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
})
score_totaux.to_csv('score/score_model_wrapper_' + name_track +
"2.csv",
index=False)
dim += 1
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
F1_fisher.append(
tools.compute_F1_score(labels_test[i],
predict_labels_fisher[i],
list_states))
F1_wrapper.append(
tools.compute_F1_score(labels_test[i],
predict_labels_wrapper[i],
list_states))
total += 1
F1_f.append(np.mean(F1_fisher))
F1_w.append(np.mean(F1_wrapper))
F1_fisher_temp += np.mean(F1_fisher)
F1_wrapper_temp += np.mean(F1_wrapper)
index_nbr_f.append(nbr_features - 1)
index_nbr_f.append(nbr_features - 1)
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(
{ name_track: score_total[num_track]})
score_totaux = pd.concat([score_totaux, df_track], axis=1)
score_totaux = ranking_features(score_totaux, tracks, method_sort)
score_totaux.to_csv(path_save + '/' + file_name + str(iteration+1), index=False)
# 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,
})
score_totaux.to_csv('score_pca' + '_' + name_track + ".csv", index=False)
list_states_posture_final = sorted(
list_states_posture_final)
for i in range(len(predict_labels)):
MCC_combined.append(
tools.compute_MCC_score(
labels_test_detailed_posture[i], labels_final[i],
list_states_posture_final))
MCC_detailed.append(
tools.compute_MCC_score(
labels_test_detailed_posture[i],
predict_labels_detailed_posture[i],
list_states_posture_final))
MCC_general.append(
tools.compute_F1_score(labels_test[i],
predict_labels[i],
list_states_posture))
MCC_details.append(
tools.compute_F1_score(labels_test_details[i],
predict_labels_details[i],
list_states_details))
F1_combined.append(
tools.compute_F1_score(labels_test_detailed_posture[i],
labels_final[i],
list_states_posture_final))
F1_detailed.append(
tools.compute_F1_score(
labels_test_detailed_posture[i],
predict_labels_detailed_posture[i],
list_states_posture_final))
