VectorAssembler#

class pyspark.ml.feature.VectorAssembler(*, inputCols=None, outputCol=None, handleInvalid='error')[source]#

A feature transformer that merges multiple columns into a vector column.

New in version 1.4.0.

Examples

>>> df = spark.createDataFrame([(1, 0, 3)], ["a", "b", "c"])
>>> vecAssembler = VectorAssembler(outputCol="features")
>>> vecAssembler.setInputCols(["a", "b", "c"])
VectorAssembler...
>>> vecAssembler.transform(df).head().features
DenseVector([1.0, 0.0, 3.0])
>>> vecAssembler.setParams(outputCol="freqs").transform(df).head().freqs
DenseVector([1.0, 0.0, 3.0])
>>> params = {vecAssembler.inputCols: ["b", "a"], vecAssembler.outputCol: "vector"}
>>> vecAssembler.transform(df, params).head().vector
DenseVector([0.0, 1.0])
>>> vectorAssemblerPath = temp_path + "/vector-assembler"
>>> vecAssembler.save(vectorAssemblerPath)
>>> loadedAssembler = VectorAssembler.load(vectorAssemblerPath)
>>> loadedAssembler.transform(df).head().freqs == vecAssembler.transform(df).head().freqs
True
>>> dfWithNullsAndNaNs = spark.createDataFrame(
...    [(1.0, 2.0, None), (3.0, float("nan"), 4.0), (5.0, 6.0, 7.0)], ["a", "b", "c"])
>>> vecAssembler2 = VectorAssembler(inputCols=["a", "b", "c"], outputCol="features",
...    handleInvalid="keep")
>>> vecAssembler2.transform(dfWithNullsAndNaNs).show()
+---+---+----+-------------+
|  a|  b|   c|     features|
+---+---+----+-------------+
|1.0|2.0|NULL|[1.0,2.0,NaN]|
|3.0|NaN| 4.0|[3.0,NaN,4.0]|
|5.0|6.0| 7.0|[5.0,6.0,7.0]|
+---+---+----+-------------+
...
>>> vecAssembler2.setParams(handleInvalid="skip").transform(dfWithNullsAndNaNs).show()
+---+---+---+-------------+
|  a|  b|  c|     features|
+---+---+---+-------------+
|5.0|6.0|7.0|[5.0,6.0,7.0]|
+---+---+---+-------------+
...

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.
`getHandleInvalid`()	Gets the value of handleInvalid 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.
`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`.
`setInputCols`(value)	Sets the value of `inputCols`.
`setOutputCol`(value)	Sets the value of `outputCol`.
`setParams`(self, \*[, inputCols, outputCol, ...])	Sets params for this VectorAssembler.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`handleInvalid`
`inputCols`
`outputCol`
`params`	Returns all params ordered by name.

Methods Documentation

clear(param)#: Clears a param from the param map if it has been explicitly set.

copy(extra=None)#

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)#: 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=None)#

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()#: Gets the value of handleInvalid 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. Raises an error if neither is set.

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.

classmethod load(path)#: Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod 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)[source]#: Sets the value of handleInvalid.

setInputCols(value)[source]#: Sets the value of inputCols.

setOutputCol(value)[source]#: Sets the value of outputCol.

setParams(self, \*, inputCols=None, outputCol=None, handleInvalid="error")[source]#: Sets params for this VectorAssembler.

New in version 1.4.0.

transform(dataset, params=None)#

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()#: Returns an MLWriter instance for this ML instance.

Attributes Documentation

handleInvalid = Param(parent='undefined', name='handleInvalid', doc="How to handle invalid data (NULL and NaN values). Options are 'skip' (filter out rows with invalid data), 'error' (throw an error), or 'keep' (return relevant number of NaN in the output). Column lengths are taken from the size of ML Attribute Group, which can be set using `VectorSizeHint` in a pipeline before `VectorAssembler`. Column lengths can also be inferred from first rows of the data since it is safe to do so but only in case of 'error' or 'skip').")#

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

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.

uid#: A unique id for the object.