Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples

The following example shows how to precompute the gram matrix while using weighted samples with an ElasticNet.

If weighted samples are used, the design matrix must be centered and then rescaled by the square root of the weight vector before the gram matrix is computed.


sample_weight vector is also rescaled to sum to n_samples, see the

documentation for the sample_weight parameter to fit.

Let’s start by loading the dataset and creating some sample weights.

import numpy as np

from sklearn.datasets import make_regression

rng = np.random.RandomState(0)

n_samples = int(1e5)
X, y = make_regression(n_samples=n_samples, noise=0.5, random_state=rng)

sample_weight = rng.lognormal(size=n_samples)
# normalize the sample weights
normalized_weights = sample_weight * (n_samples / (sample_weight.sum()))

To fit the elastic net using the precompute option together with the sample weights, we must first center the design matrix, and rescale it by the normalized weights prior to computing the gram matrix.

X_offset = np.average(X, axis=0, weights=normalized_weights)
X_centered = X - np.average(X, axis=0, weights=normalized_weights)
X_scaled = X_centered * np.sqrt(normalized_weights)[:, np.newaxis]
gram =, X_scaled)

We can now proceed with fitting. We must passed the centered design matrix to fit otherwise the elastic net estimator will detect that it is uncentered and discard the gram matrix we passed. However, if we pass the scaled design matrix, the preprocessing code will incorrectly rescale it a second time.

from sklearn.linear_model import ElasticNet

lm = ElasticNet(alpha=0.01, precompute=gram), y, sample_weight=normalized_weights)
           precompute=array([[ 9.98809919e+04, -4.48938813e+02, -1.03237920e+03, ...,
        -2.25349312e+02, -3.53959628e+02, -1.67451144e+02],
       [-4.48938813e+02,  1.00768662e+05,  1.19112072e+02, ...,
        -1.07963978e+03,  7.47987268e+01, -5.76195467e+02],
       [-1.03237920e+03,  1.19112072e+02,  1.00393284e+05, ...,
        -3.07582983e+02,  6.66670169e+02,  2.65799352e+02],
       [-2.25349312e+02, -1.07963978e+03, -3.07582983e+02, ...,
         9.99891212e+04, -4.58195950e+02, -1.58667835e+02],
       [-3.53959628e+02,  7.47987268e+01,  6.66670169e+02, ...,
        -4.58195950e+02,  9.98350372e+04,  5.60836363e+02],
       [-1.67451144e+02, -5.76195467e+02,  2.65799352e+02, ...,
        -1.58667835e+02,  5.60836363e+02,  1.00911944e+05]]))
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