| --- |
| license: apache-2.0 |
| pretty_name: AUTOENCODIX initial-config portfolios |
| tags: |
| - autoencodix |
| - hyperparameter-optimization |
| - transfer-learning |
| - zero-shot |
| - multi-omics |
| - autoencoder |
| viewer: false |
| --- |
| |
| # AUTOENCODIX initial-config portfolios |
|
|
| This repository holds the data that |
| [`autoencodix.propose_initial_config()`](https://github.com/jan-forest/autoencodix_package) 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 |
|
|
| ```python |
| 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: |
|
|
| ```python |
| 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. |
|
|
| ```bash |
| 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: |
|
|
| ```text |
| { |
| "<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` |
| |
| ```text |
| { |
| "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. |
|
|
