Build time series sequences

build_sequences.py

def build_sequences(time_series, valid_periods, categories, train_size, test_size):
  """
  Creates all possible test sequences with size <test_size> which have
  a training sequence of <train_size> in front.
  """
  X = []
  y = []
  final_categories = []
  for ts, range, category in zip(time_series, valid_periods, categories):
    valid_ts = cut_valid(ts, range)
    size = len(valid_ts)
    splits = (size - train_size) // test_size
    if splits < 2:
      if size < train_size + test_size:
        padding_len = train_size + test_size - size
        padding = np.zeros(padding_len, dtype='float32')
        valid_ts = np.concatenate((padding, valid_ts))
        start = 0
      else:
        start = size - train_size - test_size
      X.append(valid_ts[start:-test_size])
      y.append(valid_ts[-test_size:])
      final_categories.append(category)
    else:
      ts_splitter = TimeSeriesSplit(n_splits=splits, max_train_size=train_size, test_size=test_size)
      for train_seq_ix, test_seq_ix in ts_splitter.split(valid_ts):
        X.append(valid_ts[train_seq_ix[0]:train_seq_ix[-1]+1])
        y.append(valid_ts[test_seq_ix[0]:test_seq_ix[-1]+1])
        final_categories.append(category)
  return np.array(X), np.array(y), np.array(final_categories)