def main():
parser = argparse.ArgumentParser(
description=
"Train and run k-fold cross-validation on physionet BCI 2000 dataset")
parser.add_argument("-c",
"--num_classes",
type=int,
default=4,
choices=[2, 3, 4])
parser.add_argument(
"-m",
"--model_name",
type=str,
help="Name of the model used",
default="eegA",
choices=["eegA", "eegB", "eegC", "eegD", "eegA_LSTM", "eegD_LSTM"])
parser.add_argument("-cf", "--num_conv_filters", type=int, default=32)
parser.add_argument(
'--stride',
dest='stride',
help="Whether stride is used in the last Conv2D of first block",
action='store_true')
parser.add_argument('--no-stride', dest='stride', action='store_false')
parser.set_defaults(stride=True)
parser.add_argument("-dr", "--dropout_rate", type=float, default=0.5)
parser.add_argument("-bs", "--batch_size", type=int, default=16)
parser.add_argument("-e", "--epochs", type=int, default=10)
parser.add_argument("-p",
"--patience",
help="Parameter for EarlyStopping callback",
type=int,
default=5)
parser.add_argument("-kf", "--k_fold", type=int, default=5)
parser.add_argument(
"-o",
"--output_name",
type=str,
help="logs will be put in ./logs/fit/output_name. If none is"
"provided, time at run start is chosen",
default=None)
args = parser.parse_args()
# input validation
try:
num_windows = json.load(open("./data/args_bci2000_preprocess.txt",
'r'))['num_windows']
except FileNotFoundError:
raise FileNotFoundError(
"Preprocessed data arguments not found. Run main_preprocess_data_bci2000.py and try again."
)
if num_windows == 1 and 'LSTM' in args.model_name:
raise ValueError(
"LSTM can only be chosen for data preprocessed with -w > 1")
if num_windows > 1 and 'LSTM' not in args.model_name:
raise ValueError(
"Only LSTM models can be chosen for data preprocessed with -w > 1")
if args.output_name is None:
args.output_name = datetime.now().strftime('%Y%m%d-%H%M%S')
model_factory = ModelFactory(dataset="BCI2000",
output_name=args.output_name,
model_name=args.model_name,
num_classes=args.num_classes,
num_conv_filters=args.num_conv_filters,
dropout_rate=args.dropout_rate,
use_stride=args.stride)
X, y = load_preprocessed_bci2000_data(num_classes=args.num_classes)
kf = KFold(n_splits=args.k_fold, shuffle=True, random_state=42)
for idx, [train, test] in enumerate(kf.split(X, y)):
X_train = X[train]
X_test = X[test]
y_train = y[train]
y_test = y[test]
X_train, scaler = scale_data(X_train)
X_test, _ = scale_data(X_test, scaler)
model = model_factory.get_model()
history = model.fit(x=X_train,
y=y_train,
batch_size=args.batch_size,
epochs=args.epochs,
validation_data=(X_test, y_test),
callbacks=model_factory.get_callbacks(
patience=args.patience,
log_dir_suffix=f"{idx + 1}"),
shuffle=True)
write_history(history.history, log_dir=model_factory.get_log_dir())
with open(f"{model_factory.get_log_dir()}/model_summary.txt",
'w') as file:
model.summary(print_fn=lambda x: file.write(x + '\n'))
# write parameters used for training
with open(f"{model_factory.get_log_dir()}/input_args.txt",
'w') as file:
file.write(json.dumps(args.__dict__, indent=4))
def main():
"""
Does the following:
- For each subject:
- Load preprocessed data from subject (preprocessed from 'A0XT.mat')
- Train model on ALL data from 'A0XT.mat'
- Evaluate model on test data originating from 'A0XE.mat'
"""
parser = argparse.ArgumentParser(
description="Train and run model for data set 2a of BCI Competition IV."
)
parser.add_argument(
"-m",
"--model_name",
type=str,
help="Name of the model used",
default="eegA",
choices=["eegA", "eegB", "eegC", "eegD", "eegA_LSTM", "eegD_LSTM"])
parser.add_argument("-cf", "--num_conv_filters", type=int, default=32)
parser.add_argument(
'--stride',
dest='stride',
help="Whether stride is used in the last Conv2D of first block",
action='store_true')
parser.add_argument('--no-stride', dest='stride', action='store_false')
parser.set_defaults(stride=True)
parser.add_argument("-dr", "--dropout_rate", type=float, default=0.5)
parser.add_argument("-bs", "--batch_size", type=int, default=16)
parser.add_argument("-e", "--epochs", type=int, default=10)
parser.add_argument("-p",
"--patience",
help="Parameter for EarlyStopping callback",
type=int,
default=10)
parser.add_argument("-kf", "--k_fold", type=int, default=5)
parser.add_argument(
"-o",
"--output_name",
type=str,
help="logs will be put in ./logs/fit/output_name. If none is"
"provided, time at run start is chosen",
default=None)
args = parser.parse_args()
# input validation
try:
num_windows = json.load(open("./data/args_bci2aiv_preprocess.txt",
'r'))['num_windows']
except FileNotFoundError:
raise FileNotFoundError(
"Preprocessed data arguments not found. Run main_preprocess_data_bci2aiv.py and try again."
)
if num_windows == 1 and 'LSTM' in args.model_name:
raise ValueError(
"LSTM can only be chosen for data preprocessed with -w > 1")
if num_windows > 1 and 'LSTM' not in args.model_name:
raise ValueError(
"Only LSTM models can be chosen for data preprocessed with -w > 1")
if args.output_name is None:
args.output_name = datetime.now().strftime('%Y%m%d-%H%M%S')
model_factory = ModelFactory(dataset="BCI2aIV",
output_name=args.output_name,
model_name=args.model_name,
num_conv_filters=args.num_conv_filters,
use_stride=args.stride,
dropout_rate=args.dropout_rate
) # num_classes is always 4 for this dataset
for subject_num in g.subject_num_range_bci2aiv:
X_train, y_train = load_single_subject_bci2aiv_data(
subject_num=subject_num, is_training=True)
X_test, y_test = load_single_subject_bci2aiv_data(
subject_num=subject_num, is_training=False)
X_train, scaler = scale_data(X_train)
X_test, _ = scale_data(X_test, scaler)
model = model_factory.get_model()
history = model.fit(x=X_train,
y=y_train,
batch_size=args.batch_size,
epochs=args.epochs,
validation_data=(X_test, y_test),
callbacks=model_factory.get_callbacks(
patience=args.patience,
log_dir_suffix=f"{subject_num}"),
shuffle=True)
write_history(history.history,
subject_num=subject_num,
log_dir=model_factory.get_log_dir())
with open(f"{model_factory.get_log_dir()}/model_summary.txt",
'w') as file:
model.summary(print_fn=lambda x: file.write(x + '\n'))
# write parameters used for training
with open(f"{model_factory.get_log_dir()}/input_args.txt",
'w') as file:
file.write(json.dumps(args.__dict__, indent=4))