LinearSVC¶
-
class
pyspark.ml.classification.
LinearSVC
(*, featuresCol='features', labelCol='label', predictionCol='prediction', maxIter=100, regParam=0.0, tol=1e-06, rawPredictionCol='rawPrediction', fitIntercept=True, standardization=True, threshold=0.0, weightCol=None, aggregationDepth=2, maxBlockSizeInMB=0.0)[source]¶ This binary classifier optimizes the Hinge Loss using the OWLQN optimizer. Only supports L2 regularization currently.
New in version 2.2.0.
Notes
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.
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 aggregationDepth 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 labelCol or its default value.
Gets the value of maxBlockSizeInMB or its default value.
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.
Gets the value of predictionCol or its default value.
Gets the value of rawPredictionCol or its default value.
Gets the value of regParam or its default value.
Gets the value of standardization or its default value.
Gets the value of threshold 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.
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
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.
-
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).
-
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.
-
setAggregationDepth
(value)[source]¶ Sets the value of
aggregationDepth
.New in version 2.2.0.
-
setFeaturesCol
(value)¶ Sets the value of
featuresCol
.New in version 3.0.0.
-
setFitIntercept
(value)[source]¶ Sets the value of
fitIntercept
.New in version 2.2.0.
-
setMaxBlockSizeInMB
(value)[source]¶ Sets the value of
maxBlockSizeInMB
.New in version 3.1.0.
-
setParams
(*, featuresCol='features', labelCol='label', predictionCol='prediction', maxIter=100, regParam=0.0, tol=1e-06, rawPredictionCol='rawPrediction', fitIntercept=True, standardization=True, threshold=0.0, weightCol=None, aggregationDepth=2, maxBlockSizeInMB=0.0)[source]¶ 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)¶ Sets the value of
predictionCol
.New in version 3.0.0.
-
setRawPredictionCol
(value)¶ Sets the value of
rawPredictionCol
.New in version 3.0.0.
-
setStandardization
(value)[source]¶ Sets the value of
standardization
.New in version 2.2.0.
-
write
()¶ 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 typeParam
.
-
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.')¶
-