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):
model.save_model(path_model, name_model, "load_handling_" + name_track)
transition_error = []
short_transition_error = 0
MCC = 0
F1_fisher = []
F1_wrapper = []
F1_f = []
F1_w = []
if (nbr_cross_val == 0):
model = ModelHMM()
model.train(data_win, real_labels, best_features, dim_features)
if (save):
model.save_model(path_model, name_model, "load_handling")
for n_subject in range(len(list_participant)):
data_reduce = deepcopy(data_win)
labels_reduce = deepcopy(real_labels)
if (test_generalisation):
data_gen = []
labels_gen = []
seq_subject = 0
count = []
for i in range(len(info_participant)):
if (info_participant[i] == list_participant[n_subject]):
data_gen.append(data_win[i])
labels_gen.append(real_labels[i])
F1_f = []
F1_w = []
if (nbr_cross_val == 0):
data_reduce = []
for data in data_win:
df = pd.DataFrame(data)
df.columns = list_features
data_reduce.append(df[best_features_wrapper].values)
data_ref, labels_ref, data_test, labels_test, id_train, id_test = tools.split_data_base2(
data_reduce, real_labels, ratio)
model = ModelHMM()
model.train(data_ref, labels_ref, best_features_wrapper,
dim_features)
model.save_model('model', 'test_video_action', "load_handling")
info_split = []
for seq, num_seq in zip(info_sequences,
range(len(info_sequences))):
if (num_seq in id_train):
info_split.append('training')
else:
info_split.append('testing')
df = pd.DataFrame({'Sequence': info_sequences, 'Base': info_split})
df.to_csv('model/test_video_action.csv', index=False)
sys.exit("Shut down")