dataeval.protocols.UpdateStrategy¶

class dataeval.protocols.UpdateStrategy¶

Protocol defining the interface for updating reference data in drift detectors.

Update strategies control how drift detectors maintain their reference dataset as new data arrives. Implementations must provide a __call__ method that updates the reference data based on new observations.

Examples

Creating a custom update strategy that keeps a moving average:

>>> import numpy as np
>>> from numpy.typing import NDArray
>>>
>>> class MovingAverageUpdate:
...     '''Update strategy that maintains a moving average of reference data.'''
...
...     def __init__(self, n: int, alpha: float = 0.9) -> None:
...         '''
...         Parameters
...         ----------
...         n : int
...             Maximum number of samples to maintain
...         alpha : float, default 0.9
...             Exponential moving average weight (0 < alpha < 1)
...         '''
...         self.n = n
...         self.alpha = alpha
...
...     def __call__(self, reference_data: NDArray[np.float32], data: NDArray[np.float32]) -> NDArray[np.float32]:
...         '''
...         Update reference data with exponential moving average.
...
...         Parameters
...         ----------
...         reference_data : NDArray[np.float32]
...             Current reference data of shape (n_ref, n_features)
...         data : NDArray[np.float32]
...             New observations of shape (n_new, n_features)
...
...         Returns
...         -------
...         NDArray[np.float32]
...             Updated reference data of shape (n_updated, n_features)
...         '''
...         data_flat = np.atleast_2d(np.asarray(data, dtype=np.float32))
...         # Compute moving average for overlapping samples
...         n_overlap = min(len(reference_data), len(data_flat))
...         if n_overlap > 0:
...             reference_data[:n_overlap] = (
...                 self.alpha * reference_data[:n_overlap] + (1 - self.alpha) * data_flat[:n_overlap]
...             )
...         # Append remaining new samples
...         result = np.concatenate([reference_data, data_flat[n_overlap:]], axis=0)
...         return result[-self.n :]

Using a custom update strategy with a drift detector:

>>> from dataeval.shift import DriftUnivariate
>>> import numpy as np
>>>
>>> # Create reference data
>>> ref_data = np.random.normal(0, 1, (100, 10))
>>>
>>> # Initialize drift detector with custom update strategy
>>> update_strategy = MovingAverageUpdate(n=100, alpha=0.9)
>>> detector = DriftUnivariate(method="ks", update_strategy=update_strategy).fit(ref_data)
>>>
>>> # Detect drift on new data - reference will be updated automatically
>>> new_data = np.random.normal(0.5, 1, (50, 10))
>>> result = detector.predict(new_data)

Notes

Implementations should: - Accept current reference data and new observations - Return updated reference data with consistent shape - Handle edge cases (empty arrays, size mismatches) - Maintain internal state if needed (e.g., sample counts) - Ensure output size doesn’t exceed configured limits