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Ruleset covering algorithms for explainable machine learning

Project description


And is there not also the case where we play and--make up the rules as we go along?
-Ludwig Wittgenstein

the duck-rabbit


This package implements two iterative coverage-based ruleset algorithms: IREP and RIPPERk.

Performance is similar to sklearn's DecisionTree CART implementation (see Performance Tests).

For explanation of the algorithms, see my article in Towards Data Science, or the papers below, under Useful References.


To install, use

$ pip install wittgenstein

To uninstall, use

$ pip uninstall wittgenstein


  • pandas
  • numpy


Usage syntax is similar to sklearn's. The current version, however, does require that data be passed in as a Pandas DataFrame.

Once you have loaded and split your data...

>>> import pandas as pd
>>> df = pd.read_csv(dataset_filename)
>>> from sklearn.model_selection import train_test_split # Or any other mechanism you want to use for data partitioning
>>> train, test = train_test_split(df, test_size=.33)

We can fit a ruleset classifier using RIPPER or IREP.

>>> import wittgenstein as lw
>>> ripper_clf = lw.RIPPER() # Or irep_clf = lw.IREP() to build a model using IREP
>>>, class_feat='Party') # Or call .fit with params train_X, train_y
>>> ripper_clf
<RIPPER object with fit ruleset (k=2, prune_size=0.33, dl_allowance=64)> # Hyperparameter details available in the docstrings and TDS article below

Access the underlying trained model with the ruleset_ attribute. A ruleset is a disjunction of conjunctions -- 'V' represents 'or'; '^' represents 'and'.

In other words, the model predicts positive class if any of the inner-nested condition-combinations are all true:

>>> ripper_clf.ruleset_
<Ruleset object: [physician-fee-freeze=n] V [synfuels-corporation-cutback=y^adoption-of-the-budget-resolution=y^anti-satellite-test-ban=n]>

To score our fit model:

>>> test_X = test.drop(class_feat, axis=1)
>>> test_y = test[class_feat]
>>> ripper_clf.score(test_X, test_y)

Default scoring metric is accuracy. You can pass in alternate scoring functions, including those available through sklearn:

from sklearn.metrics import precision_score, recall_score
>>> precision = clf.score(X_test, y_test, precision_score)
>>> recall = clf.score(X_test, y_test, recall_score)
>>> print(f'precision: {precision} recall: {recall})
precision: 0.9914..., recall: 0.9953...

To perform predictions:

>>> ripper_clf.predict(new_data)[:5]
[True, True, False, True, False]

We can also ask our model to tell us why it made each positive prediction that it did:

>>> ripper_clf.predict(new_data)[:5]
([True, True, False, True, True]
[<Rule object: [physician-fee-freeze=n]>],
[<Rule object: [physician-fee-freeze=n]>,
  <Rule object: [synfuels-corporation-cutback=y^adoption-of-the-budget-resolution=y^anti-satellite-test-ban=n]>], # This example met multiple sufficient conditions for a positive prediction
[<Rule object: [physician-fee-freeze=n]>],


If you encounter any issues, or if you have feedback or improvement requests for how wittgenstein could be made more helpful for you, please post them to issues, and I'll respond.


Contributions are welcome! If you are interested in contributing, let me know at or on linkedin.

Useful references

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