o .wi\@s4ddlZddlmZmZmZddlZddlmZddlmZddl m Z m Z m Z m Z mZmZmZmZmZmZmZmZddlmZmZmZddlmZded efd d Zd ed ededed eeeff ddZ  d4dedeee e!eefdee d dfddZ" d5deeeeeffdeedede d eeeff ddZ#   d6dedededeee e!eefdee de$d eeeffddZ%  d4d e dedeee e!eefdee d df d!d"Z&deeeeeffd e deeded eeeff d#d$Z'   d6deded e dedeee e!eefdee de$d eeeffd%d&Z(  d4d'e dedeee e!eefdee d df d(d)Z)deeeeeffd'e deedee ded eeeff d*d+Z*   d6deded'e dedeee e!eefdee de$d eeeffd,d-Z+     d7deded.ed/dedeee e!eefd ee d'ee dee de$d eeeeeefeeeeeeefffd0d1Z,     d7deded.ed/dedeee e!eefd ee d'ee dee de$d eeeeeefeeeeeeefffd2d3Z-dS)8N)ListOptionalUnion)Tensor)Literal) -_binary_precision_recall_curve_arg_validation%_binary_precision_recall_curve_format0_binary_precision_recall_curve_tensor_validation%_binary_precision_recall_curve_update1_multiclass_precision_recall_curve_arg_validation)_multiclass_precision_recall_curve_format4_multiclass_precision_recall_curve_tensor_validation)_multiclass_precision_recall_curve_update1_multilabel_precision_recall_curve_arg_validation)_multilabel_precision_recall_curve_format4_multilabel_precision_recall_curve_tensor_validation)_multilabel_precision_recall_curve_update)_binary_roc_compute_multiclass_roc_compute_multilabel_roc_compute)ClassificationTaskfprreturncCsd|S)zConvert fprs to specificity.)rrr{/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/functional/classification/specificity_sensitivity.py_convert_fpr_to_specificity+sr specificity sensitivity thresholdsmin_sensitivitycCs||k}|s tjd|j|jd}tjd|j|jd}||fS||||||}}}t|}||||}}||fS)Ng)devicedtypeg.A)anytorchtensorr!r"argmax)rrrr indicesmax_specbest_thresholdidxrrr_specificity_at_sensitivity0s  r+ ignore_indexcCs@t||t|tsd|krdksntd|dSdSNrrzOExpected argument `min_sensitivity` to be an float in the [0,1] range, but got )r isinstancefloat ValueError)r rr,rrr1_binary_specificity_at_sensitivity_arg_validationJs  r1rstate pos_labelcCs(t|||\}}}t|}t||||S)N)rrr+)r2rr r3rrrrrr*_binary_specificity_at_sensitivity_computeVsr4Tpredstarget validate_argscCsH|rt|||t|||t||||\}}}t|||}t|||S)a Compute the highest possible specificity value given the minimum sensitivity levels provided for binary tasks. This is done by first calculating the Receiver Operating Characteristic (ROC) curve for different thresholds and the find the specificity for a given sensitivity level. Accepts the following input tensors: - ``preds`` (float tensor): ``(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`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). 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: preds: Tensor with predictions target: Tensor with true labels min_sensitivity: float value specifying minimum sensitivity 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 `tensor` of floats, will use the indicated thresholds in the tensor as bins for the calculation. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Returns: (tuple): a tuple of 2 tensors containing: - specificity: a scalar tensor with the maximum specificity for the given sensitivity level - threshold: a scalar tensor with the corresponding threshold level Example: >>> from torchmetrics.functional.classification import binary_specificity_at_sensitivity >>> preds = torch.tensor([0, 0.5, 0.4, 0.1]) >>> target = torch.tensor([0, 1, 1, 1]) >>> binary_specificity_at_sensitivity(preds, target, min_sensitivity=0.5, thresholds=None) (tensor(1.), tensor(0.4000)) >>> binary_specificity_at_sensitivity(preds, target, min_sensitivity=0.5, thresholds=5) (tensor(1.), tensor(0.2500)) )r1r rr r4)r5r6r rr,r7r2rrr!binary_specificity_at_sensitivityas A    r8 num_classescCBt|||t|tsd|krdksntd|dSdSr-)r r.r/r0)r9r rr,rrr5_multiclass_specificity_at_sensitivity_arg_validation  r;cst||\}}dd|D}t|tr#fddt||D}n fddt||D}tdd|D}tdd|D|fS)NcSg|]}t|qSrr.0fpr_rrr zB_multiclass_specificity_at_sensitivity_compute..cg|] \}}t||qSrr+r@spsnr rrrrB c g|] \}}}t|||qSrrEr@rGrHtr rrrB cSg|]}|dqSrrr@rrrrrBrCcSrPrrrRrrrrBrC)rr.rzipr$stack)r2r9rr rrrresrrIr._multiclass_specificity_at_sensitivity_computes    rXcCsR|rt||||t||||t|||||\}}}t||||}t||||S)aCompute the highest possible specificity value given minimum sensitivity level provided for multiclass tasks. This is done by first calculating the Receiver Operating Characteristic (ROC) curve for different thresholds and the find the specificity for a given sensitivity level. Accepts the following input tensors: - ``preds`` (float tensor): ``(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`` (int tensor): ``(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). 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: preds: Tensor with predictions target: Tensor with true labels num_classes: Integer specifying the number of classes min_sensitivity: float value specifying minimum sensitivity 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 `tensor` of floats, will use the indicated thresholds in the tensor as bins for the calculation. