LinearSVC

class pyspark.ml.classification.LinearSVC(*, featuresCol: str = 'features', labelCol: str = 'label', predictionCol: str = 'prediction', maxIter: int = 100, regParam: float = 0.0, tol: float = 1e-06, rawPredictionCol: str = 'rawPrediction', fitIntercept: bool = True, standardization: bool = True, threshold: float = 0.0, weightCol: Optional[str] = None, aggregationDepth: int = 2, maxBlockSizeInMB: float = 0.0)

This binary classifier optimizes the Hinge Loss using the OWLQN optimizer. Only supports L2 regularization currently.

New in version 2.2.0.

Notes

Linear SVM Classifier

Examples

>>> from pyspark.sql import Row
>>> from pyspark.ml.linalg import Vectors
>>> df = sc.parallelize([
...     Row(label=1.0, features=Vectors.dense(1.0, 1.0, 1.0)),
...     Row(label=0.0, features=Vectors.dense(1.0, 2.0, 3.0))]).toDF()
>>> svm = LinearSVC()
>>> svm.getMaxIter()
100
>>> svm.setMaxIter(5)
LinearSVC...
>>> svm.getMaxIter()
5
>>> svm.getRegParam()
0.0
>>> svm.setRegParam(0.01)
LinearSVC...
>>> svm.getRegParam()
0.01
>>> model = svm.fit(df)
>>> model.setPredictionCol("newPrediction")
LinearSVCModel...
>>> model.getPredictionCol()
'newPrediction'
>>> model.setThreshold(0.5)
LinearSVCModel...
>>> model.getThreshold()
0.5
>>> model.getMaxBlockSizeInMB()
0.0
>>> model.coefficients
DenseVector([0.0, -1.0319, -0.5159])
>>> model.intercept
2.579645978780695
>>> model.numClasses
2
>>> model.numFeatures
3
>>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0, -1.0, -1.0))]).toDF()
>>> model.predict(test0.head().features)
1.0
>>> model.predictRaw(test0.head().features)
DenseVector([-4.1274, 4.1274])
>>> result = model.transform(test0).head()
>>> result.newPrediction
1.0
>>> result.rawPrediction
DenseVector([-4.1274, 4.1274])
>>> svm_path = temp_path + "/svm"
>>> svm.save(svm_path)
>>> svm2 = LinearSVC.load(svm_path)
>>> svm2.getMaxIter()
5
>>> model_path = temp_path + "/svm_model"
>>> model.save(model_path)
>>> model2 = LinearSVCModel.load(model_path)
>>> model.coefficients[0] == model2.coefficients[0]
True
>>> model.intercept == model2.intercept
True
>>> 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.
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).
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.
setAggregationDepth(value)	Sets the value of aggregationDepth.
setFeaturesCol(value)	Sets the value of featuresCol.
setFitIntercept(value)	Sets the value of fitIntercept.
setLabelCol(value)	Sets the value of labelCol.
setMaxBlockSizeInMB(value)	Sets the value of maxBlockSizeInMB.
setMaxIter(value)	Sets the value of maxIter.
setParams(*[, featuresCol, labelCol, …])	setParams(self, *, featuresCol=”features”, labelCol=”label”, predictionCol=”prediction”, maxIter=100, regParam=0.0, tol=1e-6, rawPredictionCol=”rawPrediction”, fitIntercept=True, standardization=True, threshold=0.0, weightCol=None, aggregationDepth=2, maxBlockSizeInMB=0.0): Sets params for Linear SVM Classifier.
setPredictionCol(value)	Sets the value of predictionCol.
setRawPredictionCol(value)	Sets the value of rawPredictionCol.
setRegParam(value)	Sets the value of regParam.
setStandardization(value)	Sets the value of standardization.
setThreshold(value)	Sets the value of threshold.
setTol(value)	Sets the value of tol.
setWeightCol(value)	Sets the value of weightCol.
write()	Returns an MLWriter instance for this ML instance.

Attributes

aggregationDepth
featuresCol
fitIntercept
labelCol
maxBlockSizeInMB
maxIter
params	Returns all params ordered by name.
predictionCol
rawPredictionCol
regParam
standardization
threshold
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.

getAggregationDepth() → int

Gets the value of aggregationDepth 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.

getLabelCol() → str

Gets the value of labelCol or its default value.

getMaxBlockSizeInMB() → float

Gets the value of maxBlockSizeInMB or its default value.

getMaxIter() → int

Gets the value of maxIter 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.

getRawPredictionCol() → str

Gets the value of rawPredictionCol or its default value.

getRegParam() → float

Gets the value of regParam or its default value.

getStandardization() → bool

Gets the value of standardization or its default value.

getThreshold() → float

Gets the value of threshold 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.

setAggregationDepth(value: int) → pyspark.ml.classification.LinearSVC

Sets the value of aggregationDepth.

New in version 2.2.0.

setFeaturesCol(value: str) → P

Sets the value of featuresCol.

New in version 3.0.0.

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

Sets the value of fitIntercept.

New in version 2.2.0.

setLabelCol(value: str) → P

Sets the value of labelCol.

New in version 3.0.0.

setMaxBlockSizeInMB(value: float) → pyspark.ml.classification.LinearSVC

Sets the value of maxBlockSizeInMB.

New in version 3.1.0.

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

Sets the value of maxIter.

New in version 2.2.0.

setParams(*, featuresCol: str = 'features', labelCol: str = 'label', predictionCol: str = 'prediction', maxIter: int = 100, regParam: float = 0.0, tol: float = 1e-06, rawPredictionCol: str = 'rawPrediction', fitIntercept: bool = True, standardization: bool = True, threshold: float = 0.0, weightCol: Optional[str] = None, aggregationDepth: int = 2, maxBlockSizeInMB: float = 0.0) → pyspark.ml.classification.LinearSVC

setParams(self, *, featuresCol=”features”, labelCol=”label”, predictionCol=”prediction”, maxIter=100, regParam=0.0, tol=1e-6, rawPredictionCol=”rawPrediction”, fitIntercept=True, standardization=True, threshold=0.0, weightCol=None, aggregationDepth=2, maxBlockSizeInMB=0.0): Sets params for Linear SVM Classifier.

New in version 2.2.0.

setPredictionCol(value: str) → P

Sets the value of predictionCol.

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.LinearSVC

Sets the value of regParam.

New in version 2.2.0.

setStandardization(value: bool) → pyspark.ml.classification.LinearSVC

Sets the value of standardization.

New in version 2.2.0.

setThreshold(value: float) → pyspark.ml.classification.LinearSVC

Sets the value of threshold.

New in version 2.2.0.

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

Sets the value of tol.

New in version 2.2.0.

setWeightCol(value: str) → pyspark.ml.classification.LinearSVC

Sets the value of weightCol.

New in version 2.2.0.

write() → pyspark.ml.util.JavaMLWriter

Returns an MLWriter instance for this ML instance.

Attributes Documentation

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

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

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

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).')

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.')

Word2VecModel LinearSVCModel