'payload': payload,
'upload_time': the_time
}
time.sleep(0.01)
shuffledQids = training_queries.keys()[:]
random.shuffle(shuffledQids)
while True:
counter = 0
time.sleep(0.01)
for qid in shuffledQids:
counter += 1
#print qid, counter, 'of', qlen
q = training_queries[qid]
c = user_model.get_clicks(uploads[qid]['ranked_lerot_list'],
q.get_labels(),
query=q,
ranker_list=uploads[qid]['payload'],
upload_time=uploads[qid]['upload_time'])
if c is not None: break
if c is not None:
print c
break
s = learner.update_solution(c)
eval = evaluation.evaluate_all(s, test_queries)
print eval
with open('evaluation', 'a') as f:
f.write(str(eval) + '\n')
if runid == 1000:
break
import sys, random
import include
import lerot.retrieval_system, lerot.environment, lerot.evaluation, lerot.query
#learner = lerot.retrieval_system.ListwiseLearningSystem(46, '-w random -c comparison.TeamDraft -r ranker.DeterministicRankingFunction -s 3 -d 0.1 -a 0.01')
learner = lerot.retrieval_system.PairwiseLearningSystem(46,
"--init_weights random --epsilon 0.0 --eta 0.001 --ranker "
"ranker.DeterministicRankingFunction --ranker_tie first")
user_model = lerot.environment.CascadeUserModel('--p_click 0:0.0,1:.5,2:1.0 --p_stop 0:0.0,1:0.0,2:0.0')
evaluation = lerot.evaluation.NdcgEval()
training_queries = lerot.query.load_queries(sys.argv[1], 46)
test_queries = lerot.query.load_queries(sys.argv[2], 46)
while True:
q = training_queries[random.choice(training_queries.keys())]
l = learner.get_ranked_list(q)
c = user_model.get_clicks(l, q.get_labels())
s = learner.update_solution(c)
print evaluation.evaluate_all(s, test_queries)
shuffledQids = training_queries.keys()[:]
random.shuffle(shuffledQids) #shuffle query ids list
while True:
for qid in shuffledQids:
q = training_queries[qid]
c = user_model.get_clicks(uploads[qid]['current_l'],
q.get_labels())
if isinstance(c, np.ndarray): break
if isinstance(c, np.ndarray):
break
q = training_queries[qid]
learner.get_ranked_list(q) #, firstTime
learner.current_l = uploads[qid]['current_l']
s = learner.update_solution(c) #update learner using clicks
eval = evaluation.evaluate_all(learner.ranker, test_queries)
out_path = os.path.abspath(
os.path.join(os.path.dirname(__file__), os.pardir)
) + '/output_data/pairwise_local_evaluation_data/fold' + str(
fold) + '_' + (str(repetition))
evalString += str(eval)
evalString += '\n'
print eval
if runid >= 100 and runid % 100 == 0:
with open(out_path, 'a') as f:
f.write(str(evalString))
evalString = ''
with open(
os.path.abspath(
os.path.join(os.path.dirname(__file__), os.pardir))
+ '/output_data/pairwise_local_evaluation_data/' +
#print qid, counter, 'of', qlen
#['__feature_vectors__', '__docids__', '__labels__', '__qid__', '__comments__']
q = training_queries[qid]
l = learner.get_ranked_list(q)
payload, the_time = user_model.upload_run(q, l, runid)
uploads[qid] = {'ranked_lerot_list':l, 'payload':payload, 'upload_time':the_time}
time.sleep(0.01)
shuffledQids = training_queries.keys()[:]
random.shuffle(shuffledQids)
while True:
counter = 0
time.sleep(0.01)
for qid in shuffledQids:
counter += 1
#print qid, counter, 'of', qlen
q = training_queries[qid]
c = user_model.get_clicks(uploads[qid]['ranked_lerot_list'], q.get_labels(),
query=q, ranker_list=uploads[qid]['payload'], upload_time=uploads[qid]['upload_time'])
if c is not None: break
if c is not None:
print c
break
s = learner.update_solution(c)
eval = evaluation.evaluate_all(s, test_queries)
print eval
with open('evaluation', 'a') as f:
f.write(str(eval)+'\n')
if runid == 1000:
break
'current_query': q
}
shuffledQids = training_queries.keys()[:]
random.shuffle(shuffledQids)#shuffle query ids list
while True:
for qid in shuffledQids:
q = training_queries[qid]
c = user_model.get_clicks(uploads[qid]['current_l'], q.get_labels())
if isinstance(c, np.ndarray):break
if isinstance(c, np.ndarray):
break
q = training_queries[qid]
learner.get_ranked_list(q)#, firstTime
learner.current_l = uploads[qid]['current_l']
s = learner.update_solution(c)#update learner using clicks
eval = evaluation.evaluate_all(learner.ranker, test_queries)
out_path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir))+'/output_data/pairwise_local_evaluation_data/fold'+str(fold)+'_'+(str(repetition))
evalString += str(eval)
evalString += '\n'
print eval
if runid >= 100 and runid % 100 == 0:
with open(out_path, 'a') as f:
f.write(str(evalString))
evalString = ''
with open(os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir))+'/output_data/pairwise_local_evaluation_data/'+'learnerPickle.c', 'wb') as fp:
pickle.dump(learner, fp)
if runid == 1000:
break
