TF = dict()
i = 0
for review in reviews_feature:
i += 1
disp.tempPrint(str(i))
TF[review] = Counter()
for token in reviews_feature[review]:
TF[review][token] = float(reviews_feature[review][token]) / float(max(reviews_feature[review].values()))
print "> Computing IDF"
IDF = dict()
i = 0
for token in alltoken:
i += 1
disp.tempPrint(str(i))
IDF[token] = log(float(n) / float(len(alltoken[token])))
print "> Computing TFIDF"
TFIDF = dict()
i = 0
for review in reviews_feature:
i += 1
disp.tempPrint(str(i))
TFIDF[review] = Counter()
for token in reviews_feature[review]:
TFIDF[review][token] = TF[review][token] * IDF[token]
# SGD linear regression
data.saveFile(TFIDF, root + '/computed/TFIDF.pkl')
# Tools
from utils import data
from collections import Counter
def dot(csparse, c):
r = 0
for key in csparse:
r += csparse[key] * c[key]
return r
print "> Loading"
root = data.getParent(__file__)
print "loading TFIDF matrix"
TFIDF = data.loadFile(root + '/computed/TFIDF.pkl')
print "loading weights"
weights, bias = data.loadFile(root + "/computed/linear_regression_weights.pkl")
predict = dict()
for review in TFIDF:
predict[review] = bias + dot(TFIDF[review], weights)
data.saveFile(predict, root + "/computed/linear_regression_predict.pkl")
# number of reviews a token has to appear to be kept hardthreshold = 2 print "> Loading data" alltoken = data.loadFile(root + '/computed/alltoken.pkl') print "> Scanning data" print "Loading file", filename reviews_feature = dict() reviews_score = dict() tok = Tokenizer(preserve_case=True) # extracting tokens for line in data.generateLine(filename): review = json.loads(line) reviewid = review['review_id'] text = tok.ngrams(review['text'], 1, 3) score = int(review['stars']) # filtering tokens by the ones in the model text = filter(lambda k: k in alltoken, text) reviews_feature[reviewid] = Counter(text) reviews_score[reviewid] = score print "> End of full scan" print "> Saving" data.saveFile(reviews_feature, root + "/computed/reviews_feature.pkl") data.saveFile(reviews_score, root + "/computed/reviews_score.pkl")
# Tools from utils import data from linear_regression_sgd import sgd print "> Loading" root = data.getParent(__file__) print "loading review scores" target = data.loadFile(root + '/computed/reviews_score.pkl') print "loading TFIDF matrix" TFIDF = data.loadFile(root + '/computed/TFIDF.pkl') nReviews = len(target) print "> Optimizing with SGD" RMSE, weights, bias = sgd(TFIDF, target, alpha=0.001, epsilon=0.001) print "Alpha = ", alpha, " -- RMSE = ", RMSE data.saveFile((weights, bias), root + "/computed/linear_regression_weights.pkl")
