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# Copyright The 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 _tie_average_dcg(target: Tensor, preds: Tensor, discount_cumsum: Tensor) -> Tensor:
"""Translated version of sklearns `_tie_average_dcg` function.
Args:
target: ground truth about each document relevance.
preds: estimated probabilities of each document to be relevant.
discount_cumsum: cumulative sum of the discount.
Returns:
The cumulative gain of the tied elements.
"""
_, inv, counts = torch.unique(-preds, return_inverse=True, return_counts=True)
ranked = torch.zeros_like(counts, dtype=torch.float32)
ranked.scatter_add_(0, inv, target.to(dtype=ranked.dtype))
ranked = ranked / counts
groups = counts.cumsum(dim=0) - 1
discount_sums = torch.zeros_like(counts, dtype=torch.float32)
discount_sums[0] = discount_cumsum[groups[0]]
discount_sums[1:] = discount_cumsum[groups].diff()
return (ranked * discount_sums).sum()
def _dcg_sample_scores(target: Tensor, preds: Tensor, top_k: int, ignore_ties: bool) -> Tensor:
"""Translated version of sklearns `_dcg_sample_scores` function.
Args:
target: ground truth about each document relevance.
preds: estimated probabilities of each document to be relevant.
top_k: consider only the top k elements
ignore_ties: If True, ties are ignored. If False, ties are averaged.
Returns:
The cumulative gain
"""
discount = 1.0 / (torch.log2(torch.arange(target.shape[-1], device=target.device) + 2.0))
discount[top_k:] = 0.0
if ignore_ties:
ranking = preds.argsort(descending=True)
ranked = target[ranking]
cumulative_gain = (discount * ranked).sum()
else:
discount_cumsum = discount.cumsum(dim=-1)
cumulative_gain = _tie_average_dcg(target, preds, discount_cumsum)
return cumulative_gain
def retrieval_normalized_dcg(preds: Tensor, target: Tensor, top_k: Optional[int] = None) -> Tensor:
"""Compute `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.
top_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 ``top_k`` parameter is not `None` or an integer larger than 0
Example:
>>> from torchmetrics.functional.retrieval 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)
top_k = preds.shape[-1] if top_k is None else top_k
if not (isinstance(top_k, int) and top_k > 0):
raise ValueError("`top_k` has to be a positive integer or None")
gain = _dcg_sample_scores(target, preds, top_k, ignore_ties=False)
normalized_gain = _dcg_sample_scores(target, target, top_k, ignore_ties=True)
# filter undefined scores
all_irrelevant = normalized_gain == 0
gain[all_irrelevant] = 0
gain[~all_irrelevant] /= normalized_gain[~all_irrelevant]
return gain.mean()
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