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from typing import List
def calculate_hit_rate(retrieved_ids: List[str], relevant_id: str) -> int:
"""
Hit rate: Checks if the relevant document is present in the retrieved results.
Args:
retrieved_ids: List of retrieved document IDs.
relevant_id: The ID of the correct/relevant document.
Returns:
1 if hit, 0 if miss.
"""
return 1 if relevant_id in retrieved_ids else 0
def calculate_mrr(retrieved_ids: List[str], relevant_id: str) -> float:
"""
Mean Reciprocal Rank: The reciprocal of the rank of the first relevant document.
Higher rank means better performance (max = 1.0).
Args:
retrieved_ids: List of retrieved document IDs.
relevant_id: The ID of the correct/relevant document.
Returns:
1/rank if found, 0 if not found.
"""
try:
rank = retrieved_ids.index(relevant_id) + 1 # +1 because index starts from 0
return 1.0 / rank
except ValueError:
return 0.0
def calculate_precision_at_k(
retrieved_ids: List[str], relevant_ids: List[str], k: int = None
) -> float:
"""
Precision@K: How many relevant documents are in the top-K results.
Args:
retrieved_ids: List of retrieved document IDs.
relevant_ids: List of relevant document IDs (can be multiple).
k: Cut-off point (if None, use all retrieved_ids).
Returns:
Precision score (0.0 - 1.0).
"""
if k is not None:
retrieved_ids = retrieved_ids[:k]
if len(retrieved_ids) == 0:
return 0.0
relevant_set = set(relevant_ids)
retrieved_set = set(retrieved_ids)
hits = len(relevant_set.intersection(retrieved_set))
return hits / len(retrieved_ids)
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