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A Python 3 wrapper around Live-and-Let-Live (\(L^3\)) classifier binaries implementing the scikit-learn estimator interface. The associative classifier was originally published in [1].

When imported, the package looks for \(L^3\) compiled binaries in the user’s $HOME directory. If they are not found, it downloads them. If you mind letting the wrapper do this for you, you can download the binaries for macOS Catalina or Ubuntu 18.04.

[1]Elena Baralis, Silvia Chiusano, and Paolo Garza. 2008. A Lazy Approach to Associative Classification. IEEE Trans. Knowl. Data Eng. 20, 2 (2008), 156–171. https://doi.org/10.1109/TKDE.2007.190677


Install using pip with:

pip install l3wrapper

Or, download a wheel or source archive from PyPI.


The package is dependent on numpy, scikit-learn, tqdm, and requests.


By design, the classifier is intended for categorical/discrete attributes. Therefore, using subtypes of numpy.number to fit the model is not allowed.

Simple classification

A sample usage with the Car Evaluation dataset:

>>> from l3wrapper.l3wrapper import L3Classifier
>>> import numpy as np
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.metrics import accuracy_score
>>> X = np.loadtxt('car.data', dtype=object, delimiter=',')
>>> y = X[:, -1]
>>> X = X[:, :-1]
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
>>> clf = L3Classifier().fit(X_train, y_train)
>>> accuracy_score(y_test, clf.predict(X_test))

Column names and interpretable rules

Use the column_names and save_human_readable parameters to obtain an interpretable representation of the model:

>>> column_names = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety']
>>> clf = L3Classifier().fit(X_train, y_train, column_names=column_names, save_human_readable=True)

The snippet will generate the level1 and level2 rule sets. An excerpt is:

0 persons:4,safety:high,maint:low,buying:high acc 12 100.0 4
1 doors:2,buying:vhigh,safety:med,lug_boot:med unacc 11 100.0 4

in the form:

<rule_id>\t<antecedent>\t<class label>\t<support count>\t<confidence(%)>\t<rule length>

Known limitations

  • fixed The parallel training of multiple models cause failures (e.g. using ``GridSearchCV``, ``joblib`` or custom parallelism through ``multiprocessing`` with ``njobs>1``).
  • The scikit-learn’s utility check_estimator still doesn’t work, as L3Classifier doesn’t support numerical input.


The underlying \(L^3\) binaries are currently available for macOS and Ubuntu.

The package is currently tested with Python 3.6+.


The MIT License.


l3wrapper was written by g8a9.

Indices and tables