Resolving torcmetrics

Resolving torchmetrics issue.

docs/userguide/torch_forecasting_models.md

@@ -485,9 +485,6 @@ transformer = Scaler()
 train = transformer.fit_transform(train)
 val = transformer.transform(val)
 
-# any TorchMetric or val_loss can be used as the monitor
-torch_metrics = torchmetrics.regression.MeanAbsolutePercentageError()
-
 # MLflow setup
 ## Run this command with environment activated: mlflow ui --port xxxx  (e.g. 5000, 5001, 5002)
 # Copy and paste url from command line to web browser
@@ -515,13 +512,12 @@ with mlflow.start_run(nested=True) as run:
 
     # Define model hyperparameters to log
     params = {
-              "model_type": "Darts_Pytorch_model",
               "input_chunk_length": 24,
               "output_chunk_length": 12,
               "n_epochs": 500,
               "model_name": "NBEATS_MLflow",
               "log_tensorboard": True,
-              "torch_metrics": "torchmetrics.regression.MeanAbsolutePercentageError()",
+              "torch_metrics": MeanAbsolutePercentageError(),
               "nr_epochs_val_period": 1,
         }
 
@@ -531,7 +527,6 @@ with mlflow.start_run(nested=True) as run:
     # create the model
     model = NBEATSModel(
         **params,
-        torch_metrics=torch_metrics,
         )
 
     # use validation dataset