def getPCATraingAndTesting(thresh):
allData = loadData()
trainingData, testingData = do.partionData(allData, .8)
trainingX = trainingData.loc[:, features]
trainingY = trainingData.loc[:,'label']
testingX = testingData.loc[:, features]
testingY = testingData.loc[:, 'label']
#Standardize features
#trainingX = StandardScaler().fit_transform(trainingX)
pca = PCA()
#Run PCA decomposition
principalComponents = pca.fit_transform(trainingX)
#Compute and print the number of components that PCA will extract
numPcaComponents = findBestPCAFeatures(pca.explained_variance_ratio_, thresh)
print(f'Components: {numPcaComponents}')
principalDf = pd.DataFrame(principalComponents)
trainingX = principalDf.iloc[:, 0:numPcaComponents+1]
#Plot how each component affects the label
#plotPcaComponentsAffectingY(principalDf, trainingY)
testingX = pd.DataFrame(pca.transform(testingX))
testingX = testingX.iloc[:, 0:numPcaComponents+1]
return trainingX, trainingY, testingX, testingY
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")
