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Classifier. #48

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faf3cc2
model
GernotMaier Jan 30, 2026
a976a8f
Merge branch 'main' into gh-g3
GernotMaier Jan 30, 2026
fe81c3a
early stopping
GernotMaier Jan 30, 2026
56f5870
eval metric
GernotMaier Jan 30, 2026
3e7816b
hyperparameter adaption for g/h
GernotMaier Jan 31, 2026
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1 change: 1 addition & 0 deletions docs/changes/48.maintenance.md
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Original file line number Diff line number Diff line change
@@ -0,0 +1 @@
Add early stopping to classification. Increase number of estimators.
11 changes: 6 additions & 5 deletions src/eventdisplay_ml/hyper_parameters.py
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Expand Up @@ -29,14 +29,15 @@
"model": None,
"hyper_parameters": {
"objective": "binary:logistic",
"eval_metric": "logloss", # TODO AUC ?
"n_estimators": 100, # TODO probably too low
"max_depth": 6,
"learning_rate": 0.1,
"eval_metric": ["logloss", "auc"],
"n_estimators": 5000,
"early_stopping_rounds": 50,
"max_depth": 7,
"learning_rate": 0.05,
"subsample": 0.8,
"colsample_bytree": 0.8,
"random_state": None,
"n_jobs": 8,
"n_jobs": 48,
},
}
}
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5 changes: 3 additions & 2 deletions src/eventdisplay_ml/models.py
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Expand Up @@ -572,6 +572,7 @@ def train_classification(df, model_configs):
_logger.info(f"Features ({len(x_data.columns)}): {', '.join(x_data.columns)}")
model_configs["features"] = list(x_data.columns)
y_data = full_df["label"]

x_train, x_test, y_train, y_test = train_test_split(
x_data,
y_data,
Expand All @@ -585,7 +586,7 @@ def train_classification(df, model_configs):
for name, cfg in model_configs.get("models", {}).items():
_logger.info(f"Training {name}")
model = xgb.XGBClassifier(**cfg.get("hyper_parameters", {}))
model.fit(x_train, y_train)
model.fit(x_train, y_train, eval_set=[(x_test, y_test)], verbose=True)
evaluate_classification_model(model, x_test, y_test, full_df, x_data.columns.tolist(), name)
cfg["model"] = model
cfg["efficiency"] = evaluation_efficiency(name, model, x_test, y_test)
Expand Down Expand Up @@ -631,7 +632,7 @@ def _log_energy_bin_counts(df):

_logger.info(f"Energy bin weights (inverse-count, normalized): {inverse_counts}")

# Calculate multiplicity weights (inverse frequency)
# Calculate multiplicity weights (prioritize higher-multiplicity events)
mult_counts = df["DispNImages"].value_counts()
_logger.info("Training events per multiplicity:")
for mult, count in mult_counts.items():
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