Metadata-Version: 2.4
Name: adaptive-conformal-inference
Version: 0.1.0
Summary: Adaptive Conformal Inference Under Distribution Shift
Author: Emre Kuru
License: MIT
Project-URL: Homepage, https://github.com/emrekuru/aci-python
Project-URL: Paper, https://arxiv.org/abs/2106.00170
Keywords: conformal inference,distribution shift,uncertainty quantification,prediction sets
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.24
Provides-Extra: examples
Requires-Dist: arch>=6.0; extra == "examples"
Requires-Dist: matplotlib>=3.7; extra == "examples"
Requires-Dist: statsmodels>=0.14; extra == "examples"
Requires-Dist: yfinance>=0.2; extra == "examples"
Provides-Extra: dev
Requires-Dist: arch>=6.0; extra == "dev"
Requires-Dist: statsmodels>=0.14; extra == "dev"
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: matplotlib>=3.7; extra == "dev"
Requires-Dist: yfinance>=0.2; extra == "dev"
Dynamic: license-file

# adaptive-conformal-inference

Python implementation of **Adaptive Conformal Inference (ACI)** from:

> Isaac Gibbs and Emmanuel J. Candès. *Adaptive Conformal Inference Under Distribution Shift.* NeurIPS 2021.
> [arXiv:2106.00170](https://arxiv.org/abs/2106.00170)

ACI adapts conformal prediction sets to distribution shift by tracking a single parameter that adjusts the quantile level online, provably achieving target coverage over long time intervals.

## Installation

```bash
pip install adaptive-conformal-inference
```

For running the example scripts (stock data + plots):

```bash
pip install adaptive-conformal-inference[examples]
```

## Quick Start

```python
from aci import ACITracker

# Core: model-agnostic alpha tracker
tracker = ACITracker(alpha=0.1, gamma=0.005)

for t in range(T):
    # Your conformal prediction logic here...
    err_t = 1.0 if y_true not in prediction_set else 0.0
    tracker.update(err_t)
    next_alpha = tracker.alpha_t  # use this for the next prediction set
```

## Modules

| Module | Description |
|--------|-------------|
| `aci.tracker.ACITracker` | Core alpha update (Simple and Momentum rules) |

Non-essential forecasting/data pipelines are intentionally kept in `examples/`:
- `examples/figure1/` (self-contained Figure 1 example files)
- `examples/figure2/` (self-contained Figure 2 example files)

## Reproducing Paper Figures

```bash
# Figure 1: Volatility prediction with normalized score (4 stocks)
python examples/figure1/reproduce.py

# Figure 2: Volatility prediction with unnormalized score (4 stocks)
python examples/figure2/reproduce.py
```

## Reproduced Figures From the Paper 

### Figure 1 (Normalized Score)

| Our reproduction | Paper figure |
|---|---|
| Our Figure 1 | Paper Figure 1 |

### Figure 2 (Unnormalized Score)

| Our reproduction | Paper figure |
|---|---|
| Our Figure 2 | Paper Figure 2 |

## Citation

```bibtex
  @article{gibbs2021adaptive,
    title={Adaptive conformal inference under distribution shift},
    author={Gibbs, Isaac and Candes, Emmanuel},
    journal={Advances in Neural Information Processing Systems},
    volume={34},
    pages={1660--1672},
    year={2021}
  }
```
