def main():
global user_matrix, user_bias, item_matrix, item_bias, user_score, item_score, global_bias, tfidf_predictor, vec_predictor
base_dir = '../../paper/data/dianping/mf/'
user_item_score_file = os.path.join(
base_dir, 'train/comment.keyword.train.user_item_star')
user_matrix_file = os.path.join(
base_dir, 'out/comment.keyword.train.user_item_star.user')
item_matrix_file = os.path.join(
base_dir, 'out/comment.keyword.train.user_item_star.item')
test_file = os.path.join(base_dir, '../comment.keyword.test')
#test_file = os.path.join(base_dir, 'train/comment.mongo.train')
logging.info('loading user matrix...')
user_matrix, user_bias = load_nmf_matrix(user_matrix_file, print_log=True)
logging.info('loading item matrix...')
item_matrix, item_bias = load_nmf_matrix(item_matrix_file, print_log=True)
logging.info('loading item score...')
user_score, item_score, global_bias = load_user_item_score(
user_item_score_file, print_log=True)
logging.info('global_bias:%f' % global_bias)
#load vector
vector_directory = "../../paper/data/dianping/w2v/vector"
model_directory = "../../paper/data/dianping/lr_model/"
user_vector = os.path.join(vector_directory,
"comment.keyword.train.user.vector.200")
shop_vector = os.path.join(vector_directory,
"comment.keyword.train.shop.vector.200")
vector_model_file = os.path.join(model_directory, "w2v_200_lr")
vec_predictor = vec_lr_predictor(user_vector, shop_vector,
vector_model_file)
user_trained_shops = load_user_trained_shops(user_item_score_file)
shop_ids = load_ids(shop_vector)
user_ids = load_ids(user_vector)
fout = file('./predict_res.all', 'w')
user_count = 0
for user_id in user_ids:
if user_count % 1000 == 0:
logging.info('user count:%d' % user_count)
user_count += 1
if user_id not in user_trained_shops: continue
#predict_res = []
heap = MinSizeHeap(10)
for shop_id in shop_ids:
# if shop in training data, ignore it
if shop_id in user_trained_shops[user_id]: continue
heap.push((vector_score_function(user_id, shop_id), shop_id))
heap.sort()
#sorted_res = sorted(predict_res, key=lambda x: x[0], reverse=True)[0]
#res = ['%s:%lf' % (shop_id, score) for score, shop_id in sorted_res]
#fout.write('%s\t%s\n' % (user_id, '\t'.join(res)))
for score, shop_id in heap.arr:
fout.write('%s\t%s\t%s\n' % (user_id, shop_id, score))
fout.close()
def main():
global user_matrix, user_bias, item_matrix, item_bias, user_score, item_score, global_bias, tfidf_predictor, vec_predictor
base_dir = '../../paper/data/dianping/mf/'
user_item_score_file = os.path.join(base_dir, 'train/comment.keyword.train.user_item_star')
user_matrix_file = os.path.join(base_dir, 'out/comment.keyword.train.user_item_star.user')
item_matrix_file = os.path.join(base_dir, 'out/comment.keyword.train.user_item_star.item')
test_file = os.path.join(base_dir, '../comment.keyword.test')
#test_file = os.path.join(base_dir, 'train/comment.mongo.train')
logging.info('loading user matrix...')
user_matrix, user_bias = load_nmf_matrix(user_matrix_file, print_log=True)
logging.info('loading item matrix...')
item_matrix, item_bias = load_nmf_matrix(item_matrix_file, print_log=True)
logging.info('loading item score...')
user_score, item_score, global_bias = load_user_item_score(user_item_score_file, print_log=True)
logging.info('global_bias:%f' % global_bias)
#load vector
vector_directory = "../../paper/data/dianping/w2v/vector"
model_directory = "../../paper/data/dianping/lr_model/"
user_vector = os.path.join(vector_directory, "comment.keyword.train.user.vector.200")
shop_vector = os.path.join(vector_directory, "comment.keyword.train.shop.vector.200")
vector_model_file = os.path.join(model_directory, "w2v_200_lr")
vec_predictor = vec_lr_predictor(user_vector, shop_vector, vector_model_file)
user_trained_shops = load_user_trained_shops(user_item_score_file)
shop_ids = load_ids(shop_vector)
user_ids = load_ids(user_vector)
fout = file('./predict_res.all', 'w')
user_count = 0
for user_id in user_ids:
if user_count % 1000 == 0:
logging.info('user count:%d' % user_count)
user_count += 1
if user_id not in user_trained_shops: continue
#predict_res = []
heap = MinSizeHeap(10)
for shop_id in shop_ids:
# if shop in training data, ignore it
if shop_id in user_trained_shops[user_id]: continue
heap.push((vector_score_function(user_id, shop_id), shop_id))
heap.sort()
#sorted_res = sorted(predict_res, key=lambda x: x[0], reverse=True)[0]
#res = ['%s:%lf' % (shop_id, score) for score, shop_id in sorted_res]
#fout.write('%s\t%s\n' % (user_id, '\t'.join(res)))
for score, shop_id in heap.arr:
fout.write('%s\t%s\t%s\n' % (user_id, shop_id, score))
fout.close()
def main():
global user_matrix, user_bias, item_matrix, item_bias, user_score, item_score, global_bias, tfidf_predictor, vec_predictor
base_dir = '../../paper/data/dianping/mf/'
user_item_score_file = os.path.join(
base_dir, 'train/comment.keyword.train.user_item_star')
user_matrix_file = os.path.join(
base_dir, 'out/comment.keyword.train.user_item_star.user')
item_matrix_file = os.path.join(
base_dir, 'out/comment.keyword.train.user_item_star.item')
test_file = os.path.join(base_dir, '../comment.keyword.test')
#test_file = os.path.join(base_dir, 'train/comment.mongo.train')
logging.info('loading user matrix...')
