WeightedModelWrapper issue with linear models with intercept

[vsyrgkanis]

It might be that the weighted model wrapper has a small bug in the case of linear models that fit an intercept:

def _weighted_inputs(self, X, y, sample_weight):
    normalized_weights = X.shape[0] * sample_weight / np.sum(sample_weight)
    sqrt_weights = np.sqrt(normalized_weights)
    weight_mat = np.diag(sqrt_weights)
    return np.matmul(weight_mat, X), np.matmul(weight_mat, y)

If there is an intercept then we also want the intercept to be multiplied by the weight, so this transformation would be invalid. The user can fix it by augmenting X with an intercept and then calling a linear model without intercept. But this might be cumbersome.

[kbattocchi]

I agree that this could be an issue, but it's not clear to me how we might fix it - as far as I can see there's no simple transformation of X and y that will work when fitting a model with an intercept. Do you have any suggestions?

[vasilismsr]

#75 Addressed this.