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d814291 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | from __future__ import annotations
import math
from dataclasses import dataclass, field
@dataclass(slots=True)
class EvalMetrics:
episodes: int = 0
success: int = 0
total_steps: int = 0
total_tool_calls: int = 0
total_redundant_tool_calls: int = 0
total_reward: float = 0.0
deanonymization_total: int = 0
deanonymization_success: int = 0
graph_f1_scores: list[float] = field(default_factory=list)
total_knowledge_carrier: float = 0.0
total_knowledge_indexing: float = 0.0
total_connectivity: float = 0.0
total_format_reward: float = 0.0
total_relation_informativeness: float = 0.0
total_entity_informativeness: float = 0.0
total_diversity: float = 0.0
total_soft_shaping: float = 0.0
total_connectivity_gain: float = 0.0
total_compactness: float = 0.0
total_spawn_count: int = 0
total_spawn_finished_subtasks: int = 0
total_spawn_critical_steps: int = 0
@staticmethod
def _sigmoid_temperature(value: float, temperature: float = 2.0) -> float:
scaled = float(value) / max(1e-6, float(temperature))
if scaled >= 0:
z = math.exp(-scaled)
return 1.0 / (1.0 + z)
z = math.exp(scaled)
return z / (1.0 + z)
def add(self, info: dict, task_type: str, graph_f1: float) -> None:
self.episodes += 1
ok = info.get("agent_answer") == info.get("task_answer")
self.success += int(ok)
self.total_steps += int(info.get("step_count", 0))
self.total_tool_calls += int(info.get("tool_calls", 0))
self.total_redundant_tool_calls += int(info.get("redundant_tool_calls", 0))
self.total_reward += float(info.get("total_reward", 0.0))
self.graph_f1_scores.append(graph_f1)
components = info.get("reward_components", {})
self.total_knowledge_carrier += float(components.get("knowledge_carrier", 0.0))
self.total_knowledge_indexing += float(components.get("knowledge_indexing", 0.0))
self.total_connectivity += float(components.get("connectivity", 0.0))
self.total_format_reward += float(components.get("format_reward", 0.0))
self.total_relation_informativeness += float(components.get("relation_informativeness", 0.0))
self.total_entity_informativeness += float(components.get("entity_informativeness", 0.0))
self.total_diversity += float(components.get("diversity", 0.0))
self.total_soft_shaping += float(components.get("soft_shaping", 0.0))
self.total_connectivity_gain += float(components.get("connectivity_gain", 0.0))
self.total_compactness += float(components.get("compactness", 0.0))
self.total_spawn_count += int(info.get("spawn_count", 0))
self.total_spawn_finished_subtasks += int(info.get("spawn_finished_subtasks", 0))
self.total_spawn_critical_steps += int(info.get("spawn_critical_steps", 0))
if task_type == "identity_resolution":
self.deanonymization_total += 1
self.deanonymization_success += int(ok)
def summary(self) -> dict:
episodes = max(1, self.episodes)
task_success_rate = self.success / episodes
tool_efficiency = 1.0 - (self.total_redundant_tool_calls / max(1, self.total_tool_calls))
avg_graph_f1 = sum(self.graph_f1_scores) / max(1, len(self.graph_f1_scores))
deanonymization_accuracy = self.deanonymization_success / max(1, self.deanonymization_total)
avg_reward_raw = self.total_reward / episodes
avg_reward = self._sigmoid_temperature(avg_reward_raw, temperature=2.0)
avg_knowledge_carrier = self.total_knowledge_carrier / episodes
avg_knowledge_indexing = self.total_knowledge_indexing / episodes
avg_connectivity = self.total_connectivity / episodes
avg_relation_informativeness = self.total_relation_informativeness / episodes
avg_entity_informativeness = self.total_entity_informativeness / episodes
avg_diversity = self.total_diversity / episodes
avg_soft_shaping = self.total_soft_shaping / episodes
avg_connectivity_gain = self.total_connectivity_gain / episodes
avg_compactness = self.total_compactness / episodes
avg_spawn_count = self.total_spawn_count / episodes
spawn_completion = self.total_spawn_finished_subtasks / max(1, self.total_spawn_count)
avg_spawn_critical_steps = self.total_spawn_critical_steps / episodes
spawn_latency_signal = 1.0 / max(1.0, avg_spawn_critical_steps)
spawn_signal = max(0.0, min(1.0, 0.6 * spawn_completion + 0.4 * spawn_latency_signal))
reward_norm = avg_reward
retrieval_signal = max(0.0, min(1.0, 0.5 + 0.35 * avg_knowledge_carrier + 0.35 * avg_knowledge_indexing))
structural_signal = max(
0.0,
min(
1.0,
0.5
+ 0.25 * avg_connectivity
+ 0.20 * avg_relation_informativeness
+ 0.20 * avg_entity_informativeness
+ 0.15 * avg_diversity
+ 0.10 * avg_connectivity_gain,
),
)
leaderboard_score = (
0.28 * task_success_rate
+ 0.20 * avg_graph_f1
+ 0.12 * tool_efficiency
+ 0.12 * deanonymization_accuracy
+ 0.14 * retrieval_signal
+ 0.09 * structural_signal
+ 0.05 * reward_norm
+ 0.04 * spawn_signal
)
return {
"task_success_rate": task_success_rate,
"tool_efficiency": tool_efficiency,
"avg_graph_f1": avg_graph_f1,
"avg_steps_to_solution": self.total_steps / episodes,
"deanonymization_accuracy": deanonymization_accuracy,
"avg_reward": avg_reward,
"avg_knowledge_carrier_reward": avg_knowledge_carrier,
"avg_knowledge_indexing_reward": avg_knowledge_indexing,
"avg_connectivity_reward": avg_connectivity,
"avg_format_reward": self.total_format_reward / episodes,
"avg_relation_informativeness_reward": avg_relation_informativeness,
"avg_entity_informativeness_reward": avg_entity_informativeness,
"avg_diversity_reward": avg_diversity,
"avg_soft_shaping_reward": avg_soft_shaping,
"avg_connectivity_gain_reward": avg_connectivity_gain,
"avg_compactness_reward": avg_compactness,
"avg_spawn_count": avg_spawn_count,
"spawn_completion_rate": spawn_completion,
"avg_spawn_critical_steps": avg_spawn_critical_steps,
"spawn_signal": spawn_signal,
"retrieval_signal": retrieval_signal,
"structural_signal": structural_signal,
"leaderboard_score": leaderboard_score,
}
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