ENH - add support for intercept in SqrtLasso

[PascalCarrivain]

Context of the PR

This PR adds support for intercept in SqrtLasso estimator.
Closes #96

Checks before merging PR

• added documentation for any new feature
• added unittests
• edited the what's new

[Badr-MOUFAD]

Thanks @PascalCarrivain for the PR!
Here are some remarks

[mathurinm]

@Badr-MOUFAD merge if happy!

[Badr-MOUFAD]

Many thanks @PascalCarrivain for the hard work 💪

Just a small bit before merging

We didn't add tests for the intercept (we might be vulnerable to bugs later)

I'm not aware of any package to check against it the new feature, any suggestions @mathurinm?

With that being said, we can leverage the equivalence between the problem with intercept and without as the datafit is a norm_2 hence setting its gradient w.r.t. the intercept to zero implies setting the residual to zero

$$ \begin{equation} \min_{w, w_0} \| y - (Xw - w_0 \mathbb{1}) \| + \lambda \| w \|_1 \Longleftrightarrow \begin{cases} \min_w & \| \bar{y} - \bar{X}w \| + \lambda \| w \|_1 \\\ \text{where} & w_0 = \text{mean}(y) - \langle \text{mean\_col}(X), \hat{w} \rangle \end{cases} \end{equation} $$

[Badr-MOUFAD]

WDYT about this refactoring?

Suggested change

	if sqrt_lasso.fit_intercept:
	np.testing.assert_equal(sqrt_lasso.coef_[:-1], 0)
	else:
	np.testing.assert_equal(sqrt_lasso.coef_, 0)
	np.testing.assert_equal(sqrt_lasso.coef_[:n_features], 0)