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README.md

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+---
+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 lookup table 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.
+
+The portfolios were computed **offline** by fitting Syne Tune's `ZeroShotTransfer` scheduler to the
+**BBOmix** benchmark: about 105,000 AUTOENCODIX training runs across several architectures, two
+datasets and many downstream tasks. At call time no optimizer runs. The package just looks up the
+JSON and validates the result with Pydantic.
+
+## 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"]
+```
+
+The package downloads `initial_configs.json` from here on first use and caches it 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
+```
+
+See the *Propose Initial Config* tutorial in the AUTOENCODIX docs for all options (`objective`,
+`budget_epochs`, `dataset`, `top_k`, overrides).
+
+## 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` |
+
+### Publishing a new version (maintainers)
+
+```bash
+hf upload autoencodix/initial-configs initial_configs.json initial_configs.json \
+   --repo-type dataset --commit-message "Portfolio v2: <what changed>"
+hf repo tag create autoencodix/initial-configs v2 --repo-type dataset -m "<what changed>"
+```
+
+Then add a row to the table above. The new file must keep the layout described below. The
+package checks dataset names and budgets against whatever the file contains, 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).
+
+## License
+
+Apache-2.0, the same license as the AUTOENCODIX package.