def eval(model, data_eval, voc_size, epoch):
eval_len = len(data_eval)
# evaluate
print('')
model.eval()
y_pred_prob = np.zeros((eval_len, voc_size[-1]))
y_gt = y_pred_prob.copy()
y_pred = y_pred_prob.copy()
for step, input in enumerate(data_eval):
pred_prob, target = model(input)
pred_prob = F.sigmoid(pred_prob[0]).detach().cpu().numpy()
target = target[0].detach().cpu().numpy()
pred = pred_prob.copy()
pred[pred >= 0.35] = 1
pred[pred < 0.35] = 0
y_pred[step, :] = pred
y_pred_prob[step, :] = pred_prob
y_gt[step, :] = target
llprint('\rEval--Epoch: %d, Step: %d/%d' %
(epoch, step, len(data_eval)))
js, auc, p_1, p_3, p_5, f1, auprc = metric(y_gt, y_pred, y_pred_prob)
llprint(
'\tJS: %.4f, AUC: %.4f, P1: %.4f, P3: %.4f, P5: %.4f, F1: %.4f, AUPRC: %.4F\n'
% (js, auc, p_1, p_3, p_5, f1, auprc))
def eval(model, data_eval, voc_size, epoch):
# evaluate
print('')
model.eval()
smm_record = []
ja, prauc, avg_p, avg_r, avg_f1 = [[] for _ in range(5)]
case_study = defaultdict(dict)
med_cnt = 0
visit_cnt = 0
for step, input in enumerate(data_eval):
if len(input) < 2: # visit > 2
continue
y_gt = []
y_pred = []
y_pred_prob = []
y_pred_label = []
for i in range(1, len(input)):
y_pred_label_tmp = []
y_gt_tmp = np.zeros(voc_size[2])
y_gt_tmp[input[i][2]] = 1
y_gt.append(y_gt_tmp)
target_output1 = model(input[:i])
target_output1 = F.sigmoid(target_output1).detach().cpu().numpy()[0]
y_pred_prob.append(target_output1)
y_pred_tmp = target_output1.copy()
y_pred_tmp[y_pred_tmp >= 0.3] = 1
y_pred_tmp[y_pred_tmp < 0.3] = 0
y_pred.append(y_pred_tmp)
for idx, value in enumerate(y_pred_tmp):
if value == 1:
y_pred_label_tmp.append(idx)
y_pred_label.append(y_pred_label_tmp)
med_cnt += len(y_pred_label_tmp)
visit_cnt += 1
smm_record.append(y_pred_label)
adm_ja, adm_prauc, adm_avg_p, adm_avg_r, adm_avg_f1 = multi_label_metric(np.array(y_gt), np.array(y_pred),
np.array(y_pred_prob))
case_study[adm_ja] = {'ja': adm_ja, 'patient':input, 'y_label':y_pred_label}
ja.append(adm_ja)
prauc.append(adm_prauc)
avg_p.append(adm_avg_p)
avg_r.append(adm_avg_r)
avg_f1.append(adm_avg_f1)
llprint('\rEval--Epoch: %d, Step: %d/%d' % (epoch, step, len(data_eval)))
dill.dump(case_study, open(os.path.join('saved', model_name, 'case_study.pkl'), 'wb'))
# ddi rate
ddi_rate = ddi_rate_score(smm_record)
llprint('\tDDI Rate: %.4f, Jaccard: %.4f, PRAUC: %.4f, AVG_PRC: %.4f, AVG_RECALL: %.4f, AVG_F1: %.4f\n' % (
ddi_rate, np.mean(ja), np.mean(prauc), np.mean(avg_p), np.mean(avg_r), np.mean(avg_f1)
))
print('avg med', med_cnt / visit_cnt)
return ddi_rate, np.mean(ja), np.mean(prauc), np.mean(avg_p), np.mean(avg_r), np.mean(avg_f1)
def eval(model, data_eval, voc_size, epoch):
model.eval()
ja, prauc, avg_p, avg_r, avg_f1 = [[] for _ in range(5)]
smm_record = []
med_cnt, visit_cnt = 0, 0
for step, input in enumerate(data_eval):
y_gt = []
y_pred = []
y_pred_prob = []
y_pred_label = []
for adm_index, adm in enumerate(input):
output_logits = model(adm)
y_gt_tmp = np.zeros(voc_size[2])
y_gt_tmp[adm[2]] = 1
y_gt.append(y_gt_tmp)
# prediction prod
output_logits = output_logits.detach().cpu().numpy()
# prediction med set
out_list, sorted_predict = sequence_output_process(
output_logits, [voc_size[2], voc_size[2] + 1])
y_pred_label.append(sorted(sorted_predict))
y_pred_prob.append(np.mean(output_logits[:, :-2], axis=0))
# prediction label
y_pred_tmp = np.zeros(voc_size[2])
y_pred_tmp[out_list] = 1
y_pred.append(y_pred_tmp)
visit_cnt += 1
med_cnt += len(sorted_predict)
smm_record.append(y_pred_label)
adm_ja, adm_prauc, adm_avg_p, adm_avg_r, adm_avg_f1 = \
sequence_metric(np.array(y_gt), np.array(y_pred), np.array(y_pred_prob), np.array(y_pred_label))
ja.append(adm_ja)
prauc.append(adm_prauc)
avg_p.append(adm_avg_p)
avg_r.append(adm_avg_r)
avg_f1.append(adm_avg_f1)
llprint('\rtest step: {} / {}'.format(step, len(data_eval)))
# ddi rate
ddi_rate = ddi_rate_score(smm_record,
path='../data/output/ddi_A_final.pkl')
llprint(
'\nDDI Rate: {:.4}, Jaccard: {:.4}, PRAUC: {:.4}, AVG_PRC: {:.4}, AVG_RECALL: {:.4}, AVG_F1: {:.4}, AVG_MED: {:.4}\n'
.format(ddi_rate, np.mean(ja), np.mean(prauc), np.mean(avg_p),
np.mean(avg_r), np.mean(avg_f1), med_cnt / visit_cnt))
return ddi_rate, np.mean(ja), np.mean(prauc), np.mean(avg_p), np.mean(
avg_r), np.mean(avg_f1), med_cnt / visit_cnt
def eval(model, data_eval, voc_size, epoch):
# evaluate
print('')
model.eval()
smm_record = []
ja, prauc, avg_p, avg_r, avg_f1 = [[] for _ in range(5)]
for step, input in enumerate(data_eval):
y_gt = []
y_pred = []
y_pred_prob = []
y_pred_label = []
input1_hidden, input2_hidden, target_hidden = None, None, None
prev_target = None
for adm_idx, adm in enumerate(input):
target_output1, [input1_hidden,
input2_hidden, target_hidden] = model(
adm, prev_target,
[input1_hidden, input2_hidden, target_hidden])
prev_target = adm[2]
y_pred_label_tmp = []
y_gt_tmp = np.zeros(voc_size[2])
y_gt_tmp[adm[2]] = 1
y_gt.append(y_gt_tmp)
target_output1 = F.sigmoid(
target_output1).detach().cpu().numpy()[0]
y_pred_prob.append(target_output1)
y_pred_tmp = target_output1.copy()
y_pred_tmp[y_pred_tmp >= 0.5] = 1
y_pred_tmp[y_pred_tmp < 0.5] = 0
y_pred.append(y_pred_tmp)
for idx, value in enumerate(y_pred_tmp):
if value == 1:
y_pred_label_tmp.append(idx)
y_pred_label.append(y_pred_label_tmp)
smm_record.append(y_pred_label)
adm_ja, adm_prauc, adm_avg_p, adm_avg_r, adm_avg_f1 = multi_label_metric(
np.array(y_gt), np.array(y_pred), np.array(y_pred_prob))
ja.append(adm_ja)
prauc.append(adm_prauc)
avg_p.append(adm_avg_p)
avg_r.append(adm_avg_r)
avg_f1.append(adm_avg_f1)
llprint('\rEval--Epoch: %d, Step: %d/%d' %
(epoch, step, len(data_eval)))
# ddi rate
ddi_rate = ddi_rate_score(smm_record)
llprint(
'\tDDI Rate: %.4f, Jaccard: %.4f, PRAUC: %.4f, AVG_PRC: %.4f, AVG_RECALL: %.4f, AVG_F1: %.4f\n'
% (ddi_rate, np.mean(ja), np.mean(prauc), np.mean(avg_p),
np.mean(avg_r), np.mean(avg_f1)))
return ddi_rate, np.mean(ja), np.mean(prauc), np.mean(avg_p), np.mean(
avg_r), np.mean(avg_f1)
def eval(model, data_eval, voc_size, epoch):
model.eval()
smm_record = []
ja, prauc, avg_p, avg_r, avg_f1 = [[] for _ in range(5)]
med_cnt, visit_cnt = 0, 0
for step, input in enumerate(data_eval):
y_gt, y_pred, y_pred_prob, y_pred_label = [], [], [], []
if len(input) < 2: continue
for i in range(1, len(input)):
target_output = model(input[:i])
y_gt_tmp = np.zeros(voc_size[2])
y_gt_tmp[input[i][2]] = 1
y_gt.append(y_gt_tmp)
# prediction prob
target_output = F.sigmoid(target_output).detach().cpu().numpy()[0]
y_pred_prob.append(target_output)
# prediction med set
y_pred_tmp = target_output.copy()
y_pred_tmp[y_pred_tmp >= 0.4] = 1
y_pred_tmp[y_pred_tmp < 0.4] = 0
y_pred.append(y_pred_tmp)
# prediction label
y_pred_label_tmp = np.where(y_pred_tmp == 1)[0]
y_pred_label.append(y_pred_label_tmp)
med_cnt += len(y_pred_label_tmp)
visit_cnt += 1
smm_record.append(y_pred_label)
adm_ja, adm_prauc, adm_avg_p, adm_avg_r, adm_avg_f1 =\
multi_label_metric(np.array(y_gt), np.array(y_pred), np.array(y_pred_prob))
ja.append(adm_ja)
prauc.append(adm_prauc)
avg_p.append(adm_avg_p)
avg_r.append(adm_avg_r)
avg_f1.append(adm_avg_f1)
llprint('\rtest step: {} / {}'.format(step, len(data_eval)))
# ddi rate
ddi_rate = ddi_rate_score(smm_record,
path='../data/output/ddi_A_final.pkl')
llprint(
'\nDDI Rate: {:.4}, Jaccard: {:.4}, PRAUC: {:.4}, AVG_PRC: {:.4}, AVG_RECALL: {:.4}, AVG_F1: {:.4}, AVG_MED: {:.4}\n'
.format(ddi_rate, np.mean(ja), np.mean(prauc), np.mean(avg_p),
np.mean(avg_r), np.mean(avg_f1), med_cnt / visit_cnt))
return ddi_rate, np.mean(ja), np.mean(prauc), np.mean(avg_p), np.mean(
avg_r), np.mean(avg_f1), med_cnt / visit_cnt
def eval(model, data_eval, voc_size, epoch):
# evaluate
print('')
model.eval()
ja, prauc, avg_p, avg_r, avg_f1 = [[] for _ in range(5)]
records = []
med_cnt, visit_cnt = 0, 0
for step, input in enumerate(data_eval):
y_gt = []
y_pred = []
y_pred_prob = []
y_pred_label = []
i1_state, i2_state, i3_state = None, None, None
for adm in input:
y_gt_tmp = np.zeros(voc_size[2])
y_gt_tmp[adm[2]] = 1
y_gt.append(y_gt_tmp)
output_logits, i1_state, i2_state, i3_state = model(
adm, i1_state, i2_state, i3_state)
output_logits = output_logits.detach().cpu().numpy()
out_list, sorted_predict = sequence_output_process(
output_logits, [voc_size[2], voc_size[2] + 1])
y_pred_label.append(sorted_predict)
y_pred_prob.append(np.mean(output_logits[:, :-2], axis=0))
y_pred_tmp = np.zeros(voc_size[2])
