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Source code for ignite.metrics.fbeta

from typing import Callable, Optional, Union

import torch

from ignite.metrics.metrics_lambda import MetricsLambda
from ignite.metrics.precision import Precision
from ignite.metrics.recall import Recall

__all__ = ["Fbeta"]


[docs]def Fbeta( beta: float, average: bool = True, precision: Optional[Precision] = None, recall: Optional[Recall] = None, output_transform: Optional[Callable] = None, device: Union[str, torch.device] = torch.device("cpu"), ) -> MetricsLambda: r"""Calculates F-beta score. .. math:: F_\beta = \left( 1 + \beta^2 \right) * \frac{ \text{precision} * \text{recall} } { \left( \beta^2 * \text{precision} \right) + \text{recall} } where :math:`\beta` is a positive real factor. - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. - `y_pred` must be in the following shape (batch_size, num_categories, ...) or (batch_size, ...). - `y` must be in the following shape (batch_size, ...). Args: beta: weight of precision in harmonic mean average: if True, F-beta score is computed as the unweighted average (across all classes in multiclass case), otherwise, returns a tensor with F-beta score for each class in multiclass case. precision: precision object metric with `average=False` to compute F-beta score recall: recall object metric with `average=False` to compute F-beta score output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. It is used only if precision or recall are not provided. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. Returns: MetricsLambda, F-beta metric Examples: For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`. .. include:: defaults.rst :start-after: :orphan: Binary case .. testcode:: 1 P = Precision(average=False) R = Recall(average=False) metric = Fbeta(beta=1.0, precision=P, recall=R) metric.attach(default_evaluator, "f-beta") y_true = torch.tensor([1, 0, 1, 1, 0, 1]) y_pred = torch.tensor([1, 0, 1, 0, 1, 1]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["f-beta"]) .. testoutput:: 1 0.7499... Multiclass case .. testcode:: 2 P = Precision(average=False) R = Recall(average=False) metric = Fbeta(beta=1.0, precision=P, recall=R) metric.attach(default_evaluator, "f-beta") y_true = torch.tensor([2, 0, 2, 1, 0, 1]) y_pred = torch.tensor([ [0.0266, 0.1719, 0.3055], [0.6886, 0.3978, 0.8176], [0.9230, 0.0197, 0.8395], [0.1785, 0.2670, 0.6084], [0.8448, 0.7177, 0.7288], [0.7748, 0.9542, 0.8573], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["f-beta"]) .. testoutput:: 2 0.5222... F-beta can be computed for each class as done below: .. testcode:: 3 P = Precision(average=False) R = Recall(average=False) metric = Fbeta(beta=1.0, average=False, precision=P, recall=R) metric.attach(default_evaluator, "f-beta") y_true = torch.tensor([2, 0, 2, 1, 0, 1]) y_pred = torch.tensor([ [0.0266, 0.1719, 0.3055], [0.6886, 0.3978, 0.8176], [0.9230, 0.0197, 0.8395], [0.1785, 0.2670, 0.6084], [0.8448, 0.7177, 0.7288], [0.7748, 0.9542, 0.8573], ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["f-beta"]) .. testoutput:: 3 tensor([0.5000, 0.6667, 0.4000], dtype=torch.float64) The elements of `y` and `y_pred` should have 0 or 1 values. Thresholding of predictions can be done as below: .. testcode:: 4 def thresholded_output_transform(output): y_pred, y = output y_pred = torch.round(y_pred) return y_pred, y P = Precision(average=False, output_transform=thresholded_output_transform) R = Recall(average=False, output_transform=thresholded_output_transform) metric = Fbeta(beta=1.0, precision=P, recall=R) metric.attach(default_evaluator, "f-beta") y_true = torch.tensor([1, 0, 1, 1, 0, 1]) y_pred = torch.tensor([0.6, 0.2, 0.9, 0.4, 0.7, 0.65]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics["f-beta"]) .. testoutput:: 4 0.7499... """ if not (beta > 0): raise ValueError(f"Beta should be a positive integer, but given {beta}") if precision is not None and output_transform is not None: raise ValueError("If precision argument is provided, output_transform should be None") if recall is not None and output_transform is not None: raise ValueError("If recall argument is provided, output_transform should be None") if precision is None: precision = Precision( output_transform=(lambda x: x) if output_transform is None else output_transform, # type: ignore[arg-type] average=False, device=device, ) elif precision._average: raise ValueError("Input precision metric should have average=False") if recall is None: recall = Recall( output_transform=(lambda x: x) if output_transform is None else output_transform, # type: ignore[arg-type] average=False, device=device, ) elif recall._average: raise ValueError("Input recall metric should have average=False") fbeta = (1.0 + beta ** 2) * precision * recall / (beta ** 2 * precision + recall + 1e-15) if average: fbeta = fbeta.mean().item() return fbeta

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