8000 FIX Ensure coef_ is an ndarray when fitting LassoLars (#8160) · sergeyf/scikit-learn@6868707 · GitHub
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FIX Ensure coef_ is an ndarray when fitting LassoLars (scikit-learn#8160)
* Fix scikit-learngh-1615: ensure self.coef_ is an ndarray
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‎doc/whats_new.rst

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@@ -194,6 +194,10 @@ Bug fixes
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would be raised on trying to stack matrices with different dimensions.
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:issue:`8093` by :user:`Peter Bull <pjbull>`.
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- Fix a bug where :func:`sklearn.linear_model.LassoLars.fit` sometimes
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left `coef_` as a list, rather than an ndarray.
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:issue:`8160` by :user:`CJ Carey <perimosocordiae>`.
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API changes summary
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-------------------
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‎