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be9f416 92e9724 be9f416 92e9724 be9f416 8270f31 be9f416 92e9724 be9f416 92e9724 be9f416 92e9724 be9f416 92e9724 be9f416 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | """Configuration objects for Ontology Architect experiments."""
from __future__ import annotations
from dataclasses import asdict, dataclass, fields
import json
from pathlib import Path
from typing import Any
@dataclass(frozen=True)
class UniverseConfig:
seed: int = 13
family: str = "thermal_split"
split: str = "train"
observation_window: int = 18
future_window: int = 6
max_steps: int = 8
dt: float = 0.15
sensor_noise: float = 0.04
process_noise: float = 0.01
anomaly_rate: float = 0.08
anomaly_scale: float = 1.4
drift_interval: int = 4
@dataclass(frozen=True)
class SandboxConfig:
mode: str = "subprocess"
container_image: str = "ontology_architect-env:latest"
timeout_seconds: float = 3.0
memory_mb: int = 256
cpus: float = 1.0
allowed_imports: tuple[str, ...] = (
"abc",
"collections",
"dataclasses",
"enum",
"functools",
"itertools",
"json",
"math",
"numpy",
"random",
"scipy",
"statistics",
"typing",
)
@dataclass(frozen=True)
class RewardConfig:
mdl_lambda: float = 0.0005
mdl_complexity_mode: str = "ast"
prediction_sigma: float = 0.12
anomaly_bonus: float = 2.0
drift_bonus: float = 1.0
false_paradigm_shift_penalty: float = 0.25
paradigm_shift_min_structural_delta: float = 0.15
execution_error_penalty: float = -25.0
max_theory_chars: int = 12000
stability_weight: float = 0.3
stability_threshold: float = 0.2
@dataclass(frozen=True)
class FeedbackConfig:
peer_review_window: int = 8
lineage_window: int = 8
divergence_sigma: float = 2.0
@dataclass(frozen=True)
class TrainingConfig:
model_id: str = ""
output_dir: str = "artifacts/checkpoints"
curriculum_path: str = "artifacts/curriculum/oracle.jsonl"
report_dir: str = "artifacts/reports"
group_size: int = 4
max_steps: int = 100
batch_size: int = 1
checkpoint_steps: int = 10
@dataclass(frozen=True)
class ExperimentConfig:
universe: UniverseConfig = UniverseConfig()
sandbox: SandboxConfig = SandboxConfig()
reward: RewardConfig = RewardConfig()
feedback: FeedbackConfig = FeedbackConfig()
training: TrainingConfig = TrainingConfig()
def _coerce_section(section: dict[str, Any] | None, cls: type) -> Any:
section = section or {}
allowed = {field.name for field in fields(cls)}
clean = {key: value for key, value in section.items() if key in allowed}
if cls is SandboxConfig and "allowed_imports" in clean:
clean["allowed_imports"] = tuple(clean["allowed_imports"])
return cls(**clean)
def config_from_dict(data: dict[str, Any] | None) -> ExperimentConfig:
data = data or {}
return ExperimentConfig(
universe=_coerce_section(data.get("universe"), UniverseConfig),
sandbox=_coerce_section(data.get("sandbox"), SandboxConfig),
reward=_coerce_section(data.get("reward"), RewardConfig),
feedback=_coerce_section(data.get("feedback"), FeedbackConfig),
training=_coerce_section(data.get("training"), TrainingConfig),
)
def load_config(path: str | Path | None = None) -> ExperimentConfig:
if path is None:
return ExperimentConfig()
with Path(path).open("r", encoding="utf-8") as handle:
return config_from_dict(json.load(handle))
def save_config(config: ExperimentConfig, path: str | Path) -> None:
target = Path(path)
target.parent.mkdir(parents=True, exist_ok=True)
with target.open("w", encoding="utf-8") as handle:
json.dump(asdict(config), handle, indent=2, sort_keys=True)
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