RuleTree
Interpretable decision trees whose nodes can be split in different ways (a threshold on a feature, the distance from an example, the distance of a time series from a pattern, ...), on tabular data, time series and images. Documentation: https://fismimosa.github.io/RuleTree/
- Classification, regression (also with several targets) and clustering trees with the scikit-learn API.
- Stumps for tabular data, time series and images, usable together in the same tree.
- Rules as text or drawings (matplotlib or Graphviz), feature contributions of each prediction.
- Trees saved to and loaded from JSON, grown further with
partial_fit. - Gradient boosting, XGBoost and explainable boosting machines of RuleTrees.
- Metrics to evaluate the models: predictions, clusters, size of the trees and fairness.
- New stumps written by combining a transformer (the features) and a splitter (the split).
Installation
RuleTree requires Python 3.10 or later:
pip install RuleTree # or, from the root of the repository: pip install .
pip install "RuleTree[shapelets]" # optional: shapelet, proximity and partial pivot stumps (numba)
pip install "RuleTree[plot]" # optional: drawing the trees with matplotlib (plot_tree)
pip install "RuleTree[graphviz]" # optional: Graphviz drawings (also requires the Graphviz binaries)
pip install "RuleTree[all]" # all the optional features
Example
from sklearn.datasets import load_iris
from RuleTree import RuleTreeClassifier
X, y = load_iris(return_X_y=True)
model = RuleTreeClassifier(max_depth=2, random_state=0).fit(X, y)
model.print_rules(model.get_rules(columns_names=["sepal length", "sepal width",
"petal length", "petal width"]))
|--- petal width <= 0.8
| output: 0
|--- petal width > 0.8
| |--- petal width <= 1.75
| | output: 1
| |--- petal width > 1.75
| | output: 2
Trees with other stumps and other types of data:
from RuleTree.stumps.classification import DecisionTreeStumpClassifier, ShapeletTreeStumpClassifier
# X: tabular data, X_ts: time series of shape (n_samples, n_signals, length)
model = RuleTreeClassifier(max_depth=3, stump_selection="best",
base_stumps=[DecisionTreeStumpClassifier(), ShapeletTreeStumpClassifier()])
model.fit(X=X, X_ts=X_ts, y=y)
Documentation
The user guide is in docs/guides:
- Getting started: classification, regression, clustering.
- How RuleTree works: trees, nodes, stumps and their components.
- Stumps: the available stumps, which one to use and how to combine them.
- Tabular data, time series and images.
- Reading the tree: rules, drawings, nodes, feature contributions.
- Growing and updating trees:
partial_fit,update_statisticsand the cache of the stumps. - Saving and loading trees: what
to_dictsaves and howfrom_dictrebuilds a tree. - Ensembles: boosting of RuleTrees.
- Evaluating the models: metrics of predictions, clusters, tree size and fairness; all but the tree size need only the output of a model, so they evaluate any model.
- Extending the library: writing new stumps, transformers and splitters.
The HTML documentation, with the API reference, is built with Sphinx:
pip install -r requirements.txt -r requirements.docs.txt
make -C docs html # docs/_build/html/index.html
The figures of the guides (docs/_static/figures, each in a light and a dark version) are drawn
from fitted models by python docs/make_figures.py (it needs matplotlib): run it again when a
change affects them.
More examples are in the notebooks of Examples, which the HTML documentation runs
again at every build. To cite RuleTree, see docs/citing.rst.
Tests
pip install ".[test]"
pytest
The tests include the docstring examples and the code of the guides, whose printed output is compared with the one in the guides.
1 maintainer
Metadata
1 maintainer
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