y_pred_tmp[out_list] = 1
y_pred.append(y_pred_tmp)
visit_cnt += 1
med_cnt += len(y_pred_tmp)
records.append(y_pred_label)
adm_ja, adm_prauc, adm_avg_p, adm_avg_r, adm_avg_f1 = sequence_metric(
np.array(y_gt), np.array(y_pred), np.array(y_pred_prob),
np.array(y_pred_label))
ja.append(adm_ja)
prauc.append(adm_prauc)
avg_p.append(adm_avg_p)
avg_r.append(adm_avg_r)
avg_f1.append(adm_avg_f1)
llprint('\rEval--Epoch: %d, Step: %d/%d' %
(epoch, step, len(data_eval)))
# ddi rate
ddi_rate = ddi_rate_score(records)
llprint(
'\tDDI Rate: %.4f, Jaccard: %.4f, PRAUC: %.4f, AVG_PRC: %.4f, AVG_RECALL: %.4f, AVG_F1: %.4f, AVG_Med: %.4f\n'
% (ddi_rate, np.mean(ja), np.mean(prauc), np.mean(avg_p),
np.mean(avg_r), np.mean(avg_f1), med_cnt / visit_cnt))
return ddi_rate, np.mean(ja), np.mean(prauc), np.mean(avg_p), np.mean(
avg_r), np.mean(avg_f1)
def eval(model, data_eval, voc_size, epoch):
# evaluate
print('')
model.eval()
smm_record = []
auc, p_1, p_3, p_5, f1, prauc = [[] for _ in range(6)]
for step, input in enumerate(data_eval):
y_gt = []
y_pred = []
y_pred_prob = []
y_pred_label = []
input1_hidden, input2_hidden, target_hidden = None, None, None
for adm in input:
y_pred_label_tmp = []
y_gt_tmp = np.zeros(voc_size[2])
y_gt_tmp[adm[2]] = 1
y_gt.append(y_gt_tmp)
target_output1, output_logits, output_labels, [
input1_hidden, input2_hidden, target_hidden
] = model(adm, [input1_hidden, input2_hidden, target_hidden])
target_output1 = F.sigmoid(
target_output1).detach().cpu().numpy()[0]
a = np.argsort(target_output1)[::-1]
b = np.max(output_logits, axis=-1)
y_pred_prob.append(target_output1)
y_pred_tmp = target_output1.copy()
y_pred_tmp[y_pred_tmp >= 0.5] = 1
y_pred_tmp[y_pred_tmp < 0.5] = 0
y_pred.append(y_pred_tmp)
for idx, value in enumerate(y_pred_tmp):
if value == 1:
y_pred_label_tmp.append(idx)
y_pred_label.append(y_pred_label_tmp)
smm_record.append(y_pred_label)
adm_auc, adm_p_1, adm_p_3, adm_p_5, adm_f1, adm_prauc = multi_label_metric(
np.array(y_gt), np.array(y_pred), np.array(y_pred_prob))
auc.append(adm_auc)
p_1.append(adm_p_1)
p_3.append(adm_p_3)
p_5.append(adm_p_5)
f1.append(adm_f1)
prauc.append(adm_prauc)
llprint('\rEval--Epoch: %d, Step: %d/%d' %
(epoch, step, len(data_eval)))
llprint(
'\tAUC: %.4f, P1: %.4f, P3: %.4f, P5: %.4f, F1: %.4f, PRAUC: %.4f\n' %
(np.mean(auc), np.mean(p_1), np.mean(p_3), np.mean(p_5), np.mean(f1),
np.mean(prauc)))
dill.dump(obj=smm_record, file=open('../data/smm_records.pkl', 'wb'))
def eval(model, data_eval, voc_size, epoch):
# evaluate
print('')
model.eval()
auc, p_1, p_3, p_5, f1 = [[] for _ in range(5)]
for step, input in enumerate(data_eval):
y_gt = []
y_pred = []
y_pred_prob = []
i1_state, i2_state, i3_state = None, None, None
for adm in input:
y_gt_tmp = np.zeros(voc_size[2])
y_gt_tmp[adm[2]] = 1
y_gt.append(y_gt_tmp)
output_logits, i1_state, i2_state, i3_state = model(
adm, i1_state, i2_state, i3_state)
a = torch.argmax(output_logits, dim=-1)
y_pred_prob_tmp = torch.mean(output_logits,
dim=0).detach().cpu().numpy()[:-2]
y_pred_prob.append(y_pred_prob_tmp) # remove start and end token
# target_output1 = F.sigmoid(target_output1).detach().cpu().numpy()[0]
# a = np.argsort(target_output1)[::-1]
# b = np.max(output_logits,axis=-1)
# y_pred_prob.append(target_output1)
y_pred_tmp = y_pred_prob_tmp.copy()
y_pred_tmp[y_pred_tmp >= 0.3] = 1
y_pred_tmp[y_pred_tmp < 0.3] = 0
y_pred.append(y_pred_tmp)
adm_auc, adm_p_1, adm_p_3, adm_p_5, adm_f1 = metric(
np.array(y_gt), np.array(y_pred), np.array(y_pred_prob))
auc.append(adm_auc)
p_1.append(adm_p_1)
p_3.append(adm_p_3)
p_5.append(adm_p_5)
f1.append(adm_f1)
llprint('\rEval--Epoch: %d, Step: %d/%d' %
(epoch, step, len(data_eval)))
llprint(
'\tAUC: %.4f, P1: %.4f, P3: %.4f, P5: %.4f, F1: %.4f\n' %
(np.mean(auc), np.mean(p_1), np.mean(p_3), np.mean(p_5), np.mean(f1)))
def eval(model, data_eval, voc_size, epoch):
eval_len = len(data_eval)
# evaluate
print('')
model.eval()
y_pred_prob = np.zeros((eval_len, voc_size[-1]))
y_gt = y_pred_prob.copy()
y_pred = y_pred_prob.copy()
for step, input in enumerate(data_eval):
pre_outputs, pre_labels, last_outputs, last_labels = model(input)
last_outputs = F.softmax(last_outputs, dim=-1)
last_v, last_arg = torch.max(last_outputs, dim=-1)
last_v = last_v.detach().cpu().numpy()
last_arg = last_arg.detach().cpu().numpy()
def filter_other_token(x):
if x[1] >= voc_size[-1]:
return False
return True
try:
last_v, last_arg = zip(
*filter(filter_other_token, zip(last_v, last_arg)))
except Exception:
last_v, last_arg = [], []
last_v, last_arg = list(last_v), list(last_arg)
target = last_labels.detach().cpu().numpy()[:-1] # remove end token
pred_prob = np.zeros(voc_size[-1])
pred_prob[last_arg] = last_v
pred = pred_prob.copy()
pred[last_arg] = 1
y_pred[step, :] = pred
y_pred_prob[step, :] = pred_prob
y_gt[step, target] = 1
llprint('\rEval--Epoch: %d, Step: %d/%d' %
(epoch, step, len(data_eval)))
js, auc, p_1, p_3, p_5, f1, auprc = metric(y_gt, y_pred, y_pred_prob)
llprint(
'\tJS: %.4f, AUC: %.4f, P1: %.4f, P3: %.4f, P5: %.4f, F1: %.4f, AUPRC: %.4F\n'
% (js, auc, p_1, p_3, p_5, f1, auprc))
def eval(model, data_eval, voc_size, epoch):
# evaluate
print('')
model.eval()
auc, p_1, p_3, p_5, f1 = [[] for _ in range(5)]
for step, input in enumerate(data_eval):
y_gt = []
y_pred = []
y_pred_prob = []
input1_hidden, input2_hidden, target_hidden = None, None, None
for adm in input:
y_gt_tmp = np.zeros(voc_size[2])
y_gt_tmp[adm[2]] = 1
y_gt.append(y_gt_tmp)
output2_logits, output2_labels = model.seq_evaluate(target_hidden,
max_len=15)
target_output1, _, [
input1_hidden, input2_hidden, target_hidden
] = model(adm, [input1_hidden, input2_hidden, target_hidden])
target_output1 = F.sigmoid(
target_output1).detach().cpu().numpy()[0][0]
y_pred_prob.append(target_output1)
y_pred_tmp = target_output1.copy()
y_pred_tmp[y_pred_tmp >= 0.5] = 1
y_pred_tmp[y_pred_tmp < 0.5] = 0
y_pred.append(y_pred_tmp)
print('adm')
adm_auc, adm_p_1, adm_p_3, adm_p_5, adm_f1 = metric(
y_gt, y_pred, y_pred_prob)
auc.append(adm_auc)
p_1.append(adm_p_1)
p_3.append(adm_p_3)
p_5.append(adm_p_5)
f1.append(adm_f1)
llprint('\rEval--Epoch: %d, Step: %d/%d' %
(epoch, step, len(data_eval)))
llprint(
'\tAUC: %.4f, P1: %.4f, P3: %.4f, P5: %.4f, F1: %.4f\n' %
(np.mean(auc), np.mean(p_1), np.mean(p_3), np.mean(p_5), np.mean(f1)))
def predictSuffixAndTimeForPrefix(args, model, preprocess_manager, current,
predict, ground_truth):
in_time = 1
start = len(ground_truth["prefix_event"])
for i in range(start, predict["size"]):
input_vec, num_features_all, num_features_activities = preprocess_manager.encode_test_set_add(
args, current["line"], current["times"], current["times3"],
current["line_add"], args.batch_size_test)
Y = model.predict(input_vec, verbose=0)
y_event = Y[0][0]
y_time = Y[1][0][0]
prediction = preprocess_manager.getSymbol(y_event)
# update of prefix (event)
current["line"] += prediction
predict["predicted"] += prediction
# update of prefix for suffix prediction (time + context)
if prediction == '!':
y_time = 0
# note times2 will be automatically calculated based on times in the method "encode_test_set_add()"
current["times"].append(y_time)
current["times3"].append(current["times3"][-1] +
timedelta(seconds=y_time))
predict["suffix_time"] = predict["suffix_time"] + y_time
current["line_add"].append(current["line_add"][0])
else:
# update of prefix for suffix prediction (time + context)
if y_time < 0:
y_time = 0
y_time = y_time * preprocess_manager.divisor3
current["times"].append(y_time)
current["times3"].append(current["times3"][-1] +
timedelta(seconds=y_time))
predict["suffix_time"] = predict["suffix_time"] + y_time
current["line_add"].append(current["line_add"][0])
# termination; predict size = max sequence length - 1
if prediction == '!' or len(current["line"]) == predict["size"]:
util.llprint('\n! termination suffix prediction ... \n')
break
util.llprint("Prefix+Suffix-Time-%s: %f" %
(i, ground_truth["prefix_time"] + predict["suffix_time"]))
util.llprint("Baseline-%s: %f" %
(i, preprocess_manager.avg_time_training_cases))
if ground_truth["prefix_time"] + predict[
"suffix_time"] > preprocess_manager.avg_time_training_cases:
in_time = 0
deviation_in_time = (ground_truth["prefix_time"] + predict["suffix_time"]
) / preprocess_manager.avg_time_training_cases
return predict, in_time, deviation_in_time
def roc_auc_dnc(y_gt, y_prob):
all_micro = []
for b in range(len(y_gt):
all_micro.append(roc_auc_score(y_gt[b], y_prob[b], average='micro'))
return np.mean(all_micro)
def precision_at_k_v2(y_gt, y_prob, k=3):
precision = 0
sort_index = np.argsort(y_prob, axis=-1)[:, ::-1][:, :k]
for i in range(len(y_gt)):
TP = 0
for j in range(len(sort_index[i])):
if y_gt[i, sort_index[i, j]] == 1:
TP += 1
precision += TP / len(sort_index[i])
return precision / len(y_gt)
def metric(y_eval, y_pred, y_pred_prob):
auc = roc_auc_dnc(y_eval, y_pred_prob)
p_1 = precision_at_k_v2(y_eval, y_pred_prob, k=1)
p_3 = precision_at_k_v2(y_eval, y_pred_prob, k=3)
p_5 = precision_at_k_v2(y_eval, y_pred_prob, k=5)
f1 = f1_dnc(y_eval, y_pred)
return auc, p_1, p_3, p_5, f1
def eval(model, data_eval, voc_size, epoch):
# evaluate
print('')
model.eval()
auc, p_1, p_3, p_5, f1 = [[] for _ in range(5)]
for step, input in enumerate(data_eval):
y_gt = []
y_pred = []
y_pred_prob = []
input1_hidden, input2_hidden, target_hidden = None, None, None
for adm in input:
y_gt_tmp = np.zeros(voc_size[2])
y_gt_tmp[adm[2]] = 1
y_gt.append(y_gt_tmp)
output2_logits, output2_labels = model.seq_evaluate(target_hidden, max_len=15)
target_output1, _, [input1_hidden, input2_hidden, target_hidden] = model(adm, [input1_hidden, input2_hidden, target_hidden])
target_output1 = F.sigmoid(target_output1).detach().cpu().numpy()[0][0]
y_pred_prob.append(target_output1)
y_pred_tmp = target_output1.copy()
y_pred_tmp[y_pred_tmp>=0.5] = 1
y_pred_tmp[y_pred_tmp<0.5] = 0
y_pred.append(y_pred_tmp)
print('adm')
adm_auc, adm_p_1, adm_p_3, adm_p_5, adm_f1 = metric(y_gt, y_pred, y_pred_prob)
auc.append(adm_auc)
p_1.append(adm_p_1)
p_3.append(adm_p_3)
p_5.append(adm_p_5)
f1.append(adm_f1)
llprint('\rEval--Epoch: %d, Step: %d/%d' % (epoch, step, len(data_eval)))
llprint('\tAUC: %.4f, P1: %.4f, P3: %.4f, P5: %.4f, F1: %.4f\n' % (
np.mean(auc), np.mean(p_1), np.mean(p_3), np.mean(p_5), np.mean(f1)
))
# last_outputs = F.softmax(last_outputs, dim=-1)
# last_v, last_arg = torch.max(last_outputs, dim=-1)
# last_v = last_v.detach().cpu().numpy()
# last_arg = last_arg.detach().cpu().numpy()
#
# def filter_other_token(x):
# if x[1] >= voc_size[-1]:
# return False
# return True
# try:
# last_v, last_arg = zip(*filter(filter_other_token, zip(last_v, last_arg)))
# except Exception:
# last_v, last_arg = [], []
#
# last_v, last_arg = list(last_v), list(last_arg)
# target = last_labels.detach().cpu().numpy()[:-1] # remove end token
#
# pred_prob = np.zeros(voc_size[-1])
# pred_prob[last_arg] = last_v
# pred = pred_prob.copy()
# pred[last_arg] = 1
# y_pred[step, :] = pred
# y_pred_prob[step, :] = pred_prob
# y_gt[step, target] = 1
def main():
if not os.path.exists(os.path.join("saved", model_name)):
os.makedirs(os.path.join("saved", model_name))
data_path = '../data/records.pkl'
voc_path = '../data/voc.pkl'
device = torch.device('cuda:0')
data = dill.load(open(data_path, 'rb'))
voc = dill.load(open(voc_path, 'rb'))
diag_voc, pro_voc, med_voc = voc['diag_voc'], voc['pro_voc'], voc['med_voc']
split_point = int(len(data) * 2 / 3)
data_train = data[:split_point]
eval_len = int(len(data[split_point:]) / 2)
data_eval = data[split_point:split_point + eval_len]
EPOCH = 30
LR = 0.001
EVAL = True
voc_size = (len(diag_voc.idx2word), len(pro_voc.idx2word), len(med_voc.idx2word))
model = RNN_Two(voc_size, device=device)
if EVAL:
pass
#model.load_state_dict(torch.load(open(os.path.join("saved", model_name, resume_name), 'rb')))
model.to(device=device)
criterion1 = nn.BCEWithLogitsLoss().to(device)
criterion2 = nn.CrossEntropyLoss().to(device)
optimizer = Adam(model.parameters(), lr=LR)
if EVAL:
eval(model, data_eval, voc_size, 0)
else:
for epoch in range(EPOCH):
loss_record1 = []
loss_record2 = []
start_time = time.time()
model.train()
for step, input in enumerate(data_train):
input1_hidden, input2_hidden, target_hidden = None, None, None
for adm in input:
loss1_target = np.zeros((1, voc_size[2]))
loss1_target[:, adm[2]] = 1
loss2_target = adm[2] + [voc_size[2]+1]
target_output1, target_output2, [input1_hidden, input2_hidden, target_hidden] = model(adm, [input1_hidden, input2_hidden, target_hidden])
loss1 = criterion1(target_output1, torch.LongTensor(loss1_target).to(device))
loss2 = criterion2(target_output2, torch.LongTensor(loss2_target).to(device))
optimizer.zero_grad()
loss1.backward()
loss2.backward()
optimizer.step()
loss_record1.append(loss1.item())
loss_record2.append(loss2.item())
llprint('\rTrain--Epoch: %d, Step: %d/%d' % (epoch, step, len(data_train)))
eval(model, data_eval, voc_size, epoch)
end_time = time.time()
elapsed_time = (end_time - start_time) / 60
llprint('\tEpoch: %d, Loss1: %.4f, Loss2: %.4f One Epoch Time: %.2fm, Appro Left Time: %.2fh\n' % (epoch,
np.mean(loss_record1),
np.mean(loss_record2),
elapsed_time,
elapsed_time * (
EPOCH - epoch - 1)/60))
torch.save(model.state_dict(), open( os.path.join('saved', model_name, 'Epoch_%d_Loss1_%.4f.model' % (epoch, np.mean(loss_record1))), 'wb'))
print('')
# test
torch.save(model.state_dict(), open(
os.path.join('saved', model_name, 'final.model'), 'wb'))
if __name__ == '__main__':
main()
def calc_metrics(args):
prefix = 0
prefix_all_enabled = 1
prediction = list()
gt_label = list()
result_dir = args.result_dir
if args.cross_validation == False:
result_dir_fold = result_dir + args.data_set.split(
".csv")[0] + "__" + args.task + "_0.csv"
else:
result_dir_fold = result_dir + args.data_set.split(
".csv"
)[0] + "__" + args.task + "_%d" % args.iteration_cross_validation + ".csv"
with open(result_dir_fold, 'r') as csvfile:
reader = csv.reader(csvfile,
delimiter=';',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
next(reader)
for row in reader:
if row == []:
continue
else:
if int(row[1]) == prefix or prefix_all_enabled == 1:
gt_label.append(row[2])
prediction.append(row[3])
util.llprint("\n\n")
util.llprint("Metrics:\n")
util.llprint("Accuracy: %f\n" %
sklearn.metrics.accuracy_score(gt_label, prediction))
# calc metric for each label, and find their weighted mean
util.llprint("Precision (weighted): %f\n" %
sklearn.metrics.precision_score(
gt_label, prediction, average='weighted'))
util.llprint(
"Recall (weighted): %f\n" %
sklearn.metrics.recall_score(gt_label, prediction, average='weighted'))
util.llprint(
"F1-Score (weighted): %f\n" %
sklearn.metrics.f1_score(gt_label, prediction, average='weighted'))
# calc macro metric for each label, and find their unweighted mean
util.llprint(
"Precision (macro): %f\n" %
sklearn.metrics.precision_score(gt_label, prediction, average='macro'))
util.llprint(
"Recall (macro): %f\n" %
sklearn.metrics.recall_score(gt_label, prediction, average='macro'))
util.llprint(
"F1-Score (macro): %f\n" %
sklearn.metrics.f1_score(gt_label, prediction, average='macro'))
# calc micro metric over all examples
util.llprint(
"Precision (micro): %f\n" %
sklearn.metrics.precision_score(gt_label, prediction, average='micro'))
util.llprint(
"Recall (micro): %f\n" %
sklearn.metrics.recall_score(gt_label, prediction, average='micro'))
util.llprint(
"F1-Score (micro): %f\n\n" %
sklearn.metrics.f1_score(gt_label, prediction, average='micro'))
return sklearn.metrics.accuracy_score(
gt_label, prediction), sklearn.metrics.precision_score(
gt_label, prediction,
average='weighted'), sklearn.metrics.recall_score(
gt_label, prediction,
average='weighted'), sklearn.metrics.f1_score(
gt_label, prediction, average='weighted')
criterion = CrossEntropyLoss().to(device)
optimizer = Adam(model.parameters(), lr=LR)
for epoch in range(EPOCH):
loss_record = []
start_time = time.time()
model.train()
for step, input in enumerate(data_train):
output, y_gt = model(input)
loss = criterion(output, y_gt)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_record.append(loss.item())
llprint('\rTrain--Epoch: %d, Step: %d/%d' %
(epoch, step, len(data_train)))
# evaluate
print('')
model.eval()
y_pred_prob = np.zeros((eval_len, voc_size[-1]))
y_gt = y_pred_prob.copy()
y_pred = y_pred_prob.copy()
for step, input in enumerate(data_eval):
pred, pred_prob, target = model(input)
y_pred[step, :] = pred
y_pred_prob[step, :] = pred_prob
y_gt[step, :] = target
llprint('\rEval--Epoch: %d, Step: %d/%d' %
(epoch, step, len(data_eval)))
def test(args, preprocess_manager):
batch_size = args.batch_size_test
result_dir = args.result_dir
task = args.task
if preprocess_manager.num_features_additional > 0:
lines, caseids, lines_add, sequence_max_length, num_features_all, num_features_activities = preprocess_manager.create_test_set(
)
else:
lines, caseids, sequence_max_length, num_features_all, num_features_activities = preprocess_manager.create_test_set(
)
model = keras.models.load_model(
'%smodel_%s.h5' %
(args.checkpoint_dir, preprocess_manager.iteration_cross_validation))
predict_size = 1
data_set_name = args.data_set.split('.csv')[0]
generic_result_dir = result_dir + data_set_name + "__" + task
fold_result_dir = generic_result_dir + "_%d%s" % (
preprocess_manager.iteration_cross_validation, ".csv")
result_dir = fold_result_dir
with open(result_dir, 'w') as csvfile:
spamwriter = csv.writer(csvfile,
delimiter=';',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
spamwriter.writerow([
"CaseID", "Prefix length", "Groud truth", "Predicted",
"Levenshtein", "Damerau", "Jaccard"
])
for prefix_size in range(2, sequence_max_length):
util.llprint("\nPrefix size: %d\n" % prefix_size)
# if additional attributes exists
if preprocess_manager.num_features_additional > 0:
for line, caseid, line_add in zip(lines, caseids, lines_add):
cropped_line = ''.join(line[:prefix_size])
cropped_line_add = line_add[:prefix_size]
if '!' in cropped_line:
continue
ground_truth = ''.join(line[prefix_size:prefix_size +
predict_size])
predicted = ''
for i in range(predict_size):
if len(ground_truth) <= i:
continue
input_vec, num_features_all, num_features_activities = preprocess_manager.encode_test_set_add(
args, cropped_line, cropped_line_add, batch_size)
y = model.predict(input_vec, verbose=0)
y_char = y[0][:]
prediction = preprocess_manager.getSymbol(y_char)
cropped_line += prediction
predicted += prediction
if prediction == '!':
print('! predicted, end case')
break
output = []
if len(ground_truth) > 0:
output.append(caseid)
output.append(prefix_size)
output.append(str(ground_truth).encode("utf-8"))
output.append(str(predicted).encode("utf-8"))
output.append(
1 - distance.nlevenshtein(predicted, ground_truth))
dls = 1 - (damerau_levenshtein_distance(
str(predicted), str(ground_truth)) /
max(len(predicted), len(ground_truth)))
if dls < 0:
dls = 0
output.append(dls)
output.append(
1 - distance.jaccard(predicted, ground_truth))
spamwriter.writerow(output)
# if no additional attributes exists
else:
for line, caseid in zip(lines, caseids):
cropped_line = ''.join(line[:prefix_size])
if '!' in cropped_line:
continue
ground_truth = ''.join(line[prefix_size:prefix_size +
predict_size])
predicted = ''
for i in range(predict_size):
if len(ground_truth) <= i:
continue
input_vec = preprocess_manager.encode_test_set(
cropped_line, batch_size)
y = model.predict(input_vec, verbose=0)
y_char = y[0][:]
prediction = preprocess_manager.getSymbol(y_char)
cropped_line += prediction
predicted += prediction
if prediction == '!':
print('! predicted, end case')
break
output = []
if len(ground_truth) > 0:
output.append(caseid)
output.append(prefix_size)
output.append(str(ground_truth).encode("utf-8"))
output.append(str(predicted).encode("utf-8"))
output.append(
1 - distance.nlevenshtein(predicted, ground_truth))
dls = 1 - (damerau_levenshtein_distance(
str(predicted), str(ground_truth)) /
max(len(predicted), len(ground_truth)))
if dls < 0:
dls = 0
output.append(dls)
output.append(
1 - distance.jaccard(predicted, ground_truth))
spamwriter.writerow(output)
def fine_tune(fine_tune_name=''):
# load data
data_path = '../data/output/records_final.pkl'
voc_path = '../data/output/voc_final.pkl'
device = torch.device('cpu:0')
data = dill.load(open(data_path, 'rb'))
voc = dill.load(open(voc_path, 'rb'))
diag_voc, pro_voc, med_voc = voc['diag_voc'], voc['pro_voc'], voc[
'med_voc']
ddi_A = dill.load(open('../data/output/ddi_A_final.pkl', 'rb'))
split_point = int(len(data) * 2 / 3)
data_train = data[:split_point]
eval_len = int(len(data[split_point:]) / 2)
data_test = data[split_point:split_point + eval_len]
# data_eval = data[split_point+eval_len:]
voc_size = (len(diag_voc.idx2word), len(pro_voc.idx2word),
len(med_voc.idx2word))
model = Leap(voc_size, device=device)
model.load_state_dict(
torch.load(
open(os.path.join("saved", args.model_name, fine_tune_name),
'rb')))
model.to(device)
END_TOKEN = voc_size[2] + 1
optimizer = Adam(model.parameters(), lr=args.lr)
ddi_rate_record = []
EPOCH = 100
for epoch in range(EPOCH):
loss_record = []
start_time = time.time()
random_train_set = [
random.choice(data_train) for i in range(len(data_train))
]
for step, input in enumerate(random_train_set):
model.train()
K_flag = False
for adm in input:
target = adm[2]
output_logits = model(adm)
out_list, sorted_predict = sequence_output_process(
output_logits.detach().cpu().numpy(),
[voc_size[2], voc_size[2] + 1])
inter = set(out_list) & set(target)
union = set(out_list) | set(target)
jaccard = 0 if union == 0 else len(inter) / len(union)
K = 0
for i in out_list:
if K == 1:
K_flag = True
break
for j in out_list:
if ddi_A[i][j] == 1:
K = 1
break
loss = -jaccard * K * torch.mean(
F.log_softmax(output_logits, dim=-1))
loss_record.append(loss.item())
optimizer.zero_grad()
loss.backward(retain_graph=True)
optimizer.step()
llprint('\rtraining step: {} / {}'.format(step,
len(random_train_set)))
if K_flag:
print()
ddi_rate, ja, prauc, avg_p, avg_r, avg_f1, avg_med = eval(
model, data_test, voc_size, epoch)
# test
torch.save(
model.state_dict(),
open(os.path.join('saved', args.model_name, 'final.model'), 'wb'))
def main():
if not os.path.exists(os.path.join("saved", model_name)):
os.makedirs(os.path.join("saved", model_name))
data_path = '../data/records_final.pkl'
voc_path = '../data/voc_final.pkl'
ehr_adj_path = '../data/ehr_adj_final.pkl'
ddi_adj_path = '../data/ddi_A_final.pkl'
device = torch.device('cuda:0')
ehr_adj = dill.load(open(ehr_adj_path, 'rb'))
ddi_adj = dill.load(open(ddi_adj_path, 'rb'))
data = dill.load(open(data_path, 'rb'))
voc = dill.load(open(voc_path, 'rb'))
diag_voc, pro_voc, med_voc = voc['diag_voc'], voc['pro_voc'], voc[
'med_voc']
split_point = int(len(data) * 2 / 3)
data_train = data[:split_point]
eval_len = int(len(data[split_point:]) / 2)
data_test = data[split_point:split_point + eval_len]
data_eval = data[split_point + eval_len:]
EPOCH = 40
LR = 0.0002
TEST = args.eval
Neg_Loss = args.ddi
DDI_IN_MEM = args.ddi
TARGET_DDI = 0.05
T = 0.5
decay_weight = 0.85
voc_size = (len(diag_voc.idx2word), len(pro_voc.idx2word),
len(med_voc.idx2word))
model = GAMENet(voc_size,
ehr_adj,
ddi_adj,
emb_dim=64,
device=device,
ddi_in_memory=DDI_IN_MEM)
if TEST:
model.load_state_dict(torch.load(open(resume_name, 'rb')))
model.to(device=device)
print('parameters', get_n_params(model))
optimizer = Adam(list(model.parameters()), lr=LR)
if TEST:
eval(model, data_test, voc_size, 0)
else:
history = defaultdict(list)
best_epoch = 0
best_ja = 0
for epoch in range(EPOCH):
loss_record1 = []
start_time = time.time()
model.train()
prediction_loss_cnt = 0
neg_loss_cnt = 0
for step, input in enumerate(data_train):
for idx, adm in enumerate(input):
seq_input = input[:idx + 1]
loss1_target = np.zeros((1, voc_size[2]))
loss1_target[:, adm[2]] = 1
loss3_target = np.full((1, voc_size[2]), -1)
for idx, item in enumerate(adm[2]):
loss3_target[0][idx] = item
target_output1, batch_neg_loss = model(seq_input)
loss1 = F.binary_cross_entropy_with_logits(
target_output1,
torch.FloatTensor(loss1_target).to(device))
loss3 = F.multilabel_margin_loss(
F.sigmoid(target_output1),
torch.LongTensor(loss3_target).to(device))
if Neg_Loss:
target_output1 = F.sigmoid(
target_output1).detach().cpu().numpy()[0]
target_output1[target_output1 >= 0.5] = 1
target_output1[target_output1 < 0.5] = 0
y_label = np.where(target_output1 == 1)[0]
current_ddi_rate = ddi_rate_score([[y_label]])
if current_ddi_rate <= TARGET_DDI:
loss = 0.9 * loss1 + 0.01 * loss3
prediction_loss_cnt += 1
else:
rnd = np.exp((TARGET_DDI - current_ddi_rate) / T)
if np.random.rand(1) < rnd:
loss = batch_neg_loss
neg_loss_cnt += 1
else:
loss = 0.9 * loss1 + 0.01 * loss3
prediction_loss_cnt += 1
else:
loss = 0.9 * loss1 + 0.01 * loss3
optimizer.zero_grad()
loss.backward(retain_graph=True)
optimizer.step()
loss_record1.append(loss.item())
llprint(
'\rTrain--Epoch: %d, Step: %d/%d, L_p cnt: %d, L_neg cnt: %d'
% (epoch, step, len(data_train), prediction_loss_cnt,
neg_loss_cnt))
# annealing
T *= decay_weight
ddi_rate, ja, prauc, avg_p, avg_r, avg_f1 = eval(
model, data_eval, voc_size, epoch)
history['ja'].append(ja)
history['ddi_rate'].append(ddi_rate)
