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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 Any, List, Optional
import torch
from torch import Tensor
from torchmetrics.functional.regression.csi import _critical_success_index_compute, _critical_success_index_update
from torchmetrics.metric import Metric
from torchmetrics.utilities import dim_zero_cat
class CriticalSuccessIndex(Metric):
r"""Calculate critical success index (CSI).
Critical success index (also known as the threat score) is a statistic used weather forecasting that measures
forecast performance over inputs binarized at a specified threshold. It is defined as:
.. math:: \text{CSI} = \frac{\text{TP}}{\text{TP}+\text{FN}+\text{FP}}
Where :math:`\text{TP}`, :math:`\text{FN}` and :math:`\text{FP}` represent the number of true positives, false
negatives and false positives respectively after binarizing the input tensors.
Args:
threshold: Values above or equal to threshold are replaced with 1, below by 0
keep_sequence_dim: Index of the sequence dimension if the inputs are sequences of images. If specified,
the score will be calculated separately for each image in the sequence. If ``None``, the score will be
calculated across all dimensions.
Example:
>>> import torch
>>> from torchmetrics.regression import CriticalSuccessIndex
>>> x = torch.Tensor([[0.2, 0.7], [0.9, 0.3]])
>>> y = torch.Tensor([[0.4, 0.2], [0.8, 0.6]])
>>> csi = CriticalSuccessIndex(0.5)
>>> csi(x, y)
tensor(0.3333)
Example:
>>> import torch
>>> from torchmetrics.regression import CriticalSuccessIndex
>>> x = torch.Tensor([[[0.2, 0.7], [0.9, 0.3]], [[0.2, 0.7], [0.9, 0.3]]])
>>> y = torch.Tensor([[[0.4, 0.2], [0.8, 0.6]], [[0.4, 0.2], [0.8, 0.6]]])
>>> csi = CriticalSuccessIndex(0.5, keep_sequence_dim=0)
>>> csi(x, y)
tensor([0.3333, 0.3333])
"""
is_differentiable: bool = False
higher_is_better: bool = True
hits: Tensor
misses: Tensor
false_alarms: Tensor
hits_list: List[Tensor]
misses_list: List[Tensor]
false_alarms_list: List[Tensor]
def __init__(self, threshold: float, keep_sequence_dim: Optional[int] = None, **kwargs: Any) -> None:
super().__init__(**kwargs)
self.threshold = float(threshold)
if keep_sequence_dim and (not isinstance(keep_sequence_dim, int) or keep_sequence_dim < 0):
raise ValueError(f"Expected keep_sequence_dim to be a non-negative integer but got {keep_sequence_dim}")
self.keep_sequence_dim = keep_sequence_dim
if keep_sequence_dim is None:
self.add_state("hits", default=torch.tensor(0), dist_reduce_fx="sum")
self.add_state("misses", default=torch.tensor(0), dist_reduce_fx="sum")
self.add_state("false_alarms", default=torch.tensor(0), dist_reduce_fx="sum")
else:
self.add_state("hits_list", default=[], dist_reduce_fx="cat")
self.add_state("misses_list", default=[], dist_reduce_fx="cat")
self.add_state("false_alarms_list", default=[], dist_reduce_fx="cat")
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
hits, misses, false_alarms = _critical_success_index_update(
preds, target, self.threshold, self.keep_sequence_dim
)
if self.keep_sequence_dim is None:
self.hits += hits
self.misses += misses
self.false_alarms += false_alarms
else:
self.hits_list.append(hits)
self.misses_list.append(misses)
self.false_alarms_list.append(false_alarms)
def compute(self) -> Tensor:
"""Compute critical success index over state."""
if self.keep_sequence_dim is None:
hits = self.hits
misses = self.misses
false_alarms = self.false_alarms
else:
hits = dim_zero_cat(self.hits_list)
misses = dim_zero_cat(self.misses_list)
false_alarms = dim_zero_cat(self.false_alarms_list)
return _critical_success_index_compute(hits, misses, false_alarms)
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