Semi-Supervised Support Vector Machines (S3VM)

Python implementation of semi-supervised SVM algorithms for binary classification using both labeled and unlabeled data.

Algorithms

NewtonUTSVM: Newton-based method using two parallel planes
S3VM Constrained: Constrained formulation with MIP
S3VM Unconstrained: Unconstrained smooth optimization
MC_NDCC: Synthetic dataset generator
SRMSVM: 1-norm SVM for sparse solutions

Requirements

Python 3.x
numpy
pandas
scipy
scikit-learn

Installation

pip install numpy pandas scipy scikit-learn

Usage

Run Experiments

cd code
python main.py

Basic Example

from NewtonUTSVM import NewtonUTSVM
from utils import load_dataset, calculate_accuracy

# Load dataset
X, y, x_test, y_test, U = load_dataset("data/diabetes.csv")
y = y.reshape(y.shape[0], 1)

# Train
C = [0.1, 0.1, 0.1, 0.1, 0.3, 0.3]
model = NewtonUTSVM(X, y, U, C=C, eps=1e-4)
model.fit()

# Predict
model.predict(x_test=x_test)
predictions = model.get_preds()
accuracy = calculate_accuracy(y_test, predictions)
print(f"Accuracy: {accuracy}%")

Datasets

Real-world: diabetes, ionosphere, musk, sonar, gender, wpbc

Synthetic: NDCC datasets (100_10, 100_100, 500_10, 1000_10)

Project Structure

code/
├── main.py              # Main script
├── NewtonUTSVM.py       # NewtonUTSVM
├── S3VM_constrained.py  # Constrained S3VM
├── S3VM_unconstrained.py # Unconstrained S3VM
├── SRMSVM.py            # SRMSVM
├── MC_NDCC.py           # Dataset generator
├── utils.py             # Utilities
└── data/                # Datasets

Notes

Binary classification only (labels: +1, -1)
Datasets are automatically normalized
NewtonUTSVM requires 6 C parameters