PolynomialExpansion¶

class pyspark.ml.feature.PolynomialExpansion(*, degree: int = 2, inputCol: Optional[str] = None, outputCol: Optional[str] = None)[source]¶

Perform feature expansion in a polynomial space. As said in wikipedia of Polynomial Expansion, “In mathematics, an expansion of a product of sums expresses it as a sum of products by using the fact that multiplication distributes over addition”. Take a 2-variable feature vector as an example: (x, y), if we want to expand it with degree 2, then we get (x, x * x, y, x * y, y * y).

New in version 1.4.0.

Examples

>>> from pyspark.ml.linalg import Vectors
>>> df = spark.createDataFrame([(Vectors.dense([0.5, 2.0]),)], ["dense"])
>>> px = PolynomialExpansion(degree=2)
>>> px.setInputCol("dense")
PolynomialExpansion...
>>> px.setOutputCol("expanded")
PolynomialExpansion...
>>> px.transform(df).head().expanded
DenseVector([0.5, 0.25, 2.0, 1.0, 4.0])
>>> px.setParams(outputCol="test").transform(df).head().test
DenseVector([0.5, 0.25, 2.0, 1.0, 4.0])
>>> polyExpansionPath = temp_path + "/poly-expansion"
>>> px.save(polyExpansionPath)
>>> loadedPx = PolynomialExpansion.load(polyExpansionPath)
>>> loadedPx.getDegree() == px.getDegree()
True
>>> loadedPx.transform(df).take(1) == px.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.
`getDegree`()	Gets the value of degree or its default value.
`getInputCol`()	Gets the value of inputCol 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.
`setDegree`(value)	Sets the value of `degree`.
`setInputCol`(value)	Sets the value of `inputCol`.
`setOutputCol`(value)	Sets the value of `outputCol`.
`setParams`(self, \*[, degree, inputCol, …])	Sets params for this PolynomialExpansion.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`degree`
`inputCol`
`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

getDegree() → int[source]¶: Gets the value of degree or its default value.

New in version 1.4.0.

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

setDegree(value: int) → pyspark.ml.feature.PolynomialExpansion [source]¶: Sets the value of degree.

New in version 1.4.0.

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

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

setParams(self, \*, degree=2, inputCol=None, outputCol=None)[source]¶: Sets params for this PolynomialExpansion.

New in version 1.4.0.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.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() → pyspark.ml.util.JavaMLWriter¶: Returns an MLWriter instance for this ML instance.

Attributes Documentation

degree: pyspark.ml.param.Param[int] = Param(parent='undefined', name='degree', doc='the polynomial degree to expand (>= 1)')¶

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

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.

PCAModel

QuantileDiscretizer