FMRegressionModel¶

class pyspark.ml.regression.FMRegressionModel(java_model=None)[source]¶

Model fitted by FMRegressor.

New in version 3.0.0.

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.
`getFactorSize`()	Gets the value of factorSize or its default value.
`getFeaturesCol`()	Gets the value of featuresCol or its default value.
`getFitIntercept`()	Gets the value of fitIntercept or its default value.
`getFitLinear`()	Gets the value of fitLinear or its default value.
`getInitStd`()	Gets the value of initStd or its default value.
`getLabelCol`()	Gets the value of labelCol or its default value.
`getMaxIter`()	Gets the value of maxIter or its default value.
`getMiniBatchFraction`()	Gets the value of miniBatchFraction or its default value.
`getOrDefault`(param)	Gets the value of a param in the user-supplied param map or its default value.
`getParam`(paramName)	Gets a param by its name.
`getPredictionCol`()	Gets the value of predictionCol or its default value.
`getRegParam`()	Gets the value of regParam or its default value.
`getSeed`()	Gets the value of seed or its default value.
`getSolver`()	Gets the value of solver or its default value.
`getStepSize`()	Gets the value of stepSize or its default value.
`getTol`()	Gets the value of tol or its default value.
`getWeightCol`()	Gets the value of weightCol or its default value.
`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).
`predict`(value)	Predict label for the given features.
`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.
`setFeaturesCol`(value)	Sets the value of `featuresCol`.
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`factorSize`
`factors`	Model factor term.
`featuresCol`
`fitIntercept`
`fitLinear`
`initStd`
`intercept`	Model intercept.
`labelCol`
`linear`	Model linear term.
`maxIter`
`miniBatchFraction`
`numFeatures`	Returns the number of features the model was trained on.
`params`	Returns all params ordered by name.
`predictionCol`
`regParam`
`seed`
`solver`
`stepSize`
`tol`
`weightCol`

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

JavaParams: Copy 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

getFactorSize()¶: Gets the value of factorSize or its default value.

New in version 3.0.0.

getFeaturesCol()¶: Gets the value of featuresCol or its default value.

getFitIntercept()¶: Gets the value of fitIntercept or its default value.

getFitLinear()¶: Gets the value of fitLinear or its default value.

New in version 3.0.0.

getInitStd()¶: Gets the value of initStd or its default value.

New in version 3.0.0.

getLabelCol()¶: Gets the value of labelCol or its default value.

getMaxIter()¶: Gets the value of maxIter or its default value.

getMiniBatchFraction()¶: Gets the value of miniBatchFraction or its default value.

New in version 3.0.0.

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.

getParam(paramName)¶: Gets a param by its name.

getPredictionCol()¶: Gets the value of predictionCol or its default value.

getRegParam()¶: Gets the value of regParam or its default value.

getSeed()¶: Gets the value of seed or its default value.

getSolver()¶: Gets the value of solver or its default value.

getStepSize()¶: Gets the value of stepSize or its default value.

getTol()¶: Gets the value of tol or its default value.

getWeightCol()¶: Gets the value of weightCol or its default value.

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).

predict(value)¶: Predict label for the given features.

New in version 3.0.0.

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.

setFeaturesCol(value)¶: Sets the value of featuresCol.

New in version 3.0.0.

setPredictionCol(value)¶: Sets the value of predictionCol.

New in version 3.0.0.

transform(dataset, params=None)¶

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()¶: Returns an MLWriter instance for this ML instance.

Attributes Documentation

factorSize = Param(parent='undefined', name='factorSize', doc='Dimensionality of the factor vectors, which are used to get pairwise interactions between variables')¶

factors¶: Model factor term.

New in version 3.0.0.

featuresCol = Param(parent='undefined', name='featuresCol', doc='features column name.')¶

fitIntercept = Param(parent='undefined', name='fitIntercept', doc='whether to fit an intercept term.')¶

fitLinear = Param(parent='undefined', name='fitLinear', doc='whether to fit linear term (aka 1-way term)')¶

initStd = Param(parent='undefined', name='initStd', doc='standard deviation of initial coefficients')¶

intercept¶: Model intercept.

New in version 3.0.0.

labelCol = Param(parent='undefined', name='labelCol', doc='label column name.')¶

linear¶: Model linear term.

New in version 3.0.0.

maxIter = Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')¶

miniBatchFraction = Param(parent='undefined', name='miniBatchFraction', doc='fraction of the input data set that should be used for one iteration of gradient descent')¶

numFeatures¶: Returns the number of features the model was trained on. If unknown, returns -1

New in version 2.1.0.

params¶: Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

predictionCol = Param(parent='undefined', name='predictionCol', doc='prediction column name.')¶

regParam = Param(parent='undefined', name='regParam', doc='regularization parameter (>= 0).')¶

seed = Param(parent='undefined', name='seed', doc='random seed.')¶

solver = Param(parent='undefined', name='solver', doc='The solver algorithm for optimization. Supported options: gd, adamW. (Default adamW)')¶

stepSize = Param(parent='undefined', name='stepSize', doc='Step size to be used for each iteration of optimization (>= 0).')¶

tol = Param(parent='undefined', name='tol', doc='the convergence tolerance for iterative algorithms (>= 0).')¶

weightCol = Param(parent='undefined', name='weightCol', doc='weight column name. If this is not set or empty, we treat all instance weights as 1.0.')¶

FMRegressor

ChiSquareTest