F1_score = []
dim_score = []
feaures_save = []
dim = 11
file_wrapper = path_wrapper + 'wrapper_' + name_track + ".csv_" + str(
dim)
# for dim in range(1, 11):
while (os.path.isfile(file_wrapper)):
print(file_wrapper)
list_features_final = get_best_features(file_wrapper)[0]
dim_features = np.ones(len(list_features_final))
for n_iter in range(nbr_cross_val):
data_ref1, labels_ref, data_test, labels_test, id_train, id_test = tools.split_data_base2(
data_win2, real_labels[num_track], ratio)
data_ref = []
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
labels_gen.append(real_labels[i])
count.append(i)
del data_reduce[count[0]:count[-1] + 1]
del labels_reduce[count[0]:count[-1] + 1]
else:
n_subject = len(list_participant)
F1_wrapper_temp = 0
F1_fisher_temp = 0
for nbr_test in range(nbr_cross_val):
total = 0
data_ref, labels_ref, data_test, labels_test, id_train, id_test = tools.split_data_base2(
data_reduce, labels_reduce, ratio)
data_fisher = []
data_wrapper = []
data_win = deepcopy(data_win2)
for id_subject in id_train:
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)
model_fisher = ModelHMM()
model_fisher.train(data_fisher, labels_ref,
id_train = [[]] id_test = [[]] best_features_total = [] score_total = [[]] for i in range(len(tracks)): if(i < len(tracks)-1): score_total.append([]) for k in range(nbr_cross_val): for num_track in range(len(tracks)): if(num_track == 0): data_ref_all[k], labels_ref[num_track][k], data_test_all[k], labels_test[num_track][k], id_train[k], id_test[k] = tools.split_data_base2(data_win2, real_labels[num_track], ratio) else: for id_subject in id_train[k]: labels_ref[num_track][k].append(real_labels[num_track][id_subject]) for id_subject in id_test[k]: labels_test[num_track][k].append(real_labels[num_track][id_subject]) if(len(labels_ref) < len(tracks)): labels_ref.append([[]]) labels_test.append([[]]) if(k < nbr_cross_val-1): labels_ref[num_track].append([]) labels_test[num_track].append([])
###################################################
data_win = []
for j in range(len(df_all_data)):
data_win.append(df_all_data[j][sub_list_features].values)
F1_S = 0
MCC = 0
confusion_matrix = np.zeros(
(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)
id_test = [[]]
best_features_total = []
score_total = [[]]
for i in range(len(tracks)):
if (i < len(tracks) - 1):
score_total.append([])
for k in range(nbr_cross_val):
for num_track in range(len(tracks)):
if (num_track == 0):
data_ref_all[k], labels_ref[num_track][k], data_test_all[
k], labels_test[num_track][k], id_train[k], id_test[
k] = tools.split_data_base2(
data_win2, real_labels[num_track], ratio)
else:
for id_subject in id_train[k]:
labels_ref[num_track][k].append(
real_labels[num_track][id_subject])
for id_subject in id_test[k]:
labels_test[num_track][k].append(
real_labels[num_track][id_subject])
if (len(labels_ref) < len(tracks)):
labels_ref.append([[]])
labels_test.append([[]])
if (k < nbr_cross_val - 1):
labels_ref[num_track].append([])