mojolearn.r_regression#

mojolearn.r_regression(X, y, *, center=True, force_finite=True)[source]#

Compute the Pearson correlation of each feature with a continuous target.

Follows scikit-learn’s r_regression. Computation is float32.

Parameters:

X (array-like of shape (n_samples, n_features)) – Feature matrix.
y (array-like of shape (n_samples,)) – Target values.
center (bool, default True) – Center X and y before computing the correlation.
force_finite (bool, default True) – A constant feature or target gives a correlation of 0; with False it gives NaN.

Returns:

correlation (array of shape (n_features,)) – Pearson correlation of each feature with y.

Examples

>>> import numpy as np, mojolearn
>>> rng = np.random.default_rng(0)
>>> X = rng.random((1000, 8), dtype=np.float32)
>>> y = X[:, 0] + rng.random(1000).astype(np.float32)
>>> r = mojolearn.r_regression(X, y)