Merge pull request #148 from UCL-CORU/fix-specialty-mismatch

## Summary

- Separate services and subgroups into distinct keys (`"services"` / `"subgroups"`) in the transfer model's internal dict, so subgroup names (e.g. `paediatric`, `adult_male_young`) no longer appear alongside real services in error messages
- Return `0.0` with a warning when `get_transfer_prob` is called with a service not in the training set, instead of crashing with a `ValueError` that dumps hundreds of service names
- Shorten `warn_specialty_mismatch` messages to report counts only, without listing every mismatched specialty
- Add prettier formatting step to `tox -e docs` (pinned to `@3.1.0`) so generated notebook markdown matches the pre-commit hook and doesn't create spurious diffs
- Fix task issue template formatting

Author: zmek

.github/ISSUE_TEMPLATE/task.md

@@ -0,0 +1,5 @@
+---
+name: Task
+about: A Task to be Completed
+labels: task
+---

.github/ISSUE_TEMPLATE/task.yml

@@ -144,11 +144,13 @@ legacy_tox_ini = """
         jupyter nbconvert {toxinidir}/notebooks/*.ipynb --to markdown --output-dir={toxinidir}/docs/notebooks
         cp {toxinidir}/notebooks/README.md {toxinidir}/docs/notebooks/README.md
         python {toxinidir}/docs/notebooks/update_notebook_links.py {toxinidir}/docs/notebooks
+        npx prettier@3.1.0 --write --quote-props=as-needed {toxinidir}/docs/notebooks/*.md
         mkdocs build
     extras =
         docs
     allowlist_externals =
         cp
+        npx
 
     [tox]
     env_list =

pyproject.toml

@@ -82,15 +82,13 @@ def warn_specialty_mismatch(
     if new_in_request:
         warnings.warn(
             f"{len(new_in_request)} specialties found in the request but absent "
-            f"from the trained {source_label} (models may need retraining): "
-            f"{sorted(new_in_request)}",
+            f"from the trained {source_label} (models may need retraining).",
             stacklevel=stacklevel,
         )
     if missing_from_request:
         warnings.warn(
             f"{len(missing_from_request)} specialties present in the trained "
-            f"{source_label} but absent from the request: "
-            f"{sorted(missing_from_request)}",
+            f"{source_label} but absent from the request.",
             stacklevel=stacklevel,
         )
 

src/patientflow/predict/emergency_demand.py

@@ -1357,7 +1357,9 @@ def compute_transfer_arrivals(
                         f"got {type(source_data)}"
                     )
 
-                # Get transfer probabilities from model
+                # Get transfer probabilities from model.
+                # get_transfer_prob returns 0.0 (with a warning) for unknown
+                # services, so we only need to handle cohort-not-found here.
                 try:
                     prob_transfer = transfer_model.get_transfer_prob(
                         source_service, admission_type
@@ -1366,18 +1368,9 @@ def compute_transfer_arrivals(
                         source_service, admission_type
                     )
                 except ValueError as e:
-                    # Handle case where cohort doesn't exist in transfer model
                     if "not found in trained model" in str(e):
-                        # Skip this admission type if not trained for it
                         continue
-                    else:
-                        raise ValueError(
-                            f"Error getting transfer probabilities for '{source_service}' and admission type '{admission_type}': {e}"
-                        )
-                except KeyError as e:
-                    raise ValueError(
-                        f"Error getting transfer probabilities for '{source_service}' and admission type '{admission_type}': {e}"
-                    )
+                    raise
 
                 # Skip if no transfers from this source
                 if prob_transfer == 0: