And is there not also the case where we play and--make up the rules as we go along?
-Ludwig Wittgenstein
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 wittgensteinTo uninstall, use
$ pip uninstall wittgenstein- pandas
- numpy
- python version>=3.6
Usage syntax is similar to sklearn's.
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)Use the fit method to train a RIPPER or IREP classifier:
>>> 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 belowAccess 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:
>>> ripper_clf.ruleset_
<Ruleset [physician-fee-freeze=n] V [synfuels-corporation-cutback=y^adoption-of-the-budget-resolution=y^anti-satellite-test-ban=n]>IREP models tend be higher bias, RIPPER's higher variance.
To score our trained model, use the score function:
>>> X_test = test.drop(class_feat, axis=1)
>>> y_test = test[class_feat]
>>> ripper_clf.score(test_X, test_y)
0.9985686906328078Default 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...wittgenstein is compatible with sklearn model_selection tools such as cross_val_score and GridSearchCV, as well
as ensemblers like StackingClassifier.
Cross validation:
>>> # First dummify your categorical features and booleanize your class values 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:
>>> 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:
>>> tree = DecisionTreeClassifier(random_state=42)
>>> nb = GaussianNB(random_state=42)
>>> estimators = [("rip", ripper_clf), ("tree", tree), ("nb", nb)]
>>> ensemble_clf = StackingClassifier(estimators=estimators, final_estimator=LogisticRegression())
>>> ensemble_clf.fit(X_train, y_train)To perform predictions, use predict:
>>> ripper_clf.predict(new_data)[:5]
[True, True, False, True, False]Predict class probabilities with predict_proba:
>>> 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 using give_reasons:
>>> 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]>],
[])Sometimes you may wish to specify or modify a model (for instance, to take into account subject matter expertise, to create a baseline for scoring, to make predictions based on your own intuitions -- or perhaps out of curiosity to see how it does).
To specify your own model, use init_ruleset:
>>> ripper_clf.init_ruleset('[[physician-fee-freeze=n] V [anti-satellite-test-ban=n^physician-fee-freeze=y]]')To modify a trained model, use add_rule, replace_rule, remove_rule, or insert_rule. To alter a model by index, use replace_rule_at, etc.
>>> ripper_clf.replace_rule_at(1, '[anti-satellite-test-ban=n]')
>>> ripper_clf.insert_rule(insert_before_rule='[physician-fee-freeze=n]', new_rule='[endorse-compulsory-piracy=y]')
>>> ripper_clf.out_model()
[[endorse-compulsory-piracy=y] V
[physician-fee-freeze=n] V
[anti-satellite-test-ban=n]]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, and I'll respond.
Contributions are welcome! If you are interested in contributing, let me know at ilan.moscovitz@gmail.com or on linkedin.
- My article in Towards Data Science explaining IREP, RIPPER, and wittgenstein
- Furnkrantz-Widmer IREP paper
- Cohen's RIPPER paper
- Partial decision trees
- Bayesian Rulesets
- C4.5 paper including all the gory details on MDL
- Philosophical Investigations
- Minor bugfixes and optimizations
- 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_probareturns 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