FMRegressor#

class pyspark.ml.regression.FMRegressor(*, featuresCol='features', labelCol='label', predictionCol='prediction', factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-06, solver='adamW', seed=None)[source]#

Factorization Machines learning algorithm for regression.

solver Supports:

  • gd (normal mini-batch gradient descent)

  • adamW (default)

New in version 3.0.0.

Examples

>>> from pyspark.ml.linalg import Vectors
>>> from pyspark.ml.regression import FMRegressor
>>> df = spark.createDataFrame([
...     (2.0, Vectors.dense(2.0)),
...     (1.0, Vectors.dense(1.0)),
...     (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
>>>
>>> fm = FMRegressor(factorSize=2)
>>> fm.setSeed(16)
FMRegressor...
>>> model = fm.fit(df)
>>> model.getMaxIter()
100
>>> test0 = spark.createDataFrame([
...     (Vectors.dense(-2.0),),
...     (Vectors.dense(0.5),),
...     (Vectors.dense(1.0),),
...     (Vectors.dense(4.0),)], ["features"])
>>> model.transform(test0).show(10, False)
+--------+-------------------+
|features|prediction         |
+--------+-------------------+
|[-2.0]  |-1.9989237712341565|
|[0.5]   |0.4956682219523814 |
|[1.0]   |0.994586620589689  |
|[4.0]   |3.9880970124135344 |
+--------+-------------------+
...
>>> model.intercept
-0.0032501766849261557
>>> model.linear
DenseVector([0.9978])
>>> model.factors
DenseMatrix(1, 2, [0.0173, 0.0021], 1)
>>> model_path = temp_path + "/fm_model"
>>> model.save(model_path)
>>> model2 = FMRegressionModel.load(model_path)
>>> model2.intercept
-0.0032501766849261557
>>> model2.linear
DenseVector([0.9978])
>>> model2.factors
DenseMatrix(1, 2, [0.0173, 0.0021], 1)
>>> model.transform(test0).take(1) == model2.transform(test0).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.

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.

fit(dataset[, params])

Fits a model to the input dataset with optional parameters.

fitMultiple(dataset, paramMaps)

Fits a model to the input dataset for each param map in paramMaps.

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).

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.

setFactorSize(value)

Sets the value of factorSize.

setFeaturesCol(value)

Sets the value of featuresCol.

setFitIntercept(value)

Sets the value of fitIntercept.

setFitLinear(value)

Sets the value of fitLinear.

setInitStd(value)

Sets the value of initStd.

setLabelCol(value)

Sets the value of labelCol.

setMaxIter(value)

Sets the value of maxIter.

setMiniBatchFraction(value)

Sets the value of miniBatchFraction.

setParams(self, \*[, featuresCol, labelCol, ...])

Sets Params for FMRegressor.

setPredictionCol(value)

Sets the value of predictionCol.

setRegParam(value)

Sets the value of regParam.

setSeed(value)

Sets the value of seed.

setSolver(value)

Sets the value of solver.

setStepSize(value)

Sets the value of stepSize.

setTol(value)

Sets the value of tol.

write()

Returns an MLWriter instance for this ML instance.

Attributes

factorSize

featuresCol

fitIntercept

fitLinear

initStd

labelCol

maxIter

miniBatchFraction

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

fit(dataset, params=None)#

Fits a model to the input dataset with optional parameters.

New in version 1.3.0.

Parameters
datasetpyspark.sql.DataFrame

input dataset.

paramsdict or list or tuple, optional

an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.

Returns
Transformer or a list of Transformer

fitted model(s)

fitMultiple(dataset, paramMaps)#

Fits a model to the input dataset for each param map in paramMaps.

New in version 2.3.0.

Parameters
datasetpyspark.sql.DataFrame

input dataset.

paramMapscollections.abc.Sequence

A Sequence of param maps.

Returns
_FitMultipleIterator

A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.

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).

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.

setFactorSize(value)[source]#

Sets the value of factorSize.

New in version 3.0.0.

setFeaturesCol(value)#

Sets the value of featuresCol.

New in version 3.0.0.

setFitIntercept(value)[source]#

Sets the value of fitIntercept.

New in version 3.0.0.

setFitLinear(value)[source]#

Sets the value of fitLinear.

New in version 3.0.0.

setInitStd(value)[source]#

Sets the value of initStd.

New in version 3.0.0.

setLabelCol(value)#

Sets the value of labelCol.

New in version 3.0.0.

setMaxIter(value)[source]#

Sets the value of maxIter.

New in version 3.0.0.

setMiniBatchFraction(value)[source]#

Sets the value of miniBatchFraction.

New in version 3.0.0.

setParams(self, \*, featuresCol="features", labelCol="label", predictionCol="prediction", factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-6, solver="adamW", seed=None)[source]#

Sets Params for FMRegressor.

New in version 3.0.0.

setPredictionCol(value)#

Sets the value of predictionCol.

New in version 3.0.0.

setRegParam(value)[source]#

Sets the value of regParam.

New in version 3.0.0.

setSeed(value)[source]#

Sets the value of seed.

New in version 3.0.0.

setSolver(value)[source]#

Sets the value of solver.

New in version 3.0.0.

setStepSize(value)[source]#

Sets the value of stepSize.

New in version 3.0.0.

setTol(value)[source]#

Sets the value of tol.

New in version 3.0.0.

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

A unique id for the object.