> For the complete documentation index, see [llms.txt](https://docs.seldon.ai/alibi-detect/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.seldon.ai/alibi-detect/api-reference/utils/mapping.md).

# alibi\_detect.utils.mapping

## Functions

### `num2ord`

```python
num2ord(data: numpy.ndarray, dist: dict) -> numpy.ndarray
```

Transform numerical values into categories using the map calculated under the fit method.

| Name   | Type            | Default | Description                                                                                      |
| ------ | --------------- | ------- | ------------------------------------------------------------------------------------------------ |
| `data` | `numpy.ndarray` |         | Numpy array with the numerical data.                                                             |
| `dist` | `dict`          |         | Dict with as keys the categorical variables and as values the numerical value for each category. |

**Returns**

* Type: `numpy.ndarray`

### `ohe2ord`

```python
ohe2ord(X_ohe: numpy.ndarray, cat_vars_ohe: dict) -> Tuple[numpy.ndarray, dict]
```

Convert one-hot encoded variables to ordinal encodings.

| Name           | Type            | Default | Description                                                                                                                                             |
| -------------- | --------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `X_ohe`        | `numpy.ndarray` |         | Data with mixture of one-hot encoded and numerical variables.                                                                                           |
| `cat_vars_ohe` | `dict`          |         | Dict with as keys the first column index for each one-hot encoded categorical variable and as values the number of categories per categorical variable. |

**Returns**

* Type: `Tuple[numpy.ndarray, dict]`

### `ohe2ord_shape`

```python
ohe2ord_shape(shape: tuple, cat_vars: Optional[dict] = None, is_ohe: bool = False) -> tuple
```

Infer shape of instance if the categorical variables have ordinal instead of on-hot encoding.

| Name       | Type             | Default | Description                                                                                                |
| ---------- | ---------------- | ------- | ---------------------------------------------------------------------------------------------------------- |
| `shape`    | `tuple`          |         | Instance shape, starting with batch dimension.                                                             |
| `cat_vars` | `Optional[dict]` | `None`  | Dict with as keys the categorical columns and as values the number of categories per categorical variable. |
| `is_ohe`   | `bool`           | `False` | Whether instance is OHE.                                                                                   |

**Returns**

* Type: `tuple`

### `ord2num`

```python
ord2num(data: numpy.ndarray, dist: dict) -> numpy.ndarray
```

Transform categorical into numerical values using a mapping.

| Name   | Type            | Default | Description                                                                                      |
| ------ | --------------- | ------- | ------------------------------------------------------------------------------------------------ |
| `data` | `numpy.ndarray` |         | Numpy array with the categorical data.                                                           |
| `dist` | `dict`          |         | Dict with as keys the categorical variables and as values the numerical value for each category. |

**Returns**

* Type: `numpy.ndarray`

### `ord2ohe`

```python
ord2ohe(X_ord: numpy.ndarray, cat_vars_ord: dict) -> Tuple[numpy.ndarray, dict]
```

Convert ordinal to one-hot encoded variables.

| Name           | Type            | Default | Description                                                                                                |
| -------------- | --------------- | ------- | ---------------------------------------------------------------------------------------------------------- |
| `X_ord`        | `numpy.ndarray` |         | Data with mixture of ordinal encoded and numerical variables.                                              |
| `cat_vars_ord` | `dict`          |         | Dict with as keys the categorical columns and as values the number of categories per categorical variable. |

**Returns**

* Type: `Tuple[numpy.ndarray, dict]`