LinearSVC

class snap_ml_spark.estimator.LinearSVC(featuresCol='features', labelCol='label', predictionCol='prediction', trainingHistory=0, maxIter=1000, regParam=1.0, tol=0.001, rawPredictionCol='rawPrediction', standardization=True, weightCol=None, useGpu=False, dual=True, balanced=False, nthreads=-1, gpuMemLimit=0, verbose=False)

Linear SVM Classifier

>>> 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(maxIter=5, regParam=0.01)
>>> model = svm.fit(df)
>>> 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()
>>> result = model.transform(test0).head()
>>> result.prediction
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

getBalanced()

Gets the value of balanced or its default value.

getDual()

Gets the value of dual or its default value.

getGpuMemLimit()

Gets the value of gpuMemLimit or its default value.

getNthreads()

Gets the value of nthreads or its default value.

getTrainingHistory()

Gets the value of trainingHistory or its default value.

getUseGpu()

Gets the value of useGpu or its default value.

getVerbose()

Gets the value of verbose or its default value.

setBalanced(value)

Sets the value of balanced.

setDual(value)

Sets the value of dual.

setGpuMemLimit(value)

Sets the value of gpuMemLimit.

setNthreads(value)

Sets the value of nthreads.

setParams(featuresCol='features', labelCol='label', predictionCol='prediction', trainingHistory=0, maxIter=1000, regParam=1.0, tol=0.001, rawPredictionCol='rawPrediction', standardization=True, weightCol=None, useGpu=False, dual=True, balanced=False, nthreads=-1, gpuMemLimit=0, verbose=False)

setParams(featuresCol=”features”, labelCol=”label”, predictionCol=”prediction”, trainingHistory=0, maxIter=1000, regParam=1.0, tol=1e-3, rawPredictionCol=”rawPrediction”, standardization=True, weightCol=None, useGpu=False, dual=True, balanced=False, nthreads=-1, gpuMemLimit=0, verbose=False): Sets params for Linear SVM Classifier.

setTrainingHistory(value)

Sets the value of trainingHistory.

setUseGpu(value)

Sets the value of useGpu.

setVerbose(value)

Sets the value of verbose.