fix pytest, should work now

Author: floriankozikowski

skglm/experimental/sqrt_lasso.py

@@ -107,6 +107,7 @@ class SqrtLasso(LinearModel, RegressorMixin):
 
     def __init__(self, alpha=1., max_iter=100, max_pn_iter=100, p0=10,
                  tol=1e-4, verbose=0, fit_intercept=True):
+
         super().__init__()
         self.alpha = alpha
         self.max_iter = max_iter
@@ -147,7 +148,8 @@ def fit(self, X, y):
         self.coef_ = self.path(X_centered, y_centered, alphas=[self.alpha])[1][0]
 
         if self.fit_intercept:
-            self.intercept_ = y_mean - X_mean @ self.coef_
+            self.intercept_ = y_mean - X_mean @ self.coef_[:-1]
+            self.coef_ = self.coef_[:-1]
         else:
             self.intercept_ = 0.
         return self

skglm/experimental/tests/test_sqrt_lasso.py

@@ -109,6 +109,24 @@ def test_sqrt_lasso_with_intercept():
     y_pred = sqrt.predict(X)
     assert y_pred.shape == y.shape
 
+    # Check that coef_ and intercept_ are handled separately
+    assert sqrt.coef_.shape == (20,)
+    assert np.isscalar(sqrt.intercept_)
+
+    # Confirm prediction matches manual computation
+    manual_pred = X @ sqrt.coef_ + sqrt.intercept_
+    np.testing.assert_allclose(manual_pred, y_pred, rtol=1e-6)
+
+    np.testing.assert_allclose(
+        sqrt.intercept_, y.mean() - X.mean(axis=0) @ sqrt.coef_, rtol=1e-6
+    )
+
+    sqrt_no_intercept = SqrtLasso(
+        alpha=alpha * scal, fit_intercept=False, tol=1e-8).fit(X, y)
+    assert np.isscalar(sqrt_no_intercept.intercept_)
+    np.testing.assert_allclose(sqrt_no_intercept.predict(
+        X), X @ sqrt_no_intercept.coef_ + sqrt_no_intercept.intercept_)
+
 
 if __name__ == '__main__':
     pass