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
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]))
# indicesToKeep = finalDf['state'] == target
# ax.scatter(finalDf.loc[indicesToKeep, 'pc1']
# , finalDf.loc[indicesToKeep, 'pc2']
# , finalDf.loc[indicesToKeep, 'pc3']
# , s = 50)
# ax.legend(list_states)
# ax.grid()
for id_train in range(len(data_ref)):
data_ref[id_train] = pca.transform(data_ref[id_train])
for id_test in range(len(data_test)):
data_test[id_test] = pca.transform(data_test[id_test])
model = ModelHMM()
model.train(data_ref, labels_ref, col, dim_features)
pred_labels, proba = model.test_model(data_test)
F1_temp = []
for i in range(len(labels_test)):
F1_temp.append(tools.compute_F1_score(labels_test[i], pred_labels[i], list_states))
F1_score.append(np.mean(F1_temp))
dim_score.append(n_components)
score_totaux = pd.DataFrame(
{'nbr_components': dim_score,
'score': F1_score,
short = 0
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]):
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)
confusion_matrix += conf_mat
MCC += tools.compute_MCC_score(
predict_labels[i], labels_test[i],
list_states) / len(predict_labels)
data_win, real_labels[num_track], ratio_split)
# Keep only the data related to the final list of features
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))
'score': sorted_score,
})
best_features = sorted_features_fisher[0:n_components]
dim_features = np.ones(len(best_features))
# print(sorted_features_fisher)
data_reduce = []
for data in data_ref:
df = pd.DataFrame(data)
df.columns = list_features
data_reduce.append(df[best_features].values)
model = ModelHMM()
model.train(data_reduce, labels_ref, best_features,
dim_features)
data_reduce = []
for data in data_test:
df = pd.DataFrame(data)
df.columns = list_features
data_reduce.append(df[best_features].values)
pred_labels, proba = model.test_model(data_reduce)
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]))
for nbr_test in range(nbr_cross_val):
data_ref = []
data_test = []
for data in data_ref_all[nbr_test]:
df_data = pd.DataFrame(data, columns = list_features)
data_ref.append(df_data[sub_list_features].values)
for data in data_test_all[nbr_test]:
df_data = pd.DataFrame(data, columns = list_features)
data_test.append(df_data[sub_list_features].values)
model = ModelHMM()
model.train(data_ref, labels_ref[num_track][nbr_test], sub_list_features, np.ones(len(sub_list_features)))
#### Test
predict_labels, proba = model.test_model(data_test)
for i in range(len(predict_labels)):
F1_score.append(tools.compute_F1_score(labels_test[num_track][nbr_test][i], predict_labels[i], list_states[num_track]))
F1_S[num_track] = np.mean(F1_score)
score_total[num_track].append(F1_S[num_track])
score_totaux = pd.DataFrame(
{'best_features': best_features_total})
for name_track, num_track in zip(tracks, range(len(tracks))):
df_track = pd.DataFrame(
del data_reduce_detailted[count[0]:count[-1] + 1]
del labels_reduce_detailed[count[0]:count[-1] + 1]
del data_reduce_details[count[0]:count[-1] + 1]
del labels_reduce_details[count[0]:count[-1] + 1]
del data_reduce_action[count[0]:count[-1] + 1]
del labels_reduce_action[count[0]:count[-1] + 1]
else:
n_subject = len(list_participant)
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_reduce_posture, labels_reduce_posture, ratio)
model = ModelHMM()
model.train(data_ref, labels_ref, best_features_posture,
np.ones(len(best_features_posture)))
labels_ref_details = []
data_details_ref = []
labels_ref_detailed_posture = []
data_ref_detailed_posture = []
labels_ref_action = []
data_ref_action = []
if (test_generalisation):
for id_subject in id_train:
labels_ref_details.append(
labels_reduce_details[id_subject])
data_details_ref.append(data_reduce_details[id_subject])
labels_ref_detailed_posture.append(
labels_reduce_detailed[id_subject])