slsdt 0.0.2

Oblique decision tree using the LAHC heuristic.

SLSDT

Stochastic Local Search Decision Tree

This repository is for my first scientific initiation project.

About

Oblique Decision Tree is a algorithm for induction a machine learning method called decision tree using oblique approach.

SLSDT is a method for induction oblique decision trees using stochastic local search method called Late Acceptance Hill-Climbing (LAHC).

This project also provides a utility to read csv files and convert to the format accepted by the SLSDT method.

How to use

1. Install

pip3 install slsdt

2. read_csv

from slsdt.reader_csv import read_csv

X, y = read_csv("some_file.csv", "class_column_name")

3. slsdt

from slsdt.slsdt import SLSDT

clf = SLSDT()
clf.fit(X, y)

result = clf.predict(X)

print(result)
print(result == y)

Iris example oblique split

from sklearn import datasets
from slsdt.slsdt import SLSDT

iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the sepal width and sepal length features.
y = iris.target

mark = y != 2

# we only take the 0 (Iris-setosa) and 1 (Iris-versicolor) class labels
X = X[mark]
y = y[mark]

clf = SLSDT()
clf.fit(X, y)
clf.print_tree()

result = clf.predict(X)

print(result)
print(result == y)

Plot iris oblique split

Plot with Matplotlib using the results obtained above.

How to contribute

• Leave the :star: if you liked the project
• Fork this project
• Cloner your fork: git clone your-fork-url && cd slsdt
• Create a branch with your features: git checkout -b my-features
• Commit your changes: git commit -m 'feat: My new features'
• Send the your branch: git push origin my-features

License

This project is licensed under the EPL 2.0 License - see the LICENSE file for details.