75 lines
2.2 KiB
Python
75 lines
2.2 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Tuple
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import torch
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from pytorch_lightning.metrics.utils import _stable_1d_sort
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def _auc_update(x: torch.Tensor, y: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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if x.ndim > 1 or y.ndim > 1:
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raise ValueError(
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f'Expected both `x` and `y` tensor to be 1d, but got'
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f' tensors with dimention {x.ndim} and {y.ndim}'
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)
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if x.numel() != y.numel():
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raise ValueError(
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f'Expected the same number of elements in `x` and `y`'
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f' tensor but received {x.numel()} and {y.numel()}'
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)
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return x, y
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def _auc_compute(x: torch.Tensor, y: torch.Tensor, reorder: bool = False) -> torch.Tensor:
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if reorder:
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x, x_idx = _stable_1d_sort(x)
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y = y[x_idx]
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dx = x[1:] - x[:-1]
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if (dx < 0).any():
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if (dx <= 0).all():
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direction = -1.
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else:
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raise ValueError(
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"The `x` tensor is neither increasing or decreasing."
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" Try setting the reorder argument to `True`."
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)
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else:
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direction = 1.
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return direction * torch.trapz(y, x)
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def auc(x: torch.Tensor, y: torch.Tensor, reorder: bool = False) -> torch.Tensor:
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"""
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Computes Area Under the Curve (AUC) using the trapezoidal rule
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Args:
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x: x-coordinates
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y: y-coordinates
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reorder: if True, will reorder the arrays
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Return:
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Tensor containing AUC score (float)
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Example:
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>>> x = torch.tensor([0, 1, 2, 3])
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>>> y = torch.tensor([0, 1, 2, 2])
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>>> auc(x, y)
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tensor(4.)
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"""
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x, y = _auc_update(x, y)
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return _auc_compute(x, y, reorder=reorder)
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