completeness#

openstef_beam.metrics.metrics_deterministic.completeness(y: ndarray[tuple[Any, ...], dtype[floating]]) → float[source]#

Calculate the completeness of data.

Completeness measures the proportion of non-missing data, providing insight into data availability and potential gaps in the data.

Parameters:: y (ndarray[tuple[Any, ...], dtype[floating]]) – Values with shape (num_samples,). May contain NaN for missing data.
Returns:: The completeness as a float in range [0, 1], where 1.0 means no missing data.
Return type:: float

Example

Basic usage with energy load data

>>> import numpy as np
>>> y = np.array([100, 120, np.nan, 130, 105])
>>> completeness(y)
0.8