Interaction#
- class pyspark.ml.feature.Interaction(*, inputCols=None, outputCol=None)[source]#
Implements the feature interaction transform. This transformer takes in Double and Vector type columns and outputs a flattened vector of their feature interactions. To handle interaction, we first one-hot encode any nominal features. Then, a vector of the feature cross-products is produced.
For example, given the input feature values Double(2) and Vector(3, 4), the output would be Vector(6, 8) if all input features were numeric. If the first feature was instead nominal with four categories, the output would then be Vector(0, 0, 0, 0, 3, 4, 0, 0).
New in version 3.0.0.
Examples
>>> df = spark.createDataFrame([(0.0, 1.0), (2.0, 3.0)], ["a", "b"]) >>> interaction = Interaction() >>> interaction.setInputCols(["a", "b"]) Interaction... >>> interaction.setOutputCol("ab") Interaction... >>> interaction.transform(df).show() +---+---+-----+ | a| b| ab| +---+---+-----+ |0.0|1.0|[0.0]| |2.0|3.0|[6.0]| +---+---+-----+ ... >>> interactionPath = temp_path + "/interaction" >>> interaction.save(interactionPath) >>> loadedInteraction = Interaction.load(interactionPath) >>> loadedInteraction.transform(df).head().ab == interaction.transform(df).head().ab 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 inputCols 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.
setInputCols(value)Sets the value of
inputCols.setOutputCol(value)Sets the value of
outputCol.setParams(self, \*[, inputCols, outputCol])Sets params for this Interaction.
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
- clear(param)#
Clears a param from the param map if it has been explicitly set.
- copy(extra=None)#
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
JavaParamsCopy of this instance
- 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=None)#
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
- getInputCols()#
Gets the value of inputCols or its default value.
- getOrDefault(param)#
Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
- 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.
- classmethod load(path)#
Reads an ML instance from the input path, a shortcut of read().load(path).
- classmethod 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.
- setParams(self, \*, inputCols=None, outputCol=None)[source]#
Sets params for this Interaction.
New in version 3.0.0.
- transform(dataset, params=None)#
Transforms the input dataset with optional parameters.
New in version 1.3.0.
- Parameters
- dataset
pyspark.sql.DataFrame input dataset
- paramsdict, optional
an optional param map that overrides embedded params.
- dataset
- Returns
pyspark.sql.DataFrametransformed dataset
- write()#
Returns an MLWriter instance for this ML instance.
Attributes Documentation
- inputCols = Param(parent='undefined', name='inputCols', doc='input column names.')#
- 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 typeParam.
- uid#
A unique id for the object.