FMClassifier¶
-
class
pyspark.ml.classification.
FMClassifier
(*, 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-06, solver='adamW', thresholds=None, seed=None)[source]¶ 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.
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.
Gets the value of factorSize or its default value.
Gets the value of featuresCol or its default value.
Gets the value of fitIntercept or its default value.
Gets the value of fitLinear or its default value.
Gets the value of initStd or its default value.
Gets the value of labelCol or its default value.
Gets the value of maxIter or its default value.
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.
Gets the value of predictionCol or its default value.
Gets the value of probabilityCol or its default value.
Gets the value of rawPredictionCol or its default value.
Gets the value of regParam or its default value.
getSeed
()Gets the value of seed or its default value.
Gets the value of solver or its default value.
Gets the value of stepSize or its default value.
Gets the value of thresholds or its default value.
getTol
()Gets the value of tol or its default value.
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
Returns all params ordered by name.
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
- dataset
pyspark.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.
- dataset
- Returns
Transformer
or a list ofTransformer
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
- dataset
pyspark.sql.DataFrame
input dataset.
- paramMaps
collections.abc.Sequence
A Sequence of param maps.
- dataset
- 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.
-
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.
-
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", 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)[source]¶ Sets Params for FMClassifier.
New in version 3.0.0.
-
setPredictionCol
(value)¶ Sets the value of
predictionCol
.New in version 3.0.0.
-
setProbabilityCol
(value)¶ Sets the value of
probabilityCol
.New in version 3.0.0.
-
setRawPredictionCol
(value)¶ Sets the value of
rawPredictionCol
.New in version 3.0.0.
-
setThresholds
(value)¶ Sets the value of
thresholds
.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.')¶
-
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.')¶