a  db@sddlmZmZmZmZmZmZddlmZddl m Z ddl m Z ddl mZmZmZddlmZddlmZmZmZmZmZmZddlmZdd lmZdd lmZdd l m!Z!dd l"m#Z#m$Z$e!sgd Z%GdddeZ&GdddeZ'GdddeZ(Gddde Z)dS))AnyListOptionalSequenceTupleUnion)Tensor)Literal)_ClassificationTaskWrapper)BinaryPrecisionRecallCurveMulticlassPrecisionRecallCurveMultilabelPrecisionRecallCurve)_precision_at_recall)0_binary_recall_at_fixed_precision_arg_validation)_binary_recall_at_fixed_precision_compute1_multiclass_recall_at_fixed_precision_arg_compute4_multiclass_recall_at_fixed_precision_arg_validation1_multilabel_recall_at_fixed_precision_arg_compute4_multilabel_recall_at_fixed_precision_arg_validation)Metric) dim_zero_cat)ClassificationTask)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE)!BinaryPrecisionAtFixedRecall.plot%MulticlassPrecisionAtFixedRecall.plot%MultilabelPrecisionAtFixedRecall.plotcseZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <de ee eee efeeeedd fd d ZeeefdddZdee eeefeeedddZZS)BinaryPrecisionAtFixedRecalla7 Compute the highest possible precision value given the minimum recall thresholds provided. This is done by first calculating the precision-recall curve for different thresholds and the find the precision for a given recall level. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, ...)``. Preds should be a tensor 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, ...)``. Target should be a tensor 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. .. note:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns the following output: - ``precision`` (:class:`~torch.Tensor`): A scalar tensor with the maximum precision for the given recall level - ``threshold`` (:class:`~torch.Tensor`): A scalar tensor with the corresponding threshold level .. note:: 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: min_recall: float value specifying minimum recall threshold. 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 :class:`~torch.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 BinaryPrecisionAtFixedRecall >>> preds = tensor([0, 0.5, 0.7, 0.8]) >>> target = tensor([0, 1, 1, 0]) >>> metric = BinaryPrecisionAtFixedRecall(min_recall=0.5, thresholds=None) >>> metric(preds, target) (tensor(0.6667), tensor(0.5000)) >>> metric = BinaryPrecisionAtFixedRecall(min_recall=0.5, thresholds=5) >>> metric(preds, target) (tensor(0.6667), tensor(0.5000)) Fis_differentiableNhigher_is_betterfull_state_updateplot_lower_bound?plot_upper_boundT) min_recall thresholds ignore_index validate_argskwargsreturnc s:tj||fddi||r*t|||||_||_dS)Nr)F)super__init__rr)r&)selfr&r'r(r)r* __class__{/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/classification/precision_fixed_recall.pyr-rs  z%BinaryPrecisionAtFixedRecall.__init__r+cCs8|jdurt|jt|jfn|j}t||j|jtdSzCompute metric.N)Z reduce_fn)r'rpredstargetconfmatrr&rr.stater1r1r2computes$ z$BinaryPrecisionAtFixedRecall.computevalaxr+cCs|p|d}|||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 object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting a single value >>> from torchmetrics.classification import BinaryPrecisionAtFixedRecall >>> metric = BinaryPrecisionAtFixedRecall(min_recall=0.5) >>> metric.update(rand(10), randint(2,(10,))) >>> fig_, ax_ = metric.plot() # the returned plot only shows the maximum recall value by default .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting multiple values >>> from torchmetrics.classification import BinaryPrecisionAtFixedRecall >>> metric = BinaryPrecisionAtFixedRecall(min_recall=0.5) >>> values = [ ] >>> for _ in range(10): ... # we index by 0 such that only the maximum recall value is plotted ... values.append(metric(rand(10), randint(2,(10,)))[0]) >>> fig_, ax_ = metric.plot(values) rr:Z_plotr.r<r=r1r1r2plots)r)NNT)NN)__name__ __module__ __qualname____doc__rbool__annotations__r rr!r#floatr%rintrrrr-rr:rrrr@ __classcell__r1r1r/r2r0s. ;    rc seZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <d Z eed <dee eeeee efeeeeddfdd ZeeefdddZdeeeeefeeedddZZS) MulticlassPrecisionAtFixedRecallaCompute the highest possible precision value given the minimum recall thresholds provided. This is done by first calculating the precision-recall curve for different thresholds and the find the precision for a given recall level. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor 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, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if `ignore_index` is specified). .. note:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns a tuple of either 2 tensors or 2 lists containing: - ``precision`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the maximum precision for the given recall level per class - ``threshold`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the corresponding threshold level per class .. note:: 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 min_recall: float value specifying minimum recall threshold. 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 :class:`~torch.