# alibi\_detect.cd.cvm

## `CVMDrift`

*Inherits from:* `BaseUnivariateDrift`, `BaseDetector`, `ABC`, `DriftConfigMixin`

### Constructor

```python
CVMDrift(self, x_ref: Union[numpy.ndarray, list], p_val: float = 0.05, x_ref_preprocessed: bool = False, preprocess_at_init: bool = True, update_x_ref: Optional[Dict[str, int]] = None, preprocess_fn: Optional[Callable] = None, correction: str = 'bonferroni', n_features: Optional[int] = None, input_shape: Optional[tuple] = None, data_type: Optional[str] = None) -> None
```

| Name                 | Type                         | Default        | Description                                                                                                                                                                                                                                      |
| -------------------- | ---------------------------- | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `x_ref`              | `Union[numpy.ndarray, list]` |                | Data used as reference distribution.                                                                                                                                                                                                             |
| `p_val`              | `float`                      | `0.05`         | p-value used for significance of the CVM test. If the FDR correction method is used, this corresponds to the acceptable q-value.                                                                                                                 |
| `x_ref_preprocessed` | `bool`                       | `False`        | Whether the given reference data `x_ref` has been preprocessed yet. If `x_ref_preprocessed=True`, only the test data `x` will be preprocessed at prediction time. If `x_ref_preprocessed=False`, the reference data will also be preprocessed.   |
| `preprocess_at_init` | `bool`                       | `True`         | Whether to preprocess the reference data when the detector is instantiated. Otherwise, the reference data will be preprocessed at prediction time. Only applies if `x_ref_preprocessed=False`.                                                   |
| `update_x_ref`       | `Optional[Dict[str, int]]`   | `None`         | Reference data can optionally be updated to the last n instances seen by the detector or via reservoir sampling with size n. For the former, the parameter equals {'last': n} while for reservoir sampling {'reservoir\_sampling': n} is passed. |
| `preprocess_fn`      | `Optional[Callable]`         | `None`         | Function to preprocess the data before computing the data drift metrics.                                                                                                                                                                         |
| `correction`         | `str`                        | `'bonferroni'` | Correction type for multivariate data. Either 'bonferroni' or 'fdr' (False Discovery Rate).                                                                                                                                                      |
| `n_features`         | `Optional[int]`              | `None`         | Number of features used in the CVM test. No need to pass it if no preprocessing takes place. In case of a preprocessing step, this can also be inferred automatically but could be more expensive to compute.                                    |
| `input_shape`        | `Optional[tuple]`            | `None`         | Shape of input data.                                                                                                                                                                                                                             |
| `data_type`          | `Optional[str]`              | `None`         | Optionally specify the data type (tabular, image or time-series). Added to metadata.                                                                                                                                                             |

### Methods

#### `feature_score`

```python
feature_score(x_ref: numpy.ndarray, x: numpy.ndarray) -> Tuple[numpy.ndarray, numpy.ndarray]
```

Performs the two-sample Cramer-von Mises test(s), computing the p-value and test statistic per feature.

| Name    | Type            | Default | Description                                       |
| ------- | --------------- | ------- | ------------------------------------------------- |
| `x_ref` | `numpy.ndarray` |         | Reference instances to compare distribution with. |
| `x`     | `numpy.ndarray` |         | Batch of instances.                               |

**Returns**

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


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