Precision Recall

Functional Interface

torchmetrics.functional.precision_recall(preds, target, average='micro', mdmc_average=None, ignore_index=None, num_classes=None, threshold=0.5, top_k=None, multiclass=None)

Computes Precision

Where text{FN}` and represent the number of true positives, false negatives and false positives respecitively. With the use of top_k parameter, this metric can generalize to Recall@K and Precision@K.

The reduction method (how the recall scores are aggregated) is controlled by the average parameter, and additionally by the mdmc_average parameter in the multi-dimensional multi-class case. Accepts all inputs listed in Input types.

Parameters

• preds (Tensor) – Predictions from model (probabilities, logits or labels)

• target (Tensor) – Ground truth values

• average (str) –

  Defines the reduction that is applied. Should be one of the following:

  • 'micro' [default]: Calculate the metric globally, across all samples and classes.

  • 'macro': Calculate the metric for each class separately, and average the metrics across classes (with equal weights for each class).

  • 'weighted': Calculate the metric for each class separately, and average the metrics across classes, weighting each class by its support (tp + fn).

  • 'none' or None: Calculate the metric for each class separately, and return the metric for every class.

  • 'samples': Calculate the metric for each sample, and average the metrics across samples (with equal weights for each sample).

  Note

  What is considered a sample in the multi-dimensional multi-class case depends on the value of mdmc_average.

  Note

  If 'none' and a given class doesn’t occur in the preds or target, the value for the class will be nan.

• mdmc_average (Optional[str]) –

  Defines how averaging is done for multi-dimensional multi-class inputs (on top of the average parameter). Should be one of the following:

  • None [default]: Should be left unchanged if your data is not multi-dimensional multi-class.

  • 'samplewise': In this case, the statistics are computed separately for each sample on the N axis, and then averaged over samples. The computation for each sample is done by treating the flattened extra axes ... (see Input types) as the N dimension within the sample, and computing the metric for the sample based on that.

  • 'global': In this case the N and ... dimensions of the inputs (see Input types) are flattened into a new N_X sample axis, i.e. the inputs are treated as if they were (N_X, C). From here on the average parameter applies as usual.

• ignore_index (Optional[int]) – Integer specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. If an index is ignored, and average=None or 'none', the score for the ignored class will be returned as nan.

• num_classes (Optional[int]) – Number of classes. Necessary for 'macro', 'weighted' and None average methods.

• threshold (float) – Threshold for transforming probability or logit predictions to binary (0,1) predictions, in the case of binary or multi-label inputs. Default value of 0.5 corresponds to input being probabilities.

• top_k (Optional[int]) –

  Number of highest probability or logit score predictions considered to find the correct label, relevant only for (multi-dimensional) multi-class inputs. The default value (None) will be interpreted as 1 for these inputs.

  Should be left at default (None) for all other types of inputs.

• multiclass (Optional[bool]) – Used only in certain special cases, where you want to treat inputs as a different type than what they appear to be. See the parameter’s documentation section for a more detailed explanation and examples.

Returns

precision and recall. Their shape depends on the average parameter

• If average in ['micro', 'macro', 'weighted', 'samples'], they are a single element tensor

• If average in ['none', None], they are a tensor of shape (C, ), where C stands for the number of classes

Return type

The function returns a tuple with two elements

Raises

• ValueError – If average is not one of "micro", "macro", "weighted", "samples", "none" or None

• ValueError – If mdmc_average is not one of None, "samplewise", "global".

• ValueError – If average is set but num_classes is not provided.

• ValueError – If num_classes is set and ignore_index is not in the range [0, num_classes).

Example

>>> from torchmetrics.functional import precision_recall
>>> preds  = torch.tensor([2, 0, 2, 1])
>>> target = torch.tensor([1, 1, 2, 0])
>>> precision_recall(preds, target, average='macro', num_classes=3)
(tensor(0.1667), tensor(0.3333))
>>> precision_recall(preds, target, average='micro')
(tensor(0.2500), tensor(0.2500))