PolynomialExpansion¶
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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... >>> 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.
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).
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
Returns all params ordered by name.
Methods Documentation
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clear
(param: pyspark.ml.param.Param) → None¶ Clears a param from the param map if it has been explicitly set.
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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
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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.
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explainParams
() → str¶ Returns the documentation of all params with their optionally default values and user-supplied values.
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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
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getDegree
() → int¶ Gets the value of degree or its default value.
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getInputCol
() → str¶ Gets the value of inputCol or its default value.
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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.
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getOutputCol
() → str¶ Gets the value of outputCol or its default value.
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getParam
(paramName: str) → pyspark.ml.param.Param¶ Gets a param by its name.
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hasDefault
(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶ Checks whether a param has a default value.
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hasParam
(paramName: str) → bool¶ Tests whether this instance contains a param with a given (string) name.
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isDefined
(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶ Checks whether a param is explicitly set by user or has a default value.
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isSet
(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶ Checks whether a param is explicitly set by user.
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classmethod
load
(path: str) → RL¶ Reads an ML instance from the input path, a shortcut of read().load(path).
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classmethod
read
() → pyspark.ml.util.JavaMLReader[RL]¶ Returns an MLReader instance for this class.
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save
(path: str) → None¶ Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
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set
(param: pyspark.ml.param.Param, value: Any) → None¶ Sets a parameter in the embedded param map.
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setDegree
(value: int) → pyspark.ml.feature.PolynomialExpansion¶ Sets the value of
degree
.
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setInputCol
(value: str) → pyspark.ml.feature.PolynomialExpansion¶ Sets the value of
inputCol
.
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setOutputCol
(value: str) → pyspark.ml.feature.PolynomialExpansion¶ Sets the value of
outputCol
.
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setParams
(self, \*, degree=2, inputCol=None, outputCol=None)¶ Sets params for this PolynomialExpansion.
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transform
(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame¶ Transforms the input dataset with optional parameters.
- Parameters
- dataset
pyspark.sql.DataFrame
input dataset
- paramsdict, optional
an optional param map that overrides embedded params.
- dataset
- Returns
pyspark.sql.DataFrame
transformed dataset
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write
() → pyspark.ml.util.JavaMLWriter¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
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degree
: pyspark.ml.param.Param[int] = Param(parent='undefined', name='degree', doc='the polynomial degree to expand (>= 1)')¶
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inputCol
= Param(parent='undefined', name='inputCol', doc='input column name.')¶
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outputCol
= Param(parent='undefined', name='outputCol', doc='output column name.')¶
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params
¶ Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
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