:jx3ddlmZmZmZddlZddlmZddlmZddlm Z m Z m Z ddl m Z deded efd Zdeeeefdeeeefd efd Z dd ed edeeeeeefdeeded ef dZ dd ed ededeeeeeefdeeddeeded efdZ dd ed ededeeeeeefdeeded efdZ dd ed ededdeeeeeefdeedeedeeddeeded eeeeffdZy))ListOptionalUnionN)Tensor)Literal) binary_rocmulticlass_rocmultilabel_roc)ClassificationTaskfprtprreturnc|d|z z }tjtj|}||d||z zdz S)z>Compute Equal Error Rate (EER) for binary classification task.)torchargminabs)r r diffidxs /home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/functional/classification/eer.py_binary_eer_computersA !c'?D ,,uyy 'C HCH % **c t|tr+t|tr|jdk(r t||St j t ||Dcgc]\}}t||c}}Scc}}w)zCompute Equal Error Rate (EER).r) isinstancerndimrrstackzip)r r fts r _eer_computer!#s] #v:c6#:sxx1}"3,, ;;c#smLda+Aq1L MMLsA8 predstarget thresholds ignore_index validate_argsc@t|||||\}}}t||S)aCompute Equal Error Rate (EER) for binary classification task. .. math:: \text{EER} = \frac{\text{FAR} + \text{FRR}}{2}, \text{where} \min_t abs(FAR_t-FRR_t) The Equal Error Rate (EER) is the point where the False Positive Rate (FPR) and True Positive Rate (TPR) are equal, or in practise minimized. A lower EER value signifies higher system accuracy. Args: preds: Tensor with predictions target: Tensor with true labels 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: A single scalar with the eer score Example: >>> from torchmetrics.functional.classification import binary_eer >>> preds = torch.tensor([0, 0.5, 0.7, 0.8]) >>> target = torch.tensor([0, 1, 1, 0]) >>> binary_eer(preds, target, thresholds=None) tensor(0.5000) >>> binary_eer(preds, target, thresholds=5) tensor(0.7500) )rr!)r"r#r$r%r&r r _s r binary_eerr)-s*^UFJ mTKCa S !!r num_classesaverage)micromacroc Dt|||||||\}}} t||S)a Compute Equal Error Rate (EER) for multiclass classification task. .. math:: \text{EER} = \frac{\text{FAR} + (1 - \text{FRR})}{2}, \text{where} \min_t abs(FAR_t-FRR_t) The Equal Error Rate (EER) is the point where the False Positive Rate (FPR) and True Positive Rate (TPR) are equal, or in practise minimized. A lower EER value signifies higher system accuracy. Args: preds: Tensor with predictions target: Tensor with true labels num_classes: Integer specifying the number of classes 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. average: If aggregation of should be applied. The aggregation is applied to underlying ROC curves. By default, eer is not aggregated and a score for each class is returned. If `average` is set to ``"micro"`` , the metric will aggregate the curves by one hot encoding the targets and flattening the predictions, considering all classes jointly as a binary problem. If `average` is set to ``"macro"``, the metric will aggregate the curves by first interpolating the curves from each class at a combined set of thresholds and then average over the classwise interpolated curves. See `averaging curve objects`_ for more info on the different averaging methods. 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: If `average=None|"none"` then a 1d tensor of shape (n_classes, ) will be returned with eer score per class. If `average="macro"|"micro"` then a single scalar is returned. Example: >>> from torchmetrics.functional.classification import multiclass_eer >>> 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_eer(preds, target, num_classes=5, average="macro", thresholds=None) tensor(0.4667) >>> multiclass_eer(preds, target, num_classes=5, average=None, thresholds=None) tensor([0.0000, 0.0000, 0.6667, 