autoencodix/initial-configs

AUTOENCODIX initial-config portfolios

This repository holds the data that autoencodix.propose_initial_config() uses to suggest a strong starting hyperparameter configuration for an AUTOENCODIX model. You don't have to tune from the generic schema defaults.

Everything here comes from the BBOmix benchmark: about 105,000 AUTOENCODIX training runs across several architectures, two datasets and many downstream tasks. There are two kinds of files:

• initial_configs.json: ranked portfolios computed offline by fitting Syne Tune's ZeroShotTransfer scheduler to BBOmix. A default call just looks up this JSON and validates the result with Pydantic; no optimizer runs.
• evaluations/*.parquet + search_space.json (from v2): the BBOmix evaluations themselves, in compact form. They are used when you fix some hyperparameters (fixed={...}). The package then fits a random-forest surrogate on them and proposes the remaining hyperparameters given your fixed values.

Usage

import autoencodix as acx

# newest portfolio (revision "main"), top-ranked config for the downstream objective
config = acx.propose_initial_config("varix")

# pin a published version for reproducibility
config = acx.propose_initial_config("varix", revision="v1")

# list the available versions
acx.tuning.list_portfolio_versions()   # e.g. ["v1", "v2"]

# fix some hyperparameters, propose the rest given them (needs v2 or later)
config = acx.propose_initial_config("varix", fixed={"latent_dim": 16, "batch_size": 64})

The package downloads the files it needs from here on first use and caches them locally with huggingface_hub. After that it also works offline (HF_HUB_OFFLINE=1). You can also use a portfolio of your own:

acx.propose_initial_config("varix", repo_id="<user>/<repo>")                    # your own HF dataset
acx.propose_initial_config("varix", artifact_path="path/to/initial_configs.json")  # local file, no Hub

With artifact_path and fixed, the evaluation files are expected in this repository's layout next to the JSON (search_space.json, evaluations/<architecture>.parquet).

See the Propose Initial Config tutorial in the AUTOENCODIX docs for all options (objective, budget_epochs, dataset, top_k, fixed, overrides).

fixed vs. overrides

Keyword overrides (propose_initial_config("varix", latent_dim=16)) only overwrite that field of the static proposal. The other values stay the ones tuned together with a different latent_dim. fixed={"latent_dim": 16} re-proposes the other hyperparameters for latent_dim=16.

In a leave-one-task-out check on the v2 data, we held out each task, fixed one of latent_dim, n_layers, batch_size or k_filter at several values, and compared proposals on the held-out task among configs with that value. Conditional proposals reached the 87.7th percentile on average, compared with the 72.4th for the static top config + override. Conditional was better in 81% of 546 cases, and it was better on average for every architecture and both objectives.

Versions

main always points to the newest portfolio. So with the default revision="main", proposals can change when a new version is published. Each published portfolio is also tagged, and you can pin that tag.

Tag	Description
v1	Initial BBOmix zero-shot portfolio: vanillix, varix, ontix, disentanglix; datasets tcga, schc
v2	Same portfolio as v1, plus evaluations/*.parquet and search_space.json for conditional proposals (fixed=)

Publishing a new version (maintainers)

The evaluation files are produced by benchmarking/tuning/export_evaluations.py in the AUTOENCODIX repository, from the local Syne Tune blackbox repository.

python benchmarking/tuning/export_evaluations.py --out <dir>      # evaluations/ + search_space.json
hf upload autoencodix/initial-configs <dir> . --repo-type dataset \
   --commit-message "Portfolio v3: <what changed>"
hf repo tag create autoencodix/initial-configs v3 --repo-type dataset -m "<what changed>"

Then add a row to the table above. The files must keep the layouts described below. The package checks dataset names and budgets against whatever the files contain, so you can add new datasets or budgets without a package release.

File layout

initial_configs.json is a single nested JSON object:

{
  "<architecture>": {                     # vanillix | varix | ontix | disentanglix
    "combined": <scope>,                  # pooled across all source datasets (recommended default)
    "<dataset>": <scope>,                 # one BBOmix dataset, e.g. "tcga", "schc"
    ...
  },
  ...
}

<scope> = {
  "downstream":     [<config>, ...],      # ranked best-first, full training budget
  "reconstruction": {
    "<budget_epochs>": [<config>, ...],   # ranked best-first, budgets 10, 25, 50, 100, 150, 200, 300
    ...
  }
}

Each <config> is a flat mapping of AUTOENCODIX config fields. It's passed straight to the matching config class (VanillixConfig, VarixConfig, ...). In v1, every list holds 10 ranked configs.

Architecture	Hyperparameters
vanillix	k_filter, n_layers, enc_factor, latent_dim, batch_size, learning_rate, drop_p, weight_decay, epochs, checkpoint_interval, loss_reduction
varix	as vanillix + beta
ontix	as vanillix + beta, without latent_dim
disentanglix	as vanillix + beta_mi, beta_tc, beta_dimKL

Objectives

• downstream: maximizes an aggregate downstream classification score. The score was only measured at the final epoch, so these configs always use the full training budget (epochs = 300).
• reconstruction: minimizes reconstruction loss, which was recorded every epoch. Each budget key holds configs tuned for training that many epochs (epochs and checkpoint_interval are set to the budget).

search_space.json

{
  "constants": {"epochs": 300, "checkpoint_interval": 300, "loss_reduction": "sum"},  # held constant in BBOmix
  "budgets": [10, 25, 50, 100, 150, 200, 300],    # reconstruction budgets in the evaluation tables
  "architectures": {
    "<architecture>": {
      "<hp>": {"choices": [...], "low": .., "high": ..},              # grid-sampled (integer) hps
      "<hp>": {"scale": "log" | "linear", "low": .., "high": ..},    # continuous hps
      ...
    }
  }
}

low/high are the range actually covered by the evaluations, which can be narrower than the declared BBOmix search space. Only these hyperparameters can be fixed. Fixed values outside the range trigger a warning, because the proposal is then extrapolated.

evaluations/<architecture>.parquet

One row per (task, config). All tasks of a dataset share the same 1000 sampled configs.

Column	Meaning
dataset	BBOmix dataset, e.g. tcga, schc
task	BBOmix task (data modality combination, plus ontology for ontix)
config_id	Index of the config within its dataset
<hp> columns	Hyperparameter values, as in search_space.json
downstream	metric_avg_ml_task_performance at the final epoch, mean over 3 seeds (higher is better)
reconstruction_<b>	metric_valid_recon_loss at epoch b, mean over 3 seeds (lower is better)

License

Apache-2.0, the same license as the AUTOENCODIX package.