history['avg_p'].append(avg_p)
history['avg_r'].append(avg_r)
history['avg_f1'].append(avg_f1)
history['prauc'].append(prauc)
end_time = time.time()
elapsed_time = (end_time - start_time) / 60
llprint(
'\tEpoch: %d, Loss: %.4f, One Epoch Time: %.2fm, Appro Left Time: %.2fh\n'
% (epoch, np.mean(loss_record1), elapsed_time, elapsed_time *
(EPOCH - epoch - 1) / 60))
torch.save(
model.state_dict(),
open(
os.path.join(
'saved', model_name,
'Epoch_%d_JA_%.4f_DDI_%.4f.model' %
(epoch, ja, ddi_rate)), 'wb'))
print('')
if epoch != 0 and best_ja < ja:
best_epoch = epoch
best_ja = ja
dill.dump(history,
open(os.path.join('saved', model_name, 'history.pkl'), 'wb'))
# test
torch.save(
model.state_dict(),
open(os.path.join('saved', model_name, 'final.model'), 'wb'))
print('best_epoch:', best_epoch)
def trainIters(encoder, decoder, n_iters, print_every=1000, plot_every=100, learning_rate=0.01):
start = time.time()
print_loss_total = 0 # Reset every print_every
encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate)
training_pairs = [tensorsFromPair(random.choice(train_pairs))
for i in range(n_iters)]
criterion = nn.CrossEntropyLoss()
history = defaultdict(list)
for epoch in range(30):
for iter in range(1, n_iters + 1):
training_pair = training_pairs[iter - 1]
input_tensor = training_pair[0]
target_tensor = training_pair[1]
loss = train(input_tensor, target_tensor, encoder,
decoder, encoder_optimizer, decoder_optimizer, criterion)
print_loss_total += loss
llprint('\rTrain--Epoch: %d, Step: %d/%d' % (epoch, iter, n_iters))
print_loss_avg = print_loss_total / n_iters
print_loss_total = 0
#eval
y_gt = []
y_pred = []
y_pred_prob = []
y_pred_label = []
for pair in eval_pairs:
y_gt_tmp = np.zeros(len(med_voc.idx2word))
y_gt_tmp[np.array(pair[1])[:-1]-2] = 1
y_gt.append(y_gt_tmp)
input_tensor, output_tensor = tensorsFromPair(pair)
output_logits = evaluate(encoder, decoder, input_tensor)
output_logits = F.softmax(output_logits)
output_logits = output_logits.detach().cpu().numpy()
out_list, sorted_predict = sequence_output_process(output_logits, [SOS_token, EOS_token])
y_pred_label.append(np.array(sorted_predict)-2)
y_pred_prob.append(np.mean(output_logits[:, 2:], axis=0))
y_pred_tmp = np.zeros(len(med_voc.idx2word))
if len(out_list) != 0 :
y_pred_tmp[np.array(out_list) - 2] = 1
y_pred.append(y_pred_tmp)
ja, prauc, avg_p, avg_r, avg_f1 = sequence_metric(np.array(y_gt), np.array(y_pred),
np.array(y_pred_prob),
np.array(y_pred_label))
# ddi rate
ddi_A = dill.load(open('../data/ddi_A_final.pkl', 'rb'))
all_cnt = 0
dd_cnt = 0
for adm in y_pred_label:
med_code_set = adm
for i, med_i in enumerate(med_code_set):
for j, med_j in enumerate(med_code_set):
if j <= i:
continue
all_cnt += 1
if ddi_A[med_i, med_j] == 1 or ddi_A[med_j, med_i] == 1:
dd_cnt += 1
ddi_rate = dd_cnt / all_cnt
history['ja'].append(ja)
history['ddi_rate'].append(ddi_rate)
history['avg_p'].append(avg_p)
history['avg_r'].append(avg_r)
history['avg_f1'].append(avg_f1)
history['prauc'].append(prauc)
llprint('\n\tDDI Rate: %.4f, Jaccard: %.4f, PRAUC: %.4f, AVG_PRC: %.4f, AVG_RECALL: %.4f, AVG_F1: %.4f\n' % (
ddi_rate, ja, prauc, avg_p, avg_r, avg_f1
))
dill.dump(history, open(os.path.join('saved', model_name, 'history.pkl'), 'wb'))
torch.save(encoder.state_dict(),
open(
os.path.join('saved', model_name, 'encoder_Epoch_%d_JA_%.4f_DDI_%.4f.model' % (epoch, ja, dd_cnt/all_cnt)),
'wb'))
torch.save(decoder.state_dict(),
open(
os.path.join('saved', model_name, 'decoder_Epoch_%d_JA_%.4f_DDI_%.4f.model' % (epoch, ja, dd_cnt/all_cnt)),
'wb'))
def main():
# load data
data_path = '../data/output/records_final.pkl'
voc_path = '../data/output/voc_final.pkl'
device = torch.device('cuda:{}'.format(args.cuda))
data = dill.load(open(data_path, 'rb'))
voc = dill.load(open(voc_path, 'rb'))
diag_voc, pro_voc, med_voc = voc['diag_voc'], voc['pro_voc'], voc[
'med_voc']
split_point = int(len(data) * 2 / 3)
data_train = data[:split_point]
eval_len = int(len(data[split_point:]) / 2)
data_test = data[split_point:split_point + eval_len]
data_eval = data[split_point + eval_len:]
voc_size = (len(diag_voc.idx2word), len(pro_voc.idx2word),
len(med_voc.idx2word))
END_TOKEN = voc_size[2] + 1
model = Leap(voc_size, device=device)
# model.load_state_dict(torch.load(open(args.resume_path, 'rb')))
if args.Test:
model.load_state_dict(torch.load(open(args.resume_path, 'rb')))
model.to(device=device)
tic = time.time()
result = []
for _ in range(10):
test_sample = np.random.choice(data_test,
round(len(data_test) * 0.8),
replace=True)
ddi_rate, ja, prauc, avg_p, avg_r, avg_f1, avg_med = eval(
model, test_sample, voc_size, 0)
result.append([ddi_rate, ja, avg_f1, prauc, avg_med])
result = np.array(result)
mean = result.mean(axis=0)
std = result.std(axis=0)
outstring = ""
for m, s in zip(mean, std):
outstring += "{:.4f} $\pm$ {:.4f} & ".format(m, s)
print(outstring)
print('test time: {}'.format(time.time() - tic))
return
model.to(device=device)
print('parameters', get_n_params(model))
optimizer = Adam(model.parameters(), lr=args.lr)
history = defaultdict(list)
best_epoch, best_ja = 0, 0
EPOCH = 50
for epoch in range(EPOCH):
tic = time.time()
print('\nepoch {} --------------------------'.format(epoch + 1))
model.train()
for step, input in enumerate(data_train):
for adm in input:
loss_target = adm[2] + [END_TOKEN]
output_logits = model(adm)
loss = F.cross_entropy(
output_logits,
torch.LongTensor(loss_target).to(device))
optimizer.zero_grad()
loss.backward(retain_graph=True)
optimizer.step()
llprint('\rtraining step: {} / {}'.format(step, len(data_train)))
print()
tic2 = time.time()
ddi_rate, ja, prauc, avg_p, avg_r, avg_f1, avg_med = eval(
model, data_eval, voc_size, epoch)
print('training time: {}, test time: {}'.format(
time.time() - tic,
time.time() - tic2))
history['ja'].append(ja)
history['ddi_rate'].append(ddi_rate)
history['avg_p'].append(avg_p)
history['avg_r'].append(avg_r)
history['avg_f1'].append(avg_f1)
history['prauc'].append(prauc)
history['med'].append(avg_med)
if epoch >= 5:
print('ddi: {}, Med: {}, Ja: {}, F1: {}, PRAUC: {}'.format(
np.mean(history['ddi_rate'][-5:]),
np.mean(history['med'][-5:]), np.mean(history['ja'][-5:]),
np.mean(history['avg_f1'][-5:]),
np.mean(history['prauc'][-5:])))
torch.save(model.state_dict(), open(os.path.join('saved', args.model_name, \
'Epoch_{}_JA_{:.4}_DDI_{:.4}.model'.format(epoch, ja, ddi_rate)), 'wb'))
if epoch != 0 and best_ja < ja:
best_epoch = epoch
best_ja = ja
print('best_epoch: {}'.format(best_epoch))
dill.dump(
history,
open(
os.path.join('saved', args.model_name,
'history_{}.pkl'.format(args.model_name)), 'wb'))
if EVAL:
eval(model, data_eval, voc_size, 0)
else:
for epoch in range(EPOCH):
loss_record = []
start_time = time.time()
model.train()
for step, input in enumerate(data_train):
output, y_gt = model(input)
loss = criterion(output, y_gt)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_record.append(loss.item())
llprint('\rTrain--Epoch: %d, Step: %d/%d' %
(epoch, step, len(data_train)))
eval(model, data_eval, voc_size, epoch)
end_time = time.time()
elapsed_time = (end_time - start_time) / 60
llprint(
'\n\tEpoch: %d, Loss: %.4f, One Epoch Time: %.2fm, Appro Left Time: %.2fh\n'
% (epoch, np.mean(loss_record), elapsed_time, elapsed_time *
(EPOCH - epoch - 1) / 60))
torch.save(
model.state_dict(),
open(
os.path.join(
'saved', model_name, 'Epoch_%d_Loss_%.4f.model' %
def create_and_encode_training_set(self, args):
lines = list()
lines_add = list()
for index, value in enumerate(
self.train_index_per_fold[self.iteration_cross_validation]):
lines.append(self.lines[value])
if self.num_features_additional > 0:
lines_add.append(self.features_additional_sequences[value])
step = 1
sentences = []
softness = 0
next_chars = []
if self.num_features_additional > 0:
sentences_add = []
if self.num_features_additional > 0:
for line, line_add in zip(lines, lines_add):
for i in range(0, len(line), step):
if i == 0:
continue
sentences.append(line[0:i])
sentences_add.append(line_add[0:i])
next_chars.append(line[i])
util.llprint("\nnb sequences: %d" % len(sentences))
util.llprint("\nadd sequences: %d" % len(sentences_add))
else:
for t, line in enumerate(lines):
for i in range(0, len(line), step):
if i == 0:
continue
sentences.append(line[0:i])
next_chars.append(line[i])
util.llprint("\nnb sequences: %d" % len(sentences))
util.llprint("\nnb Vectorization ...")
