brain-automl 0.0.7

Multimodal Decision Intelligence Engine

Brain-AI: Multimodal Decision Intelligence Engine

• Introduction
• Features
• Installation
• Contribution
• Future Improvements

Introduction

Brain-AI is a Multimodal Decision Intelligence Engine.

It is intentionally not a model-training framework by itself. Instead, it decides how multimodal pipelines are constructed, then executes those decisions through modular runtime components.

Pipeline decisions are represented as:

PipelineSpec = (modalities, granularity_strategy, fusion_strategy, model_backend, hyperparameters)

🚀 What it does

• Generates valid pipeline combinations through a dedicated decision layer.
• Executes multimodal pipelines through the Brain facade.
• Supports interchangeable granularity, fusion, and model adapter strategies.
• Produces DAG visualizations and structured experiment results.

🔥 Why it’s different

• Explicit pipeline decision intelligence, separate from model backends.
• Modular architecture with single responsibility per module.
• User overrides always take precedence over automatic component resolution.

🧩 Features

• Granularity strategies: resample, pooling, attention-style alignment.
• Fusion strategies: early, late, intermediate.
• Thin model adapters: sklearn, AutoGluon scaffold, TPOT scaffold.
• Decision engine for generating and resolving PipelineSpec combinations.
• Experiment runner and leaderboard utilities.
• DAG generation for pipeline visualization.
• LLM skills for controlled external orchestration.

🧠 LLM Skills

Skills expose safe, structured entry points for LLMs to generate specs, run experiments, compare models and visualize DAGs via brain_ai/agents/skills.

🏗 Architecture Overview

The codebase follows SRP and explicit boundaries:

• brain_ai/core: orchestrator (Brain) and compatibility exports.
• brain_ai/decision: PipelineSpec, combination generation, component resolution.
• brain_ai/granularity: alignment strategies.
• brain_ai/fusion: feature fusion strategies.
• brain_ai/models: thin backend adapters only.
• brain_ai/experiments: evaluator, runner, leaderboard.
• brain_ai/dag: DAG building and visualization.
• brain_ai/rl: policy search scaffolding (state, action, reward) with no algorithmic implementation.
• brain_ai/agents: planner/executor and LLM skills.
• brain_ai/utils: shared helpers (including synthetic multimodal dataset generator).

Design constraints enforced:

• No circular imports.
• No hidden coupling between granularity and fusion.
• Adapters remain thin wrappers.
• User override components always win over auto-resolved components.

For full developer docs, see the subpackage READMEs under brain_ai/.

Installation

⚡ Quick Start

Install the package (from source):

pip install -e .

From PyPI

This project is available at PyPI. For help in installation check instructions

python3 -m pip install brain-automl

Example usage:

from brain_ai.decision.spec import PipelineSpec
from brain_ai.core.brain import Brain
from brain_ai.decision.engine import DecisionEngine

spec = PipelineSpec(modalities=["tabular"], granularity_strategy="resample",
                    fusion_strategy="early", model_backend="sklearn", hyperparameters={})

brain = Brain(decision_engine=DecisionEngine())
# run with multimodal data: {"modalities": {...}, "y": ...}

Validation and Testing

Run the automated suite:

pytest -q

Covered checks include:

• different fusion strategies produce different outputs
• different granularity strategies produce different outputs
• full strategy combinations execute without error
• user overrides are respected
• DAG artifact generation works

Notebook walkthrough:

• examples/multimodal_test.ipynb

Development Setup (Local Installation)

For development or running examples locally, follow these steps:

# Create virtual environment
python3 -m venv .venv

# Activate environment
source .venv/bin/activate  # macOS/Linux
# OR
.\.venv\Scripts\Activate   # Windows

# Install dependencies
pip install --upgrade pip setuptools wheel
pip install -r requirements.txt
pip install -e .

Notebook Example

Use the end-to-end notebook walkthrough at:

• examples/multimodal_test.ipynb

The notebook includes:

• synthetic multimodal dataset generation
• pipeline combination generation
• batch pipeline execution and comparison table
• DAG generation for the best pipeline

If you are new to this repository, start with that notebook before exploring lower-level modules.

Coverage

Run tests with coverage:

pytest --cov=brain_ai --cov-report=term-missing

Important links

• Documentation
• Quick tour
• Project maintainer (feel free to contact)
• Future Improvements
• License

Contribution

all kinds of contributions are appreciated.

• Improving readability of documentation
• Feature Request
• Reporting bugs
• Contribute code
• Asking questions in discussions