# wittgenstein
- _And is there not also the case where we play and--make up the rules as we go along?
- -Ludwig Wittgenstein_
## Summary
This package implements two iterative coverage-based ruleset algorithms: IREP and RIPPERk.
Performance is similar to sklearn's DecisionTree CART implementation (see [Performance Tests](https://github.com/imoscovitz/ruleset/blob/master/Performance%20Tests.ipynb)).
For 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).
## Installation
To install, use
`bash
$ pip install wittgenstein
`
To uninstall, use
`bash
$ pip uninstall wittgenstein
`
## Requirements - pandas - numpy - python version>=3.6
## Usage
#### Training
Usage syntax is similar to sklearn's.
Once you have loaded and split your data...
`python
>>> 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.
`python
>>> import wittgenstein as lw
>>> ripper_clf = lw.RIPPER() # Or irep_clf = lw.IREP() to build a model using IREP
>>> ripper_clf.fit(train, class_feat='Party') # Or pass X and y data to .fit
>>> ripper_clf
<RIPPER 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, or output it with .out_model(). 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:
`python
>>> ripper_clf.ruleset_
<Ruleset [physician-fee-freeze=n] V [synfuels-corporation-cutback=y^adoption-of-the-budget-resolution=y^anti-satellite-test-ban=n]>
`
### Scoring
To score our fit model:
`python
>>> X_test = test.drop(class_feat, axis=1)
>>> y_test = test[class_feat]
>>> ripper_clf.score(test_X, test_y)
0.9985686906328078
`
Default scoring metric is accuracy. You can pass in alternate scoring functions, including those available through sklearn:
`python
>>> 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...
`
### Model selection
wittgenstein classifiers are also compatible with sklearn model_selection tools such as cross_val_score and GridSearchCV, as well
as ensemblers like StackingClassifier.
Cross validation:
`python
>>> # First dummify your categorical features to make sklearn happy
>>> X_train = pd.get_dummies(X_train, columns=X_train.select_dtypes('object').columns)
>>> y_train = y_train.map(lambda x: 1 if x=='democrat' else 0)
>>> cross_val_score(ripper, X_train, y_train)
`
Grid search:
`python
>>> param_grid = {"prune_size": [0.33, 0.5], "k": [1, 2]}
>>> grid = GridSearchCV(estimator=ripper, param_grid=param_grid)
>>> grid.fit(X_train, y_train)
`
Ensemble:
`python
>>> tree = DecisionTreeClassifier(random_state=42)
>>> estimators = [("rip", ripper_clf), ("tree", tree)]
>>> ensemble_clf = StackingClassifier(estimators=estimators, final_estimator=LogisticRegression())
>>> ensemble_clf.fit(X_train, y_train)
`
### Prediction
To perform predictions:
`python
>>> ripper_clf.predict(new_data)[:5]
[True, True, False, True, False]
`
Predict class probabilities:
```python
>>> ripper_clf.predict_proba(test)
# Pairs of negative and positive class probabilities
array([[0.01212121, 0.98787879],
[0.01212121, 0.98787879], [0.77777778, 0.22222222], [0.2 , 0.8 ], ...
`
We can also ask our model to tell us why it made each positive prediction that it did:
```python
>>> ripper_clf.predict(new_data[:5], give_reasons=True)
([True, True, False, True, True]
[<Rule [physician-fee-freeze=n]>],
[<Rule [physician-fee-freeze=n]>, <Rule [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]>],
[])
`
## Issues If you encounter any issues, or if you have feedback or improvement requests for how wittgenstein could be more helpful for you, please post them to [issues](https://github.com/imoscovitz/wittgenstein/issues), and I'll respond.
## Changelog
##### v0.2.1: 5/19/2020 - Binning bugfix and optimization
#### v0.7.0: 5/4/2020 - Algorithmic optimizations to improve training speed (~10x - ~100x) - Support for training on iterable datatypes besides DataFrames, such as numpy arrays and python lists - Compatibility with sklearn ensembling metalearners and sklearn model_selection - .predict_proba returns probas in neg, pos order - Certain parameters (hyperparameters, random_state, etc.) should now be passed into IREP/RIPPER constructors rather than the .fit method. - Sundry bugfixes
## Contributing Contributions are welcome! If you are interested in contributing, let me know at [email protected] or on [linkedin](https://www.linkedin.com/in/ilan-moscovitz/).
## Useful references - [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) - [Furnkrantz-Widmer IREP paper](https://pdfs.semanticscholar.org/f67e/bb7b392f51076899f58c53bf57d5e71e36e9.pdf) - [Cohen's RIPPER paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.107.2612&rep=rep1&type=pdf) - [Partial decision trees](https://researchcommons.waikato.ac.nz/bitstream/handle/10289/1047/uow-cs-wp-1998-02.pdf?sequence=1&isAllowed=y) - [Bayesian Rulesets](https://pdfs.semanticscholar.org/bb51/b3046f6ff607deb218792347cb0e9b0b621a.pdf) - [C4.5 paper including all the gory details on MDL](https://pdfs.semanticscholar.org/cb94/e3d981a5e1901793c6bfedd93ce9cc07885d.pdf) - [_Philosophical Investigations_](https://static1.squarespace.com/static/54889e73e4b0a2c1f9891289/t/564b61a4e4b04eca59c4d232/1447780772744/Ludwig.Wittgenstein.-.Philosophical.Investigations.pdf)