X = np.zeros(
(len(sentences), self.max_sequence_length, self.num_features_all),
dtype=np.float64)
Y = np.zeros((len(sentences), len(self.target_chars)),
dtype=np.float64)
for i, sentence in enumerate(sentences):
leftpad = self.max_sequence_length - len(sentence)
if self.num_features_additional > 0:
sentence_add = sentences_add[i]
# set training set data
for t, char in enumerate(sentence):
for c in self.chars:
if c == char:
X[i, t + leftpad, self.char_indices[c]] = 1.0
# add additional attributes
if self.num_features_all > 0:
for x in range(0, self.num_features_additional):
X[i, t + leftpad,
len(self.chars) + x] = sentence_add[t][x]
# set training set label
for c in self.target_chars:
if c == next_chars[i]:
Y[i, self.target_char_indices[c]] = 1 - softness
else:
Y[i, self.target_char_indices[c]] = softness / (
len(self.target_chars) - 1)
num_features_all = self.num_features_all
num_features_activities = self.num_features_activities
return X, Y, self.max_sequence_length, num_features_all, num_features_activities
def main():
# load data
data_path = '../data/output/records_final.pkl'
voc_path = '../data/output/voc_final.pkl'
ddi_adj_path = '../data/output/ddi_A_final.pkl'
device = torch.device('cuda:{}'.format(args.cuda))
ddi_adj = dill.load(open(ddi_adj_path, 'rb'))
data = dill.load(open(data_path, 'rb'))
voc = dill.load(open(voc_path, 'rb'))
diag_voc, pro_voc, med_voc = voc['diag_voc'], voc['pro_voc'], voc[
'med_voc']
np.random.seed(1203)
np.random.shuffle(data)
split_point = int(len(data) * 3 / 5)
data_train = data[:split_point]
eval_len = int(len(data[split_point:]) / 2)
data_test = data[split_point:split_point + eval_len]
data_eval = data[split_point + eval_len:]
voc_size = (len(diag_voc.idx2word), len(pro_voc.idx2word),
len(med_voc.idx2word))
print(voc_size)
model = DualNN(voc_size, ddi_adj, emb_dim=args.dim, device=device)
# model.load_state_dict(torch.load(open(args.resume_path, 'rb')))
if args.Test:
model.load_state_dict(torch.load(open(args.resume_path, 'rb')))
model.to(device=device)
tic = time.time()
label_list, prob_add, prob_delete = eval(model, data_eval, voc_size, 0,
1)
threshold1, threshold2 = [], []
for i in range(label_list.shape[1]):
_, _, boundary_add = roc_curve(label_list[:, i],
prob_add[:, i],
pos_label=1)
_, _, boundary_delete = roc_curve(label_list[:, i],
prob_delete[:, i],
pos_label=0)
threshold1.append(boundary_add[min(round(len(boundary_add) * 0.05),
len(boundary_add) - 1)])
threshold2.append(boundary_delete[min(
round(len(boundary_delete) * 0.05),
len(boundary_delete) - 1)])
# threshold1 = np.ones(voc_size[2]) * np.mean(threshold1)
# threshold2 = np.ones(voc_size[2]) * np.mean(threshold2)
print(np.mean(threshold1), np.mean(threshold2))
eval(model, data_test, voc_size, 0, 0, threshold1, threshold2)
print('test time: {}'.format(time.time() - tic))
return
model.to(device=device)
print('parameters', get_n_params(model))
# exit()
optimizer = RMSprop(list(model.parameters()),
lr=args.lr,
weight_decay=args.weight_decay)
# start iterations
history = defaultdict(list)
best_epoch, best_ja = 0, 0
EPOCH = 40
for epoch in range(EPOCH):
t = 0
tic = time.time()
print('\nepoch {} --------------------------'.format(epoch + 1))
model.train()
for step, input in enumerate(data_train):
if len(input) < 2: continue
loss = 0
for adm_idx, adm in enumerate(input):
if adm_idx == 0: continue
seq_input = input[:adm_idx + 1]
loss_bce_target = np.zeros((1, voc_size[2]))
loss_bce_target[:, adm[2]] = 1
loss_bce_target_last = np.zeros((1, voc_size[2]))
loss_bce_target_last[:, input[adm_idx - 1][2]] = 1
add_target = np.zeros((1, voc_size[2]))
add_target[:, np.where(loss_bce_target == 1)[1]] = 1
delete_target = np.zeros((1, voc_size[2]))
delete_target[:, np.where(loss_bce_target == 0)[1]] = 1
loss_multi_target = np.full((1, voc_size[2]), -1)
for idx, item in enumerate(adm[2]):
loss_multi_target[0][idx] = item
loss_multi_target_last = np.full((1, voc_size[2]), -1)
for idx, item in enumerate(input[adm_idx - 1][2]):
loss_multi_target_last[0][idx] = item
loss_multi_add_target = np.full((1, voc_size[2]), -1)
for i, item in enumerate(np.where(add_target == 1)[0]):
loss_multi_add_target[0][i] = item
loss_multi_delete_target = np.full((1, voc_size[2]), -1)
for i, item in enumerate(np.where(delete_target == 1)[0]):
loss_multi_delete_target[0][i] = item
add_result, delete_result = model(seq_input)
loss_bce = F.binary_cross_entropy_with_logits(add_result, torch.FloatTensor(add_target).to(device)) + \
F.binary_cross_entropy_with_logits(delete_result, torch.FloatTensor(delete_target).to(device))
loss_multi = F.multilabel_margin_loss(F.sigmoid(add_result), torch.LongTensor(loss_multi_add_target).to(device)) + \
F.multilabel_margin_loss(F.sigmoid(delete_result), torch.LongTensor(loss_multi_delete_target).to(device))
# l2 = 0
# for p in model.parameters():
# l2 = l2 + (p ** 2).sum()
loss += 0.95 * loss_bce + 0.05 * loss_multi
optimizer.zero_grad()
loss.backward(retain_graph=True)
optimizer.step()
llprint('\rtraining step: {} / {}'.format(step, len(data_train)))
print()
tic2 = time.time()
ddi_rate, ja, prauc, avg_p, avg_r, avg_f1, add, delete, avg_med = eval(
model, data_eval, voc_size, epoch)
print('training time: {}, test time: {}'.format(
time.time() - tic,
time.time() - tic2))
history['ja'].append(ja)
history['ddi_rate'].append(ddi_rate)
history['avg_p'].append(avg_p)
history['avg_r'].append(avg_r)
history['avg_f1'].append(avg_f1)
history['prauc'].append(prauc)
history['add'].append(add)
history['delete'].append(delete)
history['med'].append(avg_med)
if epoch >= 5:
print(
'ddi: {}, Med: {}, Ja: {}, F1: {}, Add: {}, Delete: {}'.format(
np.mean(history['ddi_rate'][-5:]),
np.mean(history['med'][-5:]), np.mean(history['ja'][-5:]),
np.mean(history['avg_f1'][-5:]),
np.mean(history['add'][-5:]),
np.mean(history['delete'][-5:])))
torch.save(model.state_dict(), open(os.path.join('saved', args.model_name, \
'Epoch_{}_JA_{:.4}_DDI_{:.4}.model'.format(epoch, ja, ddi_rate)), 'wb'))
if epoch != 0 and best_ja < ja:
best_epoch = epoch
best_ja = ja
print('best_epoch: {}'.format(best_epoch))
dill.dump(
history,
open(
os.path.join('saved', args.model_name,
'history_{}.pkl'.format(args.model_name)), 'wb'))
def eval(model,
data_eval,
voc_size,
epoch,
val=0,
threshold1=0.8,
threshold2=0.2):
model.eval()
smm_record = []
ja, prauc, avg_p, avg_r, avg_f1 = [[] for _ in range(5)]
med_cnt, visit_cnt = 0, 0
add_list, delete_list = [], []
for step, input in enumerate(data_eval):
y_gt, y_pred, y_pred_prob, y_pred_label = [], [], [], []
add_temp_list, delete_temp_list = [], []
if len(input) < 2: continue
for adm_idx, adm in enumerate(input):
if adm_idx == 0:
y_old = np.zeros(voc_size[2])
y_old[adm[2]] = 1
continue
y_gt_tmp = np.zeros(voc_size[2])
y_gt_tmp[adm[2]] = 1
y_gt.append(y_gt_tmp)
result_out = model(input[:adm_idx + 1])
# prediction prod
y_pred_tmp = F.sigmoid(
result_out[:, 0]).detach().cpu().numpy().tolist()
y_pred_prob.append(y_pred_tmp)
previous_set = np.where(y_old == 1)[0]
# prediction med set
# result = F.sigmoid(result).detach().cpu().numpy()[0]
assignment = torch.max(result_out, axis=1)[1].cpu().numpy()
y_old[assignment == 1] = 1
y_old[assignment == 2] = 0
y_pred.append(y_old)
# prediction label
y_pred_label_tmp = np.where(y_old == 1)[0]
y_pred_label.append(sorted(y_pred_label_tmp))
visit_cnt += 1
med_cnt += len(y_pred_label_tmp)
#### add or delete
add_gt = set(np.where(y_gt_tmp == 1)[0]) - set(previous_set)
delete_gt = set(previous_set) - set(np.where(y_gt_tmp == 1)[0])
add_pre = set(np.where(y_old == 1)[0]) - set(previous_set)
delete_pre = set(previous_set) - set(np.where(y_old == 1)[0])
add_distance = len(set(add_pre) -
set(add_gt)) + len(set(add_gt) - set(add_pre))
delete_distance = len(set(delete_pre) - set(delete_gt)) + len(
set(delete_gt) - set(delete_pre))
####
add_temp_list.append(add_distance)
delete_temp_list.append(delete_distance)
if len(add_temp_list) > 1:
add_list.append(np.mean(add_temp_list))
delete_list.append(np.mean(delete_temp_list))
elif len(add_temp_list) == 1:
add_list.append(add_temp_list[0])
delete_list.append(delete_temp_list[0])
smm_record.append(y_pred_label)
adm_ja, adm_prauc, adm_avg_p, adm_avg_r, adm_avg_f1 = multi_label_metric(
np.array(y_gt), np.array(y_pred), np.array(y_pred_prob))
ja.append(adm_ja)
prauc.append(adm_prauc)
avg_p.append(adm_avg_p)
avg_r.append(adm_avg_r)
avg_f1.append(adm_avg_f1)
llprint('\rtest step: {} / {}'.format(step, len(data_eval)))
# ddi rate
ddi_rate = ddi_rate_score(smm_record,
path='../data/output/ddi_A_final.pkl')
llprint(
'\nDDI Rate: {:.4}, Jaccard: {:.4}, AVG_F1: {:.4}, Add: {:.4}, Delete: {:.4}, AVG_MED: {:.4}\n'
.format(ddi_rate, np.mean(ja), np.mean(avg_f1), np.mean(add_list),
np.mean(delete_list), med_cnt / visit_cnt))
return ddi_rate, np.mean(ja), np.mean(prauc), np.mean(avg_p), np.mean(
