def fit(self, X_train, y_train, X_val, y_val):
if X_train.ndim != 2:
raise Exception('ValueError: `X_train` is incompatible: expected ndim=4, found ndim='+str(X_train.ndim))
elif X_val.ndim != 2:
raise Exception('ValueError: `X_val` is incompatible: expected ndim=4, found ndim='+str(X_val.ndim))
print('Dimension of training set is: {} and label is: {}'.format(X_train.shape, y_train.shape))
print('Dimension of validation set is: {} and label is: {}'.format(X_val.shape, y_val.shape))
X_all = np.concatenate((X_train, X_val),axis=0)
y_all = np.concatenate((y_train, y_val),axis=0)
# Create a list where train data indices are -1 and validation data indices are 0
tr_index = np.full((X_train.shape[0]), -1)
val_index = np.full((X_val.shape[0]), 0)
split_index = np.concatenate((tr_index, val_index), axis=0).tolist()
# Use the list to create PredefinedSplit
pds = PredefinedSplit(test_fold = split_index)
clf = GridSearchCV(estimator=SVC(), param_grid=self.tuned_parameters, cv=pds, scoring = 'accuracy')
start = time.time()
clf.fit(X_all , y_all)
end = time.time()
#Clasifying with an optimal parameter set
Optimal_params = clf.best_params_
print(Optimal_params)
classifier = SVC(**Optimal_params)
classifier.fit(X_train, y_train)
dump(classifier, self.model_path)
write_log(filepath=self.time_log, data=['time_log'], mode='w')
write_log(filepath=self.time_log, data=[end-start], mode='a')
def k_fold_cross_validation(subject):
# create object of DataLoader
loader = DataLoader(dataset=args.dataset,
train_type=args.train_type,
subject=subject,
data_format=data_format,
data_type=data_type,
dataset_path=args.data_path)
y_true, y_pred = [], []
for fold in range(1, n_folds + 1):
model_name = 'S{:03d}_fold{:02d}'.format(subject, fold)
model = EEGNet(input_shape=input_shape,
class_balancing=True,
f1_average='binary',
num_class=num_class,
loss='sparse_categorical_crossentropy',
epochs=epochs,
batch_size=batch_size,
optimizer=Adam(beta_1=0.9, beta_2=0.999, epsilon=1e-08),
lr=lr,
min_lr=min_lr,
factor=factor,
patience=patience,
es_patience=es_patience,
log_path=log_path,
model_name=model_name,
dropout_rate=dropout_rate)
# load dataset
X_train, y_train = loader.load_train_set(fold=fold)
X_val, y_val = loader.load_val_set(fold=fold)
X_test, y_test = loader.load_test_set(fold=fold)
# train and test using EEGNet
model.fit(X_train, y_train, X_val, y_val)
Y, evaluation = model.predict(X_test, y_test)
# logging
csv_file = log_path + '/S{:03d}_all_results.csv'.format(subject)
if fold == 1:
write_log(csv_file, data=evaluation.keys(), mode='w')
write_log(csv_file, data=evaluation.values(), mode='a')
y_true.append(Y['y_true'])
y_pred.append(Y['y_pred'])
tf.keras.backend.clear_session()
# writing results
np.savez(log_path + '/S{:03d}_Y_results.npz'.format(subject),
y_true=np.array(y_true),
y_pred=np.array(y_pred))
print('------------------------- S{:03d} Done--------------------------'.
format(subject))
def k_fold_cross_validation(subject):
# create object of DataLoader
loader = DataLoader(dataset=args.dataset,
train_type=args.train_type,
subject=subject,
data_format=data_format,
data_type=data_type,
dataset_path=args.data_path)
y_true, y_pred = [], []
for fold in range(1, n_folds+1):
model_name='SVM_S{:03d}_fold{:02d}'.format(subject, fold)
svm = SVM(log_path=log_path,
model_name=model_name,
num_class=num_class,
tuned_parameters=tuned_parameters)
# load dataset
X_train, y_train = loader.load_train_set(fold=fold)
X_val, y_val = loader.load_val_set(fold=fold)
X_test, y_test = loader.load_test_set(fold=fold)
# train and test using SVM
svm.fit(X_train, y_train, X_val, y_val)
Y, evaluation = svm.predict(X_test, y_test)
# logging
csv_file = log_path+'/S{:03d}_all_results.csv'.format(subject)
if fold==1:
write_log(csv_file, data=evaluation.keys(), mode='w')
write_log(csv_file, data=evaluation.values(), mode='a')
y_true.append(Y['y_true'])
y_pred.append(Y['y_pred'])
# writing file
np.savez(log_path+'/S{:03d}_Y_results.npz'.format(subject),
y_true=np.array(y_true),
y_pred=np.array(y_pred))
print('------------------------- S{:03d} Done--------------------------'.format(subject))