average_precision.py

# 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, Sequence, Union

from torch import Tensor
from typing_extensions import Literal

from torchmetrics.classification.precision_recall_curve import (
    BinaryPrecisionRecallCurve,
    MulticlassPrecisionRecallCurve,
    MultilabelPrecisionRecallCurve,
)
from torchmetrics.functional.classification.average_precision import (
    _binary_average_precision_compute,
    _multiclass_average_precision_arg_validation,
    _multiclass_average_precision_compute,
    _multilabel_average_precision_arg_validation,
    _multilabel_average_precision_compute,
)
from torchmetrics.metric import Metric
from torchmetrics.utilities.data import dim_zero_cat
from torchmetrics.utilities.enums import ClassificationTask
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE

if not _MATPLOTLIB_AVAILABLE:
    __doctest_skip__ = [
        "BinaryAveragePrecision.plot",
        "MulticlassAveragePrecision.plot",
        "MultilabelAveragePrecision.plot",
    ]


class BinaryAveragePrecision(BinaryPrecisionRecallCurve):
    r"""Compute the average precision (AP) score for binary tasks.

    The AP score summarizes a precision-recall curve as an weighted mean of precisions at each threshold, with the
    difference in recall from the previous threshold as weight:

    .. math::
        AP = \sum_{n} (R_n - R_{n-1}) P_n

    where :math:`P_n, R_n` is the respective precision and recall at threshold index :math:`n`. This value is
    equivalent to the area under the precision-recall curve (AUPRC).

    As input to ``forward`` and ``update`` the metric accepts the following input:

    - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, ...)`` containing probabilities or logits for
      each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
      sigmoid per element.
    - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)`` containing ground truth labels, and
      therefore only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the
      positive class.

    As output to ``forward`` and ``compute`` the metric returns the following output:

    - ``bap`` (:class:`~torch.Tensor`): A single scalar with the average precision score

    Additional dimension ``...`` will be flattened into the batch dimension.

    The implementation both supports calculating the metric in a non-binned but accurate version and a binned version
    that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will activate the
    non-binned  version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds`
    argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
    size :math:`\mathcal{O}(n_{thresholds})` (constant memory).

    Args:
        thresholds:
            Can be one of:

            - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from
              all the data. Most accurate but also most memory consuming approach.
            - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from
              0 to 1 as bins for the calculation.
            - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation
            - If set to an 1d `tensor` of floats, will use the indicated thresholds in the tensor as
              bins for the calculation.

        validate_args: bool indicating if input arguments and tensors should be validated for correctness.
            Set to ``False`` for faster computations.
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Example:
        >>> from torch import tensor
        >>> from torchmetrics.classification import BinaryAveragePrecision
        >>> preds = tensor([0, 0.5, 0.7, 0.8])
        >>> target = tensor([0, 1, 1, 0])
        >>> metric = BinaryAveragePrecision(thresholds=None)
        >>> metric(preds, target)
        tensor(0.5833)
        >>> bap = BinaryAveragePrecision(thresholds=5)
        >>> bap(preds, target)
        tensor(0.6667)
    """
    is_differentiable: bool = False
    higher_is_better: Optional[bool] = None
    full_state_update: bool = False
    plot_lower_bound: float = 0.0
    plot_upper_bound: float = 1.0

    def compute(self) -> Tensor:  # type: ignore[override]
        """Compute metric."""
        state = (dim_zero_cat(self.preds), dim_zero_cat(self.target)) if self.thresholds is None else self.confmat
        return _binary_average_precision_compute(state, self.thresholds)

    def plot(  # type: ignore[override]
        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

            >>> # Example plotting a single
            >>> import torch
            >>> from torchmetrics.classification import BinaryAveragePrecision
            >>> metric = BinaryAveragePrecision()
            >>> metric.update(torch.rand(20,), torch.randint(2, (20,)))
            >>> fig_, ax_ = metric.plot()

        .. plot::
            :scale: 75

            >>> # Example plotting multiple values
            >>> import torch
            >>> from torchmetrics.classification import BinaryAveragePrecision
            >>> metric = BinaryAveragePrecision()
            >>> values = [ ]
            >>> for _ in range(10):
            ...     values.append(metric(torch.rand(20,), torch.randint(2, (20,))))
            >>> fig_, ax_ = metric.plot(values)

        """
        return self._plot(val, ax)


class MulticlassAveragePrecision(MulticlassPrecisionRecallCurve):
    r"""Compute the average precision (AP) score for multiclass tasks.

