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d3a24e0 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | """Evaluation metrics for ARC-AGI-3 agent performance.
Tracks per-game/level scores, action efficiency vs. human baseline,
and world-model prediction quality (AUC, calibration).
"""
from __future__ import annotations
import logging
from collections import defaultdict
import numpy as np
logger = logging.getLogger(__name__)
class EvalMetrics:
"""Tracks and computes evaluation metrics.
Records per-step data during gameplay and computes aggregate
metrics: score progression, action distribution, efficiency,
state coverage, and model quality.
Attributes:
steps: List of per-step records.
"""
def __init__(self) -> None:
"""Initialize an empty metrics tracker."""
self.steps: list[dict] = []
def record_step(
self,
score: float,
action: str,
**extra: object,
) -> None:
"""Record a single step's metrics.
Args:
score: Current game score.
action: Action taken.
**extra: Additional metrics (novelty, confidence, etc.).
"""
self.steps.append({
"score": score,
"action": action,
**extra,
})
def compute(self) -> dict:
"""Compute aggregate metrics.
Returns:
Dict with:
- total_steps: Number of actions taken.
- final_score: Last recorded score.
- action_distribution: Dict of action → count.
- unique_actions: Number of distinct actions used.
- score_delta: Final score - initial score.
"""
if not self.steps:
return {
"total_steps": 0,
"final_score": 0.0,
"action_distribution": {},
"unique_actions": 0,
"score_delta": 0.0,
}
action_counts: dict[str, int] = defaultdict(int)
for step in self.steps:
action_counts[step["action"]] += 1
scores = [s["score"] for s in self.steps]
initial_score = scores[0] if scores else 0.0
final_score = scores[-1] if scores else 0.0
return {
"total_steps": len(self.steps),
"final_score": final_score,
"action_distribution": dict(action_counts),
"unique_actions": len(action_counts),
"score_delta": final_score - initial_score,
}
def compute_efficiency(metrics: dict, max_actions: int) -> dict:
"""Compute action efficiency relative to the action budget.
ARC-AGI-3 scores are squared and action budgets are capped at
~5× human median. This function computes how efficiently the agent
used its budget.
Args:
metrics: Output of EvalMetrics.compute().
max_actions: The action budget for this level.
Returns:
Dict with:
- budget_used: Fraction of budget used (0–1).
- actions_per_score: Actions per unit of score gained.
- efficiency_score: Composite efficiency metric (0–1, higher is better).
"""
total_steps = metrics.get("total_steps", 0)
score_delta = metrics.get("score_delta", 0.0)
budget_used = total_steps / max(1, max_actions)
actions_per_score = total_steps / max(abs(score_delta), 0.001)
# Efficiency: high score with low budget usage is best
if score_delta > 0:
efficiency_score = score_delta * (1.0 - 0.5 * budget_used)
else:
efficiency_score = 0.0
return {
"budget_used": budget_used,
"actions_per_score": actions_per_score,
"efficiency_score": min(efficiency_score, 1.0),
}
def compute_change_prediction_auc(
predictions: np.ndarray,
labels: np.ndarray,
) -> float:
"""Compute AUC for the world model's change-prediction head.
Args:
predictions: Predicted probabilities (float array).
labels: Binary labels (0 or 1).
Returns:
ROC AUC score (0–1).
"""
if len(predictions) == 0 or len(np.unique(labels)) < 2:
return 0.5
# Sort by prediction descending
order = np.argsort(-predictions)
labels_sorted = labels[order]
# Compute ROC AUC via rank-based formula
n_pos = labels.sum()
n_neg = len(labels) - n_pos
if n_pos == 0 or n_neg == 0:
return 0.5
# Rank sum
ranks = np.zeros(len(predictions))
for i, idx in enumerate(order):
ranks[idx] = len(predictions) - i
sum_ranks_pos = ranks[labels == 1].sum()
auc = (sum_ranks_pos - n_pos * (n_pos + 1) / 2) / (n_pos * n_neg)
return float(auc)
def compare_agents(
baseline: dict,
candidate: dict,
) -> dict:
"""Compare two agents' evaluation results.
Args:
baseline: Baseline agent results.
candidate: Candidate agent results.
Returns:
Dict with per-game and aggregate comparisons.
"""
comparison = {
"games": {},
"aggregate": {},
}
for game_id in baseline.get("games", {}):
if game_id in candidate.get("games", {}):
b = baseline["games"][game_id]
c = candidate["games"][game_id]
comparison["games"][game_id] = {
"score_delta": c["score"] - b["score"],
"action_delta": c["total_actions"] - b["total_actions"],
"win_delta": c["levels_won"] - b["levels_won"],
}
b_agg = baseline.get("aggregate", {})
c_agg = candidate.get("aggregate", {})
comparison["aggregate"] = {
"score_delta": c_agg.get("total_score", 0) - b_agg.get("total_score", 0),
"win_rate_delta": c_agg.get("win_rate", 0) - b_agg.get("win_rate", 0),
"action_delta": c_agg.get("total_actions", 0) - b_agg.get("total_actions", 0),
}
return comparison
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