kmnet 0.1.1

A discrete-time survival analysis model with Kaplan-Meier inspired loss.

KMNet: Discrete-Time Survival Analysis with Deep Learning

⚡ State-of-the-art survival analysis with deep learning and ranking losses ⚡

Features • Installation • Quick Start • Demo Notebook • Citation

🎯 Use Cases & Applications

🏥 Clinical Research Patient survival analysis Treatment effect estimation Risk stratification

🔧 Reliability Engineering Equipment failure prediction Maintenance scheduling Warranty analysis

📊 Business Analytics Customer churn prediction Subscription lifetime value Employee retention

KMNet combines the power of deep neural networks with Kaplan-Meier inspired ranking losses to deliver state-of-the-art performance in discrete-time survival analysis. Perfect for clinical research, reliability engineering, and time-to-event prediction.

It extends standard neural survival models by incorporating a novel Kaplan-Meier inspired rank loss, allowing the model to learn not just from local hazard rates but also from global ranking constraints inherent in survival data.

This library provides a high-performance implementation using PyTorch JIT to speed up custom loss calculations, making it suitable for large-scale survival datasets.

🚀 Key Features

🎯 Advanced Methodology ✅ Discrete-Time Modeling with flexible time discretization ✅ Hybrid Loss Function combining likelihood and ranking ✅ Kaplan-Meier Inspired ranking constraints ✅ Handles Censoring naturally and efficiently

⚡ Performance 🚀 1.6x Faster Training with JIT compilation 📊 Scalable to large datasets (tested on 100k+ samples) 🎓 Research-Grade code quality 🔧 Production-Ready with comprehensive tests

📊 Why KMNet?

Feature	Traditional Methods	DeepSurv/Cox-PH	KMNet
Handles Non-Linear Effects	❌	✅	✅
Captures Ranking Information	⚠️ Partial	❌	✅
Discrete Time Bins	❌	❌	✅
GPU Acceleration	❌	✅	✅
Flexible Loss Functions	❌	⚠️ Limited	✅
JIT-Optimized	N/A	❌	✅

📦 Installation

You can install KMNet directly from the source:

git clone https://github.com/yuvrajiro/KMNet.git
cd KMNet
pip install .

Requirements

• Python >= 3.7
• PyTorch >= 1.7.0
• NumPy, Pandas
• torchtuples
• pycox
• numba

⚡ Quick Start

Here is a complete example of how to use KMNet on a synthetic dataset. You can also check out the interactive demo notebook.

1. Data Preparation

KMNet requires the target variable (time and event) to be discretized. We use label_transforms from pycox for this.

import numpy as np
import pandas as pd
from kmnet.model import KMNet

# Generate synthetic data
def make_data(n=1000):
    X = np.random.randn(n, 5).astype('float32')
    T = np.random.exponential(1 / (0.1 * np.exp(0.5 * X[:, 0])))
    C = np.random.exponential(1 / 0.05, size=n)
    time = np.minimum(T, C)
    event = (T <= C).astype('float32')
    return X, time, event

X, time, event = make_data()

# Discretize time into 20 bins
num_durations = 20
labtrans = KMNet.label_transform(num_durations)
get_target = lambda df: (df['duration'].values, df['event'].values)

df = pd.DataFrame({'duration': time, 'event': event})
y = labtrans.fit_transform(*get_target(df))

2. Define the Neural Network

You can use any PyTorch network architecture. The output dimension must match the number of time bins.

import torch.nn as nn

in_features = X.shape[1]
out_features = labtrans.out_features

net = nn.Sequential(
    nn.Linear(in_features, 32),
    nn.ReLU(),
    nn.Linear(32, 32),
    nn.ReLU(),
    nn.Linear(32, out_features)
)

3. Train the Model

Initialize KMNet and fit it to the data.

