Classifer / VTS.

docs/changes/45.maintenance.md

@@ -0,0 +1 @@
+Update g/h separation to new sorting scheme of telescope-dependent variables.

src/eventdisplay_ml/data_processing.py

@@ -460,7 +460,7 @@ def flatten_telescope_data_vectorized(
             flat_features[f"{var}_{tel_idx}"] = data_normalized[:, tel_idx]
 
     index = _get_index(df, n_evt)
-    df_flat = flatten_telescope_variables(n_tel, flat_features, index, tel_config)
+    df_flat = flatten_telescope_variables(n_tel, flat_features, index, tel_config, analysis_type)
     return pd.concat(
         [df_flat, extra_columns(df, analysis_type, training, index, tel_config, observatory)],
         axis=1,
@@ -814,7 +814,7 @@ def apply_clip_intervals(df, n_tel=None, apply_log10=None):
                     df.loc[mask_to_log, var_base] = np.log10(df.loc[mask_to_log, var_base])
 
 
-def flatten_telescope_variables(n_tel, flat_features, index, tel_config=None):
+def flatten_telescope_variables(n_tel, flat_features, index, tel_config=None, analysis_type=None):
     """Generate dataframe for telescope variables flattened for all telescopes.
 
     Creates features for all telescope IDs, using NaN as default value for missing data.
@@ -829,13 +829,19 @@ def flatten_telescope_variables(n_tel, flat_features, index, tel_config=None):
         DataFrame index.
     tel_config : dict, optional
         Telescope configuration with 'max_tel_id' key.
+    analysis_type : str, optional
+        Type of analysis, e.g. "classification" or "stereo_analysis".
     """
     df_flat = pd.DataFrame(flat_features, index=index)
     df_flat = df_flat.astype(np.float32)
 
     # Determine max telescope ID from config or use n_tel
     max_tel_id = tel_config["max_tel_id"] if tel_config else (n_tel - 1)
 
+    keep_size_vars = analysis_type == "stereo_analysis"
+    if not keep_size_vars:
+        _logger.info(f"Dropping 'size'-related variables for {analysis_type} analysis.")
+
     new_cols = {}
     for i in range(max_tel_id + 1):  # Iterate over all possible telescopes
         if f"Disp_T_{i}" in df_flat:
@@ -844,7 +850,7 @@ def flatten_telescope_variables(n_tel, flat_features, index, tel_config=None):
         if f"loss_{i}" in df_flat and f"dist_{i}" in df_flat:
             new_cols[f"loss_loss_{i}"] = df_flat[f"loss_{i}"] ** 2
             new_cols[f"loss_dist_{i}"] = df_flat[f"loss_{i}"] * df_flat[f"dist_{i}"]
-        if f"size_{i}" in df_flat and f"dist_{i}" in df_flat:
+        if f"size_{i}" in df_flat and f"dist_{i}" in df_flat and keep_size_vars:
             new_cols[f"size_dist2_{i}"] = df_flat[f"size_{i}"] / (df_flat[f"dist_{i}"] ** 2 + 1e-6)
         if f"width_{i}" in df_flat and f"length_{i}" in df_flat:
             new_cols[f"width_length_{i}"] = df_flat[f"width_{i}"] / (df_flat[f"length_{i}"] + 1e-6)
@@ -873,6 +879,8 @@ def flatten_telescope_variables(n_tel, flat_features, index, tel_config=None):
         if f"cen_y_{i}" in df_flat and f"fpointing_dy_{i}" in df_flat:
             df_flat[f"cen_y_{i}"] = df_flat[f"cen_y_{i}"] + df_flat[f"fpointing_dy_{i}"]
         df_flat = df_flat.drop(columns=[f"fpointing_dx_{i}", f"fpointing_dy_{i}"], errors="ignore")
+        if not keep_size_vars:
+            df_flat = df_flat.drop(columns=[f"size_{i}"], errors="ignore")
 
     return df_flat
 

src/eventdisplay_ml/features.py

@@ -74,6 +74,7 @@ def telescope_features(analysis_type):
         List of telescope-level feature names.
     """
     var = [
+        "size",
         "cosphi",
         "sinphi",
         "loss",
@@ -95,7 +96,6 @@ def telescope_features(analysis_type):
 
     return [
         *var,
-        "size",
         "cen_x",
         "cen_y",
         "E",
@@ -147,9 +147,12 @@ def _classification_features():
         "MSCL",
         "ArrayPointing_Elevation",
         "ArrayPointing_Azimuth",
+        "Xcore",
+        "Ycore",
     ]
     # energy used to bin the models, but not as feature
-    return var_tel + var_array + ["Erec"]
+    # size used for sorting events during flattening, but not as feature
+    return var_tel + var_array + ["Erec", "size"]
 
 
 def clip_intervals():

src/eventdisplay_ml/geomag.py

@@ -19,7 +19,7 @@
     "CTAO-SOUTH": {
         "BX": 20.552e-6,  # Tesla
         "BY": 0.0,  # Tesla
-        "BZ": -9.367 - 6,  # Tesla
+        "BZ": -9.367e-6,  # Tesla
     },
 }
 

src/eventdisplay_ml/models.py

@@ -360,8 +360,7 @@ def process_file_chunked(analysis_type, model_configs):
         threshold_keys = sorted(
             {
                 eff
-                for n_tel_models in model_configs["models"].values()
-                for e_bin_models in n_tel_models.values()
+                for e_bin_models in model_configs["models"].values()
                 for eff in (e_bin_models.get("thresholds") or {}).keys()
             }
         )

src/eventdisplay_ml/scripts/train_xgb_classify.py

@@ -4,7 +4,7 @@
 Uses image and stereo parameters to train classification BDTs to separate
 gamma-ray events from hadronic background events.
 
-Separate BDTs are trained for 2, 3, and 4 telescope multiplicity events.
+Trains a single classifier on all telescope multiplicity events.
 """
 
 import logging