https://github.com/aalexuser/fedot-assistant
LLM-based multi-AutoML Orchestrator
https://github.com/aalexuser/fedot-assistant
Last synced: 21 days ago
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LLM-based multi-AutoML Orchestrator
- Host: GitHub
- URL: https://github.com/aalexuser/fedot-assistant
- Owner: AaLexUser
- License: bsd-3-clause
- Created: 2025-03-06T13:26:05.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-07-21T07:57:27.000Z (8 months ago)
- Last Synced: 2025-07-21T09:31:41.541Z (8 months ago)
- Language: Python
- Homepage: https://deepwiki.com/AaLexUser/Fedot-assistant
- Size: 12.6 MB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# FEDOT.ASSISTANT
[](https://itmo.ru/)
[](https://deepwiki.com/AaLexUser/Fedot-assistant)
FEDOT.ASSISTANT is an LLM-based prototype for next-generation AutoML. It combines the power of Large Language Models with automated machine learning techniques to enhance data analysis and pipeline building processes.
## 🆕 What's New
- CAAFE integration: LLM-driven feature engineering for tabular classification tasks. Enabled by default via `feature_transformers.enabled_models: [CAAFE]`. Requires installing the optional dependency group and setting an API key (see below).
## 💾 Installation
1. Install uv (A fast Python package installer and resolver):
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
2. Clone the repository:
```bash
git clone https://github.com/AaLexUser/Fedot-assistant.git
cd Fedot-assistant
```
3. Create a new virtual environment and activate it:
```bash
uv venv --python 3.10
source .venv/bin/activate # On Unix/macOS
# Or on Windows:
# .venv\Scripts\activate
```
4. Install dependencies:
```bash
uv sync
```
Optional (to use CAAFE feature generation):
```bash
uv sync --group caafe
```
## 🔧 Configuration
### Environment Setup
Set your OpenAI API key:
```bash
export FEDOTLLM_LLM_API_KEY="your-api-key-here"
```
Optional (CAAFE feature generation uses its own LLM settings; you can also put these into a `.env` file):
```bash
export CAAFE_LLM_API_KEY="your-api-key-here"
# Optional overrides (defaults work with the example config)
export CAAFE_LLM_MODEL="openai/gemini-2.0-flash"
export CAAFE_LLM_BASE_URL="https://generativelanguage.googleapis.com/v1beta/openai/"
```
### Configuration Options
The system uses YAML configuration files you can customize. The default configuration is located at `fedotllm/configs/default.yaml`. You can create your own configuration file and specify it using the `--config-path` option.
## 🚀 Quick Start
### Basic Usage
```bash
# Run with default settings
fedotllm /path/to/your/task/directory
# Use specific presets
fedotllm /path/to/your/task/directory --presets best_quality
# Custom configuration
fedotllm /path/to/your/task/directory --config-path config.yaml
# Override specific settings
fedotllm /path/to/your/task/directory -o automl.enabled=fedot -o time_limit=7200
```
### Enable or configure CAAFE (optional)
CAAFE performs LLM-driven feature engineering and currently supports classification tasks only.
```bash
# Ensure optional deps are installed
uv sync --group caafe
# Run with CAAFE enabled (default), customizing parameters on the fly
fedotllm /path/to/task \
-o "feature_transformers.enabled_models=[CAAFE]" \
-o "feature_transformers.models.CAAFE.num_iterations=5" \
-o "feature_transformers.models.CAAFE.optimization_metric=roc" \
-o "feature_transformers.models.CAAFE.eval_model=lightgdm" # or tab_pfn
```
### Task Directory Structure
Your task directory should contain:
- Training data file (e.g., `train.csv`)
- Test data file (e.g., `test.csv`)
- Sample submission file (e.g., `sample_submission.csv`)
- Task description file (e.g., `descriptions.txt`)
### Quality Presets
- `medium_quality`: Fast execution with good performance
- `best_quality`: Maximum accuracy (default)
## 🙌 Acknowledgement
Our implementation adapts code from [AutoGluon Assistant](https://github.com/autogluon/autogluon-assistant). We thank authors of this project for providing high quality open source code!