133 lines
5.1 KiB
Python
133 lines
5.1 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import functools
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from abc import ABC, abstractmethod
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from typing import Any, Callable, Optional, Union
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from collections.abc import Mapping, Sequence
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from collections import namedtuple
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import torch
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from torch import nn
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from pytorch_lightning.metrics.metric import Metric
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from pytorch_lightning.metrics.classification.precision_recall import _input_format
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from pytorch_lightning.metrics.utils import METRIC_EPS
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class Fbeta(Metric):
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"""
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Computes f_beta metric.
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Works with binary, multiclass, and multilabel data.
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Accepts logits from a model output or integer class values in prediction.
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Works with multi-dimensional preds and target.
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Forward accepts
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- ``preds`` (float or long tensor): ``(N, ...)`` or ``(N, C, ...)`` where C is the number of classes
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- ``target`` (long tensor): ``(N, ...)``
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If preds and target are the same shape and preds is a float tensor, we use the ``self.threshold`` argument.
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This is the case for binary and multi-label logits.
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If preds has an extra dimension as in the case of multi-class scores we perform an argmax on ``dim=1``.
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Args:
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num_classes: Number of classes in the dataset.
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beta: Beta coefficient in the F measure.
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threshold:
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Threshold value for binary or multi-label logits. default: 0.5
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average:
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* `'micro'` computes metric globally
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* `'macro'` computes metric for each class and then takes the mean
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multilabel: If predictions are from multilabel classification.
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compute_on_step:
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Forward only calls ``update()`` and return None if this is set to False. default: True
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dist_sync_on_step:
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Synchronize metric state across processes at each ``forward()``
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before returning the value at the step. default: False
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process_group:
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Specify the process group on which synchronization is called. default: None (which selects the entire world)
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Example:
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>>> from pytorch_lightning.metrics import Fbeta
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>>> target = torch.tensor([0, 1, 2, 0, 1, 2])
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>>> preds = torch.tensor([0, 2, 1, 0, 0, 1])
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>>> f_beta = Fbeta(num_classes=3, beta=0.5)
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>>> f_beta(preds, target)
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tensor(0.3333)
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"""
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def __init__(
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self,
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num_classes: int = 1,
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beta: float = 1.,
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threshold: float = 0.5,
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average: str = 'micro',
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multilabel: bool = False,
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compute_on_step: bool = True,
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dist_sync_on_step: bool = False,
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process_group: Optional[Any] = None,
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):
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super().__init__(
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compute_on_step=compute_on_step,
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dist_sync_on_step=dist_sync_on_step,
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process_group=process_group,
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)
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self.num_classes = num_classes
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self.beta = beta
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self.threshold = threshold
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self.average = average
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self.multilabel = multilabel
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assert self.average in ('micro', 'macro'), \
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"average passed to the function must be either `micro` or `macro`"
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self.add_state("true_positives", default=torch.zeros(num_classes), dist_reduce_fx="sum")
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self.add_state("predicted_positives", default=torch.zeros(num_classes), dist_reduce_fx="sum")
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self.add_state("actual_positives", default=torch.zeros(num_classes), dist_reduce_fx="sum")
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def update(self, preds: torch.Tensor, target: torch.Tensor):
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"""
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Update state with predictions and targets.
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Args:
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preds: Predictions from model
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target: Ground truth values
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"""
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preds, target = _input_format(self.num_classes, preds, target, self.threshold, self.multilabel)
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self.true_positives += torch.sum(preds * target, dim=1)
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self.predicted_positives += torch.sum(preds, dim=1)
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self.actual_positives += torch.sum(target, dim=1)
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def compute(self):
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"""
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Computes accuracy over state.
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"""
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if self.average == 'micro':
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precision = self.true_positives.sum().float() / (self.predicted_positives.sum() + METRIC_EPS)
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recall = self.true_positives.sum().float() / (self.actual_positives.sum() + METRIC_EPS)
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return (1 + self.beta ** 2) * (precision * recall) / (self.beta ** 2 * precision + recall)
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elif self.average == 'macro':
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precision = self.true_positives.float() / (self.predicted_positives + METRIC_EPS)
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recall = self.true_positives.float() / (self.actual_positives + METRIC_EPS)
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return ((1 + self.beta ** 2) * (precision * recall) / (self.beta ** 2 * precision + recall)).mean()
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