API - Expose get_lipschitz in QuadraticGroup (scikit-learn-contrib#200)

Author: Badr-MOUFAD

skglm/datafits/group.py

@@ -22,9 +22,6 @@ class QuadraticGroup(BaseDatafit):
     grp_ptr : array, shape (n_groups + 1,)
         The group pointers such that two consecutive elements delimit
         the indices of a group in ``grp_indices``.
-
-    lipschitz : array, shape (n_groups,)
-        The lipschitz constants for each group.
     """
 
     def __init__(self, grp_ptr, grp_indices):
@@ -34,15 +31,14 @@ def get_spec(self):
         spec = (
             ('grp_ptr', int32[:]),
             ('grp_indices', int32[:]),
-            ('lipschitz', float64[:])
         )
         return spec
 
     def params_to_dict(self):
         return dict(grp_ptr=self.grp_ptr,
                     grp_indices=self.grp_indices)
 
-    def initialize(self, X, y):
+    def get_lipschitz(self, X, y):
         grp_ptr, grp_indices = self.grp_ptr, self.grp_indices
         n_groups = len(grp_ptr) - 1
 
@@ -52,7 +48,7 @@ def initialize(self, X, y):
             X_g = X[:, grp_g_indices]
             lipschitz[g] = norm(X_g, ord=2) ** 2 / len(y)
 
-        self.lipschitz = lipschitz
+        return lipschitz
 
     def value(self, y, w, Xw):
         return norm(y - Xw) ** 2 / (2 * len(y))

skglm/solvers/group_bcd.py

@@ -67,6 +67,8 @@ def solve(self, X, y, datafit, penalty, w_init=None, Xw_init=None):
             raise ValueError(val_error_message)
 
         datafit.initialize(X, y)
+        lipschitz = datafit.get_lipschitz(X, y)
+
         all_groups = np.arange(n_groups)
         p_objs_out = np.zeros(self.max_iter)
         stop_crit = 0.  # prevent ref before assign when max_iter == 0
@@ -100,7 +102,7 @@ def solve(self, X, y, datafit, penalty, w_init=None, Xw_init=None):
 
             for epoch in range(self.max_epochs):
                 # inplace update of w and Xw
-                _bcd_epoch(X, y, w[:n_features], Xw, datafit, penalty, ws)
+                _bcd_epoch(X, y, w[:n_features], Xw, lipschitz, datafit, penalty, ws)
 
                 # update intercept
                 if self.fit_intercept:
@@ -140,15 +142,15 @@ def solve(self, X, y, datafit, penalty, w_init=None, Xw_init=None):
 
 
 @njit
-def _bcd_epoch(X, y, w, Xw, datafit, penalty, ws):
+def _bcd_epoch(X, y, w, Xw, lipschitz, datafit, penalty, ws):
     # perform a single BCD epoch on groups in ws
     grp_ptr, grp_indices = penalty.grp_ptr, penalty.grp_indices
 
     for g in ws:
         grp_g_indices = grp_indices[grp_ptr[g]: grp_ptr[g+1]]
         old_w_g = w[grp_g_indices].copy()
 
-        lipschitz_g = datafit.lipschitz[g]
+        lipschitz_g = lipschitz[g]
         grad_g = datafit.gradient_g(X, y, w, Xw, g)
 
         w[grp_g_indices] = penalty.prox_1group(