cascade-custom-miner

custom_miner — the generator you submit

A cascade DataGenerator (generator.Generator) that turns one integer seed into a corpus of univariate float series. The subnet holds the model, seeds, and compute identical between king and challenger, so the only thing that moves your score is the distribution this generator emits.

The idea: compete on prior diversity + realism

The reference/genesis generators win by covering the shapes a forecaster must handle. This one mixes 10 process families, each fully seed-deterministic and vectorised:

The mixture weights (family_weights in config.json) were tuned with local_validator against a broad multi-domain eval: strong seasonal coverage matters (most real series are seasonal) while every family keeps meaningful mass so the prior generalises across non-seasonal domains too.

Contract compliance (what cascade verify checks)

• Determinism — every value comes from one np.random.default_rng(seed) in a fixed draw order → byte-identical corpus at a fixed seed (the property the trainer audits by building twice).
• Code-only — no shipped weights, no network, no clock; imports are numpy + cascade.interface only (on the allowlist, clear of the static-guard blocklist).
• Bounded + finite — 1-D float64 series, length in [min_length, max_length], finite; _sanitize is the hard backstop.
• Fast — vectorised per family (a batched time-axis recurrence, never a per-series Python loop), so draining the full corpus_n_series (16384) stays well under max_generate_seconds.

Verify it yourself:

python -m cascade.miner.cli verify custom/custom_miner
# OK: generator would be accepted by the trainer.
#   corpus_digest (seed=0): 8ecf44e7ebbb601f…  [deterministic]

How to make it yours

1. Tune the mixture — edit family_weights in config.json (no code change), then re-run python -m custom.local_validator and watch the LCB / per-domain win-rate move. This is the cheapest, safest lever.
2. Add/replace a family — add a _yourfamily(rng, n, L) -> (n, L) builder, register it in _FAMILIES / _DEFAULT_WEIGHTS / the builders tuple. Keep it vectorised and seed-deterministic; _sanitize guarantees finiteness.
3. Re-verify + re-A/B every change: verify must stay green and you want the local KOTH verdict trending up before you deploy.

Files

custom_miner/
  generator.py      class Generator(DataGenerator) — the mixture-of-priors
  config.json       name/description, length band, family_weights
  requirements.txt  hash-locked deps (numpy only). The trainer only FORMAT-checks
                    this file (it does not reinstall — the sandbox ships the
                    allowlisted stack), so the placeholder zero-hash is accepted,
                    as in the shipped reference generators. Real hashes optional.