    The AP score summarizes a precision-recall curve as an weighted mean of precisions at each threshold, with the
    difference in recall from the previous threshold as weight:

    .. math::
        AP = \sum_{n} (R_n - R_{n-1}) P_n

    where :math:`P_n, R_n` is the respective precision and recall at threshold index :math:`n`. This value is
    equivalent to the area under the precision-recall curve (AUPRC).

    As input to ``forward`` and ``update`` the metric accepts the following input:

    - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)`` containing probabilities or logits
      for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto
      apply softmax per sample.
    - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)`` containing ground truth labels, and
      therefore only contain values in the [0, n_classes-1] range (except if `ignore_index` is specified).

    As output to ``forward`` and ``compute`` the metric returns the following output:

    - ``mcap`` (:class:`~torch.Tensor`): If `average=None|"none"` then a 1d tensor of shape (n_classes, ) will be
      returned with AP score per class. If `average="macro"|"weighted"` then a single scalar is returned.

    Additional dimension ``...`` will be flattened into the batch dimension.

    The implementation both supports calculating the metric in a non-binned but accurate version and a binned version
    that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will activate the
    non-binned  version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds`
    argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
    size :math:`\mathcal{O}(n_{thresholds} \times n_{classes})` (constant memory).

    Args:
        num_classes: Integer specifing the number of classes
        average:
            Defines the reduction that is applied over classes. Should be one of the following:

            - ``macro``: Calculate score for each class and average them
            - ``weighted``: calculates score for each class and computes weighted average using their support
            - ``"none"`` or ``None``: calculates score for each class and applies no reduction
        thresholds:
            Can be one of:

            - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from
              all the data. Most accurate but also most memory consuming approach.
            - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from
              0 to 1 as bins for the calculation.
            - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation
            - If set to an 1d `tensor` of floats, will use the indicated thresholds in the tensor as
              bins for the calculation.

        validate_args: bool indicating if input arguments and tensors should be validated for correctness.
            Set to ``False`` for faster computations.
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Example:
        >>> from torch import tensor
        >>> from torchmetrics.classification import MulticlassAveragePrecision
        >>> preds = 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 = tensor([0, 1, 3, 2])
        >>> metric = MulticlassAveragePrecision(num_classes=5, average="macro", thresholds=None)
        >>> metric(preds, target)
        tensor(0.6250)
        >>> mcap = MulticlassAveragePrecision(num_classes=5, average=None, thresholds=None)
        >>> mcap(preds, target)
        tensor([1.0000, 1.0000, 0.2500, 0.2500,    nan])
        >>> mcap = MulticlassAveragePrecision(num_classes=5, average="macro", thresholds=5)
        >>> mcap(preds, target)
        tensor(0.5000)
        >>> mcap = MulticlassAveragePrecision(num_classes=5, average=None, thresholds=5)
        >>> mcap(preds, target)
        tensor([1.0000, 1.0000, 0.2500, 0.2500, -0.0000])
    """

    is_differentiable: bool = False
    higher_is_better: Optional[bool] = None
    full_state_update: bool = False
    plot_lower_bound: float = 0.0
    plot_upper_bound: float = 1.0
    plot_legend_name: str = "Class"

    def __init__(
        self,
        num_classes: int,
        average: Optional[Literal["macro", "weighted", "none"]] = "macro",
        thresholds: Optional[Union[int, List[float], Tensor]] = None,
        ignore_index: Optional[int] = None,
        validate_args: bool = True,
        **kwargs: Any,
    ) -> None:
        super().__init__(
            num_classes=num_classes, thresholds=thresholds, ignore_index=ignore_index, validate_args=False, **kwargs
        )
        if validate_args:
            _multiclass_average_precision_arg_validation(num_classes, average, thresholds, ignore_index)
        self.average = average
        self.validate_args = validate_args

    def compute(self) -> Tensor:  # type: ignore[override]
        """Compute metric."""
        state = (dim_zero_cat(self.preds), dim_zero_cat(self.target)) if self.thresholds is None else self.confmat
        return _multiclass_average_precision_compute(state, self.num_classes, self.average, self.thresholds)

    def plot(  # type: ignore[override]
        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

