## scores.LogitScore


Base class for logit-based uncertainty scores.


Usage

``` python
scores.LogitScore(
    temperature=None,
    task="multiclass",
    per_member=False,
)
```


Supports multiclass, binary (single/two-logit), and multilabel tasks. Handles temperature fitting and input normalization for all cases.

The `per_member` flag enables handling of logits that contain multiple stochastic members per sample (e.g. ensembles or MC-dropout). When `True`, score methods compute the metric for each member and return the mean across the member axis.


## Parameters


`temperature: float or None = None`  
Optional temperature to apply to logits. If `None`, no temperature scaling is applied until [fit()](scores.PCAScore.md#seapig.scores.PCAScore.fit) is called.

`task: (``"multiclass", `<span class="st">`"binary"``, ``"multilabel"``)`</span>` = ``"multiclass"`  
Type of classification task. Determines score computation and temperature fitting loss.

`per_member: bool = ``False`  
If `True`, logits are expected to have a member dimension (e.g. for ensembles or MC-dropout). Score methods will compute the score for each member and return the mean across members


## Notes

Input shapes and label formats by task:

- `multiclass`: logits `(N, C)`, labels `(N,)` long
- `binary` single-logit: logits `(N,)` or `(N, 1)`, labels `(N,)` float/long
- `binary` two-logit: logits `(N, 2)`, labels `(N,)` long
- `multilabel`: logits `(N, C)`, labels `(N, C)` float


## See Also

[scores.SoftmaxScore](scores.SoftmaxScore.md#seapig.scores.SoftmaxScore)  

[scores.EntropyScore](scores.EntropyScore.md#seapig.scores.EntropyScore)  

[scores.EnergyScore](scores.EnergyScore.md#seapig.scores.EnergyScore)  

[scores.MarginScore](scores.MarginScore.md#seapig.scores.MarginScore)  


## Examples

``` python
import torch
from seapig.scores.logits import SoftmaxScore
logits = torch.randn(4, 3)
score = SoftmaxScore()
score.score(logits)
```


## Methods

| Name | Description |
|----|----|
| [fit()](#fit) | Fit the score on reference logits. |
| [score()](#score) | Compute uncertainty scores for query logits. |
| [select()](#select) | Select samples for prediction based on their uncertainty score. |

------------------------------------------------------------------------


#### fit()


Fit the score on reference logits.


Usage

``` python
fit(
    X=None,
    Y=None,
    model=None,
    loader=None,
    outdir=None,
    prefix=None,
    *args,
    **kwargs
)
```


This method supports two usage modes:

1.  **Precomputed logits**: Supply logits directly via `X`, with optional labels via `Y` for temperature fitting.
2.  **On-the-fly extraction**: Supply a `model` with a `.logits()` method and a `DataLoader` to extract logits automatically.

You must use either logits OR model+loader, but not both.


##### Parameters


`X: torch.Tensor or None = None`  
Reference logits. Shape depends on task (see class docstring). Required when not using `model` and `loader`.

`Y: torch.Tensor or None = None`  
Optional labels for temperature fitting. Shape/type depends on task.

`model: torch.nn.Module or None = None`  
Model with a `.logits(x)` method. Required when not using precomputed logits.

`loader: DataLoader or None = None`  
DataLoader yielding batches for inference. Required when using `model`.

`outdir: Path or str or None = None`  
Optional directory to save/load logits. Only used with `model` and `loader`.

`prefix: str or None = None`  
Optional prefix for saved files. Only used with `model` and `loader`.


##### Notes

If labels are provided, temperature is fitted to minimize NLL for the task.

------------------------------------------------------------------------


#### score()


Compute uncertainty scores for query logits.


Usage

``` python
score(query_logits)
```


##### Parameters


`query_logits: torch.Tensor`  
Logits for samples to score. Shape depends on task.


##### Returns


`torch.Tensor`  
1-D tensor of shape `(M,)`. Lower values indicate lower uncertainty.


------------------------------------------------------------------------


#### select()


Select samples for prediction based on their uncertainty score.


Usage

``` python
select(query_logits)
```


Samples with scores lower than the threshold are selected for prediction, while samples with scores higher than the threshold are excluded.


##### Parameters


`query_logits: torch.Tensor`  
Logits for samples to select. Shape depends on task.


##### Returns


`dict[str, torch.Tensor]`  
A dict with keys `'score'` (uncertainty scores) and `'selected'` (boolean mask where `True` means the sample is selected).