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 MulticlassPrecisionAtFixedRecall >>> 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 = MulticlassPrecisionAtFixedRecall(num_classes=5, min_recall=0.5, thresholds=None) >>> metric(preds, target) # doctest: +NORMALIZE_WHITESPACE (tensor([1.0000, 1.0000, 0.2500, 0.2500, 0.0000]), tensor([7.5000e-01, 7.5000e-01, 5.0000e-02, 5.0000e-02, 1.0000e+06])) >>> mcrafp = MulticlassPrecisionAtFixedRecall(num_classes=5, min_recall=0.5, thresholds=5) >>> mcrafp(preds, target) # doctest: +NORMALIZE_WHITESPACE (tensor([1.0000, 1.0000, 0.2500, 0.2500, 0.0000]), tensor([7.5000e-01, 7.5000e-01, 0.0000e+00, 0.0000e+00, 1.0000e+06])) FrNr r!r"r#r$r%ZClassplot_legend_nameT) num_classesr&r'r(r)r*r+c s>tjf|||dd||r.t||||||_||_dS)NF)rLr'r(r))r,r-rr)r&)r.rLr&r'r(r)r*r/r1r2r-s z)MulticlassPrecisionAtFixedRecall.__init__r3cCs<|jdurt|jt|jfn|j}t||j|j|jtdSr4) r'rr5r6r7rrLr&rr8r1r1r2r:s$z(MulticlassPrecisionAtFixedRecall.computer;cCs|p|d}|||S)aPlot 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 object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting a single value per class >>> from torchmetrics.classification import MulticlassPrecisionAtFixedRecall >>> metric = MulticlassPrecisionAtFixedRecall(num_classes=3, min_recall=0.5) >>> metric.update(rand(20, 3).softmax(dim=-1), randint(3, (20,))) >>> fig_, ax_ = metric.plot() # the returned plot only shows the maximum recall value by default .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting a multiple values per class >>> from torchmetrics.classification import MulticlassPrecisionAtFixedRecall >>> metric = MulticlassPrecisionAtFixedRecall(num_classes=3, min_recall=0.5) >>> values = [] >>> for _ in range(20): ... # we index by 0 such that only the maximum recall value is plotted ... values.append(metric(rand(20, 3).softmax(dim=-1), randint(3, (20,)))[0]) >>> fig_, ax_ = metric.plot(values) rr>r?r1r1r2r@s)r)NNT)NNrArBrCrDrrErFr rr!r#rGr%rKstrrHrrrrr-rr:rrrr@rIr1r1r/r2rJs2 C     rJc seZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <d Z eed <dee eeeee efeeeeddfdd ZeeefdddZdeeeeefeeedddZZS) MultilabelPrecisionAtFixedRecallaiCompute the highest possible precision value given the minimum recall thresholds provided. This is done by first calculating the precision-recall curve for different thresholds and the find the precision for a given recall level. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor 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, ...)``. Target should be a tensor 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. .. note:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns a tuple of either 2 tensors or 2 lists containing: - ``precision`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the maximum precision for the given recall level per class - ``threshold`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the corresponding threshold level per class .. note:: 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 min_recall: float value specifying minimum recall threshold. 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 :class:`~torch.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 MultilabelPrecisionAtFixedRecall >>> 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 = MultilabelPrecisionAtFixedRecall(num_labels=3, min_recall=0.5, thresholds=None) >>> metric(preds, target) (tensor([1.0000, 0.6667, 1.0000]), tensor([0.7500, 0.5500, 0.3500])) >>> mlrafp = MultilabelPrecisionAtFixedRecall(num_labels=3, min_recall=0.5, thresholds=5) >>> mlrafp(preds, target) (tensor([1.0000, 0.6667, 1.0000]), tensor([0.7500, 0.5000, 0.2500])) FrNr r!r"r#r$r%ZLabelrKT) num_labelsr&r'r(r)r*r+c s>tjf|||dd||r.t||||||_||_dS)NF)rPr'r(r))r,r-rr)r&)r.rPr&r'r(r)r*r/r1r2r-s z)MultilabelPrecisionAtFixedRecall.__init__r3cCs@|jdurt|jt|jfn|j}t||j|j|j|jt dSr4) r'rr5r6r7rrPr(r&rr8r1r1r2r:s$z(MultilabelPrecisionAtFixedRecall.computer;cCs|p|d}|||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 object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting a single value >>> from torchmetrics.classification import MultilabelPrecisionAtFixedRecall >>> metric = MultilabelPrecisionAtFixedRecall(num_labels=3, min_recall=0.5) >>> metric.update(rand(20, 3), randint(2, (20, 3))) >>> fig_, ax_ = metric.plot() # the returned plot only shows the maximum recall value by default .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting multiple values >>> from torchmetrics.classification import MultilabelPrecisionAtFixedRecall >>> metric = MultilabelPrecisionAtFixedRecall(num_labels=3, min_recall=0.5) >>> values = [ ] >>> for _ in range(10): ... # we index by 0 such that only the maximum recall value is plotted ... values.append(metric(rand(20, 3), randint(2, (20, 3)))[0]) >>> fig_, ax_ = metric.plot(values) rr>r?r1r1r2r@s)r)NNT)NNrMr1r1r/r2rODs2 D     rOc @sReZdZdZdedeeeee ee feeeeeee e e d ddZdS) PrecisionAtFixedRecallaCompute the highest possible recall value given the minimum precision thresholds provided. This is done by first calculating the precision-recall curve for different thresholds and the find the recall for a given precision level. 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.BinaryPrecisionAtFixedRecall`, :class:`~torchmetrics.classification.MulticlassPrecisionAtFixedRecall` and :class:`~torchmetrics.classification.MultilabelPrecisionAtFixedRecall` for the specific details of each argument influence and examples. 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