def doBestFeatureSelection(clf, numFeatures):
multDf = pd.read_csv(os.path.dirname(os.path.abspath(__file__))+'/data/TrainData_Labeled.csv')
multTraining, multTesting = do.partionData(multDf, .8)
bestFeatures = fs.getBestFeaturesForHigherOrderTerms(clf, multTraining, numFeatures, 'accuracy')
#bestFeatures = list(['alcohol', 'volatile acidity*total sulfur dioxide*density*', 'volatile acidity*chlorides*free sulfur dioxide*pH*', 'fixed acidity*volatile acidity*free sulfur dioxide*pH*sulphates*'])
print(bestFeatures)
trainingData = multTraining.loc[:, bestFeatures]
trainingY = multTraining['label']
trainingData.insert(loc = len(trainingData.columns),column='label', value=trainingY)
testingData = multTesting.loc[:, bestFeatures]
testingY = multTesting['label']
testingData.insert(loc = len(testingData.columns),column='label', value=testingY)
print(testingData)
do.fitTrainingData(clf, trainingData)
do.testClassifier(clf, testingData, "Random Forests")
