PCAModel#

class spark_rapids_ml.feature.PCAModel(mean_: List[float], components_: List[List[float]], explained_variance_ratio_: List[float], singular_values_: List[float], n_cols: int, dtype: str)#

Applies dimensionality reduction on an input DataFrame.

Note: Input vectors must be zero-centered to ensure PCA work properly. Spark PCA does not automatically remove the mean of the input data, so use the :py:class::~pyspark.ml.feature.StandardScaler to center the input data before invoking transform.

The input vectors can be stored in three different formats: a column of vector, a column of array, or multiple scalar columns.

Examples

>>> from spark_rapids_ml.feature import PCA
>>> data = [([-1.0, -1.0],),
...         ([0.0, 0.0],),
...         ([1.0, 1.0],),]
>>> df = spark.createDataFrame(data, ["features"])
>>> gpu_pca = PCA(k=1).setInputCol("features").setOutputCol("pca_features")
>>> gpu_model = gpu_pca.fit(df)
>>> reduced_df = gpu_model.transform(df)
>>> reduced_df.show()
+---------------------+
|         pca_features|
+---------------------+
| [-1.414213562373095]|
|                [0.0]|
|  [1.414213562373095]|
+---------------------+

Methods

`clear`(param)	Reset a Spark ML Param to its default value, setting matching cuML parameter, if exists.
`copy`([extra])
`cpu`()	Return the PySpark ML PCAModel
`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.
`getInputCol`()	Gets the value of inputCol or its default value.
`getInputCols`()	Gets the value of inputCols 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`()
`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` or `inputCols`.
`setInputCols`(value)	Sets the value of `inputCols`.
`setOutputCol`(value)	Sets the value of `outputCol`
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()

Attributes

`cuml_params`	Returns the dictionary of parameters intended for the underlying cuML class.
`explainedVariance`	Returns a vector of proportions of variance explained by each principal component.
`inputCol`
`inputCols`
`k`
`mean`	Returns the mean of the input vectors.
`num_workers`	Number of cuML workers, where each cuML worker corresponds to one Spark task running on one GPU.
`outputCol`
`params`	Returns all params ordered by name.
`pc`	Returns a principal components Matrix.

Methods Documentation

clear(param: Param) → None#: Reset a Spark ML Param to its default value, setting matching cuML parameter, if exists.

copy(extra: Optional[ParamMap] = None) → P#

cpu() → PCAModel#: Return the PySpark ML PCAModel

explainParam(param: Union[str, 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

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

getInputCols() → List[str]#: Gets the value of inputCols 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, 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) → Param#: Gets a param by its name.

hasDefault(param: Union[str, 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, Param[Any]]) → bool#: Checks whether a param is explicitly set by user or has a default value.

isSet(param: Union[str, 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() → MLReader#

save(path: str) → None#: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: Param, value: Any) → None#: Sets a parameter in the embedded param map.

setInputCol(value: Union[str, List[str]]) → P#: Sets the value of inputCol or inputCols.

setInputCols(value: List[str]) → P#: Sets the value of inputCols. Used when input vectors are stored as multiple feature columns.

setOutputCol(value: str) → P#: Sets the value of outputCol

transform(dataset: DataFrame, params: Optional[ParamMap] = None) → 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() → MLWriter#

Attributes Documentation

cuml_params#: Returns the dictionary of parameters intended for the underlying cuML class.

explainedVariance#: Returns a vector of proportions of variance explained by each principal component.

inputCol: Param[str] = Param(parent='undefined', name='inputCol', doc='input column name.')#

inputCols: Param[List[str]] = Param(parent='undefined', name='inputCols', doc='input column names.')#

k: Param[int] = Param(parent='undefined', name='k', doc='the number of principal components')#

mean#: Returns the mean of the input vectors.

num_workers#: Number of cuML workers, where each cuML worker corresponds to one Spark task running on one GPU.

outputCol: Param[str] = 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.

pc#: Returns a principal components Matrix. Each column is one principal component.