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Pattern Analysis Learner - Placeholder Implementation
This module will be fully implemented in Task 9
"""
from typing import Dict, Any
from sqlalchemy.orm import Session
class PatternAnalysisLearner:
"""
Analyzes patterns in completed interviews to identify predictive questions
and update selection weights.
NOTE: This is a placeholder implementation for Task 12 (API endpoints).
Full implementation will be completed in Task 9.
"""
def __init__(self, db_session: Session, min_dataset_size: int = 50):
"""
Initialize pattern analysis learner.
Args:
db_session: Database session
min_dataset_size: Minimum number of completed interviews required
"""
self.db_session = db_session
self.min_dataset_size = min_dataset_size
self.low_value_threshold = 0.3
def analyze_patterns(self) -> Dict[str, Any]:
"""
Run pattern analysis on completed interviews.
This is a placeholder that returns mock results.
Full implementation in Task 9 will:
1. Query completed interviews with outcomes
2. Group by outcome (hired/rejected)
3. Identify question sequences in successful interviews
4. Calculate topic/type correlations with success
5. Update question selection weights
6. Flag low-value questions
Returns:
Dictionary with analysis results
"""
# Placeholder implementation
return {
"interviews_analyzed": 0,
"questions_updated": 0,
"low_value_questions": [],
"message": "Pattern analysis not yet implemented (Task 9)"
}
def identify_predictive_questions(self, interviews) -> Dict[int, float]:
"""
Identify which questions best predict success.
Placeholder implementation.
Args:
interviews: List of completed interviews
Returns:
Dictionary mapping question_id to predictive_score
"""
return {}
def update_selection_weights(self, predictive_scores: Dict[int, float]):
"""
Update question selection weights in database.
Placeholder implementation.
Args:
predictive_scores: Dictionary mapping question_id to predictive_score
"""
pass
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