            >>> # Example plotting a single
            >>> import torch
            >>> from torchmetrics.classification import MulticlassAveragePrecision
            >>> metric = MulticlassAveragePrecision(num_classes=3)
            >>> metric.update(torch.randn(20, 3), torch.randint(3,(20,)))
            >>> fig_, ax_ = metric.plot()

        .. plot::
            :scale: 75

            >>> # Example plotting multiple values
            >>> import torch
            >>> from torchmetrics.classification import MulticlassAveragePrecision
            >>> metric = MulticlassAveragePrecision(num_classes=3)
            >>> values = [ ]
            >>> for _ in range(10):
            ...     values.append(metric(torch.randn(20, 3), torch.randint(3, (20,))))
            >>> fig_, ax_ = metric.plot(values)
        """
        return self._plot(val, ax)


class MultilabelAveragePrecision(MultilabelPrecisionRecallCurve):
    r"""Compute the average precision (AP) score for multilabel tasks.

    The AP score summarizes a precision-recall curve as an weighted mean of precisions at each threshold, with the
    difference in recall from the previous threshold as weight:

    .. math::
        AP = \sum_{n} (R_n - R_{n-1}) P_n

    where :math:`P_n, R_n` is the respective precision and recall at threshold index :math:`n`. This value is
    equivalent to the area under the precision-recall curve (AUPRC).

    As input to ``forward`` and ``update`` the metric accepts the following input:

    - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)`` containing probabilities or logits
      for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto
      apply sigmoid per element.
    - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)`` containing ground truth labels, and
      therefore only contain {0,1} values (except if `ignore_index` is specified).

    As output to ``forward`` and ``compute`` the metric returns the following output:

    - ``mlap`` (:class:`~torch.Tensor`): If `average=None|"none"` then a 1d tensor of shape (n_classes, ) will be
      returned with AP score per class. If `average="micro|macro"|"weighted"` then a single scalar is returned.

    Additional dimension ``...`` will be flattened into the batch dimension.

    The implementation both supports calculating the metric in a non-binned but accurate version and a binned
    version that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will activate
    the non-binned  version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the
    `thresholds` argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
    size :math:`\mathcal{O}(n_{thresholds} \times n_{labels})` (constant memory).

    Args:
        num_labels: Integer specifing the number of labels
        average:
            Defines the reduction that is applied over labels. Should be one of the following:

            - ``micro``: Sum score over all labels
            - ``macro``: Calculate score for each label and average them
            - ``weighted``: calculates score for each label and computes weighted average using their support
            - ``"none"`` or ``None``: calculates score for each label and applies no reduction
        thresholds:
            Can be one of:

            - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from
              all the data. Most accurate but also most memory consuming approach.
            - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from
              0 to 1 as bins for the calculation.
            - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation
            - If set to an 1d `tensor` of floats, will use the indicated thresholds in the tensor as
              bins for the calculation.

        validate_args: bool indicating if input arguments and tensors should be validated for correctness.
            Set to ``False`` for faster computations.
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Example:
        >>> from torch import tensor
        >>> from torchmetrics.classification import MultilabelAveragePrecision
        >>> preds = tensor([[0.75, 0.05, 0.35],
        ...                 [0.45, 0.75, 0.05],
        ...                 [0.05, 0.55, 0.75],
        ...                 [0.05, 0.65, 0.05]])
        >>> target = tensor([[1, 0, 1],
        ...                  [0, 0, 0],
        ...                  [0, 1, 1],
        ...                  [1, 1, 1]])
        >>> metric = MultilabelAveragePrecision(num_labels=3, average="macro", thresholds=None)
        >>> metric(preds, target)
        tensor(0.7500)
        >>> mlap = MultilabelAveragePrecision(num_labels=3, average=None, thresholds=None)
        >>> mlap(preds, target)
        tensor([0.7500, 0.5833, 0.9167])
        >>> mlap = MultilabelAveragePrecision(num_labels=3, average="macro", thresholds=5)
        >>> mlap(preds, target)
        tensor(0.7778)
        >>> mlap = MultilabelAveragePrecision(num_labels=3, average=None, thresholds=5)
        >>> mlap(preds, target)
        tensor([0.7500, 0.6667, 0.9167])
    """
    is_differentiable: bool = False
    higher_is_better: Optional[bool] = None
    full_state_update: bool = False
    plot_lower_bound: float = 0.0
    plot_upper_bound: float = 1.0
    plot_legend_name: str = "Label"

