rm init inside estimators

Author: Badr-MOUFAD

skglm/estimators.py

@@ -101,10 +101,10 @@ def _glm_fit(X, y, model, datafit, penalty, solver):
 
     n_samples, n_features = X_.shape
 
-    if issparse(X):
-        datafit.initialize_sparse(X_.data, X_.indptr, X_.indices, y)
-    else:
-        datafit.initialize(X_, y)
+    # if issparse(X):
+    #     datafit.initialize_sparse(X_.data, X_.indptr, X_.indices, y)
+    # else:
+    #     datafit.initialize(X_, y)
 
     # if model.warm_start and hasattr(model, 'coef_') and model.coef_ is not None:
     if solver.warm_start and hasattr(model, 'coef_') and model.coef_ is not None:
@@ -1373,11 +1373,11 @@ def fit(self, X, y):
                 fit_intercept=False,
             )
 
-        # solve problem
-        if not issparse(X):
-            datafit.initialize(X, y)
-        else:
-            datafit.initialize_sparse(X.data, X.indptr, X.indices, y)
+        # # solve problem
+        # if not issparse(X):
+        #     datafit.initialize(X, y)
+        # else:
+        #     datafit.initialize_sparse(X.data, X.indptr, X.indices, y)
 
         w, _, stop_crit = solver.solve(X, y, datafit, penalty)