autoresearch-multi 0.1.0

Combinatorial ML pipeline search with pruning, interaction detection, and LLM proposals

autoresearch-multi

An extension of Karpathy's autoresearch. Accepts additional ML frameworks, tests more combinations, wastes fewer runs, and feeds every result back into the next decision.

What it does

1. Decomposes Python ML pipelines into independent slots (load_data, engineer_features, build_model, evaluate) and tests combinations. Detects the framework from imports and generates appropriate model variants. Supports sklearn, PyTorch, TensorFlow, Keras, HuggingFace, and statsmodels.

2. Prunes dead ends. After the first round, losing model families are killed, dominated impls within surviving families are archived, and impls that crash 3 times are auto-skipped. EXPLOIT, COMBINE, and NARROW skip all of them.

3. Detects interactions. Finds superadditive effects that sequential testing misses, then uses them to prioritize what to try next:

   Title features solo:    +2.79%
   XGBoost solo:           +0.00%
   Predicted combo:        +2.79%
   Actual combo:           +3.91%
   Interaction delta:      +1.12% (superadditive)
   

4. Adaptive search with 4 modes. Each round feeds results into the next. Auto-selects Optuna Bayesian when search space >20 variants.

   EXPLORE   coverage guarantee: one per model family x encoding
   EXPLOIT   Optuna Bayesian (large space) or grid (small space)
   COMBINE   near-miss pairs, sorted by prior interaction signal
   NARROW    fine-tune within winning region
   

5. Proposes code via LLM (when API key is set). The LLM sees per-slot rankings, interaction effects, and dead families, not just flat results. Proposals are tested directly against the current best combo instead of restarting search. Code diffs show what changed.

6. Works on any data. CSV, OpenML dataset, or multi-file Python pipelines.

Install

pip install autoresearch-multi

With optional frameworks:

pip install autoresearch-multi[torch]    # + PyTorch
pip install autoresearch-multi[keras]    # + TensorFlow/Keras
pip install autoresearch-multi[llm]      # + Claude API
pip install autoresearch-multi[all]      # everything

Or from source:

git clone https://github.com/agalimova/autoresearch-multi.git
cd autoresearch-multi
pip install -e .

Usage

autoresearch my_data.csv                   # point at a CSV
autoresearch my_pipeline.py                # point at a Python file (auto-decomposes)
autoresearch --dataset adult               # use any OpenML dataset

Without an API key: template-based search. With any LLM key, it proposes novel model code and iteratively improves. Supports Claude, GPT-4o, Gemini, OpenRouter, Together AI, Deepseek, and Ollama (local, no key needed).

Results

LLM mode

Claude proposes novel code (ensembles, custom feature engineering) and iteratively improves across 3 rounds. Example on King-Rook vs King-Pawn, a chess endgame dataset (36 categorical columns, binary classification):

Templates:      98.06%  (24 combos, 21s)
LLM Round 1:    99.40%  (+1.33%)
LLM Round 2:    99.44%  (+0.04%)
LLM Round 3:   100.00%  (+0.56%, perfect accuracy)

Template vs LLM (same datasets)

Dataset	Baseline	Template	Percentile	With Claude	Percentile	Combos	Time
heart-statlog	84.81%	84.81%	top 25%	97.33%	top 1%	45	114s
kr-vs-kp	93.31%	98.06%	top 5%	100.00%	top 1%	48	133s
diabetes	77.22%	77.22%	top 5%	77.74%	top 5%	33	94s

Features from the community

Feature	Source
Entity-level code diffs	sem
Optuna exploit	soveshmohapatra
Resume support	buzypi
Warm-start checkpoints	soveshmohapatra
Hardware auto-detection	elementalcollision
Scaling law	Sreebhargavibalijaa
TUI dashboard	elementalcollision
Experiment dedup	mutable-state-inc

Project structure

run.py                 one command, any data
engine/
  adaptive.py          4-mode search + Optuna
  variants.py          model variant generation (sklearn, pytorch, tf, keras, hf, statsmodels)
  llm_proposer.py      LLM code proposal (Claude, GPT, Gemini)
  semantic_diff.py     entity-level code diffs (for LLM mode)
  decompose.py         auto-split Python files into slots
  slots/runner.py      combinatorial slot testing
  slots/registry.py    version tracking + interaction detection
  hardware.py          hardware auto-detection
  dashboard.py         TUI dashboard
  checkpoint.py        warm-start best model across combos (PyTorch, Keras)
  scaling.py           swarm scaling law
  merge.py             multi-agent A/B merge
extras/
  telemetry/           PII-stripped experiment telemetry (opt-out)

Telemetry

Collects anonymous experiment telemetry by default. PII auto-stripped from all text (names, emails, paths, IPs, credit cards).

Disable: export AUTORESEARCH_TELEMETRY=0

License

MIT