o .wic@sddlmZddlmZmZmZddlmZddlm Z ddl m Z ddl m Z mZmZddlmZddlmZmZmZmZmZmZdd lmZdd lmZdd lmZdd lm Z dd l!m"Z"m#Z#e shgdZ$Gddde Z%GdddeZ&GdddeZ'Gddde Z(dS))Sequence)AnyOptionalUnion)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.plotc seZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <   dd e d ee eee efdeedededdf fdd ZdeeeffddZ ddee eeefdeedefddZZS)BinaryPrecisionAtFixedRecalla6 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. .. tip:: 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||rt|||||_||_dS)Nr'F)super__init__r r'r$)selfr$r%r&r'r( __class__o/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/classification/precision_fixed_recall.pyr+ts   z%BinaryPrecisionAtFixedRecall.__init__cCs8|jdurt|jt|jfn|j}t||j|jtdSzCompute metric.N) reduce_fn)r%rpredstargetconfmatrr$r r,stater/r/r0computes$ z$BinaryPrecisionAtFixedRecall.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 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) rr8_plotr,r9r:r/r/r0plot) rNNTNN)__name__ __module__ __qualname____doc__rbool__annotations__rrrr!floatr#rintlistrrr+tupler8rrrr? __classcell__r/r/r-r0r1s@  <   rceZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <d Z eed <   ddede deeeee efdeedededdffdd ZdeeeffddZ ddeeeeefdeedefddZZS) 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). .. 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: - ``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 specifying 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])) FrNrrr r!r"r#Classplot_legend_nameT num_classesr$r%r&r'r(r)c >tjd|||dd||rt||||||_||_dS)NF)rRr%r&r'r/)r*r+rr'r$)r,rRr$r%r&r'r(r-r/r0r+  z)MulticlassPrecisionAtFixedRecall.__init__cCs<|jdurt|jt|jfn|j}t||j|j|jtdSr1) r%rr3r4r5rrRr$r r6r/r/r0r8s$z(MulticlassPrecisionAtFixedRecall.computer9r:cCr;)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>r/r/r0r?r@rrArBrCrDrErFrrGrHrrrr!rIr#rQstrrJrrKrrr+rLr8rrrr?rMr/r/r-r0rOsF  D    rOcrN) 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. .. 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: - ``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 specifying 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])) FrNrrr r!r"r#LabelrQT num_labelsr$r%r&r'r(r)c rS)NF)rYr%r&r'r/)r*r+rr'r$)r,rYr$r%r&r'r(r-r/r0r+rTz)MultilabelPrecisionAtFixedRecall.__init__cCs@|jdurt|jt|jfn|j}t||j|j|j|jt dSr1) r%rr3r4r5rrYr&r$r r6r/r/r0r8s$z(MultilabelPrecisionAtFixedRecall.computer9r:cCr;)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>r/r/r0r?r@rrArBrUr/r/r-r0rWGsF  E    rWc@steZdZdZ     ddeddeddedeee e ee fd ee d ee d ee d e d e defddZdS)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. NTclstask)binary multiclass multilabelr$r%rRrYr&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 isinstancerJ ValueErrortyperO MULTILABELrW) r[r\r$r%rRrYr&r'r(r/r/r0__new__s(       zPrecisionAtFixedRecall.__new__)NNNNT)rCrDrErFrerrIrrrJrKrrGrrrgr/r/r/r0rZs8  rZN))collections.abcrtypingrrrtorchrtyping_extensionsr torchmetrics.classification.baser2torchmetrics.classification.precision_recall_curver r r =torchmetrics.functional.classification.precision_fixed_recallr =torchmetrics.functional.classification.recall_fixed_precisionr rrrrrtorchmetrics.metricrtorchmetrics.utilities.datartorchmetrics.utilities.enumsrtorchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rrOrWrZr/r/r/r0s,