mirror of
https://github.com/open-thought/reasoning-gym.git
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95 lines
No EOL
4 KiB
Python
95 lines
No EOL
4 KiB
Python
from typing import Dict, List, Any
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from dataclasses import dataclass
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from collections import defaultdict
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import numpy as np
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@dataclass
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class AttributeMonitor:
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"""Monitors performance for a specific attribute."""
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window_size: int = 10 # Number of recent problems to track
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warmup_count: int = 10 # Number of problems before starting analysis # TODO: Implement warmup (not just level_history as can go back to level)
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degradation_threshold: float = 0.9 # Threshold for degradation
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std_plateau_threshold: float = 0.1 # Threshold for plateau
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def __post_init__(self):
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self.curriculum = None # Will be set during initialization
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self.attribute_name = None # Will be set during initialization
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self.recent_scores: List[float] = [] # List of recent accuracy scores
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self.level_history: Dict[int, List[float]] = defaultdict(list) # Scores for each difficulty level
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self.best_scores: Dict[int, float] = {} # Best smoothed score achieved at each level
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def initialize(self, curriculum: Any, attribute_name: str):
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"""Initialize monitor with curriculum and attribute."""
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self.curriculum = curriculum
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self.attribute_name = attribute_name
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self.set_level(curriculum.get_attr_level(attribute_name))
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@property
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def current_level(self) -> int:
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"""Get current level from curriculum."""
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return self.curriculum.get_attr_level(self.attribute_name)
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def increment_level(self) -> bool:
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"""Increment difficulty level using curriculum."""
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if self.curriculum.increment_attr_level(self.attribute_name):
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self.recent_scores = [] # Reset scores for new level
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return True
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return False
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def decrement_level(self) -> bool:
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"""Decrement difficulty level using curriculum."""
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if self.curriculum.decrement_attr_level(self.attribute_name):
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self.recent_scores = [] # Reset scores for new level
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return True
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return False
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def set_level(self, level: int):
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"""Set difficulty level using curriculum."""
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self.curriculum.set_attr_level(self.attribute_name, level)
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self.recent_scores = [] # Reset scores for new level
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def add_score(self, score: float):
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"""Add a new score and update metrics."""
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self.recent_scores.append(score)
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if len(self.recent_scores) > self.window_size:
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self.recent_scores.pop(0)
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self.level_history[self.current_level].append(score)
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# Update best score if current moving average is higher
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if len(self.recent_scores) >= self.window_size:
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current_avg = np.mean(self.recent_scores)
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self.best_scores[self.current_level] = max(
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current_avg,
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self.best_scores.get(self.current_level, float('-inf'))
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)
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def get_current_accuracy(self) -> float:
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"""Get the current moving average accuracy."""
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if len(self.recent_scores) < self.window_size:
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return 0.0
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return np.mean(self.recent_scores)
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# TODO: is_*, addscore merge
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def is_improving(self) -> bool:
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"""Check if performance is improving."""
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if len(self.recent_scores) < self.window_size:
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return False
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current_avg = np.mean(self.recent_scores)
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return current_avg > self.best_scores.get(self.current_level, float('-inf'))
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def is_plateau(self) -> bool:
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"""Check if performance has plateaued."""
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if len(self.recent_scores) < self.window_size:
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return False
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recent_std = np.std(self.recent_scores)
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return recent_std < self.std_plateau_threshold
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def is_degrading(self) -> bool:
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"""Check if performance is degrading."""
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if len(self.recent_scores) < self.window_size:
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return False
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current_avg = np.mean(self.recent_scores)
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return current_avg < self.best_scores.get(self.current_level, float('-inf')) * self.degradation_threshold |