l3wrapper
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 |
Installation
Install using pip with:
pip install l3wrapper
Or, download a wheel or source archive from PyPI.
Requirements
The package is dependent on numpy
, scikit-learn
, tqdm
, and requests
.
Usage
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))
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>
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.
Compatibility
The underlying L^3 binaries are currently available for macOS and Ubuntu.
The package is currently tested with Python 3.6+.
License
The MIT License.
Authors
l3wrapper was written by g8a9.