FIX Ensure coef_ is an ndarray when fitting LassoLars (scikit-learn#8160

) * Fix scikit-learngh-1615: ensure self.coef_ is an ndarray
maskani-moh · Nov 15, 2017 · 4aff885 · 4aff885
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diff --git a/doc/whats_new.rst b/doc/whats_new.rst
@@ -194,6 +194,10 @@ Bug fixes
      would be raised on trying to stack matrices with different dimensions.
      :issue:`8093` by :user:`Peter Bull <pjbull>`.
 
+   - Fix a bug where :func:`sklearn.linear_model.LassoLars.fit` sometimes
+     left `coef_` as a list, rather than an ndarray.
+     :issue:`8160` by :user:`CJ Carey <perimosocordiae>`.
+
 API changes summary
 -------------------
 

diff --git a/sklearn/linear_model/least_angle.py b/sklearn/linear_model/least_angle.py
@@ -665,9 +665,9 @@ def fit(self, X, y, Xy=None):
 
         self.alphas_ = []
         self.n_iter_ = []
+        self.coef_ = np.empty((n_targets, n_features))
 
         if self.fit_path:
-            self.coef_ = []
             self.active_ = []
             self.coef_path_ = []
             for k in xrange(n_targets):
@@ -682,15 +682,14 @@ def fit(self, X, y, Xy=None):
                 self.active_.append(active)
                 self.n_iter_.append(n_iter_)
                 self.coef_path_.append(coef_path)
-                self.coef_.append(coef_path[:, -1])
+                self.coef_[k] = coef_path[:, -1]
 
             if n_targets == 1:
                 self.alphas_, self.active_, self.coef_path_, self.coef_ = [
                     a[0] for a in (self.alphas_, self.active_, self.coef_path_,
                                    self.coef_)]
                 self.n_iter_ = self.n_iter_[0]
         else:
-            self.coef_ = np.empty((n_targets, n_features))
             for k in xrange(n_targets):
                 this_Xy = None if Xy is None else Xy[:, k]
                 alphas, _, self.coef_[k], n_iter_ = lars_path(

diff --git a/sklearn/linear_model/tests/test_least_angle.py b/sklearn/linear_model/tests/test_least_angle.py
@@ -366,8 +366,15 @@ def test_multitarget():
     X = diabetes.data
     Y = np.vstack([diabetes.target, diabetes.target ** 2]).T
     n_targets = Y.shape[1]
-
-    for estimator in (linear_model.LassoLars(), linear_model.Lars()):
+    estimators = [
+        linear_model.LassoLars(),
+        linear_model.Lars(),
+        # regression test for gh-1615
+        linear_model.LassoLars(fit_intercept=False),
+        linear_model.Lars(fit_intercept=False),
+    ]
+
+    for estimator in estimators:
         estimator.fit(X, Y)
         Y_pred = estimator.predict(X)
         alphas, active, coef, path = (estimator.alphas_, estimator.active_,