def _data_preprocess(self, dataset, fold, split):
"""Preprocess the dataset.
Returns feature, label and previous features.
Args:
- dataset: temporal, static, label, time, treatment information
- fold: Cross validation fold
- split: 'train', 'valid' or 'test'
Returns:
- x: temporal feature
- y: labels
- previous_x: previous features
"""
# Set temporal, static, label, and time information
x, s, y, t, _ = dataset.get_fold(fold, split)
# Concatenate static features
if self.static_mode == 'concatenate':
if s is not None:
x = concate_xs(x, s)
# Concatenate time information
if self.time_mode == 'concatenate':
if t is not None:
x = concate_xt(x, t)
# Define previous temporal features (push one time stamp)
previous_x = x.copy()
for i in range(len(x)):
previous_x[i][1:, :] = previous_x[i][:-1, :]
previous_x[i][0, :] = -np.ones([len(x[i][0, :])])
return x, y, previous_x
def data_preprocess(self, dataset, fold, split):
"""Preprocess the dataset.
Args:
- dataset: temporal, static, label, time, treatment information
- fold: Cross validation fold
- split: 'train', 'valid' or 'test'
Returns:
- x: temporal feature
- y_hat: predictions of the predictor model
"""
# Set temporal, static, and time information
x, s, _, t, _ = dataset.get_fold(fold, split)
# Label is the prediction of the predictor
y_hat = self.predictor_model.predict(dataset, test_split=split)
# Static & Time information concatenating
if self.static_mode == 'concatenate':
if s is not None:
x = concate_xs(x, s)
if self.time_mode == 'concatenate':
if t is not None:
x = concate_xt(x, t)
return x, y_hat
def _data_preprocess(self, dataset, fold, split):
"""Preprocess the dataset.
Returns feature and label.
Args:
- dataset: temporal, static, label, time, treatment information
- fold: Cross validation fold
- split: 'train', 'valid' or 'test'
Returns:
- x: temporal feature
- y: labels
"""
# Set temporal, static, label, and time information
x, s, y, t, _ = dataset.get_fold(fold, split)
if self.static_mode == 'concatenate':
if s is not None:
x = concate_xs(x, s)
if self.time_mode == 'concatenate':
if t is not None:
x = concate_xt(x, t)
return x, y