FIX - Compatibility with scikit-learn 1.2.dev

[Badr-MOUFAD]

scikit-learn 1.2.dev breaks down the actual code. debug_script.py provides a small snippet to reproduce.

investigation

scikit-learn seems to have added a validation step of the class parameters at the fit moment.
our Lasso estimator doesn't have the same signature as the scikit-learn (e.g. copy_X and random_state), though we inherit from it.

Therefore we get an error when comparing the constructor arguments with the parent class

click to expend error

raise ValueError(
ValueError: The parameter constraints ['alpha', 'fit_intercept', 'precompute', 'max_iter', 'copy_X', 'tol', 'warm_start', 'positive', 
'random_state', 'selection'] contain unexpected parameters {'copy_X', 'precompute', 'random_state', 'selection'}

potential fix

A straightforward fix would be to override _validate_params. But I don't think it's a reliable way to do it.

click to expend code

def _validate_params(self):
        pass

[Badr-MOUFAD]

Another fix would be to define our own parameters constraints specific to the class.

With these changes, we pass the Lasso test.

[mathurinm]

I think it's better to have the checks than to remove _validate_data

[Badr-MOUFAD]

Closing as it's no longer a problem