def build_predefined(self, *args, **kwargs):
fold = []
fold.extend([
"train" for _ in load_csv_data(
join(self.unzip_path, "train", "y_train.txt"))
for _ in range(WIN_LEN)
])
fold.extend([
"test"
for _ in load_csv_data(join(self.unzip_path, "test", "y_test.txt"))
for _ in range(WIN_LEN)
])
return pd.DataFrame(fold)
def build_fold(self, *args, **kwargs):
fold = []
fold.extend([
'train' for _ in load_csv_data(
join(self.unzip_path, 'train', 'y_train.txt'))
for _ in range(self.win_len)
])
fold.extend([
'test'
for _ in load_csv_data(join(self.unzip_path, 'test', 'y_test.txt'))
for _ in range(self.win_len)
])
return pd.DataFrame(fold)
def build_label(self, task, *args, **kwargs):
labels = []
for fold in ("train", "test"):
fold_labels = load_csv_data(
join(self.unzip_path, fold, f"y_{fold}.txt"))
labels.extend([l for l in fold_labels for _ in range(WIN_LEN)])
return self.meta.inv_lookup[task], pd.DataFrame(dict(labels=labels))
def build_label(self, task, *args, **kwargs):
labels = []
for fold in ('train', 'test'):
fold_labels = load_csv_data(
join(self.unzip_path, fold, f'y_{fold}.txt'))
labels.extend(
[l for l in fold_labels for _ in range(self.win_len)])
return self.meta.inv_lookup[task], pd.DataFrame(dict(labels=labels))
def build_data(self, loc, mod, *args, **kwargs):
x_data = []
y_data = []
z_data = []
modality = dict(accel="acc", gyro="gyro")[mod]
for fold in ("train", "test"):
for l, d in zip((x_data, y_data, z_data), ("x", "y", "z")):
ty = ["body", "total"][modality in {"accel", "acc"}]
acc = load_csv_data(
join(self.unzip_path, fold, "Inertial Signals",
f"{ty}_{modality}_{d}_{fold}.txt"),
astype="np",
)
l.extend(acc.ravel().tolist())
return np.c_[x_data, y_data, z_data]
def build_index(self, *args, **kwargs):
sub = []
for fold in ("train", "test"):
sub.extend(
load_csv_data(
join(self.unzip_path, fold, f"subject_{fold}.txt")))
index = pd.DataFrame(
dict(
subject=[si for si in sub for _ in range(WIN_LEN)],
trial=build_seq_list(subs=sub, win_len=WIN_LEN),
time=build_time(subs=sub,
win_len=WIN_LEN,
fs=float(self.meta.meta["fs"])),
))
return index
def build_index(self, *args, **kwargs):
sub = []
for fold in ('train', 'test'):
sub.extend(
load_csv_data(
join(self.unzip_path, fold, f'subject_{fold}.txt')))
index = pd.DataFrame(
dict(
subject=[si for si in sub for _ in range(self.win_len)],
trial=build_seq_list(subs=sub, win_len=self.win_len),
time=build_time(subs=sub,
win_len=self.win_len,
fs=float(self.meta.meta['fs'])),
))
return index
def build_data(self, key, *args, **kwargs):
modality, placement = key
x_data = []
y_data = []
z_data = []
modality = dict(
accel='acc',
gyro='gyro',
)[modality]
for fold in ('train', 'test'):
for l, d in zip((x_data, y_data, z_data), ('x', 'y', 'z')):
ty = ['body', 'total'][modality in {'accel', 'acc'}]
acc = load_csv_data(join(self.unzip_path, fold,
'Inertial Signals',
f'{ty}_{modality}_{d}_{fold}.txt'),
astype='np')
l.extend(acc.ravel().tolist())
return np.c_[x_data, y_data, z_data]