# dagzoo

`dagzoo` generates reproducible synthetic tabular corpora from sampled causal
structure. The default prior samples a latent DAG, assigns observable features
to latent nodes, selects one latent node for the target, and only later applies
optional missingness as an observation model over emitted features. The stable
adoption layer is a small set of named
`recipe:<name>` configs plus stable artifact contracts. Repo-local authoring
under `configs/` remains available for advanced work, but it is not the
primary public entrypoint.

```mermaid
flowchart LR
    classDef setup fill:#e1f5fe,stroke:#01579b,stroke-width:2px,color:#01579b
    classDef core fill:#fff3e0,stroke:#e65100,stroke-width:2px,color:#e65100
    classDef out fill:#f3e5f5,stroke:#4a148c,stroke-width:2px,color:#4a148c

    Seed([Root Seed]) --> RNG[Deterministic Seeding]
    RNG --> Layout[Layout & DAG Sampling]
    Layout --> Mechanisms[Random Functional Mechanisms]
    Mechanisms --> Converters[Feature Converters]
    Converters --> XComplete[Complete Feature Columns]
    Layout --> TargetNode["Selected Target Node"]
    TargetNode --> Missingness[Observation Model / Missingness]
    XComplete --> Missingness
    Missingness --> Bundle[[DatasetBundle: X_obs, y, Metadata]]

    class Seed,RNG setup
    class Layout,Mechanisms,Converters,TargetNode core
    class Bundle out
```

### From Latent DAG to Tabular Data

Unlike generators that treat each column as independent noise, `dagzoo`
generates both features and target from a latent causal structure. One node in
the sampled graph can branch into multiple observable features, and one
selected latent node emits the target through its converter stack. Optional
missingness can later censor the emitted feature table without changing how
`y` was derived.

```mermaid
flowchart LR
    classDef latent fill:#e1f5fe,stroke:#01579b,stroke-width:2px,color:#01579b,stroke-dasharray: 5 5
    classDef observable fill:#f5f5f5,stroke:#212121,stroke-width:2px,color:#212121

    subgraph LatentSpace [Latent Causal DAG]
        NodeA((Node A)) --> NodeB((Node B))
    end

    subgraph ObservableSpace [Tabular Dataset Layout]
        Feat1[Feature 1: Numeric]
        Feat2[Feature 2: Categorical]
        Feat3[Feature 3: Numeric]
        Target[Target Variable]
    end

    NodeA -. mapping .-> Feat1
    NodeA -. mapping .-> Feat2
    NodeB -. mapping .-> Feat3
    NodeB -. target mapping .-> Target

    class NodeA,NodeB latent
    class Feat1,Feat2,Feat3,Target observable

    style LatentSpace fill:#f0faff,stroke:#01579b,stroke-dasharray: 5 5
    style ObservableSpace fill:#fafafa,stroke:#212121
```

In practice, that means the target comes from one latent node, while optional
missingness only affects the observed feature values emitted afterward.

## Start

Use the packaged CLI when you want the public workflow without a repo checkout.
These are the main `dagzoo` commands most users start with:

```bash
uv tool install dagzoo

# Inspect the curated recipe catalog and see the stable public names.
dagzoo recipe list

# Generate a general-purpose baseline run under data/default_baseline/.
dagzoo generate --config recipe:default-baseline --num-datasets 25 --out data/default_baseline

# Generate a smaller numeric-heavy run with the published TabPFN-style recipe.
dagzoo generate --config recipe:tabpfn-v1-prior-approx --num-datasets 25 --out data/tabpfn_prior
```

Use a repo checkout when you want to edit configs, run docs tooling, or work on
the codebase:

```bash
./scripts/dev bootstrap
source .venv/bin/activate
.venv/bin/nox -s quick
```

For in-process training loops, use the same recipe references through the
PyTorch bridge. `build_dataloader(...)` is the in-process equivalent of running
`dagzoo generate --config recipe:<name>` from the CLI:

```python
from dagzoo import build_dataloader

# Load the same baseline recipe directly into a training loop.
loader = build_dataloader(
    "recipe:default-baseline",
    num_datasets=10,
    seed=7,
    device="cpu",
)
sample = next(iter(loader))
print(sample["X_train"].shape)
```

## Public Surface

If you're new, start with the named recipes. The public surface is small on
purpose:

- `dagzoo recipe list` shows the curated recipe catalog.
- `dagzoo generate --config recipe:<name>` generates datasets from one of those
  published recipes.
- `build_dataloader("recipe:<name>", ...)` gives you the same recipe surface
  inside Python.

`recipe:<name>` is the stable public config handle most users should reach for
first. `recipes/*.yaml` are the published YAML files behind those names, so you
can inspect exactly what a recipe contains. Repo-local `configs/*.yaml` are for
custom local authoring and may change more often than the named recipe surface.

For example, this command generates 25 datasets from the baseline recipe:

```bash
# recipe:default-baseline is the named public config.
# --out chooses the run directory on disk.
dagzoo generate --config recipe:default-baseline --num-datasets 25 --out data/default_baseline
```

That run lands under `data/default_baseline/` because the path is passed to
`--out`.

The human-facing export-contract overview lives in
`docs/output-format.md`. The exhaustive field-by-field catalog lives in
`docs/export-contract-fields.md` and is generated from
`reference/export_contract_inventory.yaml`.

### What Lands on Disk

After that generate command finishes, this is the kind of layout you should
expect under the run root:

```text
data/default_baseline/
  effective_config.yaml
  effective_config_trace.yaml
  shard_00000/
    train.parquet
    test.parquet
    dataset_catalog.ndjson
  internal/
    shard_00000/
      replay_catalog.ndjson
      lineage/
        adjacency.bitpack.bin
        adjacency.index.json
```

The `shard_*` directories hold the stable public dataset artifacts. The
`internal/` tree holds dagzoo-only replay and lineage sidecars used by tooling
such as `dagzoo filter`; it is not the stable public contract.
`effective_config.yaml` records the fully resolved config for the run, and
`effective_config_trace.yaml` records where overrides came from so the run is
reproducible. The full artifact contract lives in `docs/output-format.md`.
The exhaustive field catalog lives in `docs/export-contract-fields.md`.

## Docs

- [Start](docs/start.md)
- [Reference Packs](docs/reference-packs.md)
- [Advanced Controls](docs/usage-guide.md)
- [Artifacts & API](docs/output-format.md)
- [Export Contract Fields](docs/export-contract-fields.md)
- [How It Works](docs/how-it-works.md)
- [Feature Guides](https://bensonlee5.github.io/dagzoo/docs/features/)

## Community

- [Contributing](CONTRIBUTING.md)
- [Security](SECURITY.md)
