# causaliq-analysis

[![Python Support](https://img.shields.io/pypi/pyversions/zenodo-sync.svg)](https://pypi.org/project/zenodo-sync/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

This repo provides tools for analysing and visualising learned causal graphs, including structural metrics, stability assessment, significance tests, and publication-ready tables and charts.

## Status

🚧 **Active Development** - This repository is currently in active development, which involves:

- migrating functionality from the legacy monolithic [discovery repo](https://github.com/causaliq/discovery) 
- restructuring classes to reduce module size and improve maintainability and improve usability
- ensure CausalIQ development standards are met
- adding new analysis features e.g. graph averaging


## Features

Currently implemented:

- **Release v0.1.0 - Foundation Metrics**: CausalIQ and Bayesys structural graph metrics and KL metric.


Planned releases:

- **Release v0.2.0 - Legacy Trace**: Support for reading and writing structure learning traces in legacy pickle format (this will be superseded by a more open format).
- **Release v0.3.0 - Graph Averaging**: Graph averaging to produce arc probabilities.


## Upcoming Key Innovations

### 🧠 LLM-assisted Graph Averaging
- **Uncertain or conflicting edges** - resolved using LLM queries

### 📊 Publication-ready chart generation
- **Seaborn charts** - flexible, but standardised publication-ready chart generation

### ▦ Publication-ready table generation
- **LaTeX tables** - converts tabular analysis data into publication-ready LaTeX tables

## Integration with CausalIQ Ecosystem

- 🔍 **CausalIQ Discovery** generates causal graphs which this package evaluates and visualises.
- 🤖 **CausalIQ Workflow** can access all features of this package (through the Action interface) so that analysis and visualisation are incorporated into CausalIQ workflows.
- 🧪 **CausalIQ Papers** uses the analysis, table and chart features of this package to generate published paper assets.

## LLM Support

The following provides project-specific context for this repo which should be provided after the [personal and ecosystem context](https://github.com/causaliq/causaliq/blob/main/LLM_DEVELOPMENT_GUIDE.md):

```text
I wish to migrate the code in legacy/core/metrics.py following all CausalIQ development guidelines
so that the legacy repo can use the migrated code instead. 
```

## Quick Start

```python
# to be completed
```

## Getting started

### Prerequisites

- Git 
- Latest stable versions of Python 3.9, 3.10. 3.11 and 3.12


### Clone the new repo locally and check that it works

Clone the causaliq-analysis repo locally as normal

```bash
git clone https://github.com/causaliq/causaliq-analysis.git
```

Set up the Python virtual environments and activate the default Python virtual environment. You may see
messages from VSCode (if you are using it as your IDE) that new Python environments are being created
as the scripts/setup-env runs - these messages can be safely ignored at this stage.

```text
scripts/setup-env -Install
scripts/activate
```

Check that the causaliq-analysis CLI is working, check that all CI tests pass, and start up the local mkdocs webserver. There should be no errors  reported in any of these.

```text
causaliq-analysis --help
scripts/check_ci
mkdocs serve
```

Enter **http://127.0.0.1:8000/** in a browser and check that the 
causaliq-data documentation is visible.

If all of the above works, this confirms that the code is working successfully on your system.


## Documentation

Full API documentation is available at: **http://127.0.0.1:8000/** (when running `mkdocs serve`)

## Contributing

This repository is part of the CausalIQ ecosystem. For development setup:

1. Clone the repository
2. Run `scripts/setup-env -Install` to set up environments  
3. Run `scripts/check_ci` to verify all tests pass
4. Start documentation server with `mkdocs serve`

---

**Supported Python Versions**: 3.9, 3.10, 3.11, 3.12  
**Default Python Version**: 3.11  
**License**: MIT

