Updates following review

improver/calibration/load_and_train_quantile_regression_random_forest.py

@@ -322,8 +322,8 @@ def process(
         if len(missing_features) > 0:
             msg = (
                 "The features requested in the feature_config are absent from "
-                "the forecast parquet file and the input cubes. The missing fields are: "
-                f"{missing_features}."
+                "the forecast parquet file and the input cubes. "
+                f"The missing fields are: {missing_features}."
             )
             raise ValueError(msg)
         return forecast_df, truth_df, cube_inputs
@@ -421,9 +421,12 @@ def _add_static_features_from_cubes_to_df(
                     constr = iris.Constraint(name=feature_name)
                     # Static features can be provided either as a cube or as a column
                     # in the forecast DataFrame.
-                    if not cube_inputs.extract(constr):
+                    try:
+                        feature_cube = cube_inputs.extract_cube(constr)
+                    except iris.exceptions.ConstraintMismatchError:
+                        feature_cube = None
+                    if not feature_cube:
                         continue
-                    feature_cube = cube_inputs.extract_cube(constr)
                     feature_df = as_data_frame(feature_cube, add_aux_coords=True)
                     forecast_df = forecast_df.merge(
                         feature_df[[*self.unique_site_id_keys, feature_name]],
@@ -448,14 +451,18 @@ def filter_bad_sites(
                 - DataFrame containing the forecast data with bad sites removed.
                 - DataFrame containing the truth data with bad sites removed.
         """
-        truth_df.dropna(subset=["ob_value"], inplace=True)
+        truth_df.dropna(subset=["ob_value"] + [*self.unique_site_id_keys], inplace=True)
 
         if truth_df.empty:
             msg = "Empty truth DataFrame after removing NaNs."
             raise ValueError(msg)
 
-        forecast_index = forecast_df.set_index([*self.unique_site_id_keys]).index
-        truth_index = truth_df.set_index([*self.unique_site_id_keys]).index
+        # Include time in the index, so that forecasts will be dropped if they
+        # correspond to a site and time that is not in the truth data.
+        forecast_index = forecast_df.set_index(
+            [*self.unique_site_id_keys] + ["time"]
+        ).index
+        truth_index = truth_df.set_index([*self.unique_site_id_keys] + ["time"]).index
         forecast_df = forecast_df[forecast_index.isin(truth_index)]
         truth_df = truth_df[truth_index.isin(forecast_index)]
 

improver/calibration/quantile_regression_random_forest.py

@@ -96,14 +96,9 @@ def prep_feature(
 
         # For a subset of the input DataFrame compute the mean or standard deviation
         # over the representation column, grouped by the groupby columns.
-        if feature_name == "mean":
-            subset_df = df[subset_cols].groupby(groupby_cols).mean()
-        elif feature_name == "std":
-            subset_df = df[subset_cols].groupby(groupby_cols).std()
-        elif feature_name == "min":
-            subset_df = df[subset_cols].groupby(groupby_cols).min()
-        elif feature_name == "max":
-            subset_df = df[subset_cols].groupby(groupby_cols).max()
+        if feature_name in ["min", "max", "mean", "std"]:
+            subset_grouped = df[subset_cols].groupby(groupby_cols)
+            subset_df = getattr(subset_grouped, feature_name)()
         elif feature_name.startswith("members_below"):
             threshold = float(feature_name.split("_")[2])
             if transformation is not None:
@@ -119,7 +114,6 @@ def prep_feature(
             )
             subset_df.rename(variable_name, inplace=True)
             subset_df = subset_df.astype(orig_dtype)
-            # subset_df[variable_name].astype(orig_dtype)
         elif feature_name.startswith("members_above"):
             threshold = float(feature_name.split("_")[2])
             if transformation is not None:

improver/cli/apply_quantile_regression_random_forest.py

@@ -26,8 +26,11 @@ def process(
     Args:
         file_paths (cli.inputpaths):
             A list of input paths containing:
-            - The path to a QRF trained model in pickle file format to be used
-            for calibration.
+            - The path to the pickle file produced by training the QRF model.
+            The pickle file contains the QRF model and the transformation and
+            pre_transform_addition values if a transformation was applied. If no
+            transformation was applied then the transformation and
+            pre_transform_addition values will be None and 0, respectively.
             - The path to a NetCDF file containing the forecast to be calibrated.
             - Optionally, paths to NetCDF files containing additional predictors.
         feature_config (dict):
@@ -50,8 +53,6 @@ def process(
             A string containing the CF name of the forecast to be
             calibrated e.g. air_temperature. This will be used to separate it from
             the rest of the feature cubes, if present.
-            The names of the coordinates that uniquely identify each site,
-            e.g. "wmo_id" or "latitude,longitude".
         unique_site_id_keys (str):
             The names of the coordinates that uniquely identify each site,
             e.g. "wmo_id" or "latitude,longitude".

improver/cli/train_quantile_regression_random_forest.py

@@ -153,5 +153,7 @@ def process(
         unique_site_id_keys=unique_site_id_keys,
         **kwargs,
     )(forecast_df, truth_df, cube_inputs)
+    if result == (None, None, None):
+        return None
 
     return result

improver_tests/acceptance/test_train_quantile_regression_random_forest.py

@@ -114,3 +114,51 @@ def test_missing_inputs(
     ]
 
     assert run_cli(compulsory_args + named_args) is None
+    # Check no file has been written to disk.
+    assert not output_path.exists()
+
+
+def test_invalid_cycletime(
+    tmp_path,
+):
+    """
+    Test train-quantile-regression-random-forest CLI when no training can occur
+    because there is no valid data in the parquet files to calibrate the cycletime
+    provided.
+    """
+    kgo_dir = acc.kgo_root() / CLI
+    config_path = kgo_dir / "config.json"
+    output_path = tmp_path / "output.pickle"
+    history_path = kgo_dir / "spot_calibration_tables"
+    truth_path = kgo_dir / "spot_observation_tables"
+    compulsory_args = [history_path, truth_path]
+    named_args = [
+        "--feature-config",
+        config_path,
+        "--parquet-diagnostic-names",
+        "temperature_at_screen_level",
+        "--target-cf-name",
+        "air_temperature",
+        "--forecast-periods",
+        "6:18:6",
+        "--cycletime",
+        "20250704T0000Z",
+        "--training-length",
+        "2",
+        "--experiment",
+        "mix-latestblend",
+        "--n-estimators",
+        "10",
+        "--max-depth",
+        "5",
+        "--random-state",
+        "42",
+        "--compression-level",
+        "5",
+        "--output",
+        output_path,
+    ]
+
+    assert run_cli(compulsory_args + named_args) is None
+    # Check no file has been written to disk.
+    assert not output_path.exists()