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Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
98 lines
2.7 KiB
Python
98 lines
2.7 KiB
Python
"""
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Unit Analytics API - Helpers.
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Database access, statistical computation, and utility functions.
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"""
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import os
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import logging
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from typing import List, Dict, Optional
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logger = logging.getLogger(__name__)
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# Feature flags
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USE_DATABASE = os.getenv("GAME_USE_DATABASE", "true").lower() == "true"
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# Database singleton
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_analytics_db = None
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async def get_analytics_database():
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"""Get analytics database instance."""
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global _analytics_db
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if not USE_DATABASE:
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return None
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if _analytics_db is None:
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try:
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from unit.database import get_analytics_db
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_analytics_db = await get_analytics_db()
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logger.info("Analytics database initialized")
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except ImportError:
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logger.warning("Analytics database module not available")
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except Exception as e:
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logger.warning(f"Analytics database not available: {e}")
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return _analytics_db
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def calculate_gain_distribution(gains: List[float]) -> Dict[str, int]:
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"""Calculate distribution of learning gains into buckets."""
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distribution = {
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"< -20%": 0,
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"-20% to -10%": 0,
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"-10% to 0%": 0,
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"0% to 10%": 0,
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"10% to 20%": 0,
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"> 20%": 0,
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}
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for gain in gains:
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gain_percent = gain * 100
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if gain_percent < -20:
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distribution["< -20%"] += 1
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elif gain_percent < -10:
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distribution["-20% to -10%"] += 1
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elif gain_percent < 0:
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distribution["-10% to 0%"] += 1
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elif gain_percent < 10:
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distribution["0% to 10%"] += 1
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elif gain_percent < 20:
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distribution["10% to 20%"] += 1
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else:
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distribution["> 20%"] += 1
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return distribution
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def calculate_trend(scores: List[float]) -> str:
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"""Calculate trend from a series of scores."""
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if len(scores) < 3:
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return "insufficient_data"
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# Simple linear regression
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n = len(scores)
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x_mean = (n - 1) / 2
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y_mean = sum(scores) / n
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numerator = sum((i - x_mean) * (scores[i] - y_mean) for i in range(n))
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denominator = sum((i - x_mean) ** 2 for i in range(n))
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if denominator == 0:
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return "stable"
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slope = numerator / denominator
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if slope > 0.05:
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return "improving"
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elif slope < -0.05:
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return "declining"
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else:
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return "stable"
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def calculate_difficulty_rating(success_rate: float, avg_attempts: float) -> float:
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"""Calculate difficulty rating 1-5 based on success metrics."""
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# Lower success rate and higher attempts = higher difficulty
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base_difficulty = (1 - success_rate) * 3 + 1 # 1-4 range
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attempt_modifier = min(avg_attempts - 1, 1) # 0-1 range
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return min(5.0, base_difficulty + attempt_modifier)
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