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)
>>> 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)
New in version 3.0.0.
Methods
Attributes
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
extra – Extra parameters to copy to the new instance
- Returns
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
extra – extra param values
- Returns
merged param map
-
fit
(dataset, params=None)¶ Fits a model to the input dataset with optional parameters.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
params – 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
fitted model(s)
New in version 1.3.0.
-
fitMultiple
(dataset, paramMaps)¶ Fits a model to the input dataset for each param map in paramMaps.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
.paramMaps – A Sequence of param maps.
- Returns
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.
New in version 2.3.0.
-
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.
-
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.
-
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-06, 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.
-
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 typeParam
.
-
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).')¶