def cal_average_no_boot(multi_results):
precs = []
recs = []
fs = []
for res in multi_results:
prec, rec, f1, _ = U.cal_prec_rec_f1_v2(res, targets)
precs.append(prec)
recs.append(rec)
fs.append(f1)
return np.average(fs), np.average(precs), np.average(recs)
def get_test_result_long_v2(m, testld):
if testld is None:
return none_dic()
else:
dic = {}
testld.start = 0
outputs, targets = U.get_test_results_single_point_v2(
m, testld, U.logging.debug)
prec, rec, f1, bacc = U.cal_prec_rec_f1_v2(outputs, targets)
dic['prec'] = prec
dic['rec'] = rec
dic['f1'] = f1
dic['bacc'] = bacc
return dic
def run_test(m, epoch = 1, path = 'result.png', log_path = 'danraku.log'):
U.init_logger(log_path)
results = []
losss = []
ld.start = 0
for i in range(epoch):
loss = U.train_by_data_loader_danraku(m, mini_ld, 1, U.logging.debug)[0]
losss.append(loss)
outputs, targets = U.get_test_results(m, mini_testld)
prec, rec, f1, bacc = U.cal_prec_rec_f1_v2(outputs, targets)
results.append((prec, rec, f1, bacc))
print(f'epoch = {i}, loss = {loss}, {prec}, {rec}, {f1}')
plot_prec_rec_f1_loss(results, losss, epoch, path)
print('save to result.png')
return m, results, losss
def cal_avg_score(targets, multi_results, sampled_indexs):
sampled_targets = [targets[idx] for idx in sampled_indexs]
sampled_multi_results = []
for results in multi_results:
sampled_multi_results.append([results[idx] for idx in sampled_indexs])
f_scores = []
precs = []
recs = []
for sampled_results in sampled_multi_results:
prec, rec, f1, bacc = U.cal_prec_rec_f1_v2(sampled_results,
sampled_targets)
f_scores.append(f1)
precs.append(prec)
recs.append(rec)
return np.average(f_scores), np.average(precs), np.average(recs)
def get_test_result_segbot(m, testds):
dic = {}
if testds is None:
dic['prec'] = -1
dic['rec'] = -1
dic['f1'] = -1
dic['bacc'] = -1
else:
outputs, targets = get_test_results_segbot_v2(m, testds,
U.logging.debug)
prec, rec, f1, bacc = U.cal_prec_rec_f1_v2(outputs, targets)
dic['prec'] = prec
dic['rec'] = rec
dic['f1'] = f1
dic['bacc'] = bacc
return dic
def get_test_result(m, testld):
testld.start = 0
dic = {}
if testld is None:
dic['prec'] = -1
dic['rec'] = -1
dic['f1'] = -1
dic['bacc'] = -1
else:
outputs, targets = U.get_test_results_batch(m, testld, U.logging.debug)
prec, rec, f1, bacc = U.cal_prec_rec_f1_v2(outputs, targets)
dic['prec'] = prec
dic['rec'] = rec
dic['f1'] = f1
dic['bacc'] = bacc
return dic
def get_test_result_dic(m, testld):
testld.start = 0
dic = {}
if testld is None:
dic['prec'] = -1
dic['rec'] = -1
dic['f1'] = -1
dic['bacc'] = -1
else:
outputs, targets = get_test_result(m, testld)
prec, rec, f1, bacc = U.cal_prec_rec_f1_v2(outputs, targets)
dic['prec'] = prec
dic['rec'] = rec
dic['f1'] = f1
dic['bacc'] = bacc
return dic
def run(m, epoch = 20, path = 'result.png', log_path = 'danraku.log', ld = ld, batch=8):
U.init_logger(log_path)
results = []
losss = []
recover_batch_size = ld.batch_size
ld.batch_size = batch
ld.start = 0
print(f'Loader window size: {ld.ds.half_window_size}')
for i in range(epoch):
loss = U.train_by_data_loader_danraku(m, ld, 1, U.logging.debug)[0]
losss.append(loss)
outputs, targets = U.get_test_results(m, testld)
prec, rec, f1, bacc = U.cal_prec_rec_f1_v2(outputs, targets)
results.append((prec, rec, f1, bacc))
print(f'epoch = {i}, loss = {loss}, {prec}, {rec}, {f1}')
plot_prec_rec_f1_loss(results, losss, epoch, path)
print('save to result.png')
ld.batch_size = recover_batch_size
return m, results, losss