fix

lightautoml/transformers/seq.py

@@ -38,7 +38,7 @@ def features(self) -> List[str]:
  return self._features
 
  @staticmethod
- def get_attributes(dataset):
+ def _get_attributes(dataset):
  params = {}
  for attribute in dataset._array_like_attrs:
  _data = []
@@ -94,7 +94,7 @@ def transform(self, dataset) -> NumpyDataset:
  data_seq = dataset.to_sequence().data
  data = data_seq[:, (data_seq.shape[1] - 1) - self.current_correct_lags[::-1], :]
 
- params = self.get_attributes(dataset)
+ params = self._get_attributes(dataset)
 
  # transform
  data = np.moveaxis(data, 1, 2).reshape(len(data), -1)
@@ -128,19 +128,37 @@ def __init__(self, diffs: Union[int, List[int], np.ndarray[int]] = 10, flag_del_
  )
 
  def fit(self, dataset):
+ """Fit algorithm on seq dataset.
+
+ Args:
+ dataset: NumpyDataset.
+
+ Returns:
+ Fitted transformer.
+
+ """
  if self.flag_del_0_diff:
  self.lags = self.lags[self.lags > 0] # drop diff=0
- SeqLagTransformer.fit(self, dataset)
+ return SeqLagTransformer.fit(self, dataset)
 
  def transform(self, dataset) -> NumpyDataset:
+ """Transform input seq dataset to normal numpy representation.
+
+ Args:
+ dataset: seq.
+
+ Returns:
+ Numpy dataset with lag features.
+
+ """
  # checks here
  LAMLTransformer.transform(self, dataset)
 
  # convert to accepted dtype and get attributes
  data_seq = dataset.to_sequence().data
  data_seq_t = data_seq[:, (data_seq.shape[1] - 1) - np.array([0]), :]
 
- params = self.get_attributes(dataset)
+ params = self._get_attributes(dataset)
 
  # transform
  if 0 in self.current_correct_lags: