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Question Effectiveness Tracker for monitoring and calculating question effectiveness metrics
"""
import asyncio
import statistics
from datetime import datetime, timedelta, timezone
from typing import Dict, List, Optional
from sqlalchemy.orm import Session
from sqlalchemy import func
from models.question_metrics import QuestionMetrics
from models.conversation_entry import ConversationEntry
from modules.monitoring import get_monitor
class QuestionEffectivenessTracker:
"""
Tracks question effectiveness metrics and provides scoring for question selection.
This class records question usage asynchronously, calculates effectiveness scores
based on variance, completion rate, and time to answer, and provides caching
for frequently accessed metrics.
"""
def __init__(self, db_session: Session, cache: Optional[Dict] = None, config: Optional[Dict] = None):
"""
Initialize the Question Effectiveness Tracker.
Args:
db_session: SQLAlchemy database session
cache: Optional cache dictionary for storing metrics (defaults to empty dict)
config: Optional configuration dictionary with keys:
- min_samples: Minimum number of samples before calculating effectiveness (default: 10)
- cache_ttl: Cache time-to-live in seconds (default: 3600 = 1 hour)
- variance_weight: Weight for variance in effectiveness formula (default: 0.6)
- completion_weight: Weight for completion rate (default: 0.3)
- time_weight: Weight for time to answer (default: 0.1)
- time_normalizer: Normalizer for time to answer in seconds (default: 300)
"""
self.db_session = db_session
self.cache = cache if cache is not None else {}
self.monitor = get_monitor()
# Configuration with defaults
config = config or {}
self.min_samples = config.get('min_samples', 10)
self.cache_ttl = config.get('cache_ttl', 3600) # 1 hour in seconds
self.variance_weight = config.get('variance_weight', 0.6)
self.completion_weight = config.get('completion_weight', 0.3)
self.time_weight = config.get('time_weight', 0.1)
self.time_normalizer = config.get('time_normalizer', 300) # 5 minutes in seconds
async def record_usage(
self,
question_id: int,
score: float,
time_to_answer: int,
completed: bool
) -> None:
"""
Record question usage asynchronously and update metrics.
This method updates the question metrics in the database by:
- Incrementing usage count
- Updating score statistics for variance calculation
- Updating completion rate
- Updating average time to answer
Args:
question_id: ID of the question used
score: Score received for the answer (0.0 to 1.0)
time_to_answer: Time taken to answer in seconds
completed: Whether the question was completed (not skipped)
"""
try:
# Run database update in thread pool to avoid blocking
await asyncio.get_event_loop().run_in_executor(
None,
self._update_metrics_sync,
question_id,
score,
time_to_answer,
completed
)
# Invalidate cache for this question
cache_key = f"effectiveness_{question_id}"
if cache_key in self.cache:
del self.cache[cache_key]
except Exception as e:
# Log error but don't raise - we don't want to block interview flow
print(f"Error recording usage for question {question_id}: {e}")
def _update_metrics_sync(
self,
question_id: int,
score: float,
time_to_answer: int,
completed: bool
) -> None:
"""
Synchronous method to update metrics in database.
This is called by record_usage in a thread pool executor.
"""
try:
# Get or create metrics record
metrics = self.db_session.query(QuestionMetrics).filter(
QuestionMetrics.question_id == question_id
).first()
old_effectiveness = metrics.effectiveness_score if metrics else 0.0
if not metrics:
metrics = QuestionMetrics(question_id=question_id)
self.db_session.add(metrics)
# Get all scores for this question to calculate variance
scores = self.db_session.query(ConversationEntry.score).filter(
ConversationEntry.question_id == question_id,
ConversationEntry.score.isnot(None)
).all()
scores = [s[0] for s in scores] + [score] # Include current score
# Calculate variance if we have enough samples
if len(scores) >= 2:
metrics.score_variance = statistics.variance(scores)
else:
metrics.score_variance = 0.0
# Update usage count
metrics.usage_count += 1
# Update completion rate
total_attempts = self.db_session.query(func.count(ConversationEntry.id)).filter(
ConversationEntry.question_id == question_id
).scalar() or 0
completed_attempts = self.db_session.query(func.count(ConversationEntry.id)).filter(
ConversationEntry.question_id == question_id,
ConversationEntry.answer_text.isnot(None),
ConversationEntry.answer_text != ''
).scalar() or 0
if completed:
completed_attempts += 1
total_attempts += 1
metrics.completion_rate = completed_attempts / total_attempts if total_attempts > 0 else 1.0
# Calculate effectiveness score
new_effectiveness = self.calculate_effectiveness(question_id)
metrics.effectiveness_score = new_effectiveness
# Commit changes
self.db_session.commit()
# Log effectiveness update to monitoring
self.monitor.log_effectiveness_updated(
question_id=question_id,
old_score=old_effectiveness,
new_score=new_effectiveness,
usage_count=metrics.usage_count
)
except Exception as e:
self.db_session.rollback()
print(f"Error in _update_metrics_sync for question {question_id}: {e}")
raise
def calculate_effectiveness(self, question_id: int) -> float:
"""
Calculate effectiveness metric for a question.
Formula:
effectiveness = (score_variance * 0.6) + (completion_rate * 0.3) + (avg_time / 300 * 0.1)
Returns 0.0 if insufficient samples (< min_samples).
Args:
question_id: ID of the question
Returns:
Effectiveness score (0.0 to ~1.0, though can exceed 1.0 in edge cases)
"""
try:
# Get metrics from database
metrics = self.db_session.query(QuestionMetrics).filter(
QuestionMetrics.question_id == question_id
).first()
# Return 0.0 if no metrics or insufficient samples
if not metrics or metrics.usage_count < self.min_samples:
return 0.0
# Get average time to answer from conversation entries
avg_time_result = self.db_session.query(
func.avg(ConversationEntry.time_to_answer)
).filter(
ConversationEntry.question_id == question_id,
ConversationEntry.time_to_answer.isnot(None)
).scalar()
avg_time = avg_time_result if avg_time_result is not None else 0
# Handle edge cases
variance = metrics.score_variance if metrics.score_variance is not None else 0.0
completion = metrics.completion_rate if metrics.completion_rate is not None else 1.0
# Normalize time (cap at normalizer value to prevent excessive weight)
time_component = min(avg_time / self.time_normalizer, 1.0) if self.time_normalizer > 0 else 0.0
# Calculate effectiveness using weighted formula
effectiveness = (
variance * self.variance_weight +
completion * self.completion_weight +
time_component * self.time_weight
)
return effectiveness
except Exception as e:
print(f"Error calculating effectiveness for question {question_id}: {e}")
return 0.0
def get_effectiveness_scores(self, question_ids: List[int]) -> Dict[int, float]:
"""
Get effectiveness scores for multiple questions with caching.
Uses cache when available (TTL 1 hour), queries database otherwise.
Args:
question_ids: List of question IDs
Returns:
Dictionary mapping question_id to effectiveness score
"""
results = {}
questions_to_query = []
current_time = datetime.now(timezone.utc)
# Check cache first
for question_id in question_ids:
cache_key = f"effectiveness_{question_id}"
if cache_key in self.cache:
cached_data = self.cache[cache_key]
cache_time = cached_data.get('timestamp')
# Check if cache is still valid (within TTL)
if cache_time and (current_time - cache_time).total_seconds() < self.cache_ttl:
results[question_id] = cached_data['score']
# Log cache hit
self.monitor.log_cache_hit(cache_key)
else:
# Cache expired, need to refresh
self.monitor.log_cache_miss(cache_key)
questions_to_query.append(question_id)
else:
# Not in cache
self.monitor.log_cache_miss(cache_key)
questions_to_query.append(question_id)
# Query database for non-cached questions
if questions_to_query:
for question_id in questions_to_query:
try:
score = self.calculate_effectiveness(question_id)
results[question_id] = score
# Update cache
cache_key = f"effectiveness_{question_id}"
self.cache[cache_key] = {
'score': score,
'timestamp': current_time
}
except Exception as e:
print(f"Error getting effectiveness for question {question_id}: {e}")
results[question_id] = 0.0
return results
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