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):

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Clone the repository:

git clone https://github.com/AaLexUser/Fedot-assistant.git
cd Fedot-assistant

3. Create a new virtual environment and activate it:

uv venv --python 3.10
source .venv/bin/activate  # On Unix/macOS
# Or on Windows:
# .venv\Scripts\activate

4. Install dependencies:

uv sync

Optional (to use CAAFE feature generation):

uv sync --group caafe

🔧 Configuration

Environment Setup

Set your OpenAI API key:

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):

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

# 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.

# 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. We thank authors of this project for providing high quality open source code!