added init datafit in solver

Author: Badr-MOUFAD

skglm/solvers/group_prox_newton.py

@@ -68,6 +68,13 @@ def _solve(self, X, y, datafit, penalty, w_init=None, Xw_init=None):
         all_groups = np.arange(n_groups)
         stop_crit = 0.
         p_objs_out = []
+        
+        # TODO: to be isolated in a seperated method
+        is_sparse = issparse(X)
+        if is_sparse:
+            datafit.initialize_sparse(X.data, X.indptr, X.indices, y)
+        else:
+            datafit.initialize(X, y)
 
         for iter in range(self.max_iter):
             grad = _construct_grad(X, y, w, Xw, datafit, all_groups)

skglm/solvers/prox_newton.py

@@ -84,6 +84,12 @@ def _solve(self, X, y, datafit, penalty, w_init=None, Xw_init=None):
         is_sparse = issparse(X)
         if is_sparse:
             X_bundles = (X.data, X.indptr, X.indices)
+            
+        # TODO: to be isolated in a seperated method
+        if is_sparse:
+            datafit.initialize_sparse(X.data, X.indptr, X.indices, y)
+        else:
+            datafit.initialize(X, y)
 
         if self.ws_strategy == "fixpoint":
             X_square = X.multiply(X) if is_sparse else X ** 2