Merge pull request #350 from WenjieDu/dev

Remove `pypots.imputation.locf.modules` and add assertions for BTTF

pypots/forecasting/bttf/core.py

@@ -218,6 +218,11 @@ def BTTF_forecast(
 ):
     dim1, dim2, T = dense_tensor.shape
     start_time = T - pred_step
+    assert start_time > -1, (
+        "start_time should be larger than -1, "
+        "namely the number of the input tensor's time steps should be larger than pred_step."
+    )
+    assert start_time >= np.max(time_lags), "start_time should be >= max(time_lags)"
     max_count = int(np.ceil(pred_step / multi_step))
     tensor_hat = np.zeros((dim1, dim2, max_count * multi_step))
 

pypots/imputation/locf/model.py

@@ -13,14 +13,15 @@
 import numpy as np
 import torch
 
-from .modules.core import locf_numpy, locf_torch
+from .core import locf_numpy, locf_torch
 from ..base import BaseImputer
 from ...utils.logging import logger
 
 
 class LOCF(BaseImputer):
     """LOCF (Last Observed Carried Forward) imputation method. A naive imputation method that fills missing values
-    with the last observed value. Simple but commonly used in practice.
+    with the last observed value. When time-series data gets inverse on the time dimension, this method can also be
+    seen as NOCB (Next Observation Carried Backward). Simple but commonly used in practice.
 
     Parameters
     ----------