no scale

Author: mathurinm

debug.py

@@ -18,8 +18,8 @@ def generate_dummy_data(n_samples=1000, n_features=10, noise=0.1):
 datafit = Pinball(0.5)
 penalty = L1(alpha=0.1)
 solver = PDCD_WS(
-    max_iter=10,
-    max_epochs=100,
+    max_iter=50,
+    max_epochs=500,
     tol=1e-2,
     warm_start=False,
     verbose=1,
@@ -33,10 +33,11 @@ def generate_dummy_data(n_samples=1000, n_features=10, noise=0.1):
 
 X, y = generate_dummy_data(
     n_samples=1000, # if this is reduced to 100 samples, it converges
-    n_features=10,
+    n_features=11,
 )
 # y -= y.mean()
 # y += 0.1
+y /= 10
 scaler = StandardScaler()
 X_scaled = scaler.fit_transform(X)
 

skglm/experimental/pdcd_ws.py

@@ -102,8 +102,8 @@ def _solve(self, X, y, datafit, penalty, w_init=None, Xw_init=None):
         # Despite violating the conditions mentioned in [1]
         # this choice of steps yield in practice a convergent algorithm
         # with better speed of convergence
-        dual_step = 1 / norm(X, ord=2) / 10
-        primal_steps = 1 / norm(X, axis=0, ord=2) / 10
+        dual_step = 1 / norm(X, ord=2)
+        primal_steps = 1 / norm(X, axis=0, ord=2)
 
         # primal vars
         w = np.zeros(n_features) if w_init is None else w_init