def main():
align.load_cache()
foo = map(int, question_type.classify_questions(1)['Where'])
trainIDs = foo[:-3]
validationIDs = foo[-3:]
# print trainIDs
# trainIDs=range(201,220)
# validationIDs=range(339,339)
# testIDs=range(338,338)
evaluator_combinations = [
# [],
# [seq_length,
[punc_loc]
# bag_of_words,
# novelty_bool]
# [pos_test]
# [seq_length,punc_loc,question_apposition,rewrite_apposition,pos_test,vector_bag,bag_of_words,novelty_bool] #,novelty_count]
# [novelty_count]
]
evaluatorCombinationID = 1
for evaluators in evaluator_combinations:
y_train, x_train = question_learning_data(evaluators, trainIDs)
# print y_train
trained = train(mlpy.Srda, y_train, x_train)
results = run_question_predictions(evaluators, trained, validationIDs)
writeAnswers(
answerFile(results),
'results/combination' + str(evaluatorCombinationID) + '.txt')
evaluatorCombinationID = evaluatorCombinationID + 1
align.load_cache()
align.save_cache()
def main():
align.load_cache()
#foo=map(int,question_type.classify_questions(1)['Where'])
#Where questions
foo=[202, 211, 223, 226, 227, 243, 245, 249, 258, 266, 272, 283, 304, 306, 317, 318, 356, 359, 368, 369, 373, 385, 393]
evaluatorCombinationID=9000
# trainIDs=foo[:-6]
# validationIDs=foo[-6:]
trainIDs=foo
validationIDs=[10025,10026]
evaluator_combinations=[
[punc_loc,novelty_bool],
[punc_loc],
[novelty_bool]
#Don't use because they're weird
# vector_bag,
# [seq_length]
# [punc_loc]
]
for evaluators in evaluator_combinations:
y_train,x_train = question_learning_data(evaluators,trainIDs)
# print y_train
trained=train(mlpy.Srda,y_train,x_train)
results=run_question_predictions(evaluators,trained,validationIDs)
writeAnswers(answerFile(results),'results/combination'+str(evaluatorCombinationID)+'.txt')
evaluatorCombinationID=evaluatorCombinationID+1
align.load_cache()
align.save_cache()
def main():
align.load_cache()
foo=map(int,question_type.classify_questions(1)['Where'])
trainIDs=foo[:-3]
validationIDs=foo[-3:]
# print trainIDs
# trainIDs=range(201,220)
# validationIDs=range(339,339)
# testIDs=range(338,338)
evaluator_combinations=[
# [],
# [seq_length,
[punc_loc]
# bag_of_words,
# novelty_bool]
# [pos_test]
# [seq_length,punc_loc,question_apposition,rewrite_apposition,pos_test,vector_bag,bag_of_words,novelty_bool] #,novelty_count]
# [novelty_count]
]
evaluatorCombinationID=1
for evaluators in evaluator_combinations:
y_train,x_train = question_learning_data(evaluators,trainIDs)
# print y_train
trained=train(mlpy.Srda,y_train,x_train)
results=run_question_predictions(evaluators,trained,validationIDs)
writeAnswers(answerFile(results),'results/combination'+str(evaluatorCombinationID)+'.txt')
evaluatorCombinationID=evaluatorCombinationID+1
align.load_cache()
align.save_cache()
def question_candidates(q_id):
'''Select some useful subset of the candidates for a particular question.
Return them in a list.
'''
init.get_corpus(qNum=q_id)
foo = cache_file(q_id)
candidate = cache_chunkers.uncache_chunks(open(foo))[q_id]
new_l = []
for c in candidate:
if (c[3] == "NP"):
dist = align_question_distance(get_question(q_id), c)
if dist[0] < DIST_CUTOFF and dist[1] > SCORE_CUTOFF:
new_l.append(c)
align.save_cache()
print len(new_l)
return new_l
def question_candidates(q_id):
'''Select some useful subset of the candidates for a particular question.
Return them in a list.
'''
init.get_corpus(qNum=q_id)
foo=cache_file(q_id)
candidate = cache_chunkers.uncache_chunks(open(foo))[q_id]
new_l = []
for c in candidate:
if (c[3] == "NP"):
dist = align_question_distance(get_question(q_id), c)
if dist[0] < DIST_CUTOFF and dist[1] > SCORE_CUTOFF:
new_l.append(c)
align.save_cache()
print len(new_l)
return new_l