misc

seq2seq_time/data/data.py

@@ -28,6 +28,10 @@ class RegressionForecastData:
         
         # Check processing
         self.check()
+
+    @property
+    def columns_past(self):
+        return set(self.df.columns)-set(self.columns_forecast)-set(self.columns_target)
     
     def download(self) -> pd.DataFrame:
         """Implement this method to download data and return raw df"""
@@ -54,8 +58,8 @@ class RegressionForecastData:
         
     def to_datasets(self, window_past: int, window_future: int, valid:bool=False) -> Tuple[Seq2SeqDataSet, Seq2SeqDataSet]:
         """Convert to torch datasets"""
-        ds_train = Seq2SeqDataSet(df_train, window_past=window_past, window_future=window_future, columns_target=self.columns_target, columns_past=self.columns_past)
-        ds_test = Seq2SeqDataSet(df_test, window_past=window_past, window_future=window_future, columns_target=self.columns_target, columns_past=self.columns_past)
+        ds_train = Seq2SeqDataSet(self.df_train, window_past=window_past, window_future=window_future, columns_target=self.columns_target, columns_past=self.columns_past)
+        ds_test = Seq2SeqDataSet(self.df_test, window_past=window_past, window_future=window_future, columns_target=self.columns_target, columns_past=self.columns_past)
         return ds_train, ds_test
     
     def __repr__(self):

seq2seq_time/data/dataset.py

@@ -31,7 +31,7 @@ class Seq2SeqDataSet(torch.utils.data.Dataset):
         assert df.index.freq is not None, 'should have freq'
         assert_no_objects(df)
 
-        self.df = df
+        self.df = df.dropna(subset=columns_target)
 
         self.window_past = window_past
         self.window_future = window_future

seq2seq_time/data/tidal.py

@@ -1,5 +1,6 @@
 import uptide
 import pandas as pd
+import numpy as np
 
 # https://en.wikipedia.org/wiki/Theory_of_tides#Harmonic_analysis
 default_tidal_constituents = [