mioXpektron.analysis.ml

Machine-learning benchmarking for aligned m/z feature matrices.

Functions

evaluate_all_models(models, data_dict, *[, ...])	Benchmark a mapping of classifiers and return a sorted results table.
evaluate_model(name, model, data_dict, *[, ...])	Fit one classifier and return hold-out and CV metrics.
explain_with_shap(model, data_dict, savepath, *)	SHAP beeswarm and bar plots when shap is installed.
get_benchmark_models(*[, random_state, ...])	Return a compact set of classifiers suitable for m/z matrices.
model_needs_scaling(model)	Return True when a classifier benefits from feature scaling.
plot_feature_importance(model, ...[, top_n])	Plot absolute LR coefficients or RF feature importances.
plot_model_comparison(results_df, savepath, *)	Bar chart of top model accuracies.
plot_roc_curves(results_df, data_dict, ...)	Plot ROC curves for the top-performing probabilistic models.
prepare_ml_data(data, *[, label_col, ...])	Prepare an aligned matrix for supervised classification benchmarks.
transform_features(X, *[, method])	Apply a variance-stabilising transform before embedding or ML.

mioXpektron.analysis.ml.model_needs_scaling(model)

Return True when a classifier benefits from feature scaling.

Parameters:

model (Any)

Return type:

bool

mioXpektron.analysis.ml.transform_features(X, *, method='log1p')

Apply a variance-stabilising transform before embedding or ML.

Parameters:

• X (ndarray)

• method (str)

Return type:

ndarray

mioXpektron.analysis.ml.prepare_ml_data(data, *, label_col='Group', sample_col='SampleName', test_size=0.2, random_state=42, transform='log1p', scale_features=True, handle_missing='zero')

Prepare an aligned matrix for supervised classification benchmarks.

Parameters:

• data (DataFrame | Tuple[DataFrame, Series]) – Either a table with metadata columns or a (X, y) tuple from prepare_matrix().

• label_col (str)

• sample_col (str)

• test_size (float)

• random_state (int)

• transform (str)

• scale_features (bool)

• handle_missing (str)

Return type:

Dict[str, Any]

mioXpektron.analysis.ml.get_benchmark_models(*, random_state=42, include_boosting=True)

Return a compact set of classifiers suitable for m/z matrices.

Parameters:

• random_state (int)

• include_boosting (bool)

Return type:

Dict[str, Any]

mioXpektron.analysis.ml.evaluate_model(name, model, data_dict, *, cv_folds=5, max_train_time_for_cv=30.0)

Fit one classifier and return hold-out and CV metrics.

Parameters:

• name (str)

• model (Any)

• data_dict (Mapping[str, Any])

• cv_folds (int)

• max_train_time_for_cv (float)

Return type:

Tuple[Dict[str, Any], Any]

mioXpektron.analysis.ml.evaluate_all_models(models, data_dict, *, dataset_name='dataset')

Benchmark a mapping of classifiers and return a sorted results table.

Parameters:

• models (Mapping[str, Any])

• data_dict (Mapping[str, Any])

• dataset_name (str)

Return type:

DataFrame

mioXpektron.analysis.ml.plot_model_comparison(results_df, savepath, *, dataset_name='dataset', top_n=10)

Bar chart of top model accuracies.

Parameters:

• results_df (DataFrame)

• savepath (str)

• dataset_name (str)

• top_n (int)

Return type:

None

mioXpektron.analysis.ml.plot_roc_curves(results_df, data_dict, models, savepath, *, top_n=2)

Plot ROC curves for the top-performing probabilistic models.

Parameters:

• results_df (DataFrame)

• data_dict (Mapping[str, Any])

• models (Mapping[str, Any])

• savepath (str)

• top_n (int)

Return type:

None

mioXpektron.analysis.ml.plot_feature_importance(model, feature_names, savepath, *, title, top_n=15)

Plot absolute LR coefficients or RF feature importances.

Parameters:

• model (Any)

• feature_names (List[str])

• savepath (str)

• title (str)

• top_n (int)

Return type:

None

mioXpektron.analysis.ml.explain_with_shap(model, data_dict, savepath, *, max_samples=100, max_background=200)

SHAP beeswarm and bar plots when shap is installed.

Parameters:

• model (Any)

• data_dict (Mapping[str, Any])

• savepath (str)

• max_samples (int)

• max_background (int)

Return type:

ndarray | None