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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 collections.abc import Sequence
from typing import Any, Callable, Optional, Union
import torch
from torch import Tensor, tensor
from typing_extensions import Literal
from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out
from torchmetrics.retrieval.base import RetrievalMetric, _retrieval_aggregate
from torchmetrics.utilities.data import _flexible_bincount, dim_zero_cat
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["RetrievalFallOut.plot"]
class RetrievalFallOut(RetrievalMetric):
"""Compute `Fall-out`_.
Works with binary target data. Accepts float predictions from a model output.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, ...)``
- ``target`` (:class:`~torch.Tensor`): A long or bool tensor of shape ``(N, ...)``
- ``indexes`` (:class:`~torch.Tensor`): A long tensor of shape ``(N, ...)`` which indicate to which query a
prediction belongs
As output to ``forward`` and ``compute`` the metric returns the following output:
- ``fallout@k`` (:class:`~torch.Tensor`): A tensor with the computed metric
All ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten at the beginning,
so that for example, a tensor of shape ``(N, M)`` is treated as ``(N * M, )``. Predictions will be first grouped by
``indexes`` and then will be computed as the mean of the metric over each query.
Args:
empty_target_action:
Specify what to do with queries that do not have at least a negative ``target``. Choose from:
- ``'neg'``: those queries count as ``0.0`` (default)
- ``'pos'``: those queries count as ``1.0``
- ``'skip'``: skip those queries; if all queries are skipped, ``0.0`` is returned
- ``'error'``: raise a ``ValueError``
ignore_index: Ignore predictions where the target is equal to this number.
top_k: Consider only the top k elements for each query (default: `None`, which considers them all)
aggregation:
Specify how to aggregate over indexes. Can either a custom callable function that takes in a single tensor
and returns a scalar value or one of the following strings:
- ``'mean'``: average value is returned
- ``'median'``: median value is returned
- ``'max'``: max value is returned
- ``'min'``: min value is returned
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Raises:
ValueError:
If ``empty_target_action`` is not one of ``error``, ``skip``, ``neg`` or ``pos``.
ValueError:
If ``ignore_index`` is not `None` or an integer.
ValueError:
If ``top_k`` is not ``None`` or not an integer greater than 0.
Example:
>>> from torchmetrics.retrieval import RetrievalFallOut
>>> indexes = tensor([0, 0, 0, 1, 1, 1, 1])
>>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2])
>>> target = tensor([False, False, True, False, True, False, True])
>>> rfo = RetrievalFallOut(top_k=2)
>>> rfo(preds, target, indexes=indexes)
tensor(0.5000)
"""
is_differentiable: bool = False
higher_is_better: bool = False
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
def __init__(
self,
empty_target_action: str = "pos",
ignore_index: Optional[int] = None,
top_k: Optional[int] = None,
aggregation: Union[Literal["mean", "median", "min", "max"], Callable] = "mean",
**kwargs: Any,
) -> None:
super().__init__(
empty_target_action=empty_target_action,
ignore_index=ignore_index,
aggregation=aggregation,
**kwargs,
)
if top_k is not None and not (isinstance(top_k, int) and top_k > 0):
raise ValueError("`top_k` has to be a positive integer or None")
self.top_k = top_k
def compute(self) -> Tensor:
"""First concat state ``indexes``, ``preds`` and ``target`` since they were stored as lists.
After that, compute list of groups that will help in keeping together predictions about the same query. Finally,
for each group compute the `_metric` if the number of negative targets is at least 1, otherwise behave as
specified by `self.empty_target_action`.
"""
indexes = dim_zero_cat(self.indexes)
preds = dim_zero_cat(self.preds)
target = dim_zero_cat(self.target)
indexes, indices = torch.sort(indexes)
preds = preds[indices]
target = target[indices]
split_sizes = _flexible_bincount(indexes).detach().cpu().tolist()
res = []
for mini_preds, mini_target in zip(
torch.split(preds, split_sizes, dim=0), torch.split(target, split_sizes, dim=0)
):
if not (1 - mini_target).sum():
if self.empty_target_action == "error":
raise ValueError("`compute` method was provided with a query with no negative target.")
if self.empty_target_action == "pos":
res.append(tensor(1.0))
elif self.empty_target_action == "neg":
res.append(tensor(0.0))
else:
# ensure list contains only float tensors
res.append(self._metric(mini_preds, mini_target))
return (
_retrieval_aggregate(torch.stack([x.to(preds) for x in res]), aggregation=self.aggregation)
if res
else tensor(0.0).to(preds)
)
def _metric(self, preds: Tensor, target: Tensor) -> Tensor:
return retrieval_fall_out(preds, target, top_k=self.top_k)
def plot(
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
ax: An matplotlib axis object. If provided will add plot to that axis
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> import torch
>>> from torchmetrics.retrieval import RetrievalFallOut
>>> # Example plotting a single value
>>> metric = RetrievalFallOut()
>>> metric.update(torch.rand(10,), torch.randint(2, (10,)), indexes=torch.randint(2,(10,)))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> import torch
>>> from torchmetrics.retrieval import RetrievalFallOut
>>> # Example plotting multiple values
>>> metric = RetrievalFallOut()
>>> values = []
>>> for _ in range(10):
... values.append(metric(torch.rand(10,), torch.randint(2, (10,)), indexes=torch.randint(2,(10,))))
>>> fig, ax = metric.plot(values)
"""
return self._plot(val, ax)
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