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        "author": "Ilan Moscovitz",
        "author_email": "ilan.moscovitz@gmail.com",
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        "description": "# wittgenstein\n\n_And is there not also the case where we play and--make up the rules as we go along?  \n  -Ludwig Wittgenstein_\n\n![the duck-rabbit](https://github.com/imoscovitz/wittgenstein/blob/master/duck-rabbit.jpg)\n\n## Summary\n\nThis package implements two iterative coverage-based ruleset algorithms: IREP and RIPPERk.\n\nPerformance is similar to sklearn's DecisionTree CART implementation (see [Performance Tests](https://github.com/imoscovitz/ruleset/blob/master/Performance%20Tests.ipynb)).\n\nFor explanation of the algorithms, see my article in _Towards Data Science_, or the papers below, under [Useful References](https://github.com/imoscovitz/wittgenstein#useful-references).\n\n## Installation\n\nTo install, use\n```bash\n$ pip install wittgenstein\n```\n\nTo uninstall, use\n```bash\n$ pip uninstall wittgenstein\n```\n\n## Requirements\n- pandas\n- numpy\n\n## Usage\n\nUsage syntax is similar to sklearn's. The current version, however, does require that data be passed in as a Pandas DataFrame.\n\nOnce you have loaded and split your data...\n```python\n>>> import pandas as pd\n>>> df = pd.read_csv(dataset_filename)\n>>> from sklearn.model_selection import train_test_split # Or any other mechanism you want to use for data partitioning\n>>> train, test = train_test_split(df, test_size=.33)\n```\nWe can fit a ruleset classifier using RIPPER or IREP.\n```python\n>>> import wittgenstein as lw\n>>> ripper_clf = lw.RIPPER() # Or irep_clf = lw.IREP() to build a model using IREP\n>>> ripper_clf.fit(train, class_feat='Party') # Or call .fit with params train_X, train_y\n>>> ripper_clf\n<RIPPER object with fit ruleset (k=2, prune_size=0.33, dl_allowance=64)> # Hyperparameter details available in the docstrings and TDS article below\n```\n\nAccess the underlying trained model with the ruleset_ attribute. A ruleset is a disjunction of conjunctions -- 'V' represents 'or'; '^' represents 'and'.\n\nIn other words, the model predicts positive class if any of the inner-nested condition-combinations are all true:\n```python\n>>> ripper_clf.ruleset_\n<Ruleset object: [physician-fee-freeze=n] V [synfuels-corporation-cutback=y^adoption-of-the-budget-resolution=y^anti-satellite-test-ban=n]>\n```\nTo score our fit model:\n```python\n>>> test_X = test.drop(class_feat, axis=1)\n>>> test_y = test[class_feat]\n>>> ripper_clf.score(test_X, test_y)\n0.9985686906328078\n```\nDefault scoring metric is accuracy. You can pass in alternate scoring functions, including those available through sklearn:\n```python\nfrom sklearn.metrics import precision_score, recall_score\n>>> precision = clf.score(X_test, y_test, precision_score)\n>>> recall = clf.score(X_test, y_test, recall_score)\n>>> print(f'precision: {precision} recall: {recall})\nprecision: 0.9914..., recall: 0.9953...\n```\nTo perform predictions:\n```python\n>>> ripper_clf.predict(new_data)[:5]\n[True, True, False, True, False]\n```\nWe can also ask our model to tell us why it made each positive prediction that it did:\n```python\n>>> ripper_clf.predict(new_data)[:5]\n([True, True, False, True, True]\n[<Rule object: [physician-fee-freeze=n]>],\n[<Rule object: [physician-fee-freeze=n]>,\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\n[],\n[<Rule object: [physician-fee-freeze=n]>],\n[])\n```\n\n## Issues\nIf 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](https://github.com/imoscovitz/wittgenstein/issues), and I'll respond.\n\n## Contributing\nContributions are welcome! If you are interested in contributing, let me know at ilan.moscovitz@gmail.com or on [linkedin](https://www.linkedin.com/in/ilan-moscovitz/).\n\n## Useful references\n- [My article in _Towards Data Science_ explaining IREP, RIPPER, and wittgenstein](https://towardsdatascience.com/how-to-perform-explainable-machine-learning-classification-without-any-trees-873db4192c68)\n- [Furnkrantz-Widmer IREP paper](https://pdfs.semanticscholar.org/f67e/bb7b392f51076899f58c53bf57d5e71e36e9.pdf)\n- [Cohen's RIPPER paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.107.2612&rep=rep1&type=pdf)\n- [Partial decision trees](https://researchcommons.waikato.ac.nz/bitstream/handle/10289/1047/uow-cs-wp-1998-02.pdf?sequence=1&isAllowed=y)\n- [Bayesian Rulesets](https://pdfs.semanticscholar.org/bb51/b3046f6ff607deb218792347cb0e9b0b621a.pdf)\n- [C4.5 paper including all the gory details on MDL](https://pdfs.semanticscholar.org/cb94/e3d981a5e1901793c6bfedd93ce9cc07885d.pdf)\n- [_Philosophical Investigations_](https://static1.squarespace.com/static/54889e73e4b0a2c1f9891289/t/564b61a4e4b04eca59c4d232/1447780772744/Ludwig.Wittgenstein.-.Philosophical.Investigations.pdf)",
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