FMClassificationModel¶
-
class
pyspark.ml.classification.
FMClassificationModel
(java_model=None)[source]¶ Model fitted by
FMClassifier
.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.
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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.
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explainParams
()¶ Returns the documentation of all params with their optionally default values and user-supplied values.
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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
-
getFactorSize
()¶ Gets the value of factorSize or its default value.
New in version 3.0.0.
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getFeaturesCol
()¶ Gets the value of featuresCol or its default value.
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getFitIntercept
()¶ Gets the value of fitIntercept or its default value.
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getFitLinear
()¶ Gets the value of fitLinear or its default value.
New in version 3.0.0.
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getInitStd
()¶ Gets the value of initStd or its default value.
New in version 3.0.0.
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getLabelCol
()¶ Gets the value of labelCol or its default value.
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getMaxIter
()¶ Gets the value of maxIter or its default value.
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getMiniBatchFraction
()¶ Gets the value of miniBatchFraction or its default value.
New in version 3.0.0.
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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.
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getParam
(paramName)¶ Gets a param by its name.
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getPredictionCol
()¶ Gets the value of predictionCol or its default value.
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getProbabilityCol
()¶ Gets the value of probabilityCol or its default value.
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getRawPredictionCol
()¶ Gets the value of rawPredictionCol or its default value.
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getRegParam
()¶ Gets the value of regParam or its default value.
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getSeed
()¶ Gets the value of seed or its default value.
-
getSolver
()¶ Gets the value of solver or its default value.
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getStepSize
()¶ Gets the value of stepSize or its default value.
-
getThresholds
()¶ Gets the value of thresholds or its default value.
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getTol
()¶ Gets the value of tol or its default value.
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hasDefault
(param)¶ Checks whether a param has a default value.
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hasParam
(paramName)¶ Tests whether this instance contains a param with a given (string) name.
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isDefined
(param)¶ Checks whether a param is explicitly set by user or has a default value.
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isSet
(param)¶ Checks whether a param is explicitly set by user.
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classmethod
load
(path)¶ Reads an ML instance from the input path, a shortcut of read().load(path).
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predict
(value)¶ Predict label for the given features.
New in version 3.0.0.
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predictProbability
(value)¶ Predict the probability of each class given the features.
New in version 3.0.0.
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predictRaw
(value)¶ Raw prediction for each possible label.
New in version 3.0.0.
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classmethod
read
()¶ Returns an MLReader instance for this class.
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save
(path)¶ Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
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set
(param, value)¶ Sets a parameter in the embedded param map.
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setFeaturesCol
(value)¶ Sets the value of
featuresCol
.New in version 3.0.0.
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setPredictionCol
(value)¶ Sets the value of
predictionCol
.New in version 3.0.0.
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setProbabilityCol
(value)¶ Sets the value of
probabilityCol
.New in version 3.0.0.
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setRawPredictionCol
(value)¶ Sets the value of
rawPredictionCol
.New in version 3.0.0.
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setThresholds
(value)¶ Sets the value of
thresholds
.New in version 3.0.0.
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transform
(dataset, params=None)¶ Transforms 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.
- Returns
transformed dataset
New in version 1.3.0.
-
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')¶
-
factors
¶ Model factor term.
New in version 3.0.0.
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featuresCol
= Param(parent='undefined', name='featuresCol', doc='features column name.')¶
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fitIntercept
= Param(parent='undefined', name='fitIntercept', doc='whether to fit an intercept term.')¶
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fitLinear
= Param(parent='undefined', name='fitLinear', doc='whether to fit linear term (aka 1-way term)')¶
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initStd
= Param(parent='undefined', name='initStd', doc='standard deviation of initial coefficients')¶
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intercept
¶ Model intercept.
New in version 3.0.0.
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labelCol
= Param(parent='undefined', name='labelCol', doc='label column name.')¶
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linear
¶ Model linear term.
New in version 3.0.0.
-
maxIter
= Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')¶
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miniBatchFraction
= Param(parent='undefined', name='miniBatchFraction', doc='fraction of the input data set that should be used for one iteration of gradient descent')¶
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numClasses
¶ Number of classes (values which the label can take).
New in version 2.1.0.
-
numFeatures
¶ Returns the number of features the model was trained on. If unknown, returns -1
New in version 2.1.0.
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params
¶ Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
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predictionCol
= Param(parent='undefined', name='predictionCol', doc='prediction column name.')¶
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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.')¶
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rawPredictionCol
= Param(parent='undefined', name='rawPredictionCol', doc='raw prediction (a.k.a. confidence) column name.')¶
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regParam
= Param(parent='undefined', name='regParam', doc='regularization parameter (>= 0).')¶
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seed
= Param(parent='undefined', name='seed', doc='random seed.')¶
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solver
= Param(parent='undefined', name='solver', doc='The solver algorithm for optimization. Supported options: gd, adamW. (Default adamW)')¶
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stepSize
= Param(parent='undefined', name='stepSize', doc='Step size to be used for each iteration of optimization (>= 0).')¶
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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.")¶
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tol
= Param(parent='undefined', name='tol', doc='the convergence tolerance for iterative algorithms (>= 0).')¶
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