user_matrix, user_bias = load_nmf_matrix(user_matrix_file, print_log=True)
logging.info('loading item matrix...')
item_matrix, item_bias = load_nmf_matrix(item_matrix_file, print_log=True)
logging.info('loading item score...')
user_score, item_score, global_bias = load_user_item_score(
user_item_score_file, print_log=True)
logging.info('global_bias:%f' % global_bias)
#load vector
tfidf_directory = "../../paper/data/dianping/tfidf/vector"
vector_directory = "../../paper/data/dianping/w2v/vector"
model_directory = "../../paper/data/dianping/lr_model/"
tfidf_user_vector = os.path.join(tfidf_directory,
"comment.keyword.train.user.vector.1000")
tfidf_shop_vector = os.path.join(tfidf_directory,
"comment.keyword.train.shop.vector.1000")
user_vector = os.path.join(vector_directory,
"comment.keyword.train.user.vector")
shop_vector = os.path.join(vector_directory,
"comment.keyword.train.shop.vector")
tfidf_model_file = os.path.join(model_directory, "tfidf_top10K")
vector_model_file = os.path.join(model_directory, "w2v_500")
#tfidf_predictor = tfidf_lr_predictor(tfidf_user_vector, tfidf_shop_vector, tfidf_model_file)
vec_predictor = vec_lr_predictor(user_vector, shop_vector,
vector_model_file)
logging.info('calculating rmse...')
#rmse = cal_rmse(test_file, mf_score_function)
rmse = cal_rmse(test_file, vector_score_function)
print 'rmse:%lf' % rmse
logging.info('user_miss:%d, item_miss:%d, all_miss: %d' %
(user_miss, item_miss, all_miss))
#logging.info('tfidf_predictor.hit:%d, miss:%d' % (tfidf_predictor.hit, tfidf_predictor.miss))
logging.info('vec_predictor.hit:%d, miss:%d' %
(vec_predictor.hit, vec_predictor.miss))
def main():
global user_matrix, user_bias, item_matrix, item_bias, user_score, item_score, global_bias, tfidf_predictor, vec_predictor
base_dir = "../../paper/data/dianping/mf/"
user_item_score_file = os.path.join(base_dir, "train/comment.keyword.train.user_item_star")
user_matrix_file = os.path.join(base_dir, "out/comment.keyword.train.user_item_star.user")
item_matrix_file = os.path.join(base_dir, "out/comment.keyword.train.user_item_star.item")
test_file = os.path.join(base_dir, "../comment.keyword.test")
# test_file = os.path.join(base_dir, 'train/comment.mongo.train')
logging.info("loading user matrix...")
user_matrix, user_bias = load_nmf_matrix(user_matrix_file, print_log=True)
logging.info("loading item matrix...")
item_matrix, item_bias = load_nmf_matrix(item_matrix_file, print_log=True)
logging.info("loading item score...")
user_score, item_score, global_bias = load_user_item_score(user_item_score_file, print_log=True)
logging.info("global_bias:%f" % global_bias)
# load vector
tfidf_directory = "../../paper/data/dianping/tfidf/vector"
vector_directory = "../../paper/data/dianping/w2v/vector"
model_directory = "../../paper/data/dianping/lr_model/"
tfidf_user_vector = os.path.join(tfidf_directory, "comment.keyword.train.user.vector.1000")
tfidf_shop_vector = os.path.join(tfidf_directory, "comment.keyword.train.shop.vector.1000")
user_vector = os.path.join(vector_directory, "comment.keyword.train.user.vector")
shop_vector = os.path.join(vector_directory, "comment.keyword.train.shop.vector")
tfidf_model_file = os.path.join(model_directory, "tfidf_top10K")
vector_model_file = os.path.join(model_directory, "w2v_500")
# tfidf_predictor = tfidf_lr_predictor(tfidf_user_vector, tfidf_shop_vector, tfidf_model_file)
vec_predictor = vec_lr_predictor(user_vector, shop_vector, vector_model_file)
logging.info("calculating rmse...")
# rmse = cal_rmse(test_file, mf_score_function)
rmse = cal_rmse(test_file, vector_score_function)
print "rmse:%lf" % rmse
logging.info("user_miss:%d, item_miss:%d, all_miss: %d" % (user_miss, item_miss, all_miss))
# logging.info('tfidf_predictor.hit:%d, miss:%d' % (tfidf_predictor.hit, tfidf_predictor.miss))
logging.info("vec_predictor.hit:%d, miss:%d" % (vec_predictor.hit, vec_predictor.miss))
