Source code for pytorch_lightning.metrics.functional.confusion_matrix

# 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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# Unless required by applicable law or agreed to in writing, software
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from typing import Optional

import torch

from pytorch_lightning.metrics.utils import _input_format_classification
from pytorch_lightning.utilities import rank_zero_warn


def _confusion_matrix_update(preds: torch.Tensor,
                             target: torch.Tensor,
                             num_classes: int,
                             threshold: float = 0.5) -> torch.Tensor:
    preds, target = _input_format_classification(preds, target, threshold)
    unique_mapping = (target.view(-1) * num_classes + preds.view(-1)).to(torch.long)
    bins = torch.bincount(unique_mapping, minlength=num_classes ** 2)
    confmat = bins.reshape(num_classes, num_classes)
    return confmat


def _confusion_matrix_compute(confmat: torch.Tensor,
                              normalize: Optional[str] = None) -> torch.Tensor:
    allowed_normalize = ('true', 'pred', 'all', None)
    assert normalize in allowed_normalize, \
        f"Argument average needs to one of the following: {allowed_normalize}"
    confmat = confmat.float()
    if normalize is not None:
        if normalize == 'true':
            cm = confmat / confmat.sum(axis=1, keepdim=True)
        elif normalize == 'pred':
            cm = confmat / confmat.sum(axis=0, keepdim=True)
        elif normalize == 'all':
            cm = confmat / confmat.sum()
        nan_elements = cm[torch.isnan(cm)].nelement()
        if nan_elements != 0:
            cm[torch.isnan(cm)] = 0
            rank_zero_warn(f'{nan_elements} nan values found in confusion matrix have been replaced with zeros.')
        return cm
    return confmat


def confusion_matrix(
        preds: torch.Tensor,
        target: torch.Tensor,
        num_classes: int,
        normalize: Optional[str] = None,
        threshold: float = 0.5
) -> torch.Tensor:
    """
    Computes the confusion matrix. 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.

    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:
        preds: (float or long tensor), Either a ``(N, ...)`` tensor with labels or
            ``(N, C, ...)`` where C is the number of classes, tensor with logits/probabilities
        target: ``target`` (long tensor), tensor with shape ``(N, ...)`` with ground true labels
        num_classes: Number of classes in the dataset.
        normalize: Normalization mode for confusion matrix. Choose from

            - ``None``: no normalization (default)
            - ``'true'``: normalization over the targets (most commonly used)
            - ``'pred'``: normalization over the predictions
            - ``'all'``: normalization over the whole matrix

        threshold:
            Threshold value for binary or multi-label logits. default: 0.5

    Example:

        >>> from pytorch_lightning.metrics.functional import confusion_matrix
        >>> target = torch.tensor([1, 1, 0, 0])
        >>> preds = torch.tensor([0, 1, 0, 0])
        >>> confusion_matrix(preds, target, num_classes=2)
        tensor([[2., 0.],
                [1., 1.]])
    """
    confmat = _confusion_matrix_update(preds, target, num_classes, threshold)
    return _confusion_matrix_compute(confmat, normalize)