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Source code for catalyst.metrics.f1_score

from typing import Optional, Union

import torch

from catalyst.metrics.classification import precision_recall_fbeta_support


[docs]def fbeta_score( outputs: torch.Tensor, targets: torch.Tensor, beta: float = 1.0, eps: float = 1e-7, argmax_dim: int = -1, num_classes: Optional[int] = None, ) -> Union[float, torch.Tensor]: """ Counts fbeta score for given ``outputs`` and ``targets``. Args: outputs: A list of predicted elements targets: A list of elements that are to be predicted beta: beta param for f_score eps: epsilon to avoid zero division argmax_dim: int, that specifies dimension for argmax transformation in case of scores/probabilities in ``outputs`` num_classes: int, that specifies number of classes if it known Raises: Exception: If ``beta`` is a negative number. Returns: float: F_1 score. """ if beta < 0: raise Exception("beta parameter should be non-negative") _p, _r, fbeta, _ = precision_recall_fbeta_support( outputs=outputs, targets=targets, beta=beta, eps=eps, argmax_dim=argmax_dim, num_classes=num_classes, ) return fbeta
[docs]def f1_score( outputs: torch.Tensor, targets: torch.Tensor, eps: float = 1e-7, argmax_dim: int = -1, num_classes: Optional[int] = None, ) -> Union[float, torch.Tensor]: """ Fbeta_score with beta=1. Args: outputs: A list of predicted elements targets: A list of elements that are to be predicted eps: epsilon to avoid zero division argmax_dim: int, that specifies dimension for argmax transformation in case of scores/probabilities in ``outputs`` num_classes: int, that specifies number of classes if it known Returns: float: F_1 score """ score = fbeta_score( outputs=outputs, targets=targets, beta=1, eps=eps, argmax_dim=argmax_dim, num_classes=num_classes, ) return score
__all__ = ["f1_score", "fbeta_score"]