    def __init__(
        self,
        num_labels: int,
        average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
        thresholds: Optional[Union[int, List[float], Tensor]] = None,
        ignore_index: Optional[int] = None,
        validate_args: bool = True,
        **kwargs: Any,
    ) -> None:
        super().__init__(
            num_labels=num_labels, thresholds=thresholds, ignore_index=ignore_index, validate_args=False, **kwargs
        )
        if validate_args:
            _multilabel_average_precision_arg_validation(num_labels, average, thresholds, ignore_index)
        self.average = average
        self.validate_args = validate_args

    def compute(self) -> Tensor:  # type: ignore[override]
        """Compute metric."""
        state = (dim_zero_cat(self.preds), dim_zero_cat(self.target)) if self.thresholds is None else self.confmat
        return _multilabel_average_precision_compute(
            state, self.num_labels, self.average, self.thresholds, self.ignore_index
        )

    def plot(  # type: ignore[override]
        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

            >>> # Example plotting a single
            >>> import torch
            >>> from torchmetrics.classification import MultilabelAveragePrecision
            >>> metric = MultilabelAveragePrecision(num_labels=3)
            >>> metric.update(torch.rand(20,3), torch.randint(2, (20,3)))
            >>> fig_, ax_ = metric.plot()

        .. plot::
            :scale: 75

            >>> # Example plotting multiple values
            >>> import torch
            >>> from torchmetrics.classification import MultilabelAveragePrecision
            >>> metric = MultilabelAveragePrecision(num_labels=3)
            >>> values = [ ]
            >>> for _ in range(10):
            ...     values.append(metric(torch.rand(20,3), torch.randint(2, (20,3))))
            >>> fig_, ax_ = metric.plot(values)
        """
        return self._plot(val, ax)


class AveragePrecision:
    r"""Compute the average precision (AP) score.

    The AP score summarizes a precision-recall curve as an weighted mean of precisions at each threshold, with the
    difference in recall from the previous threshold as weight:

    .. math::
        AP = \sum_{n} (R_n - R_{n-1}) P_n

    where :math:`P_n, R_n` is the respective precision and recall at threshold index :math:`n`. This value is
    equivalent to the area under the precision-recall curve (AUPRC).

    This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
    :mod:`BinaryAveragePrecision`, :mod:`MulticlassAveragePrecision` and :mod:`MultilabelAveragePrecision`
    for the specific details of each argument influence and examples.

    Legacy Example:
        >>> from torch import tensor
        >>> pred = tensor([0, 0.1, 0.8, 0.4])
        >>> target = tensor([0, 1, 1, 1])
        >>> average_precision = AveragePrecision(task="binary")
        >>> average_precision(pred, target)
        tensor(1.)

        >>> pred = 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 = tensor([0, 1, 3, 2])
        >>> average_precision = AveragePrecision(task="multiclass", num_classes=5, average=None)
        >>> average_precision(pred, target)
        tensor([1.0000, 1.0000, 0.2500, 0.2500,    nan])
    """

    def __new__(  # type: ignore[misc]
        cls,
        task: Literal["binary", "multiclass", "multilabel"],
        thresholds: Optional[Union[int, List[float], Tensor]] = None,
        num_classes: Optional[int] = None,
        num_labels: Optional[int] = None,
        average: Optional[Literal["macro", "weighted", "none"]] = "macro",
        ignore_index: Optional[int] = None,
        validate_args: bool = True,
        **kwargs: Any,
    ) -> Metric:
        """Initialize task metric."""
        task = ClassificationTask.from_str(task)
        kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
        if task == ClassificationTask.BINARY:
            return BinaryAveragePrecision(**kwargs)
        if task == ClassificationTask.MULTICLASS:
            if not isinstance(num_classes, int):
                raise ValueError(f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`")
            return MulticlassAveragePrecision(num_classes, average, **kwargs)
        if task == ClassificationTask.MULTILABEL:
            if not isinstance(num_labels, int):
                raise ValueError(f"`num_labels` is expected to be `int` but `{type(num_labels)} was passed.`")
            return MultilabelAveragePrecision(num_labels, average, **kwargs)
        raise ValueError(f"Task {task} not supported!")