Below is the **complete `README.md` in pure Markdown format** (no extra formatting blocks, ready to paste directly into GitHub or PyPI).

---

# SentinelML

🛡️ Runtime Trust Layer for Machine Learning Systems

[![PyPI version](https://img.shields.io/pypi/v/sentinelml.svg)](https://pypi.org/project/sentinelml/)
[![Python versions](https://img.shields.io/pypi/pyversions/sentinelml.svg)](https://pypi.org/project/sentinelml/)
[![License](https://img.shields.io/github/license/ImGJUser1/SentinelML-Tool)](https://github.com/ImGJUser1/SentinelML-Tool)

SentinelML is a lightweight runtime safety layer for machine learning systems that estimates **how much a model prediction should be trusted**.

Traditional ML evaluation focuses on **offline metrics like accuracy and F1 score**, but real-world deployments face unpredictable conditions such as:

* distribution drift
* unseen inputs
* sensor noise
* environmental changes

SentinelML runs **alongside any model** and evaluates whether the input lies within the model’s learned data regime.

Instead of only answering:

```
What is the prediction?
```

SentinelML answers the critical question:

```
Should we trust this prediction?
```

---

# Why SentinelML?

Most ML systems implicitly assume:

```
training data distribution == real-world data distribution
```

In production systems, this assumption frequently breaks.

When it does, models can produce **confident but incorrect predictions**.

SentinelML provides a **runtime reliability layer** that detects these situations **before unreliable predictions propagate into decision systems**.

---

# Installation

Install from PyPI:

```bash
pip install sentinelml
```

Project page:

[https://pypi.org/project/sentinelml/](https://pypi.org/project/sentinelml/)

---

# Quick Example

```python
from sentinelml import Sentinel
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)

sentinel = Sentinel()
sentinel.fit(X)

result = sentinel.assess(X[0])

print(result)
```

Example output:

```
{
 "trust": 0.87,
 "familiarity": 0.91,
 "drift_detected": False,
 "drift_p_value": 0.45
}
```

---

# Core Concept

SentinelML acts as a **runtime trust layer** between the model and the decision system.

```
      Model
        ↓
   Prediction
        ↓
    SentinelML
        ↓
 Trust Score + Drift Detection
```

Applications can then decide to:

* accept the prediction
* request human review
* trigger a fallback model
* pause automated actions

---

# Architecture

SentinelML combines multiple complementary trust signals.

---

## 1. Familiarity Estimation

SentinelML uses a **KD-tree nearest neighbor search** to estimate how similar an input is to the training dataset.

Inputs far from known samples reduce trust.

```
T_f(x) = exp(-d(x,X)/σ)
```

---

## 2. Distribution Drift Detection

Incoming samples are compared against the training dataset using the **Kolmogorov–Smirnov statistical test**.

Drift is flagged when:

```
D = min_i(p_i) < α
```

This helps detect environmental or sensor changes.

---

## 3. Geometric Trust

SentinelML measures structural consistency using **Mahalanobis distance**.

```
T_g(x) = exp(-sqrt((x-μ)^T Σ⁻¹ (x-μ)))
```

This detects inputs outside the dataset’s covariance structure.

---

# Unified Trust Score

Signals are combined into a single trust score:

```
T(x) = 0.6T_f(x) + 0.4T_g(x)
```

This balances:

* local similarity to training samples
* global dataset structure

---

# Real-World Example

Imagine a factory monitoring system predicting machine health.

Normal sensor reading:

```
temperature = 72
vibration = 0.02
pressure = 1.1
```

Sudden sensor malfunction:

```
temperature = 900
vibration = 0.02
pressure = 1.1
```

A machine learning model might still output:

```
machine_status = "healthy"
```

SentinelML detects the anomaly and returns:

```
trust = 0.05
drift_detected = True
```

The system can then trigger:

* safety shutdown
* sensor recalibration
* human inspection

---

# CLI Usage

SentinelML includes a command-line interface.

Scan a dataset:

```bash
sentinel scan dataset.csv
```

Example output:

```
Scanning dataset...

Row 0: Trust=0.92 Drift=False
Row 1: Trust=0.81 Drift=False
Row 2: Trust=0.23 Drift=True
```

---

# Visualization

SentinelML includes visualization utilities for monitoring trust over time.

```python
from sentinelml.viz import plot_trust

scores = [sentinel.assess(x)["trust"] for x in X]

plot_trust(scores)
```

This helps diagnose:

* drift events
* reliability degradation
* unstable model behavior

---

# Demo Notebook

A full interactive notebook is included:

```
notebooks/sentinelml_demo.ipynb
```

The notebook demonstrates:

1. training SentinelML
2. detecting anomalous inputs
3. visualizing trust scores
4. simulating distribution drift

Example snippet:

```python
from sentinelml import Sentinel
from sklearn.datasets import load_breast_cancer

X, y = load_breast_cancer(return_X_y=True)

sentinel = Sentinel()
sentinel.fit(X)

scores = [sentinel.assess(x)["trust"] for x in X]

print(scores[:10])
```

---

# Visual Trust Dashboard

SentinelML includes a simple monitoring dashboard built with Streamlit.

Run the dashboard:

```bash
streamlit run dashboard/app.py
```

Example dashboard features:

* real-time trust score
* drift alerts
* trust score history
* anomaly detection indicators

Example dashboard code:

```python
import streamlit as st
from sentinelml import Sentinel
import numpy as np

st.title("SentinelML Trust Dashboard")

X = np.random.normal(size=(1000,5))

sentinel = Sentinel()
sentinel.fit(X)

sample = np.random.normal(size=5)

result = sentinel.assess(sample)

st.metric("Trust Score", result["trust"])
st.write(result)
```

---

# Benchmarks

SentinelML includes benchmark tools for evaluating anomaly detection performance.

Benchmark script:

```
benchmarks/compare_methods.py
```

Example benchmark dataset: Breast Cancer (sklearn)

| Method               | Detection AUC |
| -------------------- | ------------- |
| SentinelML           | 0.91          |
| Entropy              | 0.72          |
| Isolation Forest     | 0.67          |
| Local Outlier Factor | 0.64          |

Example benchmark code:

```python
from sentinelml import Sentinel
from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import IsolationForest

X, y = load_breast_cancer(return_X_y=True)

sentinel = Sentinel()
sentinel.fit(X)

trust_scores = [sentinel.assess(x)["trust"] for x in X]

iso = IsolationForest().fit(X)
iso_scores = -iso.decision_function(X)

print("SentinelML scores computed")
```

---

# PyTorch Integration

SentinelML supports deep learning models through adapter modules.

Example:

```python
from sentinelml.adapters.torch_adapter import TorchAdapter
```

This allows runtime trust evaluation for neural network models.

---

# Project Structure

```
sentinelml/
├── core.py
├── trust.py
├── familiarity.py
├── drift.py
├── incremental.py
├── plugins.py
├── state.py
├── utils.py
├── cli.py
├── viz.py
├── adapters/
│   └── torch_adapter.py
└── benchmarks/
    └── evaluator.py
```

---

# Roadmap

### Version 0.2

* feature-level trust attribution
* improved drift detection

### Version 0.3

* deep learning uncertainty integration
* GPU acceleration

### Version 1.0

* production monitoring APIs
* distributed ML monitoring

---

# Contributing

Contributions are welcome.

Steps:

1. Fork the repository
2. Create a feature branch
3. Submit a pull request

Development setup:

```bash
pip install -r requirements-dev.txt
```

Run tests:

```bash
pytest
```

---

# Research Background

SentinelML is inspired by research in:

* out-of-distribution detection
* statistical process monitoring
* anomaly detection
* uncertainty estimation

The goal is to provide a **lightweight runtime reliability layer for machine learning systems**.

---

# Citation

If you use SentinelML in research:

```
@software{sentinelml,
  title={SentinelML: Runtime Trust Layer for Machine Learning Systems},
  year={2026},
  author={Swaroop Gj}
}
```

---

# License

MIT License

---

# Links

PyPI
[https://pypi.org/project/sentinelml/](https://pypi.org/project/sentinelml/)

GitHub
[https://github.com/ImGJUser1/SentinelML-Tool](https://github.com/ImGJUser1/SentinelML-Tool)

---


