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)

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.

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.

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.

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.

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.

setFeaturesCol(value: str) → P¶: Sets the value of featuresCol.

setFitIntercept(value: bool) → pyspark.ml.classification.FMClassifier ¶: Sets the value of fitIntercept.

setFitLinear(value: bool) → pyspark.ml.classification.FMClassifier ¶: Sets the value of fitLinear.

setInitStd(value: float) → pyspark.ml.classification.FMClassifier ¶: Sets the value of initStd.

setLabelCol(value: str) → P¶: Sets the value of labelCol.

setMaxIter(value: int) → pyspark.ml.classification.FMClassifier ¶: Sets the value of maxIter.

setMiniBatchFraction(value: float) → pyspark.ml.classification.FMClassifier ¶: Sets the value of miniBatchFraction.

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.

setPredictionCol(value: str) → P¶: Sets the value of predictionCol.

setProbabilityCol(value: str) → P¶: Sets the value of probabilityCol.

setRawPredictionCol(value: str) → P¶: Sets the value of rawPredictionCol.

setRegParam(value: float) → pyspark.ml.classification.FMClassifier ¶: Sets the value of regParam.

setSeed(value: int) → pyspark.ml.classification.FMClassifier ¶: Sets the value of seed.

setSolver(value: str) → pyspark.ml.classification.FMClassifier ¶: Sets the value of solver.

setStepSize(value: float) → pyspark.ml.classification.FMClassifier ¶: Sets the value of stepSize.

setThresholds(value: List[float]) → P¶: Sets the value of thresholds.

setTol(value: float) → pyspark.ml.classification.FMClassifier ¶: Sets the value of tol.

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