# alibi\_detect.cd.preprocess

## Functions

### `classifier_uncertainty`

```python
classifier_uncertainty(x: Union[numpy.ndarray, list], model_fn: Callable, preds_type: str = 'probs', uncertainty_type: str = 'entropy', margin_width: float = 0.1) -> numpy.ndarray
```

Evaluate model\_fn on x and transform predictions to prediction uncertainties.

| Name               | Type                         | Default     | Description                                                                                                                                                                                                |
| ------------------ | ---------------------------- | ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `x`                | `Union[numpy.ndarray, list]` |             | Batch of instances.                                                                                                                                                                                        |
| `model_fn`         | `Callable`                   |             | Function that evaluates a classification model on x in a single call (contains batching logic if necessary).                                                                                               |
| `preds_type`       | `str`                        | `'probs'`   | Type of prediction output by the model. Options are 'probs' (in \[0,1]) or 'logits' (in \[-inf,inf]).                                                                                                      |
| `uncertainty_type` | `str`                        | `'entropy'` | Method for determining the model's uncertainty for a given instance. Options are 'entropy' or 'margin'.                                                                                                    |
| `margin_width`     | `float`                      | `0.1`       | Width of the margin if uncertainty\_type = 'margin'. The model is considered uncertain on an instance if the highest two class probabilities it assigns to the instance differ by less than margin\_width. |

**Returns**

* Type: `numpy.ndarray`

### `regressor_uncertainty`

```python
regressor_uncertainty(x: Union[numpy.ndarray, list], model_fn: Callable, uncertainty_type: str = 'mc_dropout', n_evals: int = 25) -> numpy.ndarray
```

Evaluate model\_fn on x and transform predictions to prediction uncertainties.

| Name               | Type                         | Default        | Description                                                                                                                                                                                                                  |
| ------------------ | ---------------------------- | -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `x`                | `Union[numpy.ndarray, list]` |                | Batch of instances.                                                                                                                                                                                                          |
| `model_fn`         | `Callable`                   |                | Function that evaluates a regression model on x in a single call (contains batching logic if necessary).                                                                                                                     |
| `uncertainty_type` | `str`                        | `'mc_dropout'` | Method for determining the model's uncertainty for a given instance. Options are 'mc\_dropout' or 'ensemble'. The former should output a scalar per instance. The latter should output a vector of predictions per instance. |
| `n_evals`          | `int`                        | `25`           | The number of times to evaluate the model under different dropout configurations. Only relavent when using the 'mc\_dropout' uncertainty type.                                                                               |

**Returns**

* Type: `numpy.ndarray`


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