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f718f5f954
The quota ceiling was closed-form only. This adds per-node stake so a run can show nodes actually breaking it. - assign_stake: uniform, or heavy-tailed zipf (s ~ 1/rank^a), which is what makes the ceiling bite -- the head sits orders of magnitude above it, the tail far below; - inferred_alpha: converts true relative stake to the sigma/D_hat the lottery actually weighs, so a low estimate inflates every node alpha; - simulate_epoch_emissions: measures the budget over a full epoch. Overrun happens at epoch scale and needs no graph, so this is cheap: proposals are Binomial over the epoch slots, a proposal cancels the next cover, and a node stays at exactly its quota until its wins no longer fit -- at which point it emits more often than everyone else, which is the signal cover traffic exists to suppress. Measured against the closed form at N=20,000, zipf stake, over an epoch: the predicted ceiling falls inside the transition band every time, and at D_hat/D = 1 the smallest overrunning node sits at 0.1468% against a predicted 0.1475%. The D_hat/D normalisation is confirmed empirically -- deflating the estimate to 0.64 pulls the measured ceiling down with it, as the (D_hat/D)*alpha_max form requires. With heavy-tailed stake 99.7% of nodes comply and only the head breaks; the largest holder at 9.5% stake is some 65x over its allowance. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
67 lines
2.9 KiB
Python
67 lines
2.9 KiB
Python
import dataclasses
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import pytest
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from pd.config import SimConfig, SweepConfig
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def test_key_covers_every_field():
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fields = [f.name for f in dataclasses.fields(SimConfig)]
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assert len(SimConfig().key()) == len(fields)
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# n_regions=2 in the base so the region/churn fields can each be varied on their own
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# (region_locality and churn_mode="regional" both require n_regions >= 2)
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base = SimConfig(n_regions=2)
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for name in fields:
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cur = getattr(base, name)
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alt = {"n_nodes": 2000, "degree": 4, "n_regions": 4, "region_locality": 0.5,
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"blend_hops": 2, "max_blend_delay": 5,
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"unresponsive_frac": 0.2, "churn_mode": "regional", "redundancy": 2,
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"cover_rate_mult": 2.0, "block_interval_slots": 60, "slots_per_epoch": 1000,
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"stake_inference_ratio": 0.7, "traffic_window_slots": 100,
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"stake_dist": "zipf", "stake_zipf_a": 1.5,
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"n_rounds": 10, "transport_jitter_mean_ms": 1.0,
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"processing_lags_ms": (11.0, 51.0, 101.0), "processing_lag_probs": (0.6, 0.3, 0.1),
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"link_latency_dist": "fixed", "link_latency_mean_ms": 1.0,
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"coverage_pcts": (25.0,), "f_adv": 0.1, "adversary_mode": "worstcase_coverage",
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"n_placements": 1, "worstcase_max_n": 5, "graph_seed": 99, "replicate": 1,
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"root_seed": 7}[name]
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assert alt != cur
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assert dataclasses.replace(base, **{name: alt}).key() != base.key(), name
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@pytest.mark.parametrize("kw", [
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{"n_nodes": 999}, # odd
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{"degree": 1000}, # >= n
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{"blend_hops": 0}, # < 1
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{"f_adv": 1.0}, # >= 1
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{"max_blend_delay": -1},
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{"processing_lag_probs": (0.5, 0.4)}, # doesn't sum to 1 (with default 3 lags -> len mismatch)
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{"link_latency_dist": "bogus"},
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{"adversary_mode": "bogus"},
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])
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def test_validation_rejects(kw):
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with pytest.raises(ValueError):
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SimConfig(**kw)
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def test_sweep_grids_and_collapse():
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sw = SweepConfig(n_nodes=[1000, 10000], degree=[4, 8], blend_hops=[2, 3],
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max_blend_delay=[0, 3], f_adv=[0.0, 0.2],
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adversary_mode=["random", "worstcase_coverage"], seeds=3)
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assert len(sw.graph_cells()) == 2 * 2 * 3
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assert len(sw.prop_grid()) == 2 * 2
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# f_adv=0 collapses to a single (mode-irrelevant) row; f_adv=0.2 keeps both modes
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assert sw.adv_grid() == [(0.0, "random"), (0.2, "random"), (0.2, "worstcase_coverage")]
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def test_from_dict_rejects_unknown():
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with pytest.raises(ValueError):
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SweepConfig.from_dict({"nonsense": [1]})
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def test_base_config_coerces_tuples():
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sw = SweepConfig(base={"processing_lags_ms": [10.0, 90.0], "processing_lag_probs": [0.3, 0.7]})
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cfg = sw.base_config(1000, 8, 0)
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assert cfg.processing_lags_ms == (10.0, 90.0)
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assert isinstance(cfg.key(), tuple)
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