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.

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.

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.

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.

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

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

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

setMaxBlockSizeInMB(value: float) → pyspark.ml.classification.LinearSVC ¶: Sets the value of maxBlockSizeInMB.

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

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.

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

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

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

setStandardization(value: bool) → pyspark.ml.classification.LinearSVC ¶: Sets the value of standardization.

setThreshold(value: float) → pyspark.ml.classification.LinearSVC ¶: Sets the value of threshold.

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

setWeightCol(value: str) → pyspark.ml.classification.LinearSVC ¶: Sets the value of weightCol.

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