Merge remote-tracking branch 'origin/main' into fix_intercept_SqrtLasso

Author: floriankozikowski

skglm/experimental/sqrt_lasso.py

@@ -106,11 +106,13 @@ class SqrtLasso(LinearModel, RegressorMixin):
     """
 
     def __init__(self, alpha=1., max_iter=100, max_pn_iter=100, p0=10,
-<<<<<<< HEAD
+<< << << < HEAD
                  tol=1e-4, verbose=0, fit_intercept=True):
-=======
-                 tol=1e-4, verbose=0, fit_intercept=False):
->>>>>>> 69bf74f (first try, add support for fit_intercept in sqrtLasso, TODOS: review if correct, clean up, pot. add support for sparse X (not sure if that works), enhance docstring)
+
+
+== == == =
+                 tol = 1e-4, verbose = 0, fit_intercept = False):
+>>>>>> > 69bf74f (first try , add support for fit_intercept in sqrtLasso, TODOS: review if correct, clean up, pot. add support for sparse X (not sure if that works), enhance docstring)
         super().__init__()
         self.alpha = alpha
         self.max_iter = max_iter
@@ -138,11 +140,6 @@ def fit(self, X, y):
         self :
             Fitted estimator.
         """
-<<<<<<< HEAD
-        self.coef_ = self.path(X, y, alphas=[self.alpha])[1][0]
-        if self.fit_intercept:
-            self.intercept_ = self.coef_[-1]
-=======
         # self.coef_ = self.path(X, y, alphas=[self.alpha])[1][0]
         if self.fit_intercept:
             X_mean = X.mean(axis=0)
@@ -157,7 +154,6 @@ def fit(self, X, y):
 
         if self.fit_intercept:
             self.intercept_ = y_mean - X_mean @ self.coef_
->>>>>>> 69bf74f (first try, add support for fit_intercept in sqrtLasso, TODOS: review if correct, clean up, pot. add support for sparse X (not sure if that works), enhance docstring)
         else:
             self.intercept_ = 0.
         return self

skglm/experimental/tests/test_sqrt_lasso.py

@@ -77,7 +77,11 @@ def test_PDCD_WS(with_dual_init):
     penalty = L1(alpha)
 
     w = PDCD_WS(dual_init=dual_init).solve(X, y, datafit, penalty)[0]
+<<<<<<< HEAD
     clf = SqrtLasso(alpha=alpha, fit_intercept=False, tol=1e-12).fit(X, y)
+=======
+    clf = SqrtLasso(alpha=alpha, tol=1e-12).fit(X, y)
+>>>>>>> origin/main
     np.testing.assert_allclose(clf.coef_, w, atol=1e-6)