rm init inside solvers

Author: Badr-MOUFAD

skglm/solvers/anderson_cd.py

@@ -80,10 +80,8 @@ def _solve(self, X, y, datafit, penalty, w_init=None, Xw_init=None):
 
         is_sparse = sparse.issparse(X)
         if is_sparse:
-            datafit.initialize_sparse(X.data, X.indptr, X.indices, y)
             lipschitz = datafit.get_lipschitz_sparse(X.data, X.indptr, X.indices, y)
         else:
-            datafit.initialize(X, y)
             lipschitz = datafit.get_lipschitz(X, y)
 
         if len(w) != n_features + self.fit_intercept:

skglm/solvers/fista.py

@@ -51,12 +51,10 @@ def _solve(self, X, y, datafit, penalty, w_init=None, Xw_init=None):
         Xw = Xw_init.copy() if Xw_init is not None else np.zeros(n_samples)
 
         if X_is_sparse:
-            datafit.initialize_sparse(X.data, X.indptr, X.indices, y)
             lipschitz = datafit.get_global_lipschitz_sparse(
                 X.data, X.indptr, X.indices, y
             )
         else:
-            datafit.initialize(X, y)
             lipschitz = datafit.get_global_lipschitz(X, y)
 
         for n_iter in range(self.max_iter):

skglm/solvers/group_bcd.py

@@ -76,10 +76,8 @@ def _solve(self, X, y, datafit, penalty, w_init=None, Xw_init=None):
 
         is_sparse = issparse(X)
         if is_sparse:
-            datafit.initialize_sparse(X.data, X.indptr, X.indices, y)
             lipschitz = datafit.get_lipschitz_sparse(X.data, X.indptr, X.indices, y)
         else:
-            datafit.initialize(X, y)
             lipschitz = datafit.get_lipschitz(X, y)
 
         all_groups = np.arange(n_groups)

skglm/solvers/group_prox_newton.py

@@ -69,13 +69,6 @@ def _solve(self, X, y, datafit, penalty, w_init=None, Xw_init=None):
         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/lbfgs.py

@@ -38,13 +38,6 @@ def __init__(self, max_iter=50, tol=1e-4, verbose=False):
 
     def _solve(self, X, y, datafit, penalty, w_init=None, Xw_init=None):
 
-        # 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)
-
         def objective(w):
             Xw = X @ w
             datafit_value = datafit.value(y, w, Xw)

skglm/solvers/multitask_bcd.py

@@ -54,10 +54,8 @@ def _solve(self, X, Y, datafit, penalty, W_init=None, XW_init=None):
 
         is_sparse = sparse.issparse(X)
         if is_sparse:
-            datafit.initialize_sparse(X.data, X.indptr, X.indices, Y)
             lipschitz = datafit.get_lipschitz_sparse(X.data, X.indptr, X.indices, Y)
         else:
-            datafit.initialize(X, Y)
             lipschitz = datafit.get_lipschitz(X, Y)
 
         for t in range(self.max_iter):

skglm/solvers/prox_newton.py

@@ -85,12 +85,6 @@ def _solve(self, X, y, datafit, penalty, w_init=None, Xw_init=None):
         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