arrowspace_tuner 0.1.0

Hyperparameter discovery (eps auto-tuning) for ArrowSpace via Optuna.

arrowspace_tuner

Hyperparameter discovery for ArrowSpace — automatically finds the best eps, k, and tau for your corpus using a query-free spectral objective.

Why

ArrowSpace's retrieval quality depends on three graph-construction parameters:

Parameter	What it controls
eps	Neighbourhood radius for graph edges
k	Number of nearest neighbours per node
tau	Search temperature (exploration vs. exploitation)

Setting these by hand is tedious and corpus-dependent. arrowspace_tuner uses Optuna and a label-free spectral MRR proxy to find them automatically in minutes.

Install

# Core (no pandas/plotly)
pip install arrowspace-tuner

# With HTML/CSV reporting
pip install arrowspace-tuner[report]

Quickstart

import numpy as np
import arrowspace_tuner as arrowspace

embeddings = np.load("corpus.npy")   # shape (N, D) float64

# One-liner: auto-discover eps, k, tau — runs in ~15 min on 50k corpus
aspace, gl = arrowspace.optuna(embeddings)

# Search as normal
results = aspace.search(query_embedding, gl, tau=0.8)

Power-user API

from arrowspace_tuner import EpsTuner

tuner = EpsTuner(
    n_trials  = 15,
    sample_n  = 5_000,    # 33x faster: explore on 5k, final build on full corpus
    eps_low   = 0.8,      # narrow bounds if you know your corpus geometry
    eps_high  = 2.5,
    k_low     = 15,
    k_high    = 40,
    tau_low   = 0.05,
    tau_high  = 0.5,
    n_probe   = 50,
    storage   = "sqlite:///tune.db",   # resume interrupted runs
)

aspace, gl = tuner.fit(embeddings)

print(tuner.best_params)    # {"eps": 1.615, "k": 38, "tau": 0.114}
print(tuner.best_score)     # 2.138
print(tuner.best_fiedler)   # 0.718  — graph connectivity health
print(tuner.best_mrr_proxy) # 2.896  — retrieval coherence proxy

# Save CSV + HTML plots (requires [report] extra)
tuner.save_report(out_dir="results")

Speed

The dominant cost is building the ArrowSpace graph on N vectors. With sample_n:

Setting	Per trial	15 trials	Notes
Full corpus (50k)	~23 min	~5.8h	baseline
sample_n=5_000	~1.5 min	~27 min	33x faster, same best params

The final build after the study always uses the full corpus.

Objective

The objective is a weighted composite of three spectral signals — no ground-truth labels required:

score = 0.70 * mrr_top0_spectral   # retrieval coherence
      + 0.20 * log1p(fiedler)      # graph connectivity health
      + 0.10 * log1p(var_lambda)   # spectral richness

Parallel runs

Optuna + SQLite lets you run multiple workers simultaneously:

# Terminal 1
python -m arrowspace_tuner --storage sqlite:///tune.db --trials 15

# Terminal 2 (simultaneously)
python -m arrowspace_tuner --storage sqlite:///tune.db --trials 15

Requirements

• Python ≥ 3.12
• arrowspace >= 0.26.0
• optuna >= 4.8.0
• scipy >= 1.17.1
• numpy >= 2.4.4

License

Apache-2.0 — see LICENSE.