def main():
path = "/Users/tao/Documents/coding/dataset/AmazonReviews/product_review15_category3_item4/"
with open("result_amazon_with_1534", "a") as f:
for ix in [5]:
print str(ix) + " Begin"
model_result, test_result = construction(path + str(ix) + "/")
precision, recall, fscore = f_score_of_feature(model_result, test_result)
feature = pd.read_csv(path + str(ix) + "/feature.csv")
print feature.ix[list(model_result[0])]
result = str(ix) + "\t" + str(precision) + "\t" + str(recall) + "\t" + str(fscore) + "\n"
print result
f.write(result)
print str(ix) + " End"
def main():
path = '/Users/tao/Documents/coding/dataset/AmazonReviews/product_review15_category3_item4/'
with open('result_amazon_with_1534', 'a') as f:
for ix in [5]:
print str(ix) + ' Begin'
model_result, test_result = construction(path + str(ix) + '/')
precision, recall, fscore = f_score_of_feature(
model_result, test_result)
feature = pd.read_csv(path + str(ix) + '/feature.csv')
print feature.ix[list(model_result[0])]
result = str(ix) + '\t' + str(precision) + '\t' + str(
recall) + '\t' + str(fscore) + '\n'
print result
f.write(result)
print str(ix) + ' End'
def main():
path = '/Users/tao/Documents/coding/dataset/YelpReviews/product_review5_category3_item4/'
print 'yelp 10 f'
with open('result_yelp_with_534_10f', 'a') as f:
for ix in reversed([5, 6]):
print str(ix) + ' Begin'
paper_symbol_A, paper_symbol_X, paper_symbol_Y, user_num, item_num, feature_num, train_buy_set, real_result, test_mention_feature = construction(path + str(ix) + '/')
r_plus_r_p, lamda_x, lamda_y, lamda_u, lamda_h, lamda_v, convergence, T = _get_paramenters()
for sum in r_plus_r_p:
paper_symbol_U_1, paper_symbol_U_2, paper_symbol_V, paper_symbol_H_1, paper_symbol_H_2 = compute(paper_symbol_A, paper_symbol_X, paper_symbol_Y, user_num, item_num, feature_num, sum*0.4, sum * 0.6, lamda_x, lamda_y, lamda_u, lamda_h, lamda_v, convergence, T)
#print T, paper_symbol_U_1.ix[:5,:5], (paper_symbol_U_1.dot(paper_symbol_U_2.T) + paper_symbol_H_1.dot(paper_symbol_H_2.T)).ix[:5,:5]
for alpha in [0]:#,1-0.1**2,1-0.1**3,1-0.1**4,1-0.1**5,1-0.1**6,1-0.1**7,1-0.1**8,1-0.1**9,1-0.1**10,1-0.1**11,1-0.1**12,1-0.1**13]:
#model_result = recommend_items(paper_symbol_U_1, paper_symbol_U_2, paper_symbol_V, paper_symbol_H_1, paper_symbol_H_2, train_buy_set, alpha, 50, 10)
model_result = recommend_features(paper_symbol_U_1, paper_symbol_V, 10)
precision, recall, f1_result = f_score_of_feature(model_result, test_mention_feature)
#precision, recall, f1_result = f_score(model_result, real_result)
result = str(ix) + '\t' + str(sum) + '\t' + str(precision) + '\t' + str(recall) + '\t' + str(f1_result) + '\n'
print result
f.write(result)
print str(ix) + ' End'
def main():
path = '/Users/tao/Documents/coding/dataset/YelpReviews/product_review5_category3_item4/'
print 'yelp 10 f'
with open('result_yelp_with_534_10f', 'a') as f:
for ix in reversed([5, 6]):
print str(ix) + ' Begin'
paper_symbol_A, paper_symbol_X, paper_symbol_Y, user_num, item_num, feature_num, train_buy_set, real_result, test_mention_feature = construction(
path + str(ix) + '/')
r_plus_r_p, lamda_x, lamda_y, lamda_u, lamda_h, lamda_v, convergence, T = _get_paramenters(
)
for sum in r_plus_r_p:
paper_symbol_U_1, paper_symbol_U_2, paper_symbol_V, paper_symbol_H_1, paper_symbol_H_2 = compute(
paper_symbol_A, paper_symbol_X, paper_symbol_Y, user_num,
item_num, feature_num, sum * 0.4, sum * 0.6, lamda_x,
lamda_y, lamda_u, lamda_h, lamda_v, convergence, T)
#print T, paper_symbol_U_1.ix[:5,:5], (paper_symbol_U_1.dot(paper_symbol_U_2.T) + paper_symbol_H_1.dot(paper_symbol_H_2.T)).ix[:5,:5]
for alpha in [
0
]: #,1-0.1**2,1-0.1**3,1-0.1**4,1-0.1**5,1-0.1**6,1-0.1**7,1-0.1**8,1-0.1**9,1-0.1**10,1-0.1**11,1-0.1**12,1-0.1**13]:
#model_result = recommend_items(paper_symbol_U_1, paper_symbol_U_2, paper_symbol_V, paper_symbol_H_1, paper_symbol_H_2, train_buy_set, alpha, 50, 10)
model_result = recommend_features(paper_symbol_U_1,
paper_symbol_V, 10)
precision, recall, f1_result = f_score_of_feature(
model_result, test_mention_feature)
#precision, recall, f1_result = f_score(model_result, real_result)
result = str(ix) + '\t' + str(sum) + '\t' + str(
precision) + '\t' + str(recall) + '\t' + str(
f1_result) + '\n'
print result
f.write(result)
print str(ix) + ' End'