Add a custom drift method
How to implement a new drift detection 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.
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 seriescurrent_data: pd.Series- current data series to comparefeature_type: str- feature typethreshold: 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
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
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 Profilefunc: Callable- a StatTest functionallowed_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(
name="example_test",
display_name="Example Stat Test (score)",
func=example_stat_test,
allowed_feature_types=["cat"],
)
Last modified 3mo ago