Requirements

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

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))
0.9071803852889667

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>