cascade base generator (genesis "base" king)

A self-contained cascade data generator that adapts a curated subset of TempoPFN's procedural synthetic time-series priors into a single deterministic Generator(DataGenerator). Intended as the launch/genesis king for the cascade subnet.

What's inside

file	purpose
`generator.py`	`class Generator(DataGenerator)` — the entrypoint
`config.json`	length band, per-family mixing weights, sanitisation knobs
`requirements.txt`	hash-locked, allowlisted deps (`numpy, scipy, pandas, torch, scikit-learn, gpytorch, networkx`)
`tempo_gen/`	vendored, import-rewritten TempoPFN subset (Apache-2.0)
`NOTICE`, `LICENSE`	TempoPFN attribution + Apache-2.0 text
`tests/`	contract tests + a diversity sanity check

Generator families

Ten families are mixed by weight: ForecastPFN, SineWave, SawTooth, Step, Anomaly, Spikes, OrnsteinUhlenbeck, GP-prior, KernelSynth, CauKer. Each is drawn at generate_length (2048) and deterministically random-cropped into the [min_length, max_length] band for length diversity.

The GP/kernel family — GP-prior (gpytorch), KernelSynth (scikit-learn) and CauKer (networkx + scikit-learn) — was added in v2: those deps are now on cascade's allowlist (chain.toml [dependencies]). The TempoPFN ablation shows this family carries a large share of the downstream signal. CauKer is multivariate (an SCM DAG of GP-prior nodes); each channel is flattened into its own univariate series so the emitted corpus stays 1-D. Its upstream GPU (cupy) draw was replaced with NumPy's seeded multivariate_normal to keep the generate path CPU-only and reproducible.

The pyo-backed audio generators remain excluded: pyo runs a real-time audio server and seeds via hash(), both of which break the cross-process determinism contract below (and pyo is not on the allowlist).

Determinism

The corpus is a pure function of (seed, n_series). We seed NumPy / torch / Python random from seed, run torch on CPU with deterministic algorithms, derive every per-family and per-series sub-seed from the master seed, and use a separate seeded RNG for cropping. The upstream hash()-based per-generator seed offset — salted per process by PYTHONHASHSEED — was replaced with a stable zlib.crc32 in tempo_gen/.../abstract_classes.py, so two audit runs in separate processes produce byte-identical corpora.

Verify / test

# from the cascade repo root, with deps installed and cascade importable
cascade verify ./base_generator                       # full trainer-side checks
PYTHONPATH=$PWD pytest base_generator/tests -q           # contract tests
PYTHONPATH=$PWD python base_generator/tests/diversity_check.py

Config knobs (`config.json`)

min_length / max_length — per-series length band (defaults 64 / 2048, matching chain.toml).
generate_length — length generators are drawn at before cropping (>= max_length).
batch_size — internal generation batch size (memory/throughput knob).
weights — per-family mixing weights (normalised; families with weight 0 or absent are skipped).
standardize — z-normalise each series (default false; off preserves scale diversity).
clip_sigma — if > 0, clip each series to mean ± clip_sigma·std (default 0, disabled).
max_abs_value — hard magnitude clip to keep output trainer-safe finite.

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