Source code for pytorch_lightning.metrics.classification.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, Any, Union, List

import torch

from pytorch_lightning.metrics import Metric
from pytorch_lightning.metrics.functional.average_precision import (
    _average_precision_update,
    _average_precision_compute
)
from pytorch_lightning.utilities import rank_zero_warn


class AveragePrecision(Metric):
    """
    Computes the average precision score, which summarises the precision recall
    curve into one number. Works for both binary and multiclass problems.
    In the case of multiclass, the values will be calculated based on a one-vs-the-rest approach.

    Forward accepts

    - ``preds`` (float tensor): ``(N, ...)`` (binary) or ``(N, C, ...)`` (multiclass)
      where C is the number of classes

    - ``target`` (long tensor): ``(N, ...)``

    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]
        compute_on_step:
            Forward only calls ``update()`` and return None if this is set to False. default: True
        dist_sync_on_step:
            Synchronize metric state across processes at each ``forward()``
            before returning the value at the step. default: False
        process_group:
            Specify the process group on which synchronization is called. default: None (which selects the entire world)

    Example (binary case):

        >>> pred = torch.tensor([0, 1, 2, 3])
        >>> target = torch.tensor([0, 1, 1, 1])
        >>> average_precision = AveragePrecision(pos_label=1)
        >>> average_precision(pred, target)
        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 = AveragePrecision(num_classes=5)
        >>> average_precision(pred, target)
        [tensor(1.), tensor(1.), tensor(0.2500), tensor(0.2500), tensor(nan)]

    """
    def __init__(
        self,
        num_classes: Optional[int] = None,
        pos_label: Optional[int] = None,
        compute_on_step: bool = True,
        dist_sync_on_step: bool = False,
        process_group: Optional[Any] = None,
    ):
        super().__init__(
            compute_on_step=compute_on_step,
            dist_sync_on_step=dist_sync_on_step,
            process_group=process_group,
        )

        self.num_classes = num_classes
        self.pos_label = pos_label

        self.add_state("preds", default=[], dist_reduce_fx=None)
        self.add_state("target", default=[], dist_reduce_fx=None)

        rank_zero_warn(
            'Metric `AveragePrecision` will save all targets and predictions in buffer.'
            ' For large datasets this may lead to large memory footprint.'
        )

    def update(self, preds: torch.Tensor, target: torch.Tensor):
        """
        Update state with predictions and targets.

        Args:
            preds: Predictions from model
            target: Ground truth values
        """
        preds, target, num_classes, pos_label = _average_precision_update(
            preds,
            target,
            self.num_classes,
            self.pos_label
        )
        self.preds.append(preds)
        self.target.append(target)
        self.num_classes = num_classes
        self.pos_label = pos_label

    def compute(self) -> Union[torch.Tensor, List[torch.Tensor]]:
        """
        Compute the average precision score

        Returns:
            tensor with average precision. If multiclass will return list
            of such tensors, one for each class

        """
        preds = torch.cat(self.preds, dim=0)
        target = torch.cat(self.target, dim=0)
        return _average_precision_compute(preds, target, self.num_classes, self.pos_label)