def load_best_classifier_conf():
from DataClassifierV2 import ClassifiersWrapper
from pyspark.mllib.classification import SVMWithSGD, LogisticRegressionWithSGD, LogisticRegressionWithLBFGS, NaiveBayes
myClassifier = ClassifiersWrapper()
myClassifier.addClassifier(classifier=SVMWithSGD, trainParameters={}, weight=0.3)
myClassifier.addClassifier(classifier=LogisticRegressionWithSGD, trainParameters={}, weight=0.3)
myClassifier.addClassifier(classifier=NaiveBayes, trainParameters={}, weight=0.3)
myClassifier.addClassifier(classifier=LogisticRegressionWithLBFGS, trainParameters={}, weight=0.7)
return myClassifier
#newSource.lookingAll('NASDAQ:NVDA', ['NVIDIA'])
#newSource.lookingAll('VTX:NESN', ['NESTLE'])
#newSource.lookingAll('VTX:SCMN', ['SWISSCOM'])
#newSource.lookingAll('VTX:NOVN', ['NOVARTIS'])
newsRDD = newSource.doIt()
marketSource = GoogleFinanceMarketSourceSpark(
['NASDAQ:GOOGL', 'NASDAQ:NVDA', 'VTX:NESN', 'VTX:SCMN', 'VTX:NOVN'])
newsRDD = newsRDD.map(lambda x: marketSource.addMarketStatusToNews(x))
#newsRDD = newsRDD.randomSplit([1,1,1,1,1,1,1,1,1,1,1,1,1,1,1])[0]
newsRDD.cache()
print('nb news : %d' % newsRDD.count())
dataSetMaker = DataSetMakerV2(n=config.FEATURES_CONF['vecteur_size'])
fullDataSet = dataSetMaker.process(newsRDD) # TODO change
fullDataSet.cache()
myClassifier = ClassifiersWrapper()
myClassifier.addClassifier(classifier=SVMWithSGD,
trainParameters={},
weight=0.4)
#myClassifier.addClassifier(classifier=LogisticRegressionWithSGD, trainParameters={}, weight=0.3)
myClassifier.addClassifier(classifier=NaiveBayes,
trainParameters={},
weight=0.4)
myClassifier.addClassifier(classifier=LogisticRegressionWithLBFGS,
trainParameters={},
weight=0.4)
myClassifier2 = ClassifiersWrapper()
myClassifier2.addClassifier(classifier=SVMWithSGD,
trainParameters={},
weight=0.3)