adding rmse, mse to regr

openbb_terminal/forecast/forecast_controller.py

@@ -356,6 +356,7 @@ class ForecastController(BaseController):
         naive: bool = False,
         explainability_raw: bool = False,
         export_pred_raw: bool = False,
+        metric: bool = False,
     ):
         if hidden_size:
             parser.add_argument(
@@ -531,7 +532,7 @@ class ForecastController(BaseController):
                 action="store",
                 dest="model_type",
                 default="LSTM",
-                help='Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU")',
+                help='Enter a string specifying the RNN module type ("RNN", "LSTM" or "GRU")',
             )
         if dropout is not None:
             parser.add_argument(
@@ -631,6 +632,17 @@ class ForecastController(BaseController):
                 help="Export predictions to a csv file.",
             )
 
+        if metric:
+            parser.add_argument(
+                "--metric",
+                type=str,
+                action="store",
+                dest="metric",
+                default="mape",
+                choices=["rmse", "mse", "mape"],
+                help="Calculate precision based on a specific metric (rmse, mse, mape)",
+            )
+
             # if user does not put in --dataset
         return parser
 
@@ -2561,6 +2573,7 @@ class ForecastController(BaseController):
             naive=True,
             explainability_raw=True,
             export_pred_raw=True,
+            metric=True,
         )
         ns_parser = self.parse_known_args_and_warn(
             parser,
@@ -2595,6 +2608,7 @@ class ForecastController(BaseController):
                 naive=ns_parser.naive,
                 explainability_raw=ns_parser.explainability_raw,
                 export_pred_raw=ns_parser.export_pred_raw,
+                metric=ns_parser.metric,
             )
 
     @log_start_end(log=logger)

openbb_terminal/forecast/helpers.py

@@ -11,7 +11,7 @@ import pandas as pd
 from darts import TimeSeries
 from darts.dataprocessing.transformers import MissingValuesFiller, Scaler
 from darts.explainability.shap_explainer import ShapExplainer
-from darts.metrics import mape
+from darts.metrics import mape, rmse, mse
 from darts.models.forecasting.torch_forecasting_model import GlobalForecastingModel
 from darts.utils.statistics import plot_residuals_analysis
 from pytorch_lightning.callbacks.early_stopping import EarlyStopping
@@ -477,6 +477,7 @@ def plot_forecast(
     forecast_only: bool = False,
     naive: bool = False,
     export_pred_raw: bool = False,
+    metric: str = "mape",
     external_axes: Optional[List[plt.axes]] = None,
 ):
     quant_kwargs = {}
@@ -503,7 +504,14 @@ def plot_forecast(
     if naive:
         # show naive forecast shift timeseries by 1
         naive_fcast = naive_fcast.drop_before(historical_fcast.start_time())
-        naive_precision = mape(ticker_series, naive_fcast)
+
+        # calculate precision based on metric
+        if metric == "rsme":
+            naive_precision = rmse(ticker_series, naive_fcast)
+        elif metric == "mse":
+            naive_precision = mse(ticker_series, naive_fcast)
+        elif metric == "mape":
+            naive_precision = mape(ticker_series, naive_fcast)
 
         naive_fcast.plot(
             label=f"Naive+1: {naive_precision:.2f}%",
@@ -516,7 +524,7 @@ def plot_forecast(
         pred_label += " w/ past covs"
     predicted_values.plot(label=pred_label, **quant_kwargs, color="#00AAFF")
     ax.set_title(
-        f"{name} for <{ticker_name}> for next [{n_predict}] days (MAPE={precision:.2f}%)"
+        f"{name} for <{ticker_name}> for next [{n_predict}] days ({metric.upper()}={precision:.2f}%)"
     )
     ax.set_ylabel(target_col)
     ax.set_xlabel("Date")
@@ -719,6 +727,7 @@ def get_prediction(
     train_split: float,
     forecast_horizon: int,
     n_predict: int,
+    metric: str,
 ):
     _, val = ticker_series.split_before(train_split)
 
@@ -771,10 +780,16 @@ def get_prediction(
         else:
             prediction = best_model.predict(series=ticker_series, n=n_predict)
 
-    precision = mape(
-        actual_series=val, pred_series=historical_fcast
-    )  # mape = mean average percentage error
-    console.print(f"{model_name} model obtains MAPE: {precision:.2f}% \n")
+    # calculate precision based on metric (rmse, mse, mape)
+    if metric == "rmse":
+        precision = rmse(actual_series=val, pred_series=historical_fcast)
+    elif metric == "mse":
+        precision = mse(actual_series=val, pred_series=historical_fcast)
+    elif metric == "mape":
+        precision = mape(actual_series=val, pred_series=historical_fcast)
+
+    # capalize metic name
+    console.print(f"{model_name} model obtains {metric.upper()}: {precision:.2f}% \n")
 
     # scale back
     if use_scalers and isinstance(scaler, Scaler):

openbb_terminal/forecast/regr_model.py

@@ -26,6 +26,7 @@ def get_regression_data(
     forecast_horizon: int = 5,
     output_chunk_length: int = 5,
     lags: Union[int, List[int]] = 14,
+    metric: str = "mape",
 ) -> Tuple[
     List[TimeSeries], List[TimeSeries], List[TimeSeries], float, type[RegressionModel]
 ]:
@@ -49,6 +50,8 @@ def get_regression_data(
         The length of the forecast of the model. Defaults to 1.
     lags: Union[int, List[int]]
         lagged target values to predict the next time step
+    metric: str
+        Metric to use for evaluation. Defaults to "mape".
 
     Returns
     -------
@@ -99,4 +102,5 @@ def get_regression_data(
         train_split,
         forecast_horizon,
         n_predict,
+        metric,
     )

openbb_terminal/forecast/regr_view.py

@@ -35,6 +35,7 @@ def display_regression(
     naive: bool = False,
     explainability_raw: bool = False,
     export_pred_raw: bool = False,
+    metric: str = "mape",
     external_axes: Optional[List[plt.axes]] = None,
 ):
     """Display Regression Forecasting
@@ -74,6 +75,8 @@ def display_regression(
     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.
+    metric: str
+        The metric to use for the forecast. Defaults to "mape".
     external_axes: Optional[List[plt.axes]]
         External axes to plot on
     """
@@ -100,6 +103,7 @@ def display_regression(
         forecast_horizon=forecast_horizon,
         output_chunk_length=output_chunk_length,
         lags=lags,
+        metric=metric,
     )
     probabilistic = False
     helpers.plot_forecast(
@@ -120,6 +124,7 @@ def display_regression(
         forecast_only=forecast_only,
         naive=naive,
         export_pred_raw=export_pred_raw,
+        metric=metric,
         external_axes=external_axes,
     )
     if residuals: