for data in data_ref1:
df = pd.DataFrame(data)
df.columns = list_features
data_ref.append(df[list_features_final].values)
model = ModelHMM()
model.train(data_ref, labels_ref, list_features_final,
dim_features)
data_ref = []
for data in data_test:
df = pd.DataFrame(data)
df.columns = list_features
data_ref.append(df[list_features_final].values)
pred_labels, proba = model.test_model(data_ref)
F1_temp = []
for i in range(len(labels_test)):
F1_temp.append(
tools.compute_F1_score(labels_test[i], pred_labels[i],
list_states[num_track]))
F1_score.append(np.mean(F1_temp))
dim_score.append(dim)
feaures_save.append(str(list_features_final))
score_totaux = pd.DataFrame({
'nbr_components': dim_score,
'score': F1_score,
'features': feaures_save
best_features_wrapper, dim_features)
if (save):
model.save_model(path_model, name_model, "load_handling")
#### Test
data_fisher = []
data_wrapper = []
for id_subject in id_test:
df = pd.DataFrame(data_win2[id_subject])
df.columns = list_features
data_fisher.append(df[best_features_fisher].values)
data_wrapper.append(df[best_features_wrapper].values)
predict_labels_fisher, proba = model_fisher.test_model(
data_fisher)
predict_labels_wrapper, proba = model_wrapper.test_model(
data_wrapper)
time_test = []
for id_subject in id_test:
time_test.append(timestamps[id_subject])
for i in range(len(labels_test)):
conf_mat = tools.compute_confusion_matrix(
predict_labels_fisher[i], labels_test[i], list_states)
confusion_matrix_fisher += conf_mat
conf_mat = tools.compute_confusion_matrix(
predict_labels_wrapper[i], labels_test[i], list_states)
confusion_matrix_wrapper += conf_mat
train_set = tools.reduce_data_to_features(data_train, list_features,
list_features_final)
test_set = tools.reduce_data_to_features(data_test, list_features,
list_features_final)
dim_features = np.ones(len(list_features_final))
print('DEBUG list of final features ', list_features_final)
plt.plot(train_set[0][:, 3])
plt.show()
# Training the model
model = ModelHMM()
model.train(train_set, labels_train, list_features_final, dim_features)
# Testing the model
pred_labels, proba = model.test_model(test_set)
#debug sere
#pred_labels, proba les ecrire dans un fichier
F1_temp = []
for i in range(len(labels_test)):
F1_temp.append(
tools.compute_F1_score(labels_test[i], pred_labels[i],
list_states[num_track]))
F1_score.append(np.mean(F1_temp))
print(F1_score)
if (flag_save):
(len(list_states), len(list_states)))
for nbr_test in range(nbr_cross_val):
data_ref, labels_ref, data_test, labels_test, id_train, id_test = tools.split_data_base2(
data_win, real_labels, ratio)
model = ModelHMM()
model.train(data_ref, labels_ref, sub_list_features,
np.ones(len(sub_list_features)))
#### Test
time_test = []
for id_subject in id_test:
time_test.append(timestamps[id_subject])
predict_labels, proba = model.test_model(data_test)
for i in range(len(predict_labels)):
conf_mat = tools.compute_confusion_matrix(
predict_labels[i], labels_test[i], list_states)
confusion_matrix += conf_mat
MCC += tools.compute_MCC_score(
predict_labels[i], labels_test[i],
list_states) / len(predict_labels)
prec_total, recall_total, F1_score = tools.compute_score(
confusion_matrix)
acc = tools.get_accuracy(confusion_matrix)
F1_S = F1_score
# F1_S = MCC/nbr_cross_val
if (len(score) == 0):
data_test_action = []
accu = 0
total_ = 0
for id_subject in id_test:
time_test.append(timestamps[id_subject])
labels_test_details.append(labels_details[id_subject])
data_details_test.append(data_details[id_subject])
labels_test_detailed_posture.append(
labels_posture_detailed[id_subject])
data_test_detailed_posture.append(
data_detailed_posture[id_subject])
labels_test_action.append(
labels_current_action[id_subject])
data_test_action.append(data_current_action[id_subject])
predict_labels, proba = model.test_model(data_test)
predict_labels_details, proba_details = model_details.test_model(
data_details_test)
predict_labels_detailed_posture, proba = model_detailed_posture.test_model(
data_test_detailed_posture)
predict_labels_action, proba = model_action.test_model(
data_test_action)
# time, ground_truth, prediction, id_sample_start, id_sample_end = tools.prepare_segment_analysis(time_test, predict_labels, labels_test, id_test)
# predict_labels2 = deepcopy(predict_labels)
labels_final = deepcopy(predict_labels)
list_states_posture_final = []
for i in range(len(predict_labels)):