o .wiqc@sddlmZddlmZmZmZddlmZddlm Z ddl m Z ddl m Z mZmZddlmZmZmZmZmZddlmZdd lmZdd lmZdd lmZdd lmZm Z es`gd Z!Gddde Z"GdddeZ#GdddeZ$Gddde Z%dS))Sequence)AnyOptionalUnion)Tensor)Literal)_ClassificationTaskWrapper)BinaryPrecisionRecallCurveMulticlassPrecisionRecallCurveMultilabelPrecisionRecallCurve)!_binary_average_precision_compute,_multiclass_average_precision_arg_validation%_multiclass_average_precision_compute,_multilabel_average_precision_arg_validation%_multilabel_average_precision_compute)Metric) dim_zero_cat)ClassificationTask)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE)BinaryAveragePrecision.plotMulticlassAveragePrecision.plotMultilabelAveragePrecision.plotc@seZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <d e fd d Z ddeee ee fdeed efddZdS)BinaryAveragePrecisiona 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) Fis_differentiableThigher_is_betterfull_state_updateplot_lower_bound?plot_upper_boundreturncCs0|jdurt|jt|jfn|j}t||jSzCompute metric.N) thresholdsrpredstargetconfmatr selfstater+j/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/classification/average_precision.pycomputets$ zBinaryAveragePrecision.computeNvalaxcC |||S)a: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) _plotr)r.r/r+r+r,ploty (rNN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatr!rr-rrrrrr4r+r+r+r,r/s  >    rceZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <d Z e ed <   ddedeeddeeeee efdeedededdffdd ZdefddZ d deeeeefdeedefddZZS)!MulticlassAveragePrecisionaCompute 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). For multiclass the metric is calculated by iteratively treating each class as the positive class and all other classes as the negative, which is referred to as the one-vs-rest approach. One-vs-one is currently not supported by this metric. By default the reported metric is then the average over all classes, but this behavior can be changed by setting the ``average`` argument. 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 specifying 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]) FrTrrrrr r!Classplot_legend_namemacroN num_classesaveragerBweightednoner$ ignore_index validate_argskwargsr"c >tjd|||dd||rt||||||_||_dS)NF)rCr$rHrIr+)super__init__r rDrI)r)rCrDr$rHrIrJ __class__r+r,rM  z#MulticlassAveragePrecision.__init__cCs8|jdurt|jt|jfn|j}t||j|j|jSr#)r$rr%r&r'rrCrDr(r+r+r,r-s$z"MulticlassAveragePrecision.computer.r/cCr0)aiPlot 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) r1r3r+r+r,r4r5rrBNNTr6r7r8r9r:rr;r<rrrr=r!rAstrintrrrlistrrrMr-rrrr4 __classcell__r+r+rNr,r?sH  S       r?cr>)!MultilabelAveragePrecisionaCompute 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 specifying 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]) FrTrrrrr r!LabelrArBN num_labelsrD)microrBrFrGr$rHrIrJr"c rK)NF)rYr$rHrIr+)rLrMrrDrI)r)rYrDr$rHrIrJrNr+r,rMrPz#MultilabelAveragePrecision.__init__cCs<|jdurt|jt|jfn|j}t||j|j|j|jSr#) r$rr%r&r'rrYrDrHr(r+r+r,r-s$z"MultilabelAveragePrecision.computer.r/cCr0)afPlot 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) r1r3r+r+r,r4r5rrQr6rRr+r+rNr,rWEsH  R      rWc@s~eZdZdZ      ddeddeddeeee e e fd eed eed eed d eede de defddZdS)AveragePrecisionaCompute 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 :class:`~torchmetrics.classification.BinaryAveragePrecision`, :class:`~torchmetrics.classification.MulticlassAveragePrecision` and :class:`~torchmetrics.classification.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]) NrBTclstask)binary multiclass multilabelr$rCrYrDrErHrIrJr"c Kst|}||||d|tjkrtdi|S|tjkr7t|ts.tdt |dt ||fi|S|tj krTt|tsKtdt |dt ||fi|Std|d) zInitialize task metric.)r$rHrIz+`num_classes` is expected to be `int` but `z was passed.`z*`num_labels` is expected to be `int` but `zTask z not supported!Nr+) rfrom_strupdateBINARYr MULTICLASS isinstancerT ValueErrortyper? MULTILABELrW) r\r]r$rCrYrDrHrIrJr+r+r,__new__s      zAveragePrecision.__new__)NNNrBNT)r7r8r9r:rgrrrrTrUr=rr;rrrir+r+r+r,r[s:(   r[N)&collections.abcrtypingrrrtorchrtyping_extensionsr torchmetrics.classification.baser2torchmetrics.classification.precision_recall_curver r r 8torchmetrics.functional.classification.average_precisionr r rrrtorchmetrics.metricrtorchmetrics.utilities.datartorchmetrics.utilities.enumsrtorchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rr?rWr[r+r+r+r,s(        u"