o .wib@sddlmZddlmZmZmZddlmZddlm Z ddl m Z ddl m Z mZmZddlmZmZmZmZmZmZddlmZdd lmZdd lmZdd lmZdd lm Z m!Z!esbgd Z"Gddde Z#GdddeZ$GdddeZ%Gddde Z&dS))Sequence)AnyOptionalUnion)Tensor)Literal)_ClassificationTaskWrapper)BinaryPrecisionRecallCurveMulticlassPrecisionRecallCurveMultilabelPrecisionRecallCurve)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)!BinaryRecallAtFixedPrecision.plot%MulticlassRecallAtFixedPrecision.plot%MultilabelRecallAtFixedPrecision.plotc seZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <   dd e d ee eee efdeedededdf fdd ZdeeeffddZ ddee eeefdeedefddZZS)BinaryRecallAtFixedPrecisiona7 Compute 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. 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. .. tip:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns the following output: - ``recall`` (:class:`~torch.Tensor`): A scalar tensor with the maximum recall for the given precision 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_precision: float value specifying minimum precision 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 BinaryRecallAtFixedPrecision >>> preds = tensor([0, 0.5, 0.7, 0.8]) >>> target = tensor([0, 1, 1, 0]) >>> metric = BinaryRecallAtFixedPrecision(min_precision=0.5, thresholds=None) >>> metric(preds, target) (tensor(1.), tensor(0.5000)) >>> metric = BinaryRecallAtFixedPrecision(min_precision=0.5, thresholds=5) >>> metric(preds, target) (tensor(1.), tensor(0.5000)) Fis_differentiableNhigher_is_betterfull_state_updateplot_lower_bound?plot_upper_boundT min_precision thresholds ignore_index validate_argskwargsreturnc s:tj||fddi||rt|||||_||_dS)Nr&F)super__init__r r&r#)selfr#r$r%r&r' __class__o/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/classification/recall_fixed_precision.pyr*ss   z%BinaryRecallAtFixedPrecision.__init__cCs4|jdurt|jt|jfn|j}t||j|jSzCompute metric.N)r$rpredstargetconfmatr r#r+stater.r.r/computes$z$BinaryRecallAtFixedPrecision.computevalaxcC|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 BinaryRecallAtFixedPrecision >>> metric = BinaryRecallAtFixedPrecision(min_precision=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 BinaryRecallAtFixedPrecision >>> metric = BinaryRecallAtFixedPrecision(min_precision=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) rr6_plotr+r7r8r.r.r/plot) rNNTNN)__name__ __module__ __qualname____doc__rbool__annotations__rrrr floatr"rintlistrrr*tupler6rrrr= __classcell__r.r.r,r/r0s@  <   rceZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <d Z eed <   ddede deeeee efdeedededdffdd ZdeeeffddZ ddeeeeefdeedefddZZS) MulticlassRecallAtFixedPrecisionaSCompute 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. 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. 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). .. tip:: 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: - ``recall`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the maximum recall for the given precision 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 specifying the number of classes min_precision: float value specifying minimum precision 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 MulticlassRecallAtFixedPrecision >>> 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 = MulticlassRecallAtFixedPrecision(num_classes=5, min_precision=0.5, thresholds=None) >>> metric(preds, target) (tensor([1., 1., 0., 0., 0.]), tensor([7.5000e-01, 7.5000e-01, 1.0000e+06, 1.0000e+06, 1.0000e+06])) >>> mcrafp = MulticlassRecallAtFixedPrecision(num_classes=5, min_precision=0.5, thresholds=5) >>> mcrafp(preds, target) (tensor([1., 1., 0., 0., 0.]), tensor([7.5000e-01, 7.5000e-01, 1.0000e+06, 1.0000e+06, 1.0000e+06])) FrNrrrr r!r"Classplot_legend_nameT num_classesr#r$r%r&r'r(c >tjd|||dd||rt||||||_||_dS)NF)rPr$r%r&r.)r)r*rr&r#)r+rPr#r$r%r&r'r,r.r/r*  z)MulticlassRecallAtFixedPrecision.