tazids 1.2.0

A simple machine learning library for educational purposes.

Tazids Library Documentation

Overview

Tazids is a simple yet powerful machine learning library that provides implementations for various algorithms, including Linear Regression, Decision Trees, and K-Means clustering. The library is designed to help you easily build, train, and apply machine learning models for a variety of tasks, with a focus on transparency and simplicity.

Installation

To install the Tazids library, you can use the following command:

pip install tazids

Directory Structure

The Tazids library is organized as follows:

Tazi_Ds/
├── tazids/
│   ├── __init__.py        # Initialization file for the tazids module
│   ├── regressor.py       # Contains the LinearRegression class
│   ├── tree.py            # Contains the DecisionTree class
│   ├── clustering.py      # Contains the KMeans class
└── setup.py               # Setup script for installation

Features

Linear Regression Class (LinearRegression)

The LinearRegression class implements a simple linear regression model using gradient descent. It allows you to:

• Train the model on data using gradient descent.
• Predict outcomes for new data.
• Monitor the training process with loss values.

Example Usage

import numpy as np
from tazids.regressor import LinearRegression

# Generate synthetic data
X = np.random.rand(100, 1) * 10
y = 5 * X + np.random.randn(100, 1) * 2  # Linear relationship with noise

# Initialize and train the model
model = LinearRegression()
model.fit(X, y, learning_rate=0.01, iters=1000)

# Make predictions
predictions = model.predict(X)

Decision Tree Class (DecisionTree)

The DecisionTree class implements a simple decision tree for classification tasks. It supports:

• Recursive splitting of the data to build the decision tree.
• Making predictions by traversing the tree for each input sample.

Example Usage

from tazids.tree import DecisionTree
from sklearn.datasets import load_iris

# Load dataset
data = load_iris()
X, y = data.data, data.target

# Initialize and train the model
model = DecisionTree()
model.fit(X, y)

# Make predictions
predictions = model.predict(X)

KMeans Class (KMeans)

The KMeans class implements the K-Means clustering algorithm for unsupervised learning. It clusters data points into k groups by:

• Initializing random centroids.
• Assigning points to the nearest centroid.
• Updating centroids iteratively until convergence.

Example Usage

from tazids.clustering import KMeans
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt

# Generate synthetic data
X, _ = make_blobs(n_samples=300, centers=4, cluster_std=0.6, random_state=42)

# Initialize and train the KMeans model
model = KMeans(n_iter=300, tol=1e-4)
model.fit(X, k=4)

# Predict cluster labels
predictions = model.predict(X)

# Visualize the results
plt.scatter(X[:, 0], X[:, 1], c=predictions, cmap='viridis', s=30)
plt.scatter(model.centroids[:, 0], model.centroids[:, 1], c='red', marker='x', s=200, label='Centroids')
plt.legend()
plt.title("KMeans Clustering")
plt.show()

Notes

• Tazids is designed to be simple and transparent for educational and research purposes.
• Transparency: All models are built from scratch to provide a better understanding of their inner workings.
• Learning-Oriented: Ideal for students and researchers who want to explore the basics of machine learning.
• Lightweight: Minimal dependencies to keep the library easy to use.
• For advanced use cases, consider established libraries like scikit-learn, but Tazids offers a perfect entry point to learn the fundamentals.

Made By Mohannad Tazi