def ex1(reviews, tests, predictionInfo):
allTrainingReviews = extractReviews(reviews)
allTrainingNames = extractNames(reviews)
rmse = classifiers.crossValidate(allTrainingReviews, lambda trainSet: classifiers.TwoClassifier(trainSet), 4, allTrainingNames)
print 'Average RMS error rate on all validation sets: {0}\n'.format(rmse)
classy = classifiers.TwoClassifier(allTrainingReviews)
for review in tests:
key = '{0}::{1}'.format(review['file'], review['position'])
for cat in ['food', 'service', 'venue', 'overall']:
prediction = classy.classifyDocument(review[cat + 'Review'])
predictionInfo[key][cat] = prediction
def ex2(reviews, tests, predictionInfo):
# Transforms the documents some
transReviews = []
names = []
for review in reviews:
doc = {}
for cat in ['food', 'service', 'venue', 'overall']:
doc[cat] = int(review[cat + 'Score'])
transReviews.append((doc, int(review['overallScore'])))
names.append('{0}::{1}'.format(review['file'], review['position']))
rmse = classifiers.crossValidate(transReviews, lambda trainSet: classifiers.OverallClassifier(trainSet), 4, names)
print 'Average RMS error rate on all validation sets: {0}\n'.format(rmse)
classy = classifiers.OverallClassifier(transReviews)
for review in tests:
key = '{0}::{1}'.format(review['file'], review['position'])
# Get the info from the predictions from ex1
doc = {}
for cat in ['food', 'service', 'venue']:
doc[cat] = predictionInfo[key][cat]
prediction = classy.classifyDocument(doc)
predictionInfo[key]['loneOverall'] = prediction
def ex3(reviews, tests, predictionInfo):
# Transforms the documents some
transReviews = []
names = []
for review in reviews:
for cat in ['food', 'service', 'venue', 'overall']:
transReviews.append((review[cat + 'Review'], review['reviewer']))
names.append('{0}::{1}'.format(review['file'], review['position']))
'''
for review in reviews:
doc = ''
for cat in ['food', 'service', 'venue', 'overall']:
doc += ' ' + review[cat + 'Review']
transReviews.append((doc, review['reviewer']))
names.append('{0}::{1}'.format(review['file'], review['position']))
'''
rmse = classifiers.crossValidate(transReviews, lambda trainSet: classifiers.NBClassifier(trainSet), 4, names, True)
print 'Average RMS error rate on all validation sets: {0}\n'.format(rmse)
classy = classifiers.NBClassifier(transReviews)
for test in tests:
key = '{0}::{1}'.format(test['file'], test['position'])
predictions = {}
for cat in ['food', 'service', 'venue', 'overall']:
prediction = classy.classifyDocument(review[cat + 'Review'])
if not predictions.has_key(prediction):
predictions[prediction] = 1
else:
predictions[prediction] += 1
if len(predictions) == 4:
# Random plz
finalPrediction = predictions.keys()[random.randint(0, len(predictions) - 1)]
else:
finalPrediction = max(predictions, key=predictions.get)
predictionInfo[key]['reviewer'] = finalPrediction
allTrainingReviews = extractReviews(reviews)
# res = {}
# bestRes = (10, '')
# for i in range(1, 10):
# for j in [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:
# res['{0}-{1}'.format(i, j)] = classifiers.crossValidate(allTrainingReviews, lambda trainSet: classifiers.WordWeightClassifier(trainSet, i, j))
# if res['{0}-{1}'.format(i, j)] < bestRes[0]:
# bestRes = (res['{0}-{1}'.format(i, j)], '{0}-{1}'.format(i, j))
#
# print res
# print bestRes
# sys.exit()
print 'RMSE: {0}'.format(classifiers.crossValidate(allTrainingReviews, lambda trainSet: classifiers.TwoClassifier(trainSet)))
sys.exit()
print 'RMSE: {0}'.format(classifiers.crossValidate(allTrainingReviews, lambda trainSet: classifiers.WordWeightClassifier(trainSet)))
sys.exit()
print 'RMSE: {0}'.format(classifiers.crossValidate(allTrainingReviews, lambda trainSet: classifiers.SentiClassifier(trainSet)))
sys.exit()
#TEST
setDistRmse = classifiers.crossValidate(allTrainingReviews, lambda trainSet: classifiers.SetDistClassifier(trainSet))
wwRmse = classifiers.crossValidate(allTrainingReviews, lambda trainSet: classifiers.WordWeightClassifier(trainSet))
binaryRmse = classifiers.crossValidate(allTrainingReviews, lambda trainSet: classifiers.BinaryClassSplitClassifier(trainSet))
nbRmse = classifiers.crossValidate(allTrainingReviews, lambda trainSet: classifiers.NBClassifier(trainSet), 4, 0)
print 'SetDist: {0}, WW: {1}, Binary: {2}, NB: {3}'.format(setDistRmse, wwRmse, binaryRmse, nbRmse)