mirror of
https://github.com/open-thought/reasoning-gym.git
synced 2026-04-22 16:49:06 +00:00
106 lines
No EOL
4.2 KiB
Python
106 lines
No EOL
4.2 KiB
Python
from typing import Dict, List, Any, Optional, Tuple
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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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from enum import Enum
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class PerformanceTrend(Enum):
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"""Performance trend states for an attribute."""
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INSUFFICIENT_DATA = "insufficient_data"
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IMPROVING = "improving"
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PLATEAU_HIGH_ACC = "plateau_high_acc"
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PLATEAU_LOW_ACC = "plateau_low_acc"
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DEGRADING = "degrading"
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STABLE = "stable"
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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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# TODO: Different vars for different exercises, attributes
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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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high_acc_threshold: float = 0.8 # Threshold for high accuracy
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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) -> PerformanceTrend:
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"""
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Add a new score and analyze the performance trend.
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Returns:
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PerformanceTrend: The current performance trend
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"""
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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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# Not enough data to analyze trends
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if len(self.recent_scores) < self.window_size:
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return PerformanceTrend.INSUFFICIENT_DATA
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current_avg = np.mean(self.recent_scores)
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current_best = self.best_scores.get(self.current_level, float('-inf'))
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# Update best score if current moving average is higher
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if current_avg > current_best:
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self.best_scores[self.current_level] = current_avg
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return PerformanceTrend.IMPROVING
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# Check for plateau
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recent_std = np.std(self.recent_scores)
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if recent_std < self.std_plateau_threshold:
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if current_avg > self.high_acc_threshold:
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return PerformanceTrend.PLATEAU_HIGH_ACC
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else:
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return PerformanceTrend.PLATEAU_LOW_ACC
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# Check for degradation
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if current_avg < current_best * self.degradation_threshold:
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return PerformanceTrend.DEGRADING
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return PerformanceTrend.STABLE
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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) |