PCA¶

class pyspark.ml.feature.PCA(*, k: Optional[int] = None, inputCol: Optional[str] = None, outputCol: Optional[str] = None)[source]¶

PCA trains a model to project vectors to a lower dimensional space of the top k principal components.

New in version 1.5.0.

Examples

>>> from pyspark.ml.linalg import Vectors
>>> data = [(Vectors.sparse(5, [(1, 1.0), (3, 7.0)]),),
...     (Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]),),
...     (Vectors.dense([4.0, 0.0, 0.0, 6.0, 7.0]),)]
>>> df = spark.createDataFrame(data,["features"])
>>> pca = PCA(k=2, inputCol="features")
>>> pca.setOutputCol("pca_features")
PCA...
>>> model = pca.fit(df)
>>> model.getK()
2
>>> model.setOutputCol("output")
PCAModel...
>>> model.transform(df).collect()[0].output
DenseVector([1.648..., -4.013...])
>>> model.explainedVariance
DenseVector([0.794..., 0.205...])
>>> pcaPath = temp_path + "/pca"
>>> pca.save(pcaPath)
>>> loadedPca = PCA.load(pcaPath)
>>> loadedPca.getK() == pca.getK()
True
>>> modelPath = temp_path + "/pca-model"
>>> model.save(modelPath)
>>> loadedModel = PCAModel.load(modelPath)
>>> loadedModel.pc == model.pc
True
>>> loadedModel.explainedVariance == model.explainedVariance
True
>>> loadedModel.transform(df).take(1) == model.transform(df).take(1)
True

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.
`getInputCol`()	Gets the value of inputCol or its default value.
`getK`()	Gets the value of k 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`.
`setK`(value)	Sets the value of `k`.
`setOutputCol`(value)	Sets the value of `outputCol`.
`setParams`(self, \*[, k, inputCol, outputCol])	Set params for this PCA.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`inputCol`
`k`
`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.

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

getK() → int¶: Gets the value of k or its default value.

New in version 1.5.0.

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.

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

setK(value: int) → pyspark.ml.feature.PCA [source]¶: Sets the value of k.

New in version 1.5.0.

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

setParams(self, \*, k=None, inputCol=None, outputCol=None)[source]¶: Set params for this PCA.

New in version 1.5.0.

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

Attributes Documentation

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

k = Param(parent='undefined', name='k', doc='the number of principal components')¶

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.

OneHotEncoderModel

PCAModel