riqd#

openstef_beam.metrics.riqd(y_true: ndarray[tuple[Any, ...], dtype[floating]], y_pred_lower_q: ndarray[tuple[Any, ...], dtype[floating]], y_pred_upper_q: ndarray[tuple[Any, ...], dtype[floating]], *, measurement_range_lower_q: float = 0.05, measurement_range_upper_q: float = 0.95) → float[source]#

Calculate the relative Inter Quantile Distance (rIQD).

rIQD measures the average distance between two quantiles, normalized by the measurement range.

Parameters:

  • y_true (ndarray[tuple[Any, ...], dtype[floating]]) – Ground truth values with shape (num_samples,).

  • y_pred_lower_q (ndarray[tuple[Any, ...], dtype[floating]]) – Predicted values of lower quantile with shape (num_samples,).

  • y_pred_upper_q (ndarray[tuple[Any, ...], dtype[floating]]) – Predicted values of upper quantile with shape (num_samples,).

  • measurement_range_lower_q (float) – Lower quantile for range calculation. Must be in [0, 1].

  • measurement_range_upper_q (float) – Upper quantile for range calculation. Must be in [0, 1] and greater than measurement_range_lower_q.

Returns:

The relative Inter Quantile Distance (rIQD) as a float. Returns NaN if the measurement

range is zero.

Return type:

float

Example

Basic usage with energy load data:

>>> import numpy as np
>>> y_true = np.array([100, 120, 110, 130, 105])
>>> y_pred_lower_q = np.array([90, 100, 105, 95, 85])
>>> y_pred_upper_q = np.array([110, 125, 140, 135, 90])
>>> riqd = riqd(y_true, y_pred_lower_q, y_pred_upper_q)
>>> round(riqd, 4)
0.9259
Parameters:

  • y_true (ndarray[tuple[Any, ...], dtype[floating]])

  • y_pred_lower_q (ndarray[tuple[Any, ...], dtype[floating]])

  • y_pred_upper_q (ndarray[tuple[Any, ...], dtype[floating]])

  • measurement_range_lower_q (float)

  • measurement_range_upper_q (float)

Return type:

float