0.6667, 1.0000]) >>> multiclass_eer(preds, target, num_classes=5, average="macro", thresholds=5) tensor(0.4667) >>> multiclass_eer(preds, target, num_classes=5, average=None, thresholds=5) tensor([0.0000, 0.0000, 0.6667, 0.6667, 1.0000]) )r r!) r"r#r*r$r+r%r&r r r(s rmulticlass_eerr/`s0D! ZR^`mnKCa S !!r num_labelscBt||||||\}}}t||S)a Compute Equal Error Rate (EER) for multilabel classification task. .. math:: \text{EER} = \frac{\text{FAR} + (1 - \text{FRR})}{2}, \text{where} \min_t abs(FAR_t-FRR_t) The Equal Error Rate (EER) is the point where the False Positive Rate (FPR) and True Positive Rate (TPR) are equal, or in practise minimized. A lower EER value signifies higher system accuracy. Args: preds: Tensor with predictions target: Tensor with true labels num_labels: Integer specifying the number of labels 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: A 1d tensor of shape (n_classes, ) will be returned with eer score per label. Example: >>> from torchmetrics.functional.classification import multilabel_eer >>> 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_eer(preds, target, num_labels=3, thresholds=None) tensor([0.5000, 0.5000, 0.1667]) >>> multilabel_eer(preds, target, num_labels=3, thresholds=5) tensor([0.5000, 0.7500, 0.1667]) )r r!) r"r#r0r$r%r&r r r(s rmultilabel_eerr2s-n! J VcdKCa S !!rtask)binary multiclass multilabelc tj|}|tjk(rt|||||S|tjk(r9t |t stdt|dt|||||||S|tjk(r8t |t stdt|dt||||||Std|d)aCompute Equal Error Rate (EER) metric. This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of :func:`~torchmetrics.functional.classification.binary_eer`, :func:`~torchmetrics.functional.classification.multiclass_eer` and :func:`~torchmetrics.functional.classification.multilabel_eer` for the specific details of each argument influence and examples. Args: preds: Predictions from model (logits or probabilities) target: Ground truth labels task: Type of task, either 'binary', 'multiclass' or 'multilabel' thresholds: Thresholds used for computing the ROC curve num_classes: Number of classes (for multiclass task) num_labels: Number of labels (for multilabel task) average: Method to average EER over multiple classes/labels ignore_index: Specify a target value that is ignored validate_args: Bool indicating whether to validate input arguments Legacy Example: >>> from torchmetrics.functional.classification import eer >>> preds = torch.tensor([0.13, 0.26, 0.08, 0.19, 0.34]) >>> target = torch.tensor([0, 0, 1, 1, 1]) >>> eer(preds, target, task='binary') tensor(0.5833) >>> preds = torch.tensor([[0.90, 0.05, 0.05], ... [0.05, 0.90, 0.05], ... [0.05, 0.05, 0.90], ... [0.85, 0.05, 0.10], ... [0.10, 0.10, 0.80]]) >>> target = torch.tensor([0, 1, 1, 2, 2]) >>> eer(preds, target, task='multiclass', num_classes=3, ) tensor([0.0000, 0.4167, 0.4167]) z+`num_classes` is expected to be `int` but `z was passed.`z*`num_labels` is expected to be `int` but `zTask z not supported.) r from_strBINARYr) MULTICLASSrint ValueErrortyper/ MULTILABELr2) r"r#r3r$r*r0r+r%r&s reerr?s`  & &t ,D !(((%\=QQ !,,,+s+J4P[K\J]]jkl leV[*g|]jkk !,,,*c*I$zJZI[[hij jeVZ\S`aa uTF/2 33r)NNT)NNNT)NNNNNT)typingrrrrrtyping_extensionsr*torchmetrics.functional.classification.rocrr r torchmetrics.utilities.enumsr rr!r;floatboolr)r/r2r?rrrGs)( % <+V+&+V+N vtF|# $N vtF|# $N N=A"& 0" 0" 0"sDK7890"3- 0"  0"  0"n=A37"&C" C" C"C"sDK789 C" g./ 0 C" 3- C"C" C"T=A"& 8" 8" 8"8"sDK789 8" 3- 8"  8" 8"~=A!% $37"&;4 ;4 ;4 6 7;4sDK789 ;4 # ;4  ;4g./ 0;43-;4;4 64<  ;4r