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ndcg.py
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ndcg.py
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# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
import torch
from torch import Tensor
from torchmetrics.utilities.checks import _check_retrieval_functional_inputs
def _dcg(target: Tensor) -> Tensor:
"""Computes Discounted Cumulative Gain for input tensor."""
denom = torch.log2(torch.arange(target.shape[-1], device=target.device) + 2.0)
return (target / denom).sum(dim=-1)
def retrieval_normalized_dcg(preds: Tensor, target: Tensor, k: Optional[int] = None) -> Tensor:
"""Computes `Normalized Discounted Cumulative Gain`_ (for information retrieval).
``preds`` and ``target`` should be of the same shape and live on the same device.
``target`` must be either `bool` or `integers` and ``preds`` must be `float`,
otherwise an error is raised.
Args:
preds: estimated probabilities of each document to be relevant.
target: ground truth about each document relevance.
k: consider only the top k elements (default: None, which considers them all)
Return:
a single-value tensor with the nDCG of the predictions ``preds`` w.r.t. the labels ``target``.
Raises:
ValueError:
If ``k`` parameter is not `None` or an integer larger than 0
Example:
>>> from torchmetrics.functional import retrieval_normalized_dcg
>>> preds = torch.tensor([.1, .2, .3, 4, 70])
>>> target = torch.tensor([10, 0, 0, 1, 5])
>>> retrieval_normalized_dcg(preds, target)
tensor(0.6957)
"""
preds, target = _check_retrieval_functional_inputs(preds, target, allow_non_binary_target=True)
k = preds.shape[-1] if k is None else k
if not (isinstance(k, int) and k > 0):
raise ValueError("`k` has to be a positive integer or None")
sorted_target = target[torch.argsort(preds, dim=-1, descending=True)][:k]
ideal_target = torch.sort(target, descending=True)[0][:k]
ideal_dcg = _dcg(ideal_target)
target_dcg = _dcg(sorted_target)
# filter undefined scores
all_irrelevant = ideal_dcg == 0
target_dcg[all_irrelevant] = 0
target_dcg[~all_irrelevant] /= ideal_dcg[~all_irrelevant]
return target_dcg.mean()