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Reranking Module
This module uses a cross-encoder model to rerank initial recommendations
and ensures balance between Knowledge (K) and Personality (P) assessments.
"""
import numpy as np
from typing import List, Dict
import logging
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class AssessmentReranker:
"""Reranks recommendations using cross-encoder and ensures K/P balance"""
def __init__(self, model_name: str = 'cross-encoder/ms-marco-MiniLM-L-6-v2'):
self.model_name = model_name
self.model = None
self.tokenizer = None
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
logger.info(f"Reranker using device: {self.device}")
def load_model(self):
"""Load the cross-encoder model"""
try:
logger.info(f"Loading reranking model: {self.model_name}")
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
self.model = AutoModelForSequenceClassification.from_pretrained(self.model_name)
self.model.to(self.device)
self.model.eval()
logger.info("Reranking model loaded successfully")
except Exception as e:
logger.error(f"Error loading model: {e}")
raise
def compute_cross_encoder_score(self, query: str, assessment_text: str) -> float:
"""Compute relevance score using cross-encoder"""
if self.model is None:
self.load_model()
try:
# Tokenize
inputs = self.tokenizer(
query,
assessment_text,
return_tensors='pt',
truncation=True,
max_length=512,
padding=True
)
# Move to device
inputs = {k: v.to(self.device) for k, v in inputs.items()}
# Get score
with torch.no_grad():
outputs = self.model(**inputs)
score = outputs.logits[0][0].item()
return score
except Exception as e:
logger.warning(f"Error computing cross-encoder score: {e}")
return 0.0
def create_assessment_text(self, assessment: Dict) -> str:
"""Create text representation of assessment for reranking"""
parts = []
if 'assessment_name' in assessment:
parts.append(assessment['assessment_name'])
if 'category' in assessment:
parts.append(f"Category: {assessment['category']}")
if 'test_type' in assessment:
type_full = 'Knowledge/Skill Assessment' if assessment['test_type'] == 'K' else 'Personality/Behavior Assessment'
parts.append(type_full)
if 'description' in assessment:
parts.append(assessment['description'])
return ' | '.join(parts)
def rerank(self,
query: str,
candidates: List[Dict],
top_k: int = 10,
alpha: float = 0.5) -> List[Dict]:
"""
Rerank candidates using cross-encoder scores
Args:
query: Original search query
candidates: List of candidate assessments from initial retrieval
top_k: Number of final results to return
alpha: Weight for combining embedding score and cross-encoder score
(0.0 = only cross-encoder, 1.0 = only embedding)
Returns:
Reranked list of assessments
"""
if not candidates:
return []
logger.info(f"Reranking {len(candidates)} candidates...")
# Compute cross-encoder scores
for candidate in candidates:
assessment_text = self.create_assessment_text(candidate)
ce_score = self.compute_cross_encoder_score(query, assessment_text)
# Store original embedding score
embedding_score = candidate.get('score', 0.0)
# Combine scores
combined_score = alpha * embedding_score + (1 - alpha) * ce_score
candidate['cross_encoder_score'] = ce_score
candidate['embedding_score'] = embedding_score
candidate['combined_score'] = combined_score
# Sort by combined score
reranked = sorted(candidates, key=lambda x: x['combined_score'], reverse=True)
# Select top k
reranked = reranked[:top_k]
logger.info(f"Reranking complete, returning top {len(reranked)} results")
return reranked
def ensure_balance(self,
assessments: List[Dict],
min_k: int = 1,
min_p: int = 1) -> List[Dict]:
"""
Ensure balance between Knowledge (K) and Personality (P) assessments
Args:
assessments: List of assessments
min_k: Minimum number of K assessments
min_p: Minimum number of P assessments
Returns:
Balanced list of assessments
"""
if not assessments:
return []
# Separate K and P assessments
k_assessments = [a for a in assessments if a.get('test_type') == 'K']
p_assessments = [a for a in assessments if a.get('test_type') == 'P']
logger.info(f"Initial distribution - K: {len(k_assessments)}, P: {len(p_assessments)}")
# Check if we need to adjust
if len(k_assessments) < min_k or len(p_assessments) < min_p:
logger.info("Adjusting to ensure minimum balance...")
