FMClassifier

class pyspark.ml.classification.FMClassifier(*, featuresCol: str = 'features', labelCol: str = 'label', predictionCol: str = 'prediction', probabilityCol: str = 'probability', rawPredictionCol: str = 'rawPrediction', factorSize: int = 8, fitIntercept: bool = True, fitLinear: bool = True, regParam: float = 0.0, miniBatchFraction: float = 1.0, initStd: float = 0.01, maxIter: int = 100, stepSize: float = 1.0, tol: float = 1e-06, solver: str = 'adamW', thresholds: Optional[List[float]] = None, seed: Optional[int] = None)

Factorization Machines learning algorithm for classification.

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.classification import FMClassifier
>>> df = spark.createDataFrame([
...     (1.0, Vectors.dense(1.0)),
...     (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
>>> fm = FMClassifier(factorSize=2)
>>> fm.setSeed(11)
FMClassifier...
>>> model = fm.fit(df)
>>> model.getMaxIter()
100
>>> test0 = spark.createDataFrame([
...     (Vectors.dense(-1.0),),
...     (Vectors.dense(0.5),),
...     (Vectors.dense(1.0),),
...     (Vectors.dense(2.0),)], ["features"])
>>> model.predictRaw(test0.head().features)
DenseVector([22.13..., -22.13...])
>>> model.predictProbability(test0.head().features)
DenseVector([1.0, 0.0])
>>> model.transform(test0).select("features", "probability").show(10, False)
+--------+------------------------------------------+
|features|probability                               |
+--------+------------------------------------------+
|[-1.0]  |[0.9999999997574736,2.425264676902229E-10]|
|[0.5]   |[0.47627851732981163,0.5237214826701884]  |
|[1.0]   |[5.491554426243495E-4,0.9994508445573757] |
|[2.0]   |[2.005766663870645E-10,0.9999999997994233]|
+--------+------------------------------------------+
...
>>> model.intercept
-7.316665276826291
>>> model.linear
DenseVector([14.8232])
>>> model.factors
DenseMatrix(1, 2, [0.0163, -0.0051], 1)
>>> model_path = temp_path + "/fm_model"
>>> model.save(model_path)
>>> model2 = FMClassificationModel.load(model_path)
>>> model2.intercept
-7.316665276826291
>>> model2.linear
DenseVector([14.8232])
>>> model2.factors
DenseMatrix(1, 2, [0.0163, -0.0051], 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.
getProbabilityCol()	Gets the value of probabilityCol or its default value.
getRawPredictionCol()	Gets the value of rawPredictionCol 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.
getThresholds()	Gets the value of thresholds 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 FMClassifier.
setPredictionCol(value)	Sets the value of predictionCol.
setProbabilityCol(value)	Sets the value of probabilityCol.
setRawPredictionCol(value)	Sets the value of rawPredictionCol.
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.
setThresholds(value)	Sets the value of thresholds.
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
probabilityCol
rawPredictionCol
regParam
seed
solver
stepSize
thresholds
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

fit(dataset: pyspark.sql.dataframe.DataFrame, params: Union[ParamMap, List[ParamMap], Tuple[ParamMap], None] = None) → Union[M, List[M]]

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: pyspark.sql.dataframe.DataFrame, paramMaps: Sequence[ParamMap]) → Iterator[Tuple[int, M]]

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() → int

Gets the value of factorSize or its default value.

New in version 3.0.0.

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.

New in version 3.0.0.

getInitStd() → float

Gets the value of initStd or its default value.

New in version 3.0.0.

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.

New in version 3.0.0.

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.

getProbabilityCol() → str

Gets the value of probabilityCol or its default value.

getRawPredictionCol() → str

Gets the value of rawPredictionCol 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.

getThresholds() → List[float]

Gets the value of thresholds 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).

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.

setFactorSize(value: int) → pyspark.ml.classification.FMClassifier

Sets the value of factorSize.

New in version 3.0.0.

setFeaturesCol(value: str) → P

Sets the value of featuresCol.

New in version 3.0.0.

setFitIntercept(value: bool) → pyspark.ml.classification.FMClassifier

Sets the value of fitIntercept.

New in version 3.0.0.

setFitLinear(value: bool) → pyspark.ml.classification.FMClassifier

Sets the value of fitLinear.

New in version 3.0.0.

setInitStd(value: float) → pyspark.ml.classification.FMClassifier

Sets the value of initStd.

New in version 3.0.0.

setLabelCol(value: str) → P

Sets the value of labelCol.

New in version 3.0.0.

setMaxIter(value: int) → pyspark.ml.classification.FMClassifier

Sets the value of maxIter.

New in version 3.0.0.

setMiniBatchFraction(value: float) → pyspark.ml.classification.FMClassifier

Sets the value of miniBatchFraction.

New in version 3.0.0.

setParams(self, \*, featuresCol="features", labelCol="label", predictionCol="prediction", probabilityCol="probability", rawPredictionCol="rawPrediction", 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", thresholds=None, seed=None)

Sets Params for FMClassifier.

New in version 3.0.0.

setPredictionCol(value: str) → P

Sets the value of predictionCol.

New in version 3.0.0.

setProbabilityCol(value: str) → P

Sets the value of probabilityCol.

New in version 3.0.0.

setRawPredictionCol(value: str) → P

Sets the value of rawPredictionCol.

New in version 3.0.0.

setRegParam(value: float) → pyspark.ml.classification.FMClassifier

Sets the value of regParam.

New in version 3.0.0.

setSeed(value: int) → pyspark.ml.classification.FMClassifier

Sets the value of seed.

New in version 3.0.0.

setSolver(value: str) → pyspark.ml.classification.FMClassifier

Sets the value of solver.

New in version 3.0.0.

setStepSize(value: float) → pyspark.ml.classification.FMClassifier

Sets the value of stepSize.

New in version 3.0.0.

setThresholds(value: List[float]) → P

Sets the value of thresholds.

New in version 3.0.0.

setTol(value: float) → pyspark.ml.classification.FMClassifier

Sets the value of tol.

New in version 3.0.0.

write() → pyspark.ml.util.JavaMLWriter

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.')

probabilityCol = Param(parent='undefined', name='probabilityCol', doc='Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.')

rawPredictionCol = Param(parent='undefined', name='rawPredictionCol', doc='raw prediction (a.k.a. confidence) 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).')

thresholds = Param(parent='undefined', name='thresholds', doc="Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0, excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.")

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.')

OneVsRestModel FMClassificationModel