@agramfort How would you like to calculate the cross-validated mean square error? Weighted by sample_weight or unweighted. I think this was discussed somewhere else. I would prefer the weighted version.

Furthermore, just as info, the current approach to rescale X by sqrt(sw) might use more memory copies as the unweighted version.

The diff on Github doesn't seem to take into account the merge #15436 PR, maybe merging master would help?

How would you like to calculate the cross-validated mean square error? Weighted by sample_weight or unweighted. I think this was discussed somewhere else. I would prefer the weighted version.

I couldn't find the corresponding issue, maybe it could be worth opening one. There was #15651 but it's a different topic.

In the short term, I think we want to be consistent with GridSearchCV(ElasticNet()).fit(X, y, sample_weight). As far as I can tell from

@jjerphan If you feel comfortable, I'd appreciate your review approval very much:smirk:

Decision in yesterday's dev meeting (2021-05-25): Remove use_weights_in_cv. This functionality will come with SLEP006 (hopefully) and makes this PR non-controversial.

To have it easily referenced, here is the code that is needed for use_weights_in_cv:

• LinearModelCV

  """
  use_weights_in_cv : bool, default=False
      If `True`, the MSE over test folds is calculated as a weighted average,
      weighted by the sum of `sample_weight` of each test fold. Here,
      `sample_weight=None` acts like `sample_weight=1`, which means the sum
      of weights in the test fold is the number of observations.
  """

• fit

  if not self.use_weights_in_cv:
      # The mean is computed over folds.
      mean_mse = np.mean(mse_paths, axis=1)
  else:
      if sample_weight is None:
          # Note that both len(test) and sample_weight[test].sum() can
          # have different values for different folds.
          sw_paths = [len(test) for train, test in folds]
      else:
          sw_paths = [sample_weight[test].sum() for train, test in folds]
      # The average is computed over folds.
      mean_mse = np.average(mse_paths, axis=1, weights=sw_paths)

• test_enet_cv_sample_weight_correctness

  @pytest.mark.parametrize("use_weights_in_cv", [True, False])
  @pytest.mark.parametrize("fit_intercept", [True, False])
  def test_enet_cv_sample_weight_correctness(use_weights_in_cv, fit_intercept):
      """Test that ElasticNetCV with sample weights gives correct results."""
      ...
      if use_weights_in_cv:
        # Note: The order of groups from LeaveOneGroupOut is the same for both.
        assert_allclose(reg_sw.mse_path_, reg.mse_path_)

@rth Do you want to merge? @agramfort and @jjerphan have already approved. This PR only adds sample_weight to some fit methods, nothing more API-wise. test_enet_cv_grid_search assures equivalency with GridSearchCV.

@lorentzenchr you need to rebase this one

@rth Main merged and all CI green.