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FBeta Score

Module Interface

class torchmetrics.FBetaScore(num_classes=None, beta=1.0, threshold=0.5, average='micro', mdmc_average=None, ignore_index=None, top_k=None, multiclass=None, compute_on_step=None, **kwargs)[source]

Computes F-score, specifically:

F_\beta = (1 + \beta^2) * \frac{\text{precision} * \text{recall}}
{(\beta^2 * \text{precision}) + \text{recall}}

Where \beta is some positive real factor. Works with binary, multiclass, and multilabel data. Accepts logit scores or probabilities 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 to convert into integer labels. This is the case for binary and multi-label logits and probabilities.

If preds has an extra dimension as in the case of multi-class scores we perform an argmax on dim=1.

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

  • beta (float) – Beta coefficient in the F measure.

  • 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.

  • 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.

  • top_k (Optional[int]) –

    Number of the highest probability or logit score predictions considered finding 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.

  • compute_on_step (Optional[bool]) –

    Forward only calls update() and returns None if this is set to False.

    Deprecated since version v0.8: Argument has no use anymore and will be removed v0.9.

  • kwargs (Dict[str, Any]) – Additional keyword arguments, see Advanced metric settings for more info.

Raises

ValueError – If average is none of "micro", "macro", "weighted", "none", None.

Example

>>> import torch
>>> from torchmetrics import FBetaScore
>>> target = torch.tensor([0, 1, 2, 0, 1, 2])
>>> preds = torch.tensor([0, 2, 1, 0, 0, 1])
>>> f_beta = FBetaScore(num_classes=3, beta=0.5)
>>> f_beta(preds, target)
tensor(0.3333)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()[source]

Computes f-beta over state.

Return type

Tensor

Functional Interface

torchmetrics.functional.fbeta_score(preds, target, beta=1.0, average='micro', mdmc_average=None, ignore_index=None, num_classes=None, threshold=0.5, top_k=None, multiclass=None)[source]

Computes f_beta metric.

F_{\beta} = (1 + \beta^2) * \frac{\text{precision} * \text{recall}}
{(\beta^2 * \text{precision}) + \text{recall}}

Works with binary, multiclass, and multilabel data. Accepts probabilities or 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 to convert into integer labels. This is the case for binary and multi-label logits or probabilities.

If preds has an extra dimension as in the case of multi-class scores we perform an argmax on dim=1.

The reduction method (how the precision 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

  • beta (float) – beta coefficient

  • 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.

Return type

Tensor

Returns

The shape of the returned tensor depends on the average parameter

  • If average in ['micro', 'macro', 'weighted', 'samples'], a one-element tensor will be returned

  • If average in ['none', None], the shape will be (C,), where C stands for the number of classes

Example

>>> from torchmetrics.functional import fbeta_score
>>> target = torch.tensor([0, 1, 2, 0, 1, 2])
>>> preds = torch.tensor([0, 2, 1, 0, 0, 1])
>>> fbeta_score(preds, target, num_classes=3, beta=0.5)
tensor(0.3333)
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