mayini-framework 0.9.0

A comprehensive deep learning framework built from scratch in Python with PyTorch-like API

Mayini Framework

Mayini is a comprehensive, from-scratch Deep Learning and Machine Learning framework built in pure Python. Designed with a clean, PyTorch-like API, it aims to demystify underlying AI mechanics while providing robust, high-performance features for deep neural networks, classical ML models, evolutionary algorithms, and multimodal data preprocessing.

Whether you're researching new topologies, learning the mathematics of backpropagation, or deploying classical models, Mayini has you covered.

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📦 Installation

Install Mayini directly from PyPI via pip:

pip install mayini-framework==0.8.1

If you wish to edit the source code and contribute:

git clone https://github.com/your-username/mayini.git
cd mayini
pip install -e .[dev]

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🚀 Key Modules & Architecture

The framework is divided into five highly independent yet composable pillars:

1. 🧠 mayini.tensor (The Autograd Engine)

A custom multidimensional array computing engine built on top of NumPy. It features a complete define-by-run Automatic Differentiation (Autograd) computational graph, ensuring mathematically guaranteed gradients for neural network backpropagation.

• Precision preserved elegantly across massive operations (float64 strict type preservation).
• Full broadcasting semantics for operations (add, matmul, pow, sum).
• Topologically sorted backwards passes.

2. 🔌 mayini.nn (Deep Learning)

A rich PyTorch-like Neural Network library mapping seamlessly to the Tensor backend.

• Layers: Linear, Conv2D, RNN
• Activations: ReLU, Sigmoid, Tanh, Softmax
• Optimizers: Adam, RMSprop, SGD (with momentum)
• Losses: MSELoss, CrossEntropyLoss, BCELoss
• Structure: Standardized Module inheritance blocks with recursive parameter registration.

3. 🤖 mayini.ml (Classical Machine Learning)

Robust statistical and classical machine learning algorithms, optimized for clarity and speed.

• Supervised: Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, Naive Bayes, Linear & Logistic Regression.
• Unsupervised: K-Means Clustering, PCA (Principal Component Analysis), Isolation Forests (Anomaly Detection).
• Ensembles: Bagging, Boosting (AdaBoost/Gradient style), Voting implementations.

4. 🧬 mayini.neat (NeuroEvolution of Augmenting Topologies)

A custom, fully-featured implementation of NEAT. Instead of relying on gradient descent, NEAT evolves both the topological structure and weights of artificial neural networks using genetic algorithms.

• Dynamically mutate network structures (add nodes, add connections).
• Speciation to protect topological innovations from being wiped out early.
• Custom activation registries and highly-configurable fitness evaluators.

5. 🛠️ mayini.preprocessing (Multimodal Pipelines & Gradio Widget)

An enterprise-grade multimodal data preprocessor spanning Text, Image, Audio, and Video.

• No-Code Interactive Widget: Launch a Gradio-powered UI to visualize and build your preprocessing pipelines instantly! Run cleaning, transformations, and feature extractions directly from your browser.
• Text: TF-IDF, Stemming, Tokenization, Text Normalization.
• Image: Bilinear Resizing, Sobel Edge Detection, Datagen Augmentations.
• Audio: STFT Spectrograms, MFCC feature extraction, Pitch Shifting.

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💻 Code Examples & Quick Start

Example 1: Basic Autograd Calculus

from mayini.tensor import Tensor

# Define variables with gradient tracking
x = Tensor([2.0], requires_grad=True)
y = Tensor([3.0], requires_grad=True)

# Build a computational graph
z = (x ** 2) + (x * y)

# Compute gradients
z.backward()

print(f"z = {z.data}")         # z = 10.0
print(f"dz/dx = {x.grad}")     # 2x + y = 7.0 
print(f"dz/dy = {y.grad}")     # x = 2.0

Example 2: Training a Multilayer Perceptron (MLP)

from mayini.tensor import Tensor
import mayini.nn as nn
import mayini.optim as optim

# 1. Define Model Architecture
class MLP(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(2, 16)
        self.fc2 = nn.Linear(16, 1)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        out = self.relu(self.fc1(x))
        return self.sigmoid(self.fc2(out))

model = MLP()

# 2. Setup Optimizer & Loss Metric
optimizer = optim.Adam(model.parameters(), lr=0.01)
criterion = nn.BCELoss()

# 3. Create Dataset (XOR problem)
X = Tensor([[0, 0], [0, 1], [1, 0], [1, 1]])
Y = Tensor([[0], [1], [1], [0]])

# 4. Training Loop
for epoch in range(200):
    optimizer.zero_grad()
    predictions = model(X)
    loss = criterion(predictions, Y)
    loss.backward()
    optimizer.step()

print(f"Final Model Loss: {loss.data}")

Example 3: Training an SVM (Classical ML)

import numpy as np
from mayini.ml.supervised.svm import SVM

# Generate random dummy data
X = np.array([[1, 2], [1, 3], [5, 6], [6, 5]])
y = np.array([1, 1, -1, -1])

# Initialize Support Vector Machine
model = SVM(learning_rate=0.001, lambda_param=0.01, n_iters=1000)
model.fit(X, y)

# Predict
predictions = model.predict(np.array([[2, 2], [5, 5]]))
print(f"Predictions: {predictions}")

Example 4: Launching the Gradio Preprocessor UI

Tired of formatting complex data arrays blindly? Use the built-in Gradio widget to instantly mock your pipelines:

from mayini.preprocessing.widget import launch_widget

# Automatically opens the local server at http://localhost:7860
launch_widget()

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🛠️ Testing & Stability

Mayini relies on rigorous regression testing to ensure robust deployments.

• The framework guarantees 100% test suite stability.
• The mayini.tensor operations are validated against scipy.optimize finite difference algorithms to ensure mathematically flawless differentiation boundaries.

Run the test suite locally:

pytest -v test/

🤝 Contributing

Contributions are always welcome. To get started:

1. Fork the codebase and branch off main.
2. Ensure you add robust unittests (pytest) for any mathematical algorithm updates.
3. Submit a Pull Request.

📄 License

This project is licensed under the MIT License. See the LICENSE file for the full legal layout.