print '\n'
from Model import Model
test_model = Model()
test_model.UpdateBaselines(train_reviews)
print "Mean: " + str(test_model.mean)
print('training model')
from nltk.corpus import stopwords
test_model.exclude = stopwords.words('english')
test_model.Train(train_reviews)
print('done, running evaluations.\n')
from Evaluation import evaluate_rigorous_dist
evaluate_rigorous_dist(test_reviews, test_model.wordReviewNet, modePercentage, test_model)
#test_model.Guess(test_reviews[0]["review"], test_reviews[0]["score"], True)
### Generate confusion matrix of scores
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
score_true = []
score_guess = []
