> For the complete documentation index, see [llms.txt](https://docs.seldon.ai/alibi-explain/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-explain/api-reference/explainers/anchors/anchor_explanation.md).

# alibi.explainers.anchors.anchor\_explanation

## `AnchorExplanation`

### Constructor

```python
AnchorExplanation(self, exp_type: str, exp_map: dict) -> None
```

| Name       | Type   | Default | Description                                                            |
| ---------- | ------ | ------- | ---------------------------------------------------------------------- |
| `exp_type` | `str`  |         | Type of explainer: tabular, text or image.                             |
| `exp_map`  | `dict` |         | Dictionary with the anchors and explainer metadata for an observation. |

### Methods

#### `coverage`

```python
coverage(partial_index: Optional[int] = None) -> float
```

| Name            | Type            | Default | Description                                                                                                                                                |
| --------------- | --------------- | ------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `partial_index` | `Optional[int]` | `None`  | Get the result coverage until a certain index. For example, if the result has precisions `[0.1, 0.5, 0.95]` and `partial_index=1`, this will return `0.5`. |

**Returns**

* Type: `float`

#### `examples`

```python
examples(only_different_prediction: bool = False, only_same_prediction: bool = False, partial_index: Optional[int] = None) -> Union[list, numpy.ndarray]
```

| Name                        | Type            | Default | Description                                                                                                 |
| --------------------------- | --------------- | ------- | ----------------------------------------------------------------------------------------------------------- |
| `only_different_prediction` | `bool`          | `False` | If `True`, will only return examples where the result makes a different prediction than the original model. |
| `only_same_prediction`      | `bool`          | `False` | If `True`, will only return examples where the result makes the same prediction than the original model.    |
| `partial_index`             | `Optional[int]` | `None`  | Get the examples from the partial result until a certain index.                                             |

**Returns**

* Type: `Union[list, numpy.ndarray]`

#### `features`

```python
features(partial_index: Optional[int] = None) -> list
```

| Name            | Type            | Default | Description                                                                                                                                        |
| --------------- | --------------- | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------- |
| `partial_index` | `Optional[int]` | `None`  | Get the result until a certain index. For example, if the result uses `segment_labels=(1, 2, 3)` and `partial_index=1`, this will return `[1, 2]`. |

**Returns**

* Type: `list`

#### `names`

```python
names(partial_index: Optional[int] = None) -> list
```

| Name            | Type            | Default | Description                                                                                                                                     |
| --------------- | --------------- | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------- |
| `partial_index` | `Optional[int]` | `None`  | Get the result until a certain index. For example, if the result is `(A=1, B=2, C=2)` and `partial_index=1`, this will return `["A=1", "B=2"]`. |

**Returns**

* Type: `list`

#### `precision`

```python
precision(partial_index: Optional[int] = None) -> float
```

| Name            | Type            | Default | Description                                                                                                                                                 |
| --------------- | --------------- | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `partial_index` | `Optional[int]` | `None`  | Get the result precision until a certain index. For example, if the result has precisions `[0.1, 0.5, 0.95]` and `partial_index=1`, this will return `0.5`. |

**Returns**

* Type: `float`