def batchCalcTimePrefScore():
"""
Batch work process.
Access DB's predicttokens collection and task collection.
Calculate score, and save it back to the DB.
"""
from pymongo import MongoClient
import tokenizer
client = MongoClient(CONST_DB_ADDR, CONST_DB_PORT)
# print client.database_names()
db = client.test
# print db.collection_names()
task_collection = db.tasks;
token_collection = db.predicttokens;
for c in task_collection.find():
# Run for all tasks.
# We may want to run only for non-completed tasks because they are
# the only tasks that time preference score matters.
# However, for now we don't have enough data, so we need to use every
# tasks even if it is already completed.
# get score for all time slots.
content = c['name'] + ' ' + c['description']
tokens = tokenizer.extractor(content);
score = getTimePrefScore(c['userId'], token_collection, tokens)
task_collection.update_one({'_id': c['_id']}, {'$set': {'timePreferenceScore': score}});
def batchCalcTimePrefScore():
"""
Batch work process.
Access DB's predicttokens collection and task collection.
Calculate score, and save it back to the DB.
"""
from pymongo import MongoClient
import tokenizer
client = MongoClient(CONST_DB_ADDR, CONST_DB_PORT)
# print client.database_names()
db = client.test
# print db.collection_names()
task_collection = db.tasks
token_collection = db.predicttokens
for c in task_collection.find():
# Run for all tasks.
# We may want to run only for non-completed tasks because they are
# the only tasks that time preference score matters.
# However, for now we don't have enough data, so we need to use every
# tasks even if it is already completed.
# get score for all time slots.
content = c['name'] + ' ' + c['description']
tokens = tokenizer.extractor(content)
score = getTimePrefScore(c['userId'], token_collection, tokens)
task_collection.update_one({'_id': c['_id']},
{'$set': {
'timePreferenceScore': score
}})
def tokenizer_execute(language, page_html, link):
obj = tokenizer.tokenizer(language)
obj.generate_tokens()
obj2 = tokenizer.semantic_tokenizer(obj.tokens)
obj2.generate_tokens()
obj3 = tokenizer.extractor(obj.tokens, obj2.semantic_tokens)
if not page_html:
returned_result = obj3.start_extract(link)
return returned_result
else:
returned_result = obj3.start_extract_without_fetch(page_html)
return returned_result
def main():
import sys
import json
result = {}
try:
result["tokens"] = tokenizer.extractor(sys.argv[1])
except:
with open('errlog.txt', 'w') as f:
f.write(sys.exc_info()[0])
sys.exit(1)
print json.dumps(result)
sys.exit(0)
def main():
import sys
import json
result = {}
try:
result["tokens"] = tokenizer.extractor(sys.argv[1])
except:
with open('errlog.txt', 'w') as f:
f.write(sys.exc_info()[0])
sys.exit(1);
print json.dumps(result)
sys.exit(0)
def updateSyllabusToFirebase():
text = extractor(reader(g.file).full_text).final_list
db = firebase.database()
l = []
for i in text:
topics = i.split(",")
for j in topics:
te = getContent(j)
data = {"topic": j, "isVideo": te.isVideo, "summary": te.summary}
db.child("syllabus").child(g.year_).child(g.branch_).child(g.subject_).push(data)
l.append(te.summary)
with open("new.txt", "w") as outfile:
outfile.write("\n".join(i for i in l))
print("DONE!!")
