rnn metric

openbb_terminal/forecast/rnn_model.py

@@ -36,6 +36,7 @@ def get_rnn_data(
     input_chunk_size: int = 14,
     force_reset: bool = True,
     save_checkpoints: bool = True,
+    metric: str = "mape",
 ) -> Tuple[
     Optional[List[type[TimeSeries]]],
     Optional[List[type[TimeSeries]]],
@@ -77,6 +78,8 @@ def get_rnn_data(
     save_checkpoints: bool
         Whether or not to automatically save the untrained model and checkpoints from training.
         Defaults to True.
+    metric: str
+        Metric to use for model selection. Defaults to "mape".
 
     Returns
     -------
@@ -145,4 +148,5 @@ def get_rnn_data(
         train_split,
         forecast_horizon,
         n_predict,
+        metric,
     )

openbb_terminal/forecast/rnn_view.py

@@ -42,6 +42,7 @@ def display_rnn_forecast(
     end_date: Optional[datetime] = None,
     naive: bool = False,
     export_pred_raw: bool = False,
+    metric: str = "mape",
     external_axes: Optional[List[plt.axes]] = None,
 ):
     """Display RNN forecast
@@ -94,6 +95,10 @@ def display_rnn_forecast(
     naive: bool
         Whether to show the naive baseline. This just assumes the closing price will be the same
         as the previous day's closing price. Defaults to False.
+    export_pred_raw: bool
+        Whether to export the raw predictions. Defaults to False.
+    metric: str
+        The metric to use for the forecast. Defaults to "mape".
     external_axes: Optional[List[plt.axes]]
         External axes to plot on
     """
@@ -124,6 +129,7 @@ def display_rnn_forecast(
         input_chunk_size=input_chunk_size,
         force_reset=force_reset,
         save_checkpoints=save_checkpoints,
+        metric=metric,
     )
     if ticker_series == []:
         return
@@ -148,6 +154,7 @@ def display_rnn_forecast(
         forecast_only=forecast_only,
         naive=naive,
         export_pred_raw=export_pred_raw,
+        metric=metric,
         external_axes=external_axes,
     )
     if residuals: