print(seen)
sleep(1)
return seen
if __name__ == "__main__":
from util import flags
flags.define_string("title_index", None, "Pickle of all titles")
flags.define_string("label_path", None, "Where we write page associations")
flags.define_string("database", None, "Question database")
flags.define_string("performance_output", None, "Where we write user performance")
flags.define_string("user", None, "User identifier")
flags.InitFlags()
seen = already_answered(flags.performance_output, flags.user)
al = ActiveLearner(None, flags.label_path)
print("Loading question db %s" % flags.database)
db = QuestionDatabase(flags.database)
pw = PerformanceWriter(flags.performance_output, flags.user)
tf = TitleFinder(open(flags.title_index))
questions = db.questions_by_tournament("High School Championship")
for qid in questions:
question = questions[qid]
if question.fold == "train" or qid in seen:
continue
choices = list(tf.query(question.answer))
# Get what and when the human answered
wp, idx, ans = get_answer([question.text[x] for x in sorted(question.text)], question.answer, question.page)
return seen
if __name__ == "__main__":
from util import flags
flags.define_string("title_index", None, "Pickle of all titles")
flags.define_string("label_path", None, "Where we write page associations")
flags.define_string("database", None, "Question database")
flags.define_string("performance_output", None,
"Where we write user performance")
flags.define_string("user", None, "User identifier")
flags.InitFlags()
seen = already_answered(flags.performance_output, flags.user)
al = ActiveLearner(None, flags.label_path)
print("Loading question db %s" % flags.database)
db = QuestionDatabase(flags.database)
pw = PerformanceWriter(flags.performance_output, flags.user)
tf = TitleFinder(open(flags.title_index))
questions = db.questions_by_tournament("High School Championship")
for qid in questions:
question = questions[qid]
if question.fold == "train" or qid in seen:
continue
choices = list(tf.query(question.answer))
# Get what and when the human answered
wp, idx, ans = get_answer(
[question.text[x] for x in sorted(question.text)], question.answer,
args.add_argument('--titles',
type=str,
default='data/wiki_index.pkl',
help='page title candiates')
args.add_argument('--labels',
type=str,
default='data/map/ans_to_wiki',
help='write page assignment answers')
args = args.parse_args()
# Open up the database
d = QuestionDatabase(args.database)
page_diversity = d.answer_map(normalize)
# Set up the active learner for writing assignments
al = ActiveLearner(None, args.labels)
existing_labels = set(x[0] for x in al.human_labeled())
# get the candidates we want to assign to pages
answers = d.unmatched_answers(existing_labels)
print(answers.keys()[:10])
# Open up the title finder
tf = TitleFinder(open(args.titles))
for ans, count in sorted(answers.items(),
key=lambda x: sum(x[1].values()),
reverse=True):
if ans in kBAD_ANSWERS:
continue
choices = list(tf.query(ans))
if __name__ == "__main__":
args = argparse.ArgumentParser('Interactive assign pages to questions')
args.add_argument('--database', type=str, default='data/questions.db',
help='sqlite3 database of questions')
args.add_argument('--titles', type=str, default='data/wiki_index.pkl',
help='page title candiates')
args.add_argument('--labels', type=str, default='data/map/ans_to_wiki',
help='write page assignment answers')
args = args.parse_args()
# Open up the database
d = QuestionDatabase(args.database)
page_diversity = d.answer_map(normalize)
# Set up the active learner for writing assignments
al = ActiveLearner(None, args.labels)
existing_labels = set(x[0] for x in al.human_labeled())
# get the candidates we want to assign to pages
answers = d.unmatched_answers(existing_labels)
print(answers.keys()[:10])
# Open up the title finder
tf = TitleFinder(open(args.titles))
for ans, count in sorted(answers.items(), key=lambda x: sum(x[1].values()),
reverse=True):
if ans in kBAD_ANSWERS:
continue
choices = list(tf.query(ans))
print("--------- (%i)" % sum(count.values()))
import pickle
import time
from page_assignment.active_learning_for_matching import ActiveLearner
from util.qdb import QuestionDatabase
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="apply wikipedia pages")
parser.add_argument("--db", default='data/questions.db', type=str,
help="The question database")
parser.add_argument("--match_location", type=str,
default='data/map/ans_to_wiki_',
help="Where we read matches learned")
flags = parser.parse_args()
start = time.time()
print("Loading db..")
db = QuestionDatabase(flags.db)
print("Loading classifier...")
classifier = ActiveLearner(None, flags.match_location, [])
for question, page in classifier.human_labeled():
ans_type = ""
db.set_answer_page(question, page, ans_type)
print(question, page, "GIVEN", ans_type)
