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| |
|
|
| from __future__ import annotations |
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|
| from dataclasses import dataclass |
| from typing import Literal |
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| from .statistics import ChannelMetrics |
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|
| EvalPath = Literal["ordinary", "contest", "large_n"] |
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|
| @dataclass(frozen=True, slots=True) |
| class ContestCandidate: |
| index: int |
| route_log_probability: float |
| energy: float |
| standard_error: float |
| walker_tail_std: float |
| in_tie_set: bool |
|
|
| def as_dict(self) -> dict[str, int | float | bool]: |
| return { |
| "index": self.index, |
| "route_log_probability": self.route_log_probability, |
| "energy": self.energy, |
| "standard_error": self.standard_error, |
| "walker_tail_std": self.walker_tail_std, |
| "in_tie_set": self.in_tie_set, |
| } |
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|
|
| @dataclass(frozen=True, slots=True) |
| class ContestResult: |
| winner: int |
| reason: str |
| candidates: tuple[ContestCandidate, ...] |
|
|
| def as_dict(self) -> dict: |
| return { |
| "winner": self.winner, |
| "reason": self.reason, |
| "candidates": [candidate.as_dict() for candidate in self.candidates], |
| } |
|
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|
|
| @dataclass(frozen=True, slots=True) |
| class EvalMetric: |
| step: int |
| energy: float |
| energy_std: float |
| step_walltime: float |
| walltime: float |
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|
|
| @dataclass(frozen=True, slots=True) |
| class EvalResult: |
| path: EvalPath |
| route: tuple[int, ...] |
| route_log_probability: float | None |
| measurements: int |
| walltime_seconds: float |
| energy: ChannelMetrics |
| channels: dict[str, ChannelMetrics] |
| contest: ContestResult | None = None |
|
|
| def as_dict(self) -> dict: |
| result = { |
| "measurements": self.measurements, |
| "walltime_seconds": self.walltime_seconds, |
| "energy": self.energy.mean, |
| "energy_std": self.energy.local_energy_std, |
| "channels": {name: metrics.mean for name, metrics in self.channels.items()}, |
| } |
| if self.energy.lag1_autocorrelation is not None: |
| result["energy_lag1_autocorrelation"] = self.energy.lag1_autocorrelation |
| return result |
|
|