avg_r), np.mean(avg_f1), np.mean(add_list), np.mean(
delete_list), med_cnt / visit_cnt
def main():
# load data
data_path = '../data/output/records_final.pkl'
voc_path = '../data/output/voc_final.pkl'
ddi_adj_path = '../data/output/ddi_A_final.pkl'
device = torch.device('cuda:{}'.format(args.cuda))
ddi_adj = dill.load(open(ddi_adj_path, 'rb'))
data = dill.load(open(data_path, 'rb'))
voc = dill.load(open(voc_path, 'rb'))
diag_voc, pro_voc, med_voc = voc['diag_voc'], voc['pro_voc'], voc[
'med_voc']
np.random.seed(1203)
np.random.shuffle(data)
split_point = int(len(data) * 3 / 5)
data_train = data[:split_point]
eval_len = int(len(data[split_point:]) / 2)
data_test = data[split_point:split_point + eval_len]
data_eval = data[split_point + eval_len:]
voc_size = (len(diag_voc.idx2word), len(pro_voc.idx2word),
len(med_voc.idx2word))
print(voc_size)
model = SimNN(voc_size, ddi_adj, emb_dim=args.dim, device=device)
# model.load_state_dict(torch.load(open(args.resume_path, 'rb')))
if args.Test:
model.load_state_dict(torch.load(open(args.resume_path, 'rb')))
model.to(device=device)
tic = time.time()
eval(model, data_test, voc_size, 0)
print('test time: {}'.format(time.time() - tic))
return
model.to(device=device)
print('parameters', get_n_params(model))
# exit()
optimizer = RMSprop(list(model.parameters()),
lr=args.lr,
weight_decay=args.weight_decay)
# start iterations
history = defaultdict(list)
best_epoch, best_ja = 0, 0
criterion = torch.nn.CrossEntropyLoss()
EPOCH = 40
for epoch in range(EPOCH):
t = 0
tic = time.time()
print('\nepoch {} --------------------------'.format(epoch + 1))
model.train()
for step, input in enumerate(data_train):
if len(input) < 2: continue
loss = 0
for adm_idx, adm in enumerate(input):
if adm_idx == 0: continue
seq_input = input[:adm_idx + 1]
loss_bce_target = np.zeros((1, voc_size[2]))
loss_bce_target[:, adm[2]] = 1
loss_bce_target_last = np.zeros((1, voc_size[2]))
loss_bce_target_last[:, input[adm_idx - 1][2]] = 1
target_list = loss_bce_target - loss_bce_target_last
target_list[target_list == -1] = 2
result = model(seq_input)
loss += criterion(result,
torch.LongTensor(target_list[0]).to(device))
# l2 = 0
# for p in model.parameters():
# l2 = l2 + (p ** 2).sum()
optimizer.zero_grad()
loss.backward(retain_graph=True)
optimizer.step()
llprint('\rtraining step: {} / {}'.format(step, len(data_train)))
print()
tic2 = time.time()
ddi_rate, ja, prauc, avg_p, avg_r, avg_f1, add, delete, avg_med = eval(
model, data_eval, voc_size, epoch)
print('training time: {}, test time: {}'.format(
time.time() - tic,
time.time() - tic2))
history['ja'].append(ja)
history['ddi_rate'].append(ddi_rate)
history['avg_p'].append(avg_p)
history['avg_r'].append(avg_r)
history['avg_f1'].append(avg_f1)
history['prauc'].append(prauc)
history['add'].append(add)
history['delete'].append(delete)
history['med'].append(avg_med)
if epoch >= 5:
print(
'ddi: {}, Med: {}, Ja: {}, F1: {}, Add: {}, Delete: {}'.format(
np.mean(history['ddi_rate'][-5:]),
np.mean(history['med'][-5:]), np.mean(history['ja'][-5:]),
np.mean(history['avg_f1'][-5:]),
np.mean(history['add'][-5:]),
np.mean(history['delete'][-5:])))
torch.save(model.state_dict(), open(os.path.join('saved', args.model_name, \
'Epoch_{}_JA_{:.4}_DDI_{:.4}.model'.format(epoch, ja, ddi_rate)), 'wb'))
if epoch != 0 and best_ja < ja:
best_epoch = epoch
best_ja = ja
print('best_epoch: {}'.format(best_epoch))
dill.dump(
history,
open(
os.path.join('saved', args.model_name,
'history_{}.pkl'.format(args.model_name)), 'wb'))
def main():
if not os.path.exists(os.path.join("saved", model_name)):
os.makedirs(os.path.join("saved", model_name))
data_path = '../data/records.pkl'
voc_path = '../data/voc.pkl'
ehr_adj_path = '../data/ehr_adj.pkl'
ddi_adj_path = '../data/ddi_A.pkl'
device = torch.device('cuda:0')
ehr_adj = dill.load(open(ehr_adj_path, 'rb'))
ddi_adj = dill.load(open(ddi_adj_path, 'rb'))
data = dill.load(open(data_path, 'rb'))
voc = dill.load(open(voc_path, 'rb'))
diag_voc, pro_voc, med_voc = voc['diag_voc'], voc['pro_voc'], voc[
'med_voc']
split_point = int(len(data) * 2 / 3)
data_train = data[:split_point]
eval_len = int(len(data[split_point:]) / 2)
# data_eval = data[split_point:split_point + eval_len]
data_eval = data[split_point + eval_len:]
EPOCH = 30
LR = 0.001
EVAL = True
voc_size = (len(diag_voc.idx2word), len(pro_voc.idx2word),
len(med_voc.idx2word))
model = GMNN(voc_size, ehr_adj, ddi_adj, emb_dim=64, device=device)
if EVAL:
model.load_state_dict(
torch.load(
open(os.path.join("saved", model_name, resume_name), 'rb')))
model.to(device=device)
optimizer = Adam(list(model.parameters()), lr=LR)
if EVAL:
eval(model, data_eval, voc_size, 0)
else:
for epoch in range(EPOCH):
loss_record1 = []
loss_record2 = []
start_time = time.time()
model.train()
for step, input in enumerate(data_train):
input1_hidden, input2_hidden, target_hidden = None, None, None
loss = 0
for adm in input:
loss1_target = np.zeros((1, voc_size[2]))
loss1_target[:, adm[2]] = 1
loss2_target = adm[2] + [adm[2][0]]
loss3_target = np.full((1, voc_size[2]), -1)
for idx, item in enumerate(adm[2]):
loss3_target[0][idx] = item
target_output1, target_output2, [
input1_hidden, input2_hidden, target_hidden
], batch_pos_loss, batch_neg_loss = model(
adm, [input1_hidden, input2_hidden, target_hidden])
loss1 = F.binary_cross_entropy_with_logits(
target_output1,
torch.FloatTensor(loss1_target).to(device))
loss2 = F.cross_entropy(
target_output2,
torch.LongTensor(loss2_target).to(device))
# loss = 9*loss1/10 + loss2/10
loss3 = F.multilabel_margin_loss(
F.sigmoid(target_output1),
torch.LongTensor(loss3_target).to(device))
loss += loss1 + 0.1 * loss3 + 0.01 * batch_neg_loss
loss_record1.append(loss.item())
loss_record2.append(loss3.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
llprint('\rTrain--Epoch: %d, Step: %d/%d' %
(epoch, step, len(data_train)))
eval(model, data_eval, voc_size, epoch)
end_time = time.time()
elapsed_time = (end_time - start_time) / 60
llprint(
'\tEpoch: %d, Loss1: %.4f, Loss2: %.4f, One Epoch Time: %.2fm, Appro Left Time: %.2fh\n'
% (epoch, np.mean(loss_record1), np.mean(loss_record2),
elapsed_time, elapsed_time * (EPOCH - epoch - 1) / 60))
torch.save(
model.state_dict(),
open(
os.path.join(
'saved', model_name, 'Epoch_%d_Loss1_%.4f.model' %
(epoch, np.mean(loss_record1))), 'wb'))
print('')
# test
torch.save(
model.state_dict(),
open(os.path.join('saved', model_name, 'final.model'), 'wb'))
def __init__(self, args):
self.num_folds = args.num_folds
self.data_dir = args.data_dir + args.data_set
util.llprint("Create structures...")
lines = []
caseids = []
lastcase = ''
line = ''
firstLine = True
numlines = 0
check_additional_features = True
features_additional_attributes = []
features_additional_events = []
features_additional_sequences = []
csvfile = open(self.data_dir, 'r')
spamreader = csv.reader(csvfile, delimiter=';', quotechar='|')
next(spamreader, None)
for row in spamreader:
# initial setting of additional features
if check_additional_features:
if len(row) == self.num_attributes_standard:
util.llprint("No additional attributes.\n")
else:
self.num_features_additional = len(
row) - self.num_attributes_standard
util.llprint("Number of additional attributes: %d\n" %
self.num_features_additional)
check_additional_features = False
if row[0] != lastcase:
caseids.append(row[0])
lastcase = row[0]
if not firstLine:
lines.append(line)
if self.num_features_additional > 0:
features_additional_sequences.append(
features_additional_events)
line = ''
if self.num_features_additional > 0:
features_additional_events = []
numlines += 1
# get values of additional attributes
if self.num_features_additional > 0:
for index in range(
self.num_attributes_standard,
self.num_attributes_standard +
self.num_features_additional):
features_additional_attributes.append(row[index])
features_additional_events.append(
features_additional_attributes)
features_additional_attributes = []
# add activity to a case
line += chr(int(row[1]) + self.ascii_offset)
firstLine = False
lines.append(line)
if self.num_features_additional > 0:
features_additional_sequences.append(features_additional_events)
numlines += 1
# get elements per fold in case of split evaluation
util.llprint("Loading Data starts... \n")
self.elems_per_fold = int(round(numlines / self.num_folds))
util.llprint("Calc max length of sequence\n")
lines = list(map(lambda x: x + '!', lines))
self.max_sequence_length = max(map(lambda x: len(x), lines))
util.llprint("Max length of sequence: %d\n" % self.max_sequence_length)
util.llprint(
"Beginn calculation of total chars and total target chars... \n")
self.chars = list(map(lambda x: set(x), lines))
self.chars = list(set().union(*self.chars))
self.chars.sort()
self.target_chars = copy.copy(self.chars)
self.chars.remove('!')
