Add pointwise error computation to method and add to forecasting unit…

… test

deepsensor/eval/__init__.py

@@ -0,0 +1 @@
+from .metrics import *

deepsensor/eval/metrics.py

@@ -0,0 +1,24 @@
+import xarray as xr
+from deepsensor.model.pred import Prediction
+
+
+def compute_errors(pred: Prediction, target: xr.Dataset) -> xr.Dataset:
+    """
+    Compute errors between predictions and targets.
+
+    Args:
+        pred: Prediction object.
+        target: Target data.
+
+    Returns:
+        xr.Dataset: Dataset of pointwise differences between predictions and targets
+        at the same valid time in the predictions. Note, the difference is positive
+        when the prediction is greater than the target.
+    """
+    errors = {}
+    for var_ID, pred_var in pred.items():
+        target_var = target[var_ID]
+        error = pred_var["mean"] - target_var.sel(time=pred_var.time)
+        error.name = f"{var_ID}"
+        errors[var_ID] = error
+    return xr.Dataset(errors)

tests/test_model.py

@@ -18,6 +18,7 @@
 from deepsensor.data.loader import TaskLoader
 from deepsensor.model.convnp import ConvNP
 from deepsensor.train.train import Trainer
+from deepsensor.eval.metrics import compute_errors
 
 from tests.utils import gen_random_data_xr, gen_random_data_pandas
 
@@ -686,9 +687,15 @@ def test_forecasting_model_predict_return_valid_times(self):
 
             if isinstance(pred_var, xr.Dataset):
                 # Check we can compute errors using the valid time coord ('time')
-                errors = pred_var["mean"] - self.da.sel(time=pred_var.time)
-                assert errors.dims == ("lead_time", "init_time", "x1", "x2")
-                assert errors.shape == pred_var["mean"].shape
+                errors = compute_errors(pred, self.da.to_dataset())
+                for var_ID in errors.keys():
+                    assert tuple(errors[var_ID].dims) == (
+                        "lead_time",
+                        "init_time",
+                        "x1",
+                        "x2",
+                    )
+                    assert errors[var_ID].shape == pred[var_ID]["mean"].shape
             elif isinstance(pred_var, pd.DataFrame):
                 # Makes coordinate checking easier by avoiding repeat values
                 pred_var = pred_var.to_xarray().isel(x1=0, x2=0)