RandomForests.py

import FeatureSelector as fs
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
import os
import DataOperations as do
from sklearn import preprocessing
import PCA

def normalizedRandomForests():
    pass

if __name__ == "__main__":
    # multDf = pd.read_csv(os.path.dirname(os.path.abspath(__file__))+'/data/TrainData_Squared.csv')
    # multTraining, multTesting = do.partionData(multDf, .8)
    rfc = RandomForestClassifier(n_estimators=500)
    # bestFeatures = fs.getBestFeaturesForHigherOrderTerms(rfc, multTraining, 7, '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(rfc, trainingData)
    # do.testClassifier(rfc, testingData, "Random Forests")

    #Test pca version:
    trainingX, trainingY, testingX, testingY = PCA.getPCATraingAndTesting(.9)
    rfc.fit(trainingX, trainingY)

    score = rfc.score(testingX, testingY)
    print(score)