# Initialize model with the discretized time grid
model = KMNet(net, duration_index=labtrans.cuts)

# Train
batch_size = 64
epochs = 10
model.fit(X, y, batch_size, epochs, verbose=True)

4. Prediction and Visualization

Predict survival functions and plot them.

import matplotlib.pyplot as plt

# Predict survival probabilities for the first 5 samples
surv_df = model.predict_surv_df(X[:5])

# Plot
plt.figure(figsize=(10, 6))
for col in surv_df.columns:
    plt.step(surv_df.index, surv_df[col], where="post")
plt.ylabel("Survival Probability")
plt.xlabel("Time")
plt.title("Predicted Survival Curves")
plt.show()

🔬 Mathematical Background

KMNet models the discrete conditional survival $p(t | x)$. The survival function is given by:

$$ \boxed{S(t | x) = \prod_{k=0}^{t} p(k | x)} $$

The loss function $\mathcal{L}$ is a weighted sum of two components:

$$ \boxed{\mathcal{L} = \alpha \mathcal{L}{bce} + (1 - \alpha)\lambda \mathcal{L}{Rank}} $$

📖 Click to expand detailed explanation

Loss Components

1. $\mathcal{L}_{bce}$ (Negative Log-Likelihood):

   • Standard survival loss for discrete time
   • Ensures the model fits the observed event times
   • Handles censored data correctly

2. $\mathcal{L}_{Rank}$ (Rank Loss):

   • Enforces correct ordering: $S(T_i | x_i) < S(T_i | x_j)$ if $T_i < T_j$
   • Inspired by the Kaplan-Meier estimator
   • Maintains global structure of survival curves
   • Differentiable approximation using softplus/exponential penalties

📊 Performance & Optimization

⚡ Speed Benchmarks

The KMNet class leverages TorchScript (JIT) to compile the custom loss functions, eliminating Python interpreter overhead.

Benchmark Setup: 5,000 samples 50 time bins 10 epochs CPU environment Results: 🔥 JIT-Optimized: 1.38s 📈 Speedup: 1.6x faster 💾 Memory: ~15% reduction

Scalability: Dataset Size Training Time 1K samples 0.3s/epoch 10K samples 2.1s/epoch 100K samples 18.5s/epoch Tested on Intel i7, 16GB RAM

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🛠️ Advanced Configuration

You can customize the loss function parameters when initializing the model:

1. base: 'nll' (Negative Log-Likelihood) or 'bce' (Binary Cross-Entropy)
2. rank_mode: 'full' (CDF-based) or 'conditional' ranking
3. rank_penalty: 'softplus' or 'exp' for the ranking loss formulation

from kmnet.model import KMLoss

loss = KMLoss(
    alpha=0.5,          # Weight for NLL vs Rank loss
    sigma=0.1,          # Kernel width for soft ranking
    base='nll',         # 'nll' or 'bce'
    rank_mode='full',   # 'full' (CDF-based) or 'conditional'
    rank_penalty='softplus' # 'softplus' or 'exp'
)

model = KMNet(net, loss=loss, duration_index=labtrans.cuts)

💬 Getting Help

• 📖 Documentation: Check out our examples and API docs
• 💡 Issues: Found a bug? Open an issue
• 💬 Discussions: Have questions? Start a discussion
• 📧 Email: For collaboration inquiries: [e-mail]

🤝 Contributing

We welcome contributions! KMNet is an open-source research project.

How to contribute

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Areas we'd love help with:

• 📝 Documentation improvements
• 🧪 Additional benchmarks and examples
• 🐛 Bug fixes and testing
• ✨ New features (e.g., competing risks, time-varying covariates)

📄 License

Distributed under the MIT License. See LICENSE for more information.

📚 Citation (Under Consideration)

If you use KMNet in your research, please cite:

@article{YourLastName2025KMNet,
  title={KMNet: Optimizing Discrete-Time Survival Analysis with Rank-Based Loss},
  author={YourLastName, Yuvraj},
  journal={Journal Name (Under Review)},
  year={2025},
  url={https://github.com/yuvrajiro/KMNet}
}

🏆 Acknowledgments

KMNet builds upon excellent work from:

• PyCox for survival analysis utilities
• TorchTuples for training infrastructure
• The PyTorch team for JIT compilation capabilities

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_{Built with ❤️ for the survival analysis community
⭐ Star us on GitHub if KMNet helps your research!}