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Returns: (tuple): a tuple of either 2 tensors or 2 lists containing - recall: an 1d tensor of size (n_classes, ) with the maximum recall for the given precision level per class - thresholds: an 1d tensor of size (n_classes, ) with the corresponding threshold level per class Example: >>> from torchmetrics.functional.classification import multiclass_specificity_at_sensitivity >>> preds = torch.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 = torch.tensor([0, 1, 3, 2]) >>> multiclass_specificity_at_sensitivity(preds, target, num_classes=5, min_sensitivity=0.5, thresholds=None) (tensor([1., 1., 0., 0., 0.]), tensor([7.5000e-01, 7.5000e-01, 5.0000e-02, 5.0000e-02, 1.0000e+06])) >>> multiclass_specificity_at_sensitivity(preds, target, num_classes=5, min_sensitivity=0.5, thresholds=5) (tensor([1., 1., 0., 0., 0.]), tensor([7.5000e-01, 7.5000e-01, 0.0000e+00, 0.0000e+00, 1.0000e+06])) )r;r r rrX)r5r6r9r rr,r7r2rrr%multiclass_specificity_at_sensitivitysF  rY num_labelscCr:r-)rr.r/r0)rZr rr,rrr5_multilabel_specificity_at_sensitivity_arg_validationr<r[c st|||\}}dd|D}t|tr$fddt||D}n fddt||D}tdd|D}tdd|D|fS)NcSr=rr>r?rrrrB3rCzB_multilabel_specificity_at_sensitivity_compute..crDrrErFrIrrrB5rJcrKrrErLrNrrrB:rOcSrPrQrrRrrrrB>rCcSrPrTrrRrrrrB?rC)rr.rrUr$rV) r2rZrr,r rrrrWrrIr._multilabel_specificity_at_sensitivity_compute+s    r\cCsT|rt||||t||||t|||||\}}}t||||}t|||||S)aCompute the highest possible specificity value given minimum sensitivity level provided for multilabel tasks. This is done by first calculating the Receiver Operating Characteristic (ROC) curve for different thresholds and the find the specificity for a given sensitivity level. Accepts the following input tensors: - ``preds`` (float tensor): ``(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`` (int tensor): ``(N, C, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). 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: preds: Tensor with predictions target: Tensor with true labels num_labels: Integer specifying the number of labels min_sensitivity: float value specifying minimum sensitivity 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 `tensor` of floats, will use the indicated thresholds in the tensor as bins for the calculation. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Returns: (tuple): a tuple of either 2 tensors or 2 lists containing - specificity: an 1d tensor of size (n_classes, ) with the maximum recall for the given precision level per class - thresholds: an 1d tensor of size (n_classes, ) with the corresponding threshold level per class Example: >>> from torchmetrics.functional.classification import multilabel_specificity_at_sensitivity >>> preds = torch.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 = torch.tensor([[1, 0, 1], ... [0, 0, 0], ... [0, 1, 1], ... [1, 1, 1]]) >>> multilabel_specificity_at_sensitivity(preds, target, num_labels=3, min_sensitivity=0.5, thresholds=None) (tensor([1.0000, 0.5000, 1.0000]), tensor([0.7500, 0.6500, 0.3500])) >>> multilabel_specificity_at_sensitivity(preds, target, num_labels=3, min_sensitivity=0.5, thresholds=5) (tensor([1.0000, 0.5000, 1.0000]), tensor([0.7500, 0.5000, 0.2500])) )r[rrrr\)r5r6rZr rr,r7r2rrr%multilabel_specificity_at_sensitivityCsJ  r]task)binary multiclass multilabelc Cs*tjdtddt|||||||||d S)a Compute the highest possible specificity value given the minimum sensitivity thresholds provided. .. warning:: This function was deprecated in v1.3.0 of Torchmetrics and will be removed in v2.0.0. Use `specificity_at_sensitivity` instead. zSThis method has will be removed in 2.0.0. Use `specificity_at_sensitivity` instead.r) stacklevel r5r6r^r rr9rZr,r7)warningswarnDeprecationWarningspecificity_at_sensitivityrcrrrspecicity_at_sensitivitys rhc Cst|}|tjkrt||||||S|tjkr1t|ts'tdt|dt |||||||S|tj krOt|tsEtdt|dt |||||||Std|)aQCompute the highest possible specificity value given the minimum sensitivity thresholds provided. This is done by first calculating the Receiver Operating Characteristic (ROC) curve for different thresholds and the find the specificity for a given sensitivity 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 :func:`~torchmetrics.functional.classification.binary_specificity_at_sensitivity`, :func:`~torchmetrics.functional.classification.multiclass_specificity_at_sensitivity` and :func:`~torchmetrics.functional.classification.multilabel_specificity_at_sensitivity` for the specific details of each argument influence and examples. z+`num_classes` is expected to be `int` but `z was passed.`z*`num_labels` is expected to be `int` but `zNot handled value: ) rfrom_strBINARYr8 MULTICLASSr.intr0typerY MULTILABELr]rcrrrrgs$       rg)NNrT)NNT)NNNNT).rdtypingrrrr$rtyping_extensionsr=torchmetrics.functional.classification.precision_recall_curverrr r r r r rrrrr*torchmetrics.functional.classification.rocrrrtorchmetrics.utilities.enumsrrr/tupler+rllistr1r4boolr8r;rXrYr[r\r]rhrgrrrrs   8        L      S      Y , ) ,