Shortcuts

Source code for pytorch_lightning.metrics.functional.average_precision

# 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, Sequence, Tuple, Union, List

import torch

from pytorch_lightning.metrics.functional.precision_recall_curve import (
    _precision_recall_curve_update,
    _precision_recall_curve_compute
)


def _average_precision_update(
        preds: torch.Tensor,
        target: torch.Tensor,
        num_classes: Optional[int] = None,
        pos_label: Optional[int] = None,
) -> Tuple[torch.Tensor, torch.Tensor, int, int]:
    return _precision_recall_curve_update(preds, target, num_classes, pos_label)


def _average_precision_compute(
        preds: torch.Tensor,
        target: torch.Tensor,
        num_classes: int,
        pos_label: int,
        sample_weights: Optional[Sequence] = None
) -> Union[List[torch.Tensor], torch.Tensor]:
    precision, recall, _ = _precision_recall_curve_compute(preds, target, num_classes, pos_label)
    # Return the step function integral
    # The following works because the last entry of precision is
    # guaranteed to be 1, as returned by precision_recall_curve
    if num_classes == 1:
        return -torch.sum((recall[1:] - recall[:-1]) * precision[:-1])

    res = []
    for p, r in zip(precision, recall):
        res.append(-torch.sum((r[1:] - r[:-1]) * p[:-1]))
    return res


[docs]def average_precision( preds: torch.Tensor, target: torch.Tensor, num_classes: Optional[int] = None, pos_label: Optional[int] = None, sample_weights: Optional[Sequence] = None, ) -> Union[List[torch.Tensor], torch.Tensor]: """ Computes the average precision score. Args: num_classes: integer with number of classes. Not nessesary to provide for binary problems. pos_label: integer determining the positive class. Default is ``None`` which for binary problem is translate to 1. For multiclass problems this argument should not be set as we iteratively change it in the range [0,num_classes-1] sample_weight: sample weights for each data point Returns: tensor with average precision. If multiclass will return list of such tensors, one for each class Example (binary case): >>> pred = torch.tensor([0, 1, 2, 3]) >>> target = torch.tensor([0, 1, 1, 1]) >>> average_precision(pred, target, pos_label=1) tensor(1.) Example (multiclass case): >>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05], ... [0.05, 0.75, 0.05, 0.05, 0.05], ... [0.05, 0.05, 0.75, 0.05, 0.05], ... [0.05, 0.05, 0.05, 0.75, 0.05]]) >>> target = torch.tensor([0, 1, 3, 2]) >>> average_precision(pred, target, num_classes=5) [tensor(1.), tensor(1.), tensor(0.2500), tensor(0.2500), tensor(nan)] """ preds, target, num_classes, pos_label = _average_precision_update(preds, target, num_classes, pos_label) return _average_precision_compute(preds, target, num_classes, pos_label, sample_weights)

© Copyright Copyright (c) 2018-2021, William Falcon et al... Revision c462b274.

Built with Sphinx using a theme provided by Read the Docs.
Read the Docs v: stable
Versions
latest
stable
1.1.6
1.1.5
1.1.4
1.1.3
1.1.2
1.1.1
1.1.0
1.0.8
1.0.7
1.0.6
1.0.5
1.0.4
1.0.3
1.0.2
1.0.1
1.0.0
0.10.0
0.9.0
0.8.5
0.8.4
0.8.3
0.8.2
0.8.1
0.8.0
0.7.6
0.7.5
0.7.4
0.7.3
0.7.2
0.7.1
0.7.0
0.6.0
0.5.3.2
0.5.3
0.4.9
release-1.0.x
Downloads
pdf
html
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.