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Source code for pytorch_lightning.metrics.classification.precision_recall

# Copyright The PyTorch Lightning team.
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# 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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from typing import Any, Optional

import torch

from pytorch_lightning.metrics.metric import Metric
from pytorch_lightning.metrics.utils import METRIC_EPS, _input_format_classification_one_hot


[docs]class Precision(Metric): r""" Computes `Precision <https://en.wikipedia.org/wiki/Precision_and_recall>`_: .. math:: \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}} Where :math:`\text{TP}` and :math:`\text{FP}` represent the number of true positives and false positives respecitively. 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 then takes the mean 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 Precision >>> target = torch.tensor([0, 1, 2, 0, 1, 2]) >>> preds = torch.tensor([0, 2, 1, 0, 0, 1]) >>> precision = Precision(num_classes=3) >>> precision(preds, target) tensor(0.3333) """ def __init__( self, num_classes: int = 1, 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.threshold = threshold self.average = average self.multilabel = multilabel assert self.average in ('micro', 'macro'), \ "average passed to the function must be either `micro` or `macro`" 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")
[docs] def update(self, preds: torch.Tensor, target: torch.Tensor): preds, target = _input_format_classification_one_hot( self.num_classes, preds, target, self.threshold, self.multilabel ) # multiply because we are counting (1, 1) pair for true positives self.true_positives += torch.sum(preds * target, dim=1) self.predicted_positives += torch.sum(preds, dim=1)
[docs] def compute(self): if self.average == 'micro': return self.true_positives.sum().float() / (self.predicted_positives.sum() + METRIC_EPS) elif self.average == 'macro': return (self.true_positives.float() / (self.predicted_positives + METRIC_EPS)).mean()
[docs]class Recall(Metric): r""" Computes `Recall <https://en.wikipedia.org/wiki/Precision_and_recall>`_: .. math:: \text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}} Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and false negatives respecitively. 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 then takes the mean 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 Recall >>> target = torch.tensor([0, 1, 2, 0, 1, 2]) >>> preds = torch.tensor([0, 2, 1, 0, 0, 1]) >>> recall = Recall(num_classes=3) >>> recall(preds, target) tensor(0.3333) """ def __init__( self, num_classes: int = 1, 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.threshold = threshold self.average = average self.multilabel = multilabel assert self.average in ('micro', 'macro'), \ "average passed to the function must be either `micro` or `macro`" self.add_state("true_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 """ preds, target = _input_format_classification_one_hot( self.num_classes, preds, target, self.threshold, self.multilabel ) # multiply because we are counting (1, 1) pair for true positives self.true_positives += torch.sum(preds * target, dim=1) self.actual_positives += torch.sum(target, dim=1)
[docs] def compute(self): """ Computes recall over state. """ if self.average == 'micro': return self.true_positives.sum().float() / (self.actual_positives.sum() + METRIC_EPS) elif self.average == 'macro': return (self.true_positives.float() / (self.actual_positives + METRIC_EPS)).mean()

© Copyright Copyright (c) 2018-2021, William Falcon et al... Revision c462b274.

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