Metrics

Metrics not included in fastai.

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MatthewsCorrCoefBinary

 MatthewsCorrCoefBinary (sample_weight=None)

Matthews correlation coefficient for single-label classification problems

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get_task_metrics

 get_task_metrics (dls, binary_metrics=None, multi_class_metrics=None,
                   regression_metrics=None, verbose=True)

All metrics applicable to multi classification have been created by Doug Williams (https://github.com/williamsdoug). Thanks a lot Doug!!

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F1_multi

 F1_multi (*args, **kwargs)

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Fbeta_multi

 Fbeta_multi (inp, targ, beta=1.0, thresh=0.5, sigmoid=True)

Computes Fbeta when inp and targ are the same size.

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balanced_accuracy_multi

 balanced_accuracy_multi (inp, targ, thresh=0.5, sigmoid=True)

Computes balanced accuracy when inp and targ are the same size.

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specificity_multi

 specificity_multi (inp, targ, thresh=0.5, sigmoid=True)

Computes specificity (true negative rate) when inp and targ are the same size.

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recall_multi

 recall_multi (inp, targ, thresh=0.5, sigmoid=True)

Computes recall when inp and targ are the same size.

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precision_multi

 precision_multi (inp, targ, thresh=0.5, sigmoid=True)

Computes precision when inp and targ are the same size.

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metrics_multi_common

 metrics_multi_common (inp, targ, thresh=0.5, sigmoid=True,
                       by_sample=False)

Computes TP, TN, FP, FN when inp and targ are the same size.

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accuracy_multi

 accuracy_multi (inp, targ, thresh=0.5, sigmoid=True, by_sample=False)

Computes accuracy when inp and targ are the same size.

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mae

 mae (inp, targ)

Mean absolute error between inp and targ.

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mape

 mape (inp, targ)

Mean absolute percentage error between inp and targ.

n_classes = 4
inp = torch.normal(0, 1, (16, 20, n_classes))
targ = torch.randint(0, n_classes, (16, 20)).to(torch.int8)
_mAP(inp, targ)
0.27493315845795063