def discriminative(dataset, cur_fold, num_latent, num_iterations):
# for cur_fold in range(5):
train, test = get_train_test(dataset,
num_folds=num_folds,
fold_num=cur_fold)
#train, valid = train_test_split(train, test_size=0.2, random_state=0)
valid = train[int(0.8 * len(train)):].copy()
train = train[:int(0.8 * len(train))].copy()
valid_gt = valid[:, 1:, :, :]
test_gt = test[:, 1:, :, :]
train_sc, valid_sc = reshape_for_sc(train), reshape_for_sc(valid)
train_data = np.array(
[train_sc[:, :, i] for i in range(1, train.shape[1])]).swapaxes(1, 2)
c = SparseCoding()
c.train(train_data, num_latent=num_latent)
valid_pred = c.disaggregate_discriminative(train_sc[:, :,
0].swapaxes(0, 1),
valid_sc[:, :,
0].swapaxes(0, 1),
num_iter=num_iterations)
valid_pred = valid_pred[-1, :, :, :]
valid_pred = valid_pred.swapaxes(0, 2).swapaxes(1, 2)
valid_pred = valid_pred.reshape(valid_pred.shape[0], valid_pred.shape[1],
-1, 24)
valid_pred = np.minimum(valid_pred, valid_gt[:, 0:1, :, :])
valid_error = {
APPLIANCE_ORDER[i + 1]:
mean_absolute_error(valid_pred[:, i, :, :].flatten(),
valid_gt[:, i, :, :].flatten())
for i in range(valid_pred.shape[1])
}
train_sc, test_sc = reshape_for_sc(train), reshape_for_sc(test)
train_data = np.array(
[train_sc[:, :, i] for i in range(1, train.shape[1])]).swapaxes(1, 2)
c = SparseCoding()
c.train(train_data, num_latent=num_latent)
test_pred = c.disaggregate_discriminative(train_sc[:, :, 0].swapaxes(0, 1),
test_sc[:, :, 0].swapaxes(0, 1),
num_iter=num_iterations)
test_pred = test_pred[-1, :, :, :]
test_pred = test_pred.swapaxes(0, 2).swapaxes(1, 2)
test_pred = test_pred.reshape(test_pred.shape[0], test_pred.shape[1], -1,
24)
test_pred = np.minimum(test_pred, test_gt[:, 0:1, :, :])
test_error = {
APPLIANCE_ORDER[i + 1]:
mean_absolute_error(test_pred[:, i, :, :].flatten(),
test_gt[:, i, :, :].flatten())
for i in range(test_pred.shape[1])
}
return valid_pred, valid_error, valid_gt, test_pred, test_error, test_gt
def non_discriminative(num_latent):
out = []
for cur_fold in range(5):
train, test = get_train_test(num_folds=num_folds, fold_num=cur_fold)
train_sc, test_sc = reshape_for_sc(train), reshape_for_sc(test)
train_data = np.array([
train_sc[:, :, i] for i in range(1, train.shape[1])
]).swapaxes(1, 2)
c = SparseCoding()
c.train(train_data, num_latent=num_latent)
pred = c.disaggregate(test_sc[:, :, 0].swapaxes(0, 1)).swapaxes(
0, 2).swapaxes(1, 2)
pred = pred.reshape(pred.shape[0], pred.shape[1], -1, 24)
out.append(pred)
return np.concatenate(out)
