| # 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: |
| |
| | family | what it contributes | |
| |--------|--------------------| |
| | `trend_seasonal_ar` | level + slope + multi-seasonal sinusoids + AR(1) noise | |
| | `regime_shift` | piecewise level & variance regimes (structural breaks) | |
| | `multiplicative` | positive level × seasonal factor × multiplicative noise | |
| | `ar2` | AR(2), stationarity-guaranteed (Levinson-Durbin), incl. near-unit-root | |
| | `integrated` | I(1)/I(2) random walks with drift | |
| | `threshold_ar` | SETAR — regime-switching nonlinear recurrence | |
| | `chaotic` | bounded chaotic maps (logistic / sine) | |
| | `rff_gp` | smooth GP-like samples via random Fourier features | |
| | `intermittent` | zero-inflated / intermittent demand | |
| | `pulse_outlier` | smooth base + sparse pulses/outliers + flat gaps | |
|
|
| The mixture weights (`family_weights` in [`config.json`](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: |
|
|
| ```bash |
| 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. |
| ``` |
|
|
