LinearSVCModel#

class pyspark.ml.classification.LinearSVCModel(java_model=None)[source]#

Model fitted by LinearSVC.

New in version 2.2.0.

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.
`evaluate`(dataset)	Evaluates the model on a test dataset.
`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.
`getAggregationDepth`()	Gets the value of aggregationDepth or its default value.
`getFeaturesCol`()	Gets the value of featuresCol or its default value.
`getFitIntercept`()	Gets the value of fitIntercept or its default value.
`getLabelCol`()	Gets the value of labelCol or its default value.
`getMaxBlockSizeInMB`()	Gets the value of maxBlockSizeInMB or its default value.
`getMaxIter`()	Gets the value of maxIter 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.
`getRawPredictionCol`()	Gets the value of rawPredictionCol or its default value.
`getRegParam`()	Gets the value of regParam or its default value.
`getStandardization`()	Gets the value of standardization or its default value.
`getThreshold`()	Gets the value of threshold 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).
`predict`(value)	Predict label for the given features.
`predictRaw`(value)	Raw prediction for each possible label.
`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.
`setFeaturesCol`(value)	Sets the value of `featuresCol`.
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`setRawPredictionCol`(value)	Sets the value of `rawPredictionCol`.
`setThreshold`(value)	Sets the value of `threshold`.
`summary`()	Gets summary (accuracy/precision/recall, objective history, total iterations) of model trained on the training set.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`aggregationDepth`
`coefficients`	Model coefficients of Linear SVM Classifier.
`featuresCol`
`fitIntercept`
`hasSummary`	Indicates whether a training summary exists for this model instance.
`intercept`	Model intercept of Linear SVM Classifier.
`labelCol`
`maxBlockSizeInMB`
`maxIter`
`numClasses`	Number of classes (values which the label can take).
`numFeatures`	Returns the number of features the model was trained on.
`params`	Returns all params ordered by name.
`predictionCol`
`rawPredictionCol`
`regParam`
`standardization`
`threshold`
`tol`
`weightCol`

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

evaluate(dataset)[source]#

Evaluates the model on a test dataset.

New in version 3.1.0.

Parameters

datasetpyspark.sql.DataFrame: Test dataset to evaluate model on.

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

getAggregationDepth()#: Gets the value of aggregationDepth or its default value.

getFeaturesCol()#: Gets the value of featuresCol or its default value.

getFitIntercept()#: Gets the value of fitIntercept or its default value.

getLabelCol()#: Gets the value of labelCol or its default value.

getMaxBlockSizeInMB()#: Gets the value of maxBlockSizeInMB or its default value.

getMaxIter()#: Gets the value of maxIter or its default value.

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.

getRawPredictionCol()#: Gets the value of rawPredictionCol or its default value.

getRegParam()#: Gets the value of regParam or its default value.

getStandardization()#: Gets the value of standardization or its default value.

getThreshold()#: Gets the value of threshold 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).

predict(value)#: Predict label for the given features.

New in version 3.0.0.

predictRaw(value)#: Raw prediction for each possible label.

New in version 3.0.0.

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.

setFeaturesCol(value)#: Sets the value of featuresCol.

New in version 3.0.0.

setPredictionCol(value)#: Sets the value of predictionCol.

New in version 3.0.0.

setRawPredictionCol(value)#: Sets the value of rawPredictionCol.

New in version 3.0.0.

setThreshold(value)[source]#: Sets the value of threshold.

New in version 3.0.0.

summary()[source]#: Gets summary (accuracy/precision/recall, objective history, total iterations) of model trained on the training set. An exception is thrown if trainingSummary is None.

New in version 3.1.0.

transform(dataset, params=None)#

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters

datasetpyspark.sql.DataFrame: input dataset
paramsdict, optional: an optional param map that overrides embedded params.

Returns

pyspark.sql.DataFrame: transformed dataset

write()#: Returns an MLWriter instance for this ML instance.

Attributes Documentation

aggregationDepth = Param(parent='undefined', name='aggregationDepth', doc='suggested depth for treeAggregate (>= 2).')#

coefficients#: Model coefficients of Linear SVM Classifier.

New in version 2.2.0.

featuresCol = Param(parent='undefined', name='featuresCol', doc='features column name.')#

fitIntercept = Param(parent='undefined', name='fitIntercept', doc='whether to fit an intercept term.')#

hasSummary#: Indicates whether a training summary exists for this model instance.

New in version 2.1.0.

intercept#: Model intercept of Linear SVM Classifier.

New in version 2.2.0.

labelCol = Param(parent='undefined', name='labelCol', doc='label column name.')#

maxBlockSizeInMB = Param(parent='undefined', name='maxBlockSizeInMB', doc='maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0.')#

maxIter = Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')#

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.

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.')#

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).')#

standardization = Param(parent='undefined', name='standardization', doc='whether to standardize the training features before fitting the model.')#

threshold = Param(parent='undefined', name='threshold', doc='The threshold in binary classification applied to the linear model prediction. This threshold can be any real number, where Inf will make all predictions 0.0 and -Inf will make all predictions 1.0.')#

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.')#

uid#: A unique id for the object.