RuleTree
RuleTree builds interpretable decision trees one node at a time. Each node is split by a stump: a threshold on a feature or on a combination of a few features (oblique split), the distance from an instance of the training set (a pivot), the distance of a time series from a shapelet, which of two pivots or shapelets is closer, a fair split, ... The same tree can mix different kinds of stumps and different types of data (tabular data, time series, images), and its rules can be read, drawn and saved.
- Classification, regression 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 Graphviz drawings, 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[graphviz]" # optional: drawing the trees (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.
- Stumps: the available stumps and how to combine them.
- Tabular data, time series and images.
- Reading the tree: rules, drawings, nodes, feature contributions.
- Saving, growing and updating trees.
- 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) 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.
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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