VectorIndexer¶

class pyspark.ml.feature.VectorIndexer(*, maxCategories: int = 20, inputCol: Optional[str] = None, outputCol: Optional[str] = None, handleInvalid: str = 'error')[source]¶

Class for indexing categorical feature columns in a dataset of Vector.

This has 2 usage modes:

Automatically identify categorical features (default behavior)

This helps process a dataset of unknown vectors into a dataset with some continuous features and some categorical features. The choice between continuous and categorical is based upon a maxCategories parameter.

Set maxCategories to the maximum number of categorical any categorical feature should have.

E.g.: Feature 0 has unique values {-1.0, 0.0}, and feature 1 values {1.0, 3.0, 5.0}. If maxCategories = 2, then feature 0 will be declared categorical and use indices {0, 1}, and feature 1 will be declared continuous.

Index all features, if all features are categorical

If maxCategories is set to be very large, then this will build an index of unique values for all features.

Warning: This can cause problems if features are continuous since this will collect ALL unique values to the driver.

E.g.: Feature 0 has unique values {-1.0, 0.0}, and feature 1 values {1.0, 3.0, 5.0}. If maxCategories >= 3, then both features will be declared categorical.

This returns a model which can transform categorical features to use 0-based indices.

Index stability:

This is not guaranteed to choose the same category index across multiple runs.
If a categorical feature includes value 0, then this is guaranteed to map value 0 to index 0. This maintains vector sparsity.
More stability may be added in the future.

TODO: Future extensions: The following functionality is planned for the future:

Preserve metadata in transform; if a feature’s metadata is already present, do not recompute.
Specify certain features to not index, either via a parameter or via existing metadata.
Add warning if a categorical feature has only 1 category.

New in version 1.4.0.

Examples

>>> from pyspark.ml.linalg import Vectors
>>> df = spark.createDataFrame([(Vectors.dense([-1.0, 0.0]),),
...     (Vectors.dense([0.0, 1.0]),), (Vectors.dense([0.0, 2.0]),)], ["a"])
>>> indexer = VectorIndexer(maxCategories=2, inputCol="a")
>>> indexer.setOutputCol("indexed")
VectorIndexer...
>>> model = indexer.fit(df)
>>> indexer.getHandleInvalid()
'error'
>>> model.setOutputCol("output")
VectorIndexerModel...
>>> model.transform(df).head().output
DenseVector([1.0, 0.0])
>>> model.numFeatures
2
>>> model.categoryMaps
{0: {0.0: 0, -1.0: 1}}
>>> indexer.setParams(outputCol="test").fit(df).transform(df).collect()[1].test
DenseVector([0.0, 1.0])
>>> params = {indexer.maxCategories: 3, indexer.outputCol: "vector"}
>>> model2 = indexer.fit(df, params)
>>> model2.transform(df).head().vector
DenseVector([1.0, 0.0])
>>> vectorIndexerPath = temp_path + "/vector-indexer"
>>> indexer.save(vectorIndexerPath)
>>> loadedIndexer = VectorIndexer.load(vectorIndexerPath)
>>> loadedIndexer.getMaxCategories() == indexer.getMaxCategories()
True
>>> modelPath = temp_path + "/vector-indexer-model"
>>> model.save(modelPath)
>>> loadedModel = VectorIndexerModel.load(modelPath)
>>> loadedModel.numFeatures == model.numFeatures
True
>>> loadedModel.categoryMaps == model.categoryMaps
True
>>> loadedModel.transform(df).take(1) == model.transform(df).take(1)
True
>>> dfWithInvalid = spark.createDataFrame([(Vectors.dense([3.0, 1.0]),)], ["a"])
>>> indexer.getHandleInvalid()
'error'
>>> model3 = indexer.setHandleInvalid("skip").fit(df)
>>> model3.transform(dfWithInvalid).count()
0
>>> model4 = indexer.setParams(handleInvalid="keep", outputCol="indexed").fit(df)
>>> model4.transform(dfWithInvalid).head().indexed
DenseVector([2.0, 1.0])

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.
`fit`(dataset[, params])	Fits a model to the input dataset with optional parameters.
`fitMultiple`(dataset, paramMaps)	Fits a model to the input dataset for each param map in paramMaps.
`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.
`setHandleInvalid`(value)	Sets the value of `handleInvalid`.
`setInputCol`(value)	Sets the value of `inputCol`.
`setMaxCategories`(value)	Sets the value of `maxCategories`.
`setOutputCol`(value)	Sets the value of `outputCol`.
`setParams`(self, \*[, maxCategories, …])	Sets params for this VectorIndexer.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`handleInvalid`
`inputCol`
`maxCategories`
`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

fit(dataset: pyspark.sql.dataframe.DataFrame, params: Union[ParamMap, List[ParamMap], Tuple[ParamMap], None] = None) → Union[M, List[M]]¶

Fits a model to the input dataset with optional parameters.

New in version 1.3.0.

Parameters

datasetpyspark.sql.DataFrame: input dataset.
paramsdict or list or tuple, optional: an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.

Returns

Transformer or a list of Transformer: fitted model(s)

fitMultiple(dataset: pyspark.sql.dataframe.DataFrame, paramMaps: Sequence[ParamMap]) → Iterator[Tuple[int, M]]¶

Fits a model to the input dataset for each param map in paramMaps.

New in version 2.3.0.

Parameters

datasetpyspark.sql.DataFrame: input dataset.
paramMapscollections.abc.Sequence: A Sequence of param maps.

Returns

_FitMultipleIterator: A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.

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.

New in version 1.4.0.

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.

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

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

setMaxCategories(value: int) → pyspark.ml.feature.VectorIndexer [source]¶: Sets the value of maxCategories.

New in version 1.4.0.

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

setParams(self, \*, maxCategories=20, inputCol=None, outputCol=None, handleInvalid="error")[source]¶: Sets params for this VectorIndexer.

New in version 1.4.0.

write() → pyspark.ml.util.JavaMLWriter¶: Returns an MLWriter instance for this ML instance.

Attributes Documentation

handleInvalid = 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 = 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.')¶

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.

VectorAssembler

VectorIndexerModel