def execute(self, base_path, signal, subjects, window, window_overlap, selector, use_classifiers, with_all_signals, times):
self.subjects = subjects
self.window = window
self.window_overlap = window_overlap
self.selector = selector
self.with_all_signals = with_all_signals
predicts_rf = []
predicts_clf = []
predicts_nbrs = []
predicts_shooter = []
testings = []
for sub in subjects:
print('sujeito = ', sub)
training_labels, training_features = self.get_data(base_path, signal, sub, 'training')
testing_labels, testing_features = self.get_data(base_path, signal, sub, 'testing')
if (np.isscalar(training_features[0])):
training_features = training_features.reshape(-1,1)
testing_features = testing_features.reshape(-1,1)
testings.insert(sub, testing_labels)
if 'forest' in use_classifiers:
rf = RandomForestClassifier(n_estimators=100, max_depth=5, oob_score=True)
rf = rf.fit(training_features, training_labels)
predictions = rf.predict(testing_features)
predicts_rf.insert(sub, predictions)
predicts_rf_subject = predictions
if 'svm' in use_classifiers:
clf = svm.SVC(gamma='scale', C=4)
clf = clf.fit(training_features, training_labels)
predictions = clf.predict(testing_features)
predicts_clf.insert(sub, predictions)
predicts_clf_subject = predictions
if 'knn' in use_classifiers:
knn = KNeighborsClassifier(n_neighbors=11, weights='distance', algorithm='auto', metric='euclidean')
knn = knn.fit(training_features, training_labels)
predictions = knn.predict(testing_features)
predicts_nbrs.insert(sub, predictions)
predicts_nbrs_subject = predictions
if 'shooter' in use_classifiers:
shoot2 = Shooter()
predictions = shoot2.choose(predicts_rf_subject, predicts_nbrs_subject, predicts_clf_subject)
predicts_shooter.insert(sub, predictions)
return predicts_rf, predicts_clf, predicts_nbrs, predicts_shooter, testings
def one_classifier_and_decision(self, selection, number):
print('Begining ' + str(number) + ' - todos com decisão ' + selection +
' ')
shoot2 = Shooter()
ecg_predicts_rf, ecg_predicts_clf, ecg_predicts_nbrs, ecg_predicts_shooter, ecg_testings = self.individual(
'ecg', selection, 0, True, ['svm'])
eda_predicts_rf, eda_predicts_clf, eda_predicts_nbrs, eda_predicts_shooter, eda_testings = self.individual(
'eda', selection, 0, True, ['forest'])
# emg_predicts_rf, emg_predicts_clf, emg_predicts_nbrs, emg_predicts_shooter, emg_testings = self.individual('emg', selection, 0, True, ['svm'])
resp_predicts_rf, resp_predicts_clf, resp_predicts_nbrs, resp_predicts_shooter, resp_testings = self.individual(
'resp', selection, 0, True, ['svm'])
# decision_predicts = []
# i = 0
# j = 0
# for i in range(self.CLASSIFICATION_TIMES):
# decision_predicts.insert(i, [])
# for j in range(len(self.BASE_SUBJECTS)):
# decisao = shoot2.choose(ecg_predicts_clf[i][j], eda_predicts_rf[i][j], emg_predicts_clf[i][j])
# decision_predicts[i].insert(j, decisao)
# evaluate = Evaluator()
# evaluate.report(self.BASE_SUBJECTS, self.CLASSIFICATION_TIMES, ecg_testings, ecg_predicts_clf, eda_predicts_rf, emg_predicts_clf, decision_predicts, '/Volumes/My Passport/TCC/Resultados3/TODOS_' + str(number) + '_' + selection + '_1.csv')
decision_predicts2 = []
i = 0
j = 0
for i in range(self.CLASSIFICATION_TIMES):
decision_predicts2.insert(i, [])
for j in range(len(self.BASE_SUBJECTS)):
decisao = shoot2.choose(ecg_predicts_clf[i][j],
eda_predicts_rf[i][j],
resp_predicts_clf[i][j])
decision_predicts2[i].insert(j, decisao)
evaluate = Evaluator()
evaluate.report(
self.BASE_SUBJECTS, self.CLASSIFICATION_TIMES, ecg_testings,
ecg_predicts_clf, eda_predicts_rf, resp_predicts_clf,
decision_predicts2, '/Volumes/My Passport/TCC/Resultados3/TODOS_' +
str(number) + '_' + selection + '_2.csv')
