bioneuralnet.metrics.evaluation

Functions

evaluate_f1m(X, y[, model_type, ...])	Evaluate macro F1-score over multiple runs.
evaluate_f1w(X, y[, model_type, ...])	Evaluate weighted F1-score over multiple runs.
evaluate_model(X, y[, model_type, ...])	Evaluate a single model (RF or XGB, classif or reg) over multiple runs, returning three tuples.
evaluate_rf(X, y[, mode, n_estimators, ...])	Shortcut function: evaluate a RandomForest (classification or regression).
evaluate_single_run(X, y[, model_type, ...])	Do one train/test split, train the specified model.
evaluate_xgb(X, y[, mode, n_estimators, ...])	Shortcut function: evaluate an XGBoost (classification or regression).
get_logger(name)	Retrieves a global logger configured to write to 'bioneuralnet.log' at the project root.
plot_grouped_performance(scores, title[, ...])	Plot grouped bar chart and save results to text file.
plot_multiple_metrics(metrics[, title_map, ...])	Consolidate multiple metric grouped performances into one figure.

Classes

Path(*args, **kwargs)	PurePath subclass that can make system calls.
XGBClassifier(*args, **kwargs)	Implementation of the scikit-learn API for XGBoost classification.
XGBRegressor(*args, **kwargs)	Implementation of the scikit-learn API for XGBoost regression.

bioneuralnet.metrics.evaluation.evaluate_f1m(X: ndarray, y: ndarray, model_type: str = 'rf_classif', n_estimators: int = 100, runs: int = 5, seed: int = 119)

Evaluate macro F1-score over multiple runs.

bioneuralnet.metrics.evaluation.evaluate_f1w(X: ndarray, y: ndarray, model_type: str = 'rf_classif', n_estimators: int = 100, runs: int = 5, seed: int = 119)

Evaluate weighted F1-score over multiple runs.

bioneuralnet.metrics.evaluation.evaluate_model(X: ndarray, y: ndarray, model_type: str = 'rf_classif', n_estimators: int = 150, runs: int = 100, seed: int = 119)

Evaluate a single model (RF or XGB, classif or reg) over multiple runs, returning three tuples. For classification:

• (accuracy_mean, accuracy_std)

• (f1_weighted_mean, f1_weighted_std)

• (f1_macro_mean, f1_macro_std)

For regression:

• (r2_mean, r2_std)

• (None, None)

• (None, None)

bioneuralnet.metrics.evaluation.evaluate_rf(X: ndarray, y: ndarray, mode: str = 'classification', n_estimators: int = 150, runs: int = 100, seed: int = 119, return_all: bool = False)

Shortcut function: evaluate a RandomForest (classification or regression).

bioneuralnet.metrics.evaluation.evaluate_single_run(X: ndarray, y: ndarray, model_type: str = 'rf_classif', n_estimators: int = 100, test_size: float = 0.3, seed: int = 119)

Do one train/test split, train the specified model.

Return: (accuracy, f1_weighted, f1_macro)

bioneuralnet.metrics.evaluation.evaluate_xgb(X: ndarray, y: ndarray, mode: str = 'classification', n_estimators: int = 150, runs: int = 100, seed: int = 119, return_all: bool = False)

Shortcut function: evaluate an XGBoost (classification or regression).

bioneuralnet.metrics.evaluation.plot_grouped_performance(scores: dict[str, dict[str, tuple[float, float]]], title: str, ylabel: str = 'Score', filename: str | Path = None)

Plot grouped bar chart and save results to text file.

bioneuralnet.metrics.evaluation.plot_multiple_metrics(metrics: dict[str, dict[str, dict[str, tuple[float, float]]]], title_map: dict[str, str] = None, ylabel_map: dict[str, str] = None, filename: Path = None)

Consolidate multiple metric grouped performances into one figure.

Adds numeric labels on top of each bar.