small changes

AIStream-Peelout · Jan 3, 2024 · ddbce90 · ddbce90
1 parent 7ddf019
commit ddbce90
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Showing 5 changed files with 4 additions and 5 deletions.
diff --git a/flood_forecast/evaluator.py b/flood_forecast/evaluator.py
@@ -368,7 +368,7 @@ def handle_later_ev(model, df_train_and_test, end_tensor, params, csv_test_loade
         model.model.train()  # sets mode to train so the dropout layers will be touched
         assert num_prediction_samples > 0
         if csv_test_loader.__class__.__name__ == "SeriesIDTestLoader":
-            raise NotImplementedError("SeriesIDTestLoader not yet supported for predictions")
+            raise NotImplementedError("SeriesIDTestLoader not yet supported for predictions.")
         prediction_samples = generate_prediction_samples(
             model,
             df_train_and_test,

diff --git a/flood_forecast/temporal_decoding.py b/flood_forecast/temporal_decoding.py
@@ -7,7 +7,7 @@ def decoding_function(model, src: torch.Tensor, trg: torch.Tensor, forecast_leng
     """This function is responsible for decoding models that use `TemporalLoader` data. The basic logic of this
     function is as follows. The data to the encoder (e.g. src) is not modified at each step of the decoding process.
     Instead only the data to the decoder (e.g. the masked trg) is changed when forecasting max_len > forecast_length.
-    New data is appended (forecast_len == 2) (decoder_seq==10) (max==20) (20 (8)->2 First 8 should
+    New data is appended (forecast_len == 2) (decoder_seq==10) (max==20) (20 (8)->2 First 8 should be the same).
 
     :param model: The PyTorch time series forecasting model that you want to use forecasting on.
     :type model: `torch.nn.Module`

diff --git a/setup.py b/setup.py
@@ -16,7 +16,7 @@
 
 setup(
     name='flood_forecast',
-    version='1.0dev',
+    version='1.00dev',
     packages=[
         'flood_forecast',
         'flood_forecast.transformer_xl',

diff --git a/tests/test_series_id.py b/tests/test_series_id.py
@@ -59,7 +59,7 @@ def test_series_test_loader(self):
         print(all_rows_orig)
         # self.assertIsInstance(all_rows_orig, pd.DataFrame)
         self.assertGreater(forecast_start, 0)
-        # self.assertIsInstance(df_train_test, pd.DataFrame)..
+        # self.assertIsInstance(df_train_test, pd.DataFrame)..l
 
     def test_eval_series_loader(self):
         # infer_on_torch_model("s")  # to-do fill in

diff --git a/tests/transformer_b_series.json b/tests/transformer_b_series.json
@@ -62,7 +62,6 @@
    "inference_params":
    {
          "datetime_start":"2020-06-06",
-         "num_prediction_samples": 11,
           "hours_to_forecast":4, 
           "test_csv_path":"tests/test_data/solar_small.csv",
           "decoder_params":{