r2#

openstef_beam.metrics.r2(y_true: ndarray[tuple[Any, ...], dtype[floating]], y_pred: ndarray[tuple[Any, ...], dtype[floating]], *, sample_weights: ndarray[tuple[Any, ...], dtype[floating]] | None = None) → float[source]#

Calculate the R² (coefficient of determination) score.

R² represents the proportion of variance in the dependent variable that is predictable from the independent variable(s). It provides a measure of how well observed outcomes are replicated by the model, based on the proportion of total variation of outcomes explained by the model.

Parameters:

y_true (ndarray[tuple[Any, ...], dtype[floating]]) – Ground truth values with shape (num_samples,).
y_pred (ndarray[tuple[Any, ...], dtype[floating]]) – Predicted values with shape (num_samples,).
sample_weights (ndarray[tuple[Any, ...], dtype[floating]] | None) – Optional weights for each sample with shape (num_samples,). If None, all samples are weighted equally.

Returns:

The R² score as a float. Best possible score is 1.0, and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the mean of y_true would get an R² score of 0.0.

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 = np.array([98, 122, 108, 135, 107])
>>> score = r2(y_true, y_pred)
>>> round(score, 3)
0.929

Perfect predictions give R² = 1.0:

>>> perfect_pred = np.array([100, 120, 110, 130, 105])
>>> r2(y_true, perfect_pred)
1.0

With sample weights:

>>> weights = np.array([1, 2, 1, 2, 1])
>>> score = r2(y_true, y_pred, sample_weights=weights)
>>> isinstance(score, float)
True

Parameters:

y_true (ndarray[tuple[Any, ...], dtype[floating]])
y_pred (ndarray[tuple[Any, ...], dtype[floating]])
sample_weights (ndarray[tuple[Any, ...], dtype[floating]] | None)

Return type:

float