Source code for catalyst.dl.callbacks.metrics.ppv_tpr_f1

from typing import List

from catalyst.dl.callbacks import MeterMetricsCallback
from catalyst.utils import meters

[docs]class PrecisionRecallF1ScoreCallback(MeterMetricsCallback): """ Calculates the global precision (positive predictive value or ppv), recall (true positive rate or tpr), and F1-score per class for each loader. .. note:: Currently, supports binary and multi-label cases. """
[docs] def __init__( self, input_key: str = "targets", output_key: str = "logits", class_names: List[str] = None, num_classes: int = 2, threshold: float = 0.5, activation: str = "Sigmoid", ): """ Args: input_key (str): input key to use for metric calculation specifies our ``y_true`` output_key (str): output key to use for metric calculation; specifies our ``y_pred`` class_names (List[str]): class names to display in the logs. If None, defaults to indices for each class, starting from 0. num_classes (int): Number of classes; must be > 1 threshold (float): threshold for outputs binarization activation (str): An torch.nn activation applied to the outputs. Must be one of ``'none'``, ``'Sigmoid'``, ``'Softmax2d'`` """ # adjusting num_classes automatically if class_names is not None num_classes = num_classes if class_names is None else len(class_names) meter_list = [ meters.PrecisionRecallF1ScoreMeter(threshold) for _ in range(num_classes) ] super().__init__( metric_names=["ppv", "tpr", "f1"], meter_list=meter_list, input_key=input_key, output_key=output_key, class_names=class_names, num_classes=num_classes, activation=activation, )
__all__ = ["PrecisionRecallF1ScoreCallback"]