LinearSVC¶
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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)[source]¶ -
This binary classifier optimizes the Hinge Loss using the OWLQN optimizer. Only supports L2 regularization currently.
>>> 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.coefficients DenseVector([0.0, -0.2792, -0.1833]) >>> model.intercept 1.0206118982229047 >>> 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([-1.4831, 1.4831]) >>> result = model.transform(test0).head() >>> result.newPrediction 1.0 >>> result.rawPrediction DenseVector([-1.4831, 1.4831]) >>> 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
New in version 2.2.0.
Methods
Attributes
Methods Documentation
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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
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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
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fit
(dataset, params=None)¶ Fits a model to 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. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.
- Returns
fitted model(s)
New in version 1.3.0.
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fitMultiple
(dataset, paramMaps)¶ Fits a model to the input dataset for each param map in paramMaps.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
.paramMaps – A Sequence of param maps.
- Returns
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.
New in version 2.3.0.
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getAggregationDepth
()¶ Gets the value of aggregationDepth or its default value.
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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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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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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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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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getStandardization
()¶ Gets the value of standardization or its default value.
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getThreshold
()¶ Gets the value of threshold or its default value.
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getTol
()¶ Gets the value of tol or its default value.
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getWeightCol
()¶ Gets the value of weightCol 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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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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setAggregationDepth
(value)[source]¶ Sets the value of
aggregationDepth
.New in version 2.2.0.
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setFeaturesCol
(value)¶ Sets the value of
featuresCol
.New in version 3.0.0.
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setFitIntercept
(value)[source]¶ Sets the value of
fitIntercept
.New in version 2.2.0.
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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)[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): Sets params for Linear SVM Classifier.
New in version 2.2.0.
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setPredictionCol
(value)¶ Sets the value of
predictionCol
.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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setStandardization
(value)[source]¶ Sets the value of
standardization
.New in version 2.2.0.
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write
()¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
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aggregationDepth
= Param(parent='undefined', name='aggregationDepth', doc='suggested depth for treeAggregate (>= 2).')¶
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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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labelCol
= Param(parent='undefined', name='labelCol', doc='label column name.')¶
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maxIter
= Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 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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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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standardization
= Param(parent='undefined', name='standardization', doc='whether to standardize the training features before fitting the model.')¶
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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.')¶
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tol
= Param(parent='undefined', name='tol', doc='the convergence tolerance for iterative algorithms (>= 0).')¶
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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.')¶
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