mamut 0.1.2

Machine Automated Modelling and Utility Toolkit

MAMUT

Machine Automated Modelling and Utility Toolkit for tabular classification.

Overview

MAMUT is a Python toolkit that automates model selection and evaluation for classification tasks on tabular data. It bundles preprocessing, Optuna-driven hyperparameter optimization, model comparison, and reporting into a single workflow built on scikit-learn and XGBoost.

Key Features

• End-to-end preprocessing: missing values, categorical encoding, skew correction, scaling, outlier filtering, imbalance handling (SMOTE/undersampling/SMOTETomek), optional feature selection, and PCA.
• Model search across common classifiers (LogisticRegression, RandomForestClassifier, SVC, XGBClassifier, MLPClassifier, GaussianNB, KNeighborsClassifier).
• Hyperparameter optimization with Optuna (TPE/Bayesian or random search).
• Report generation via evaluate() with metrics, plots, and SHAP explanations.
• Saved artifacts: fit() stores fitted models; evaluate() writes an HTML report and plots to disk.

Installation

Python 3.12 is the target runtime (see .python-version).

From PyPI:

pip install mamut

From source:

pip install -e .

For development with Poetry:

poetry install

Quickstart

from sklearn.datasets import load_iris
from mamut.wrapper import Mamut

X, y = load_iris(as_frame=True, return_X_y=True)

mamut = Mamut(n_iterations=5, optimization_method="bayes")
mamut.fit(X, y)

preds = mamut.predict(X)
proba = mamut.predict_proba(X)

Configuration Notes

• With preprocessing enabled (default), pass X as a pandas DataFrame and y as a Series.
• Targets must be categorical (float targets raise a ValueError).
• fit() performs a stratified 80/20 train/test split controlled by random_state.
• Select the optimization strategy with optimization_method="bayes" or "random_search".
• Control the search budget with n_iterations.
• Exclude models by class name (e.g., exclude_models=["SVC"]).
• Preprocessing options are passed directly into Mamut(...) (e.g., pca=True, feature_selection=True, num_imputation="knn").
• score_metric expects one of: accuracy, precision, recall, f1, balanced_accuracy, jaccard, roc_auc_score.

Outputs and Reports

• mamut.best_model_: best performing pipeline after fit.
• mamut.training_summary_: per-model scores and timings.
• mamut.optuna_studies_: Optuna studies keyed by model name.
• mamut.evaluate(): writes an HTML report to ./mamut_report/report_<timestamp>.html and stores plots in ./mamut_report/plots/.
• mamut.save_best_model(path): writes the best model to an existing directory as a .joblib file.
• fit() saves all fitted models to ./fitted_models/<timestamp>/ as .joblib files.

Development

poetry run pytest
poetry run pre-commit run --all-files
make -C docs html

Examples and Docs

• Notebooks: walkthrough.ipynb and docs/source/notebooks/walkthrough.ipynb.
• Documentation site: https://mamut.readthedocs.io/en/latest/

License

MIT. See LICENSE.