dtreenetwork 1.1.1

A Layered Decision Tree Based Model for Classification tasks

TreeNet

TreeNet is a lightweight and customizable machine learning model designed for flexible experimentation with layered network structures. It provides simple training and prediction interfaces and is well-suited for rapid prototyping and educational purposes.

Installation

You can install the package directly from PyPI:

pip install treenet

Or install it from the source:

git clone https://github.com/zeshanalvi/treenet.git
cd treenet
pip install .

Usage

Here’s a quick example of how to use TreeNet:

import numpy as np, pandas as pd, random, string
from treenet import TreeNet

# Initialize the model
model = TreeNet(layer_count=3, breath_count=2)

# Sample training data
col_labels = [''.join(random.choices(string.ascii_uppercase, k=10)) for _ in range(20)] # labels of 20 columns
trainXnp = np.random.rand(100, 20)   # 100 samples, 20 features
trainX = pd.DataFrame(trainXnp, columns=col_labels) # Dataframe
trainY = np.random.randint(0, 8, size=(100,)) # Multiclass labels with 8 classes

# Train the model
model.train(trainX, trainY)

# Sample test data
testXnp = np.random.rand(10, 20) # 10 samples, 20 features
testX = pd.DataFrame(testXnp, columns=col_labels) # Dataframe

# Predict probabilities
probabilities = model.predict_prob(testX)
print("Probabilities:\n", probabilities)

# Predict labels
predictions = model.predict(testX)
print("Predictions:\n", predictions)

API Reference

TreeNet

__init__(self, layer_count=2, breath_count=1)

Initializes a new TreeNet model.

layer_count (int): Number of layers in the network (default: 2).

breath_count (int): Number of parallel branches per layer (default: 1).

train(self, trainX, trainY)

Trains the model on given data.

trainX (np.ndarray): Training features of shape (n_samples, n_features).

trainY (np.ndarray): Training labels of shape (n_samples, 1).

predict_prob(self, testX) -> np.ndarray

Returns the probability distribution over classes for input samples.

testX (np.ndarray): Test features of shape (n_samples, n_features).

Returns: np.ndarray of shape (n_samples, n_classes).

predict(self, testX) -> np.ndarray

Returns predicted class labels for input samples.

testX (np.ndarray): Test features of shape (n_samples, n_features).

Returns: np.ndarray of shape (n_samples, 1).

Project Structure

treenet/
│
├── treenet/
│   ├── __init__.py
│   ├── model.py         # Implementation of TreeNet class
│
├── tests/
│   ├── test_model.py    # Unit tests for TreeNet
│
├── README.md
├── setup.py
├── pyproject.toml
├── LICENSE

Contributing

Contributions are welcome! Please feel free to submit a Pull Request or open an Issue.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

TreeNet was built to simplify experimentation with layered machine learning structures and provide a lightweight package for educational and prototyping use.