Add a custom drift method

How to implement a new drift detection method.


  • 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.

Code example

Notebook example with custom data drift function example:

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


  • score: float - Stat Test score (actual value)

  • drift_detected: bool - indicates is drift detected with given threshold


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)


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(
  display_name="Example Stat Test (score)",

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