `outputCol`.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`categoryMaps`	Feature value index.
`handleInvalid`
`inputCol`
`maxCategories`
`numFeatures`	Number of features, i.e., length of Vectors which this transforms.
`outputCol`
`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

getHandleInvalid() → str¶: Gets the value of handleInvalid or its default value.

getInputCol() → str¶: Gets the value of inputCol or its default value.

getMaxCategories() → int¶: Gets the value of maxCategories 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.

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.

setInputCol(value: str) → pyspark.ml.feature.VectorIndexerModel ¶: Sets the value of inputCol.

setOutputCol(value: str) → pyspark.ml.feature.VectorIndexerModel ¶: Sets the value of outputCol.

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

categoryMaps¶: Feature value index. Keys are categorical feature indices (column indices). Values are maps from original features values to 0-based category indices. If a feature is not in this map, it is treated as continuous.

handleInvalid: pyspark.ml.param.Param[str] = Param(parent='undefined', name='handleInvalid', doc="How to handle invalid data (unseen labels or NULL values). 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 of the number of categories of the feature).")¶

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

maxCategories: pyspark.ml.param.Param[int] = Param(parent='undefined', name='maxCategories', doc='Threshold for the number of values a categorical feature can take (>= 2). If a feature is found to have > maxCategories values, then it is declared continuous.')¶

numFeatures¶: Number of features, i.e., length of Vectors which this transforms.

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.

VectorIndexer

VectorSizeHint