# PolynomialExpansion¶

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

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).

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...
DenseVector([0.5, 0.25, 2.0, 1.0, 4.0])
DenseVector([0.5, 0.25, 2.0, 1.0, 4.0])
>>> polyExpansionPath = temp_path + "/poly-expansion"
>>> px.save(polyExpansionPath)
True
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. 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. Gets the value of degree or its default value. 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. 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). 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. 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

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

extra param values

Returns
dict

merged param map

getDegree() → int

Gets the value of degree or its default value.

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

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

Sets the value of degree.

setInputCol(value: str)pyspark.ml.feature.PolynomialExpansion

Sets the value of inputCol.

setOutputCol(value: str)pyspark.ml.feature.PolynomialExpansion

Sets the value of outputCol.

setParams(self, \*, degree=2, inputCol=None, outputCol=None)

Sets params for this PolynomialExpansion.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame

Transforms the input dataset with optional parameters.

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.