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Object-oriented implementations of decision tree variants

Project description

This repository will contain several variants of decision tree / ensemble classification algorithms, written in an object-oriented style. My immediate goal is to try to reproduce some of the results from this paper on canonical correlation forests, which I am testing against the same datasets.

Where possible, external parameters names will match scikit-learn’s implementations of decision trees and random forests.


One major difference from scikit-learn is that datasets and their attributes are treated as first-class objects. Additionally, all classifiers must be initialized with their training dataset (as opposed to calling fit).

from oo_trees.dataset import Dataset
from oo_trees.decision_tree import DecisionTree
from oo_trees.random_forest import RandomForest

X = examples # numpy 2D numeric array
y = outcomes # numpy 1D array

dataset = Dataset(X, y)

training_dataset, test_dataset = dataset.random_split(0.75)

d_tree = DecisionTree(training_dataset)
forest = RandomForest(training_dataset)


d_tree_confusion_matrix = d_tree.performance_on(test_dataset)
forest_confusion_matrix = forest.performance_on(test_dataset)


When initializing datasets, we assume all attributes of the training examples are categorical. If that is not the case, you can pass in an additional attribute_types variable on initialize:

from oo_trees.dataset import Dataset
from oo_trees.attribute import NumericAttribute, CategoricalAttribute

X = examples
y = outcomes

attributes = [
  NumericAttribute(index=0, name='age'),
  CategoricalAttribute(index=1, name='sex'),
  NumericAttribute(index=2, name='income')

dataset = Dataset(X, y, attributes)

The logic for finding the best split is differs for each attribute type, and in the future there may be additional type-specific parameters (such as importance or number-to-name mappings) useful for classification or display.

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