FMRegressionModel

class pyspark.ml.regression.FMRegressionModel(java_model: Optional[JavaObject] = None)

Model fitted by FMRegressor.

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: 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

getFactorSize() → int

Gets the value of factorSize or its default value.

getFeaturesCol() → str

Gets the value of featuresCol or its default value.

getFitIntercept() → bool

Gets the value of fitIntercept or its default value.

getFitLinear() → bool

Gets the value of fitLinear or its default value.

getInitStd() → float

Gets the value of initStd or its default value.

getLabelCol() → str

Gets the value of labelCol or its default value.

getMaxIter() → int

Gets the value of maxIter or its default value.

getMiniBatchFraction() → float

Gets the value of miniBatchFraction 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.

getParam(paramName: str) → pyspark.ml.param.Param

Gets a param by its name.

getPredictionCol() → str

Gets the value of predictionCol or its default value.

getRegParam() → float

Gets the value of regParam or its default value.

getSeed() → int

Gets the value of seed or its default value.

getSolver() → str

Gets the value of solver or its default value.

getStepSize() → float

Gets the value of stepSize or its default value.

getTol() → float

Gets the value of tol or its default value.

getWeightCol() → str

Gets the value of weightCol or its default value.

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).

predict(value: T) → float

Predict label for the given features.

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.

setFeaturesCol(value: str) → P

Sets the value of featuresCol.

setPredictionCol(value: str) → P

Sets the value of predictionCol.

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

factorSize: pyspark.ml.param.Param[int] = Param(parent='undefined', name='factorSize', doc='Dimensionality of the factor vectors, which are used to get pairwise interactions between variables')

factors

Model factor term.

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

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

fitLinear: pyspark.ml.param.Param[bool] = Param(parent='undefined', name='fitLinear', doc='whether to fit linear term (aka 1-way term)')

initStd: pyspark.ml.param.Param[float] = Param(parent='undefined', name='initStd', doc='standard deviation of initial coefficients')

intercept

Model intercept.

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

linear

Model linear term.

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

miniBatchFraction: pyspark.ml.param.Param[float] = 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

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.')

previous

FMRegressor

next

ChiSquareTest