# Add a custom drift method

**Pre-requisites**:

* You know how to set custom drift methods and which methods are available in the library.

If you do not find a suitable drift detection method, you can implement a custom function.

## Custom StatTest function requirements:

The StatTest function should match `(reference_data: pd.Series, current_data: pd.Series, threshold: float) -> Tuple[float, bool]` signature:

* `reference_data: pd.Series` - reference data series
* `current_data: pd.Series` - current data series to compare
* `feature_type: str` - feature type
* `threshold: float` - Stat Test threshold for drift detection

Returns:

* `score: float` - Stat Test score (actual value)
* `drift_detected: bool` - indicates is drift detected with given threshold

### Example:

```python
from typing import Tuple

import numpy as np
import pandas as pd
from scipy.stats import anderson_ksamp


def anderson_stat_test(reference_data: pd.Series, current_data: pd.Series, _feature_type: str, threshold: float) -> Tuple[float, bool]:
  p_value = anderson_ksamp(np.array([reference_data, current_data]))[2]
  return p_value, p_value < threshold
```

## StatTest meta information (StatTest class):

To use the StatTest function, we recommended writing a specific instance of the StatTest class for that function:

To create the instance of the `StatTest` class, you need:

* `name: str` - a short name used to reference the Stat Test from the options (the StatTest should be registered globally)
* `display_name: str` - a long name displayed in the Dashboard and Profile
* `func: Callable` - a StatTest function
* `allowed_feature_types: List[str]` - the list of allowed feature types to which this function can be applied (available values: `cat`, `num`)

### Example:

```python
from evidently.calculations.stattests import StatTest


def example_stat_test(reference_data, current_data, feature_type, threshold):
  return 0.1, False


example_stat_test = StatTest(
  name="example_test",
  display_name="Example Stat Test (score)",
  func=example_stat_test,
  allowed_feature_types=["cat"],
)
```