File size: 3,231 Bytes
4ff79c6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | # SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
#
# SPDX-License-Identifier: Apache-2.0
from typing import Any, Dict, List
from haystack import Document, component
@component
class DocumentMRREvaluator:
"""
Evaluator that calculates the mean reciprocal rank of the retrieved documents.
MRR measures how high the first retrieved document is ranked.
Each question can have multiple ground truth documents and multiple retrieved documents.
`DocumentMRREvaluator` doesn't normalize its inputs, the `DocumentCleaner` component
should be used to clean and normalize the documents before passing them to this evaluator.
Usage example:
```python
from haystack import Document
from haystack.components.evaluators import DocumentMRREvaluator
evaluator = DocumentMRREvaluator()
result = evaluator.run(
ground_truth_documents=[
[Document(content="France")],
[Document(content="9th century"), Document(content="9th")],
],
retrieved_documents=[
[Document(content="France")],
[Document(content="9th century"), Document(content="10th century"), Document(content="9th")],
],
)
print(result["individual_scores"])
# [1.0, 1.0]
print(result["score"])
# 1.0
```
"""
# Refer to https://www.pinecone.io/learn/offline-evaluation/ for the algorithm.
@component.output_types(score=float, individual_scores=List[float])
def run(
self, ground_truth_documents: List[List[Document]], retrieved_documents: List[List[Document]]
) -> Dict[str, Any]:
"""
Run the DocumentMRREvaluator on the given inputs.
`ground_truth_documents` and `retrieved_documents` must have the same length.
:param ground_truth_documents:
A list of expected documents for each question.
:param retrieved_documents:
A list of retrieved documents for each question.
:returns:
A dictionary with the following outputs:
- `score` - The average of calculated scores.
- `individual_scores` - A list of numbers from 0.0 to 1.0 that represents how high the first retrieved
document is ranked.
"""
if len(ground_truth_documents) != len(retrieved_documents):
msg = "The length of ground_truth_documents and retrieved_documents must be the same."
raise ValueError(msg)
individual_scores = []
for ground_truth, retrieved in zip(ground_truth_documents, retrieved_documents):
reciprocal_rank = 0.0
ground_truth_contents = [doc.content for doc in ground_truth if doc.content is not None]
for rank, retrieved_document in enumerate(retrieved):
if retrieved_document.content is None:
continue
if retrieved_document.content in ground_truth_contents:
reciprocal_rank = 1 / (rank + 1)
break
individual_scores.append(reciprocal_rank)
score = sum(individual_scores) / len(ground_truth_documents)
return {"score": score, "individual_scores": individual_scores}
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