util.llprint("Total chars: %d, target chars: %d\n" %
(len(self.chars), len(self.target_chars)))
util.llprint("Beginn creation of dicts for char handling... \n")
self.char_indices = dict((c, i) for i, c in enumerate(self.chars))
self.indices_char = dict((i, c) for i, c in enumerate(self.chars))
self.target_char_indices = dict(
(c, i) for i, c in enumerate(self.target_chars))
self.target_indices_char = dict(
(i, c) for i, c in enumerate(self.target_chars))
util.llprint("Dics for char handling created\n")
# set feature variables
self.num_features_activities = len(self.chars)
self.num_features_all = self.num_features_activities + self.num_features_additional
# set structure variables
self.lines = lines
self.caseids = caseids
if self.num_features_additional > 0:
self.features_additional_sequences = features_additional_sequences
# init validation
kFold = KFold(n_splits=self.num_folds, random_state=0, shuffle=True)
for train_index, test_index in kFold.split(lines):
self.train_index_per_fold.append(train_index)
self.test_index_per_fold.append(test_index)
def eval(model,
data_eval,
voc_size,
epoch,
val=0,
threshold1=0.3,
threshold2=0.3):
model.eval()
smm_record = []
ja, prauc, avg_p, avg_r, avg_f1 = [[] for _ in range(5)]
med_cnt, visit_cnt = 0, 0
label_list, prob_add, prob_delete = [], [], []
add_list, delete_list = [], []
for step, input in enumerate(data_eval):
y_gt, y_pred, y_pred_prob, y_pred_label = [], [], [], []
add_temp_list, delete_temp_list = [], []
if len(input) < 2: continue
for adm_idx, adm in enumerate(input):
if adm_idx == 0:
y_old = np.zeros(voc_size[2])
y_old[adm[2]] = 1
continue
y_gt_tmp = np.zeros(voc_size[2])
y_gt_tmp[adm[2]] = 1
y_gt.append(y_gt_tmp)
label_list.append(y_gt_tmp)
add_result, delete_result = model(input[:adm_idx + 1])
# prediction prod
y_pred_tmp_add = F.sigmoid(add_result).detach().cpu().numpy()[0]
y_pred_tmp_delete = F.sigmoid(
delete_result).detach().cpu().numpy()[0]
y_pred_prob.append(y_pred_tmp_add)
prob_add.append(y_pred_tmp_add)
prob_delete.append(y_pred_tmp_delete)
previous_set = np.where(y_old == 1)[0]
# prediction med set
y_old[y_pred_tmp_add >= threshold2] = 1
y_old[y_pred_tmp_delete >= threshold1] = 0
y_pred.append(y_old)
# prediction label
y_pred_label_tmp = np.where(y_old == 1)[0]
y_pred_label.append(sorted(y_pred_label_tmp))
visit_cnt += 1
med_cnt += len(y_pred_label_tmp)
#### add or delete
add_gt = set(np.where(y_gt_tmp == 1)[0]) - set(previous_set)
delete_gt = set(previous_set) - set(np.where(y_gt_tmp == 1)[0])
add_pre = set(np.where(y_old == 1)[0]) - set(previous_set)
delete_pre = set(previous_set) - set(np.where(y_old == 1)[0])
add_distance = len(set(add_pre) -
set(add_gt)) + len(set(add_gt) - set(add_pre))
delete_distance = len(set(delete_pre) - set(delete_gt)) + len(
set(delete_gt) - set(delete_pre))
####
add_temp_list.append(add_distance)
delete_temp_list.append(delete_distance)
if len(add_temp_list) > 1:
add_list.append(np.mean(add_temp_list))
delete_list.append(np.mean(delete_temp_list))
elif len(add_temp_list) == 1:
add_list.append(add_temp_list[0])
delete_list.append(delete_temp_list[0])
smm_record.append(y_pred_label)
adm_ja, adm_prauc, adm_avg_p, adm_avg_r, adm_avg_f1 = multi_label_metric(
np.array(y_gt), np.array(y_pred), np.array(y_pred_prob))
ja.append(adm_ja)
prauc.append(adm_prauc)
avg_p.append(adm_avg_p)
avg_r.append(adm_avg_r)
avg_f1.append(adm_avg_f1)
llprint('\rtest step: {} / {}'.format(step, len(data_eval)))
# ddi rate
ddi_rate = ddi_rate_score(smm_record,
path='../data/output/ddi_A_final.pkl')
# llprint('\nDDI Rate: {:.4}, Jaccard: {:.4}, PRAUC: {:.4}, AVG_PRC: {:.4}, AVG_RECALL: {:.4}, AVG_F1: {:.4}, Add: {:.4}, Delete; {:.4}, AVG_MED: {:.4}\n'.format(
# ddi_rate, np.mean(ja), np.mean(prauc), np.mean(avg_p), np.mean(avg_r), np.mean(avg_f1), np.mean(add_list), np.mean(delete_list), med_cnt / visit_cnt
# ))
# print ('-1-', ddi_rate, '-2-', np.mean(ja), '-3-', np.mean(prauc), '-4-', np.mean(avg_f1), '-5-', np.mean(add_list), '-6-', np.mean(delete_list), '-7-', med_cnt / visit_cnt)
llprint(
'\nDDI Rate: {:.4}, Jaccard: {:.4}, AVG_F1: {:.4}, Add: {:.4}, Delete: {:.4}, AVG_MED: {:.4}\n'
.format(np.float(ddi_rate), np.mean(ja), np.mean(avg_f1),
np.mean(add_list), np.mean(delete_list), med_cnt / visit_cnt))
if val == 0:
return np.float(ddi_rate), np.mean(ja), np.mean(prauc), np.mean(
avg_p), np.mean(avg_r), np.mean(avg_f1), np.mean(
add_list), np.mean(delete_list), med_cnt / visit_cnt
else:
return np.array(label_list), np.array(prob_add), np.array(prob_delete)
def train(args, preprocess_manager):
util.llprint("Loading Data starts... \n")
X, y, sequence_max_length, num_features_all, num_features_activities = preprocess_manager.create_and_encode_training_set(
args)
util.llprint("Loading Data done!\n")
print('Build model...')
# LSTM
if args.dnn_architecture == 0:
main_input = keras.layers.Input(shape=(sequence_max_length,
num_features_all),
name='main_input')
l1 = keras.layers.recurrent.LSTM(100,
implementation=2,
activation="tanh",
kernel_initializer='glorot_uniform',
return_sequences=False,
dropout=0.2)(main_input)
b1 = keras.layers.normalization.BatchNormalization()(l1)
# GRU
elif args.dnn_architecture == 1:
main_input = keras.layers.Input(shape=(sequence_max_length,
num_features_all),
name='main_input')
l1 = keras.layers.recurrent.GRU(100,
implementation=2,
activation="tanh",
kernel_initializer='glorot_uniform',
return_sequences=False,
dropout=0.2)(main_input)
b1 = keras.layers.normalization.BatchNormalization()(l1)
# RNN
elif args.dnn_architecture == 2:
main_input = keras.layers.Input(shape=(sequence_max_length,
num_features_all),
name='main_input')
l1 = keras.layers.recurrent.SimpleRNN(
100,
implementation=2,
activation="tanh",
kernel_initializer='glorot_uniform',
return_sequences=False,
dropout=0.2)(main_input)
b1 = keras.layers.normalization.BatchNormalization()(l1)
activity_output = keras.layers.Dense(
num_features_activities + 1,
activation='softmax',
name='activity_output',
kernel_initializer='glorot_uniform')(b1)
model = keras.models.Model(inputs=[main_input], outputs=[activity_output])
optimizer = keras.optimizers.Nadam(lr=args.learning_rate,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-8,
schedule_decay=0.004,
clipvalue=3)
model.compile(loss={'activity_output': 'categorical_crossentropy'},
optimizer=optimizer)
early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss',
patience=10)
model_checkpoint = keras.callbacks.ModelCheckpoint(
'%smodel_%s.h5' %
(args.checkpoint_dir, preprocess_manager.iteration_cross_validation),
monitor='val_loss',
verbose=0,
save_best_only=True,
save_weights_only=False,
mode='auto')
lr_reducer = keras.callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.5,
patience=10,
verbose=0,
mode='auto',
min_delta=0.0001,
cooldown=0,
min_lr=0)
model.summary()
start_training_time = datetime.now()
model.fit(X, {'activity_output': y},
validation_split=1 / args.num_folds,
callbacks=[early_stopping, model_checkpoint, lr_reducer],
verbose=1,
batch_size=args.batch_size_train,
epochs=args.dnn_num_epochs)
training_time = datetime.now() - start_training_time
return training_time.total_seconds()
# iterate folds
for iteration_cross_validation in range(0, args.num_folds):
preprocess_manager.iteration_cross_validation = iteration_cross_validation
training_time_seconds.append(train.train(args, preprocess_manager))
args.iteration_cross_validation = iteration_cross_validation
test.test(args, preprocess_manager)
accuracy_value, precision_value, recall_value, f1_value = metric.calc_metrics(args)
accuracy_values.append(accuracy_value)
precision_values.append(precision_value)
recall_values.append(recall_value)
f1_values.append(f1_value)
# final output
for index in range(0, len(accuracy_values)):
util.llprint("Accuracy of fold %i: %f\n" % (index + 1, accuracy_values[index]))
util.llprint("Precision of fold %i: %f\n" % (index + 1, precision_values[index]))
util.llprint("Recall of fold %i: %f\n" % (index + 1, recall_values[index]))
util.llprint("F1-Score of fold %i: %f\n" % (index + 1, f1_values[index]))
util.llprint("Training time of fold %i: %f seconds\n" % (index + 1, training_time_seconds[index]))
util.llprint(
"Average accuracy %i-fold cross-validation: %f\n" % (args.num_folds, sum(accuracy_values) / args.num_folds))
util.llprint("Average precision precision %i-fold cross-validation: %f\n" % (
args.num_folds, sum(precision_values) / args.num_folds))
util.llprint(
"Average recall %i-fold cross-validation: %f\n" % (args.num_folds, sum(recall_values) / args.num_folds))
util.llprint(
"Average f1-score %i-fold cross-validation: %f\n" % (args.num_folds, sum(f1_values) / args.num_folds))
util.llprint("Average training time in seconds %i-fold cross-validation: %f\n" % (
args.num_folds, sum(training_time_seconds) / args.num_folds))
y_pred_tmp[np.array(out_list) - 2] = 1
y_pred.append(y_pred_tmp)
ja, prauc, avg_p, avg_r, avg_f1 = sequence_metric(np.array(y_gt), np.array(y_pred),
np.array(y_pred_prob),
np.array(y_pred_label))
# ddi rate
ddi_A = dill.load(open('../data/ddi_A_final.pkl', 'rb'))
all_cnt = 0
dd_cnt = 0
for adm in y_pred_label:
med_code_set = adm
for i, med_i in enumerate(med_code_set):
for j, med_j in enumerate(med_code_set):
if j <= i:
continue
all_cnt += 1
if ddi_A[med_i, med_j] == 1 or ddi_A[med_j, med_i] == 1:
dd_cnt += 1
ddi_rate = dd_cnt / all_cnt
llprint('\n\tDDI Rate: %.4f, Jaccard: %.4f, PRAUC: %.4f, AVG_PRC: %.4f, AVG_RECALL: %.4f, AVG_F1: %.4f\n' % (
ddi_rate, ja, prauc, avg_p, avg_r, avg_f1
))
