# some implementations are adopted from https://github.com/ncoop57/cute_ranking/blob/main/cute_ranking/core.py
# the original code is licensed under Apache-2.0
from typing import List, Optional
import numpy as np
def _check_k(k):
if k is not None and k < 1:
raise ValueError(f'`k` must be >=1 or `None`')
[docs]def r_precision(binary_relevance: List[int], **kwargs) -> float:
"""R-Precision determines the precision in the fist R documents, where R is the
number of documents relevant to the query.
Relevance is considered binary by this function (nonzero is relevant).
Please note, that it is necessary to provide relevance scores for all documents,
i.e., the calculated metric is wrong, if you apply it on the Top-K scores only.
.. seealso::
https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#R-precision
:param binary_relevance: binary relevancy in rank order
:return: R-Precision
"""
binary_relevance = np.array(binary_relevance) != 0
z = binary_relevance.nonzero()[0]
if not z.size:
return 0.0
return float(np.mean(binary_relevance[: z.size]))
[docs]def precision_at_k(
binary_relevance: List[int], k: Optional[int] = None, **kwargs
) -> float:
"""Precision @K.
If `binary_relevance` is empty, 0.0 is returned.
:param binary_relevance: binary relevancy in rank order
:param k: measured on top-k
:return: precision @k
"""
_check_k(k)
if len(binary_relevance) == 0:
return 0.0
binary_relevance = np.array(binary_relevance)[:k] != 0
return float(np.mean(binary_relevance))
[docs]def hit_at_k(binary_relevance: List[int], k: Optional[int] = None, **kwargs) -> int:
"""Score is percentage of first relevant item in list that occur
:param binary_relevance: binary relevancy in rank order
:param k: measured on top-k
:return: hit @k if hit return 1 else 0
"""
_check_k(k)
return 1 if np.sum(binary_relevance[:k]) > 0 else 0
[docs]def average_precision(binary_relevance: List[int], **kwargs) -> float:
"""Score is average precision (area under PR curve)
Relevance is binary (nonzero is relevant).
:param binary_relevance: binary relevancy in rank order
:return: Average precision
"""
r = np.array(binary_relevance) != 0
out = [precision_at_k(r, k + 1) for k in range(r.size) if r[k]]
if not out:
return 0.0
return float(np.mean(out))
[docs]def reciprocal_rank(binary_relevance: List[int], **kwargs) -> float:
"""Score is reciprocal of the rank of the first relevant item
:param binary_relevance: binary relevancy in rank order
:return: Average precision
"""
rs = np.array(binary_relevance).nonzero()[0]
return 1.0 / (rs[0] + 1) if rs.size else 0.0
[docs]def recall_at_k(
binary_relevance: List[int], max_rel: int, k: Optional[int] = None, **kwargs
) -> float:
"""Score is recall after all relevant documents have been retrieved
Relevance is binary (nonzero is relevant).
:param binary_relevance: binary relevancy in rank order
:param k: measured on top-k
:param max_rel: Maximum number of documents that can be relevant
:return: Recall score
"""
_check_k(k)
binary_relevance = np.array(binary_relevance[:k]) != 0
if max_rel is None:
raise ValueError('The metric recall_at_k requires a max_rel parameter')
if np.sum(binary_relevance) > max_rel:
raise ValueError(f'Number of relevant Documents retrieved > {max_rel}')
return np.sum(binary_relevance) / max_rel
[docs]def f1_score_at_k(
binary_relevance: List[int], max_rel: int, k: Optional[int] = None, **kwargs
) -> float:
"""Score is harmonic mean of precision and recall
Relevance is binary (nonzero is relevant).
:param binary_relevance: binary relevancy in rank order
:param k: measured on top-k
:param max_rel: Maximum number of documents that can be relevant
:return: F1 score @ k
"""
_check_k(k)
p = precision_at_k(binary_relevance, k)
r = recall_at_k(binary_relevance, max_rel, k)
if (p + r) > 0:
return 2 * p * r / (p + r)
else:
return 0.0
[docs]def dcg_at_k(
relevance: List[float], method: int = 0, k: Optional[int] = None, **kwargs
):
"""Score is discounted cumulative gain (dcg)
Relevance is positive real values. Can use binary
as the previous methods.
Example from
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
:param relevance: Relevance scores (list or numpy) in rank order
(first element is the first item)
:param k: measured on top-k
:param method: If 0 then weights are [1.0, 1.0, 0.6309, 0.5, 0.4307, ...]
If 1 then weights are [1.0, 0.6309, 0.5, 0.4307, ...]
:return: Discounted cumulative gain
"""
_check_k(k)
r = np.asfarray(relevance)[:k]
if r.size:
if method == 0:
return r[0] + np.sum(r[1:] / np.log2(np.arange(2, r.size + 1)))
elif method == 1:
return np.sum(r / np.log2(np.arange(2, r.size + 2)))
else:
raise ValueError('method must be 0 or 1.')
return 0.0
[docs]def ndcg_at_k(
relevance: List[float], method: int = 0, k: Optional[int] = None, **kwargs
):
"""Calculates a normalized discounted cumulative gain (ndcg).
Relevance values can be positive real values. However, one can also use binary
scores as in other evaluation methods.
Please note, that it is necessary to provide relevance scores for all documents,
i.e., the calculated metric is wrong, if you apply it on the Top-K scores only.
Example from
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
:param relevance: Relevance scores (list or numpy) in rank order
(first element is the first item)
:param k: measured on top-k
:param method: If 0 then weights are [1.0, 1.0, 0.6309, 0.5, 0.4307, ...]
If 1 then weights are [1.0, 0.6309, 0.5, 0.4307, ...]
:return: Normalized discounted cumulative gain
"""
_check_k(k)
dcg_max = dcg_at_k(sorted(relevance, reverse=True), method=method, k=k)
if not dcg_max:
return 0.0
return dcg_at_k(relevance, method=method, k=k) / dcg_max
