def replace_score(predictor_name = 'MaxEntropy'):
from database import update_score_for_product_id, select_ft_score
from predictor import loadTrainedPredictor
from srs_local import get_ft_dicts_from_contents
'''
this function replaces all scores stored in the db with scores by vanderSentiment
'''
res = select_ft_score()
predictor = loadTrainedPredictor(predictor_name)
# print res[1]["ft_score"]
for r in res:
product_id = r["product_id"]
prod_contents = r["contents"]
prod_ft_score_dict, prod_ft_senIdx_dict = get_ft_dicts_from_contents(prod_contents, predictor)
update_score_for_product_id(product_id, prod_ft_score_dict, prod_ft_senIdx_dict)
def calculate_ft_dict_for_all_products(predictor_name):
predictor = loadTrainedPredictor()
client, db = connect_to_db()
db_product_collection = db.product_collection
cursor = db_product_collection.find()
i=0
for product in cursor:
i+=1
product_id = product['product_id']
prod_contents = product['contents']
prod_ft_score_dict, prod_ft_senIdx_dict = get_ft_dicts_from_contents(prod_contents, predictor)
query = {"product_id": product_id}
update_field = {
"ft_score": prod_ft_score_dict,
"ft_senIdx": prod_ft_senIdx_dict
}
db_product_collection.update(query, {"$set": update_field}, True)
if i%10 == 0:
print i
client.close()
