Oh, My Trees

Oh, My Trees (ohmt) is a library for hyperplane-based Decision Tree induction, which allows you to induce both Univariate (e.g., CART, C4.5) and Multivariate (OC1, Geometric) Decision Trees. It currently supports single-class classification trees, and does not support categorical variables as they don't play well with hyperplanes.

Quickstart

Installation

Installation through git:

git clone https://github.com/msetzu/oh-my-trees
mkvirtualenv -p python3.11 omt  # optional, creates virtual environment

cd oh-my-trees
pip install -r src/requirements.txt

or directly through pip:

pip install ohmt

Training trees

OMT follows the classic sklearn fit/predict interface.
You can find a full example in the examples notebook notebooks/examples.ipynb.

from ohmt.trees.multivariate import OmnivariateDT

dt = OmnivariateDT()
x = ...
y = ...

# trees all follow a similar sklearn-like training interface, with max_depth, min_samples, and min_eps as available parameters
dt.fit(x, y, max_depth=4)

OMT also offers a pruning toolkit, handled by trees.pruning.Gardener, which allows you to prune the inducted Tree. Find out more in the example notebook.

Induction algorithms

OMT offers several Tree induction algorithms

*As mirror of Interpretable AI's implementation, you need to install the appropriate license to use Optimal trees

Using Trees

You can get an explicit view of a tree by accessing:

• tree.nodes: Dict[int, Node] its nodes,
• tree.parent: Dict[int, int], tree.ancestors: Dict[int, List[int]] its parent and ancestors,
• tree.descendants: Dict[int, List[int] its descendants,
• tree.depth: Dict[int, int]: the depth of its nodes.

Trees can also be JSONized:

tree.json()

Growing your own Tree

Greedy trees follow the basic algorithmic core of

• learning step: induce a node
• if shall continue:
  • generate two children
  • recurse on the given children

We incorporate this algorithm in Tree, where step implements the node induction, thus, most greedy induction algorithms can implemented by simply overriding the step function:

    def step(self, parent_node: Optional[InternalNode],
             data: numpy.ndarray, labels: numpy.ndarray, classes: numpy.ndarray,
             direction: Optional[str] = None, depth: int = 1,
             min_eps: float = 0.000001, max_depth: int = 16, min_samples: int = 10,
             node_fitness_function: Optional[Callable] = None,
             node_hyperparameters: Optional[Dict] = None, **step_hyperparameters) -> Optional[Node]