starts, ends = F.get_valid_start_end(series_is_nan)
series_lags = F.make_lags(series, LAGS, use_smooth=True)
series_lags_corr = F.batch_autocorr(series, LAGS, starts, ends, threshold=1.05)
series_lags_corr = normalize(series_lags_corr, axis=0)[0]
series_lags_corr = Values(series_lags_corr, name='series_lags_corr')
series, series_mean, series_std = normalize(series[:, np.newaxis,
DROP_BEFORE:],
axis=2)
series_lags = np.nan_to_num(
(series_lags[:, :, DROP_BEFORE:] - series_mean) / series_std)
series_lags = Values(series_lags, 'xy_lags')
time_idxes = np.arange(series.shape[2])
trn_idx, val_idx = forward_split(time_idxes, ENC_LEN, VALID_LEN + TEST_LEN)
val_idx, test_idx = forward_split(val_idx, ENC_LEN, TEST_LEN)
trn_dl = create_seq2seq_data_loader(series,
enc_len=ENC_LEN,
dec_len=DEC_LEN,
time_idx=trn_idx,
batch_size=BATCH_SIZE,
num_iteration_per_epoch=4,
features=[series_lags, series_lags_corr],
seq_last=False,
device='cuda',
mode='train')
val_dl = create_seq2seq_data_loader(series,
enc_len=ENC_LEN,
dec_len=DEC_LEN,
@time : 2020/5/12 16:33
"""
from deepseries.models import RNN2RNN, Wave2WaveV1
from deepseries.train import Learner
from deepseries.dataset import Values, create_seq2seq_data_loader, forward_split
import numpy as np
from torch.optim import Adam
import torch
batch_size = 16
enc_len = 36
dec_len = 12
series = np.sin(np.arange(0, 1000))
series = series.reshape(1, 1, -1)
train_idx, valid_idx = forward_split(np.arange(series.shape[2]),
enc_len=14,
valid_size=200)
def test_rnn2rnn():
train_dl = create_seq2seq_data_loader(series,
enc_len=14,
dec_len=7,
time_idx=train_idx,
batch_size=12,
num_iteration_per_epoch=12,
seq_last=False)
valid_dl = create_seq2seq_data_loader(series,
enc_len=14,
dec_len=7,
time_idx=valid_idx,