# Start with empty result
result = []
# Add minimum K assessments
result.extend(k_assessments[:min_k])
# Add minimum P assessments
result.extend(p_assessments[:min_p])
# Add remaining assessments by score
remaining = [a for a in assessments if a not in result]
remaining_sorted = sorted(remaining, key=lambda x: x.get('combined_score', x.get('score', 0)), reverse=True)
# Fill up to desired total
total_needed = len(assessments)
result.extend(remaining_sorted[:total_needed - len(result)])
# Sort final result by score
result = sorted(result, key=lambda x: x.get('combined_score', x.get('score', 0)), reverse=True)
logger.info(f"Balanced distribution - K: {len([a for a in result if a.get('test_type') == 'K'])}, "
f"P: {len([a for a in result if a.get('test_type') == 'P'])}")
return result
return assessments
def rerank_and_balance(self,
query: str,
candidates: List[Dict],
top_k: int = 10,
min_k: int = 1,
min_p: int = 1,
alpha: float = 0.5) -> List[Dict]:
"""
Rerank candidates and ensure K/P balance
Args:
query: Original search query
candidates: List of candidate assessments
top_k: Number of final results
min_k: Minimum K assessments
min_p: Minimum P assessments
alpha: Weight for score combination
Returns:
Reranked and balanced list of assessments
"""
# First rerank
reranked = self.rerank(query, candidates, top_k=top_k * 2, alpha=alpha) # Get more for balancing
# Then ensure balance and trim to top_k
balanced = self.ensure_balance(reranked, min_k=min_k, min_p=min_p)
# Final trim to top_k
final_results = balanced[:top_k]
# Add rank
for i, assessment in enumerate(final_results, 1):
assessment['rank'] = i
return final_results
def normalize_scores(self, assessments: List[Dict]) -> List[Dict]:
"""Normalize scores to 0-1 range"""
if not assessments:
return assessments
scores = [a.get('combined_score', a.get('score', 0)) for a in assessments]
if not scores or max(scores) == min(scores):
return assessments
min_score = min(scores)
max_score = max(scores)
score_range = max_score - min_score
for assessment in assessments:
raw_score = assessment.get('combined_score', assessment.get('score', 0))
normalized = (raw_score - min_score) / score_range
assessment['score'] = normalized
return assessments
def main():
"""Main execution function"""
# Test the reranker
reranker = AssessmentReranker()
# Sample candidates
candidates = [
{
'assessment_name': 'Java Programming Assessment',
'category': 'Technical',
'test_type': 'K',
'description': 'Evaluates Java programming skills',
'score': 0.85
},
{
'assessment_name': 'Leadership Assessment',
'category': 'Leadership',
'test_type': 'P',
'description': 'Evaluates leadership potential',
'score': 0.75
},
{
'assessment_name': 'Python Coding Test',
'category': 'Technical',
'test_type': 'K',
'description': 'Assesses Python programming',
'score': 0.80
}
]
query = "Looking for a Java developer with strong leadership skills"
print("\n=== Reranking Test ===\n")
print(f"Query: {query}\n")
# Rerank and balance
results = reranker.rerank_and_balance(query, candidates, top_k=5, min_k=1, min_p=1)
print("Reranked Results:")
for assessment in results:
print(f"\n{assessment.get('rank', 0)}. {assessment['assessment_name']}")
print(f" Type: {assessment['test_type']}")
print(f" Embedding Score: {assessment.get('embedding_score', 0):.4f}")
print(f" Cross-Encoder Score: {assessment.get('cross_encoder_score', 0):.4f}")
print(f" Combined Score: {assessment.get('combined_score', 0):.4f}")
return reranker
if __name__ == "__main__":
main()
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