# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
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# limitations under the License.
from typing import Any, Optional
import torch
from pytorch_lightning.metrics.functional.f_beta import (
_fbeta_update,
_fbeta_compute
)
from pytorch_lightning.metrics.metric import Metric
from pytorch_lightning.utilities import rank_zero_warn
[docs]class FBeta(Metric):
r"""
Computes `F-score <https://en.wikipedia.org/wiki/F-score>`_, specifically:
.. math::
F_\beta = (1 + \beta^2) * \frac{\text{precision} * \text{recall}}
{(\beta^2 * \text{precision}) + \text{recall}}
Where :math:`\beta` is some positive real factor. Works with binary, multiclass, and multilabel data.
Accepts logits from a model output or integer class values in prediction.
Works with multi-dimensional preds and target.
Forward accepts
- ``preds`` (float or long tensor): ``(N, ...)`` or ``(N, C, ...)`` where C is the number of classes
- ``target`` (long tensor): ``(N, ...)``
If preds and target are the same shape and preds is a float tensor, we use the ``self.threshold`` argument.
This is the case for binary and multi-label logits.
If preds has an extra dimension as in the case of multi-class scores we perform an argmax on ``dim=1``.
Args:
num_classes: Number of classes in the dataset.
beta: Beta coefficient in the F measure.
threshold:
Threshold value for binary or multi-label logits. default: 0.5
average:
- ``'micro'`` computes metric globally
- ``'macro'`` computes metric for each class and uniformly averages them
- ``'weighted'`` computes metric for each class and does a weighted-average,
where each class is weighted by their support (accounts for class imbalance)
- ``'none'`` computes and returns the metric per class
multilabel: If predictions are from multilabel classification.
compute_on_step:
Forward only calls ``update()`` and return None if this is set to False. default: True
dist_sync_on_step:
Synchronize metric state across processes at each ``forward()``
before returning the value at the step. default: False
process_group:
Specify the process group on which synchronization is called. default: None (which selects the entire world)
Example:
>>> from pytorch_lightning.metrics import FBeta
>>> target = torch.tensor([0, 1, 2, 0, 1, 2])
>>> preds = torch.tensor([0, 2, 1, 0, 0, 1])
>>> f_beta = FBeta(num_classes=3, beta=0.5)
>>> f_beta(preds, target)
tensor(0.3333)
"""
def __init__(
self,
num_classes: int,
beta: float = 1.0,
threshold: float = 0.5,
average: str = "micro",
multilabel: bool = False,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
):
super().__init__(
compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group,
)
self.num_classes = num_classes
self.beta = beta
self.threshold = threshold
self.average = average
self.multilabel = multilabel
allowed_average = ("micro", "macro", "weighted", None)
if self.average not in allowed_average:
raise ValueError('Argument `average` expected to be one of the following:'
f' {allowed_average} but got {self.average}')
self.add_state("true_positives", default=torch.zeros(num_classes), dist_reduce_fx="sum")
self.add_state("predicted_positives", default=torch.zeros(num_classes), dist_reduce_fx="sum")
self.add_state("actual_positives", default=torch.zeros(num_classes), dist_reduce_fx="sum")
[docs] def update(self, preds: torch.Tensor, target: torch.Tensor):
"""
Update state with predictions and targets.
Args:
preds: Predictions from model
target: Ground truth values
"""
true_positives, predicted_positives, actual_positives = _fbeta_update(
preds, target, self.num_classes, self.threshold, self.multilabel
)
self.true_positives += true_positives
self.predicted_positives += predicted_positives
self.actual_positives += actual_positives
[docs] def compute(self) -> torch.Tensor:
"""
Computes fbeta over state.
"""
return _fbeta_compute(self.true_positives, self.predicted_positives,
self.actual_positives, self.beta, self.average)
# todo: remove in v1.2
class Fbeta(FBeta):
r"""
Computes `F-score <https://en.wikipedia.org/wiki/F-score>`_
.. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.classification.f_beta.FBeta`
"""
def __init__(
self,
num_classes: int,
beta: float = 1.0,
threshold: float = 0.5,
average: str = "micro",
multilabel: bool = False,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
):
rank_zero_warn(
"This `Fbeta` was deprecated in v1.0.x in favor of"
" `from pytorch_lightning.metrics.classification.f_beta import FBeta`."
" It will be removed in v1.2.0", DeprecationWarning
)
super().__init__(
num_classes, beta, threshold, average, multilabel, compute_on_step, dist_sync_on_step, process_group
)
[docs]class F1(FBeta):
"""
Computes F1 metric. F1 metrics correspond to a harmonic mean of the
precision and recall scores.
Works with binary, multiclass, and multilabel data.
Accepts logits from a model output or integer class values in prediction.
Works with multi-dimensional preds and target.
Forward accepts
- ``preds`` (float or long tensor): ``(N, ...)`` or ``(N, C, ...)`` where C is the number of classes
- ``target`` (long tensor): ``(N, ...)``
If preds and target are the same shape and preds is a float tensor, we use the ``self.threshold`` argument.
This is the case for binary and multi-label logits.
If preds has an extra dimension as in the case of multi-class scores we perform an argmax on ``dim=1``.
Args:
num_classes: Number of classes in the dataset.
threshold:
Threshold value for binary or multi-label logits. default: 0.5
average:
- ``'micro'`` computes metric globally
- ``'macro'`` computes metric for each class and uniformly averages them
- ``'weighted'`` computes metric for each class and does a weighted-average,
where each class is weighted by their support (accounts for class imbalance)
- ``'none'`` computes and returns the metric per class
multilabel: If predictions are from multilabel classification.
compute_on_step:
Forward only calls ``update()`` and returns None if this is set to False. default: True
dist_sync_on_step:
Synchronize metric state across processes at each ``forward()``
before returning the value at the step. default: False
process_group:
Specify the process group on which synchronization is called. default: None (which selects the entire world)
Example:
>>> from pytorch_lightning.metrics import F1
>>> target = torch.tensor([0, 1, 2, 0, 1, 2])
>>> preds = torch.tensor([0, 2, 1, 0, 0, 1])
>>> f1 = F1(num_classes=3)
>>> f1(preds, target)
tensor(0.3333)
"""
def __init__(
self,
num_classes: int,
threshold: float = 0.5,
average: str = "micro",
multilabel: bool = False,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
):
super().__init__(
num_classes=num_classes,
beta=1.0,
threshold=threshold,
average=average,
multilabel=multilabel,
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
)