__init__cCs8|jdurt|jt|jfn|j}t||j|j|jSr0)r$rr1r2r3rrPr#r4r.r.r/r6s$z(MulticlassRecallAtFixedPrecision.computer7r8cCr9)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 MulticlassRecallAtFixedPrecision >>> metric = MulticlassRecallAtFixedPrecision(num_classes=3, min_precision=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 MulticlassRecallAtFixedPrecision >>> metric = MulticlassRecallAtFixedPrecision(num_classes=3, min_precision=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<r.r.r/r=r>rr?r@rArBrCrDrrErFrrrr rGr"rOstrrHrrIrrr*rJr6rrrr=rKr.r.r,r/rMsF  F    rMcrL) MultilabelRecallAtFixedPrecisiona\Compute 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. 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. .. tip:: 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: - ``recall`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the maximum recall for the given precision 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 specifying the number of labels min_precision: float value specifying minimum precision 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 MultilabelRecallAtFixedPrecision >>> 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 = MultilabelRecallAtFixedPrecision(num_labels=3, min_precision=0.5, thresholds=None) >>> metric(preds, target) (tensor([1., 1., 1.]), tensor([0.0500, 0.5500, 0.0500])) >>> mlrafp = MultilabelRecallAtFixedPrecision(num_labels=3, min_precision=0.5, thresholds=5) >>> mlrafp(preds, target) (tensor([1., 1., 1.]), tensor([0.0000, 0.5000, 0.0000])) FrNrrrr r!r"LabelrOT num_labelsr#r$r%r&r'r(c rQ)NF)rWr$r%r&r.)r)r*rr&r#)r+rWr#r$r%r&r'r,r.r/r*rRz)MultilabelRecallAtFixedPrecision.__init__cCs<|jdurt|jt|jfn|j}t||j|j|j|jSr0) r$rr1r2r3rrWr%r#r4r.r.r/r6s$z(MultilabelRecallAtFixedPrecision.computer7r8cCr9)aaPlot 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 MultilabelRecallAtFixedPrecision >>> metric = MultilabelRecallAtFixedPrecision(num_labels=3, min_precision=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 MultilabelRecallAtFixedPrecision >>> metric = MultilabelRecallAtFixedPrecision(num_labels=3, min_precision=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<r.r.r/r=r>rr?r@rSr.r.r,r/rUFsF  E    rUc@steZdZdZ     ddeddeddedeee e ee fd ee d ee d ee d e d e defddZdS)RecallAtFixedPrecisionaCompute 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.BinaryRecallAtFixedPrecision`, :class:`~torchmetrics.classification.MulticlassRecallAtFixedPrecision` and :class:`~torchmetrics.classification.MultilabelRecallAtFixedPrecision` for the specific details of each argument influence and examples. NTclstask)binary multiclass multilabelr#r$rPrWr%r&r'r(c Kst|}|tjkrt||||fi|S|tjkr5t|ts)tdt|dt |||||fi|S|tj krUt|tsItdt|dt |||||fi|Std|d)zInitialize task metric.z+`num_classes` is expected to be `int` but `z was passed.`z*`num_labels` is expected to be `int` but `zTask z not supported!) rfrom_strBINARYr MULTICLASS isinstancerH ValueErrortyperM MULTILABELrU) rYrZr#r$rPrWr%r&r'r.r.r/__new__s(       zRecallAtFixedPrecision.__new__)NNNNT)rArBrCrDrcrrGrrrHrIrrErrrer.r.r.r/rXs8  rXN)'collections.abcrtypingrrrtorchrtyping_extensionsr torchmetrics.classification.baser2torchmetrics.classification.precision_recall_curver r r =torchmetrics.functional.classification.recall_fixed_precisionr r rrrrtorchmetrics.metricrtorchmetrics.utilities.datartorchmetrics.utilities.enumsrtorchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rrMrUrXr.r.r.r/s*