# acc = util.model_roc_score(learner, test_dat)
acc = util.model_score(learner, test_dat)
test_acc.append(acc)
learned_size.append(total_pool_size - prof.get_pool_size() + init_size)
if ITER_ENABLE:
if count < 0: break
count -= 1
return test_acc, learned_size
pool_dat = load_pool()
init_dat = load_init()
test_dat = load_test()
train_pool = np.array(gen_land_pool(pool_dat))
shuffle(train_pool)
print "[info]Start passive learning..."
test_acc_ps, learned_size_ps = run_stl_landm(pool_dat,
init_dat,
test_dat,
do_active=False)
util.curve_to_csv("res/ps_stl_non.csv", test_acc_ps, learned_size_ps)
print "[info]Start active learning..."
test_acc_ac, learned_size_ac = run_stl_landm(pool_dat,
init_dat,
def reload_dat():
gc.collect()
pool_dat = load_pool()
init_dat = load_init()
test_dat = load_test()
return pool_dat, init_dat, test_dat
eng.addpath("/home/stpanda/Dropbox/STDreamSoft/Academics/SeniorThesis/Projects/al_ella/ml")
eng.addpath(eng.genpath(ELLA_DIR))
# res = eng.runExperimentActiveTask()
# print res
######## Panda ######
# Comparing Multiple Active Learner vs ELLA + ATS vs ELLA + ATS + AL vs
# ELLA + AL
# This file runs ELLA + Active Task Selection
#####################
## Load all files
test_dat = util.add_bias(load_test())
pool_dat = util.add_bias(load_pool())
init_dat = util.add_bias(load_init())
init_size = util.dat_size(init_dat)
## Init ELLA Model with init set ##
ella_model = ELLA(eng, init_dat)
init_acc = ella_score(ella_model, test_dat)
test_acc = [init_acc]
learned_size = [init_size]
prof = Professor(init_dat, pool_dat, multi_t=True, random=True)
total_pool_size = prof.get_pool_size()
print "train pool size", total_pool_size
# ### Training Until No more data available OR Reach the set N_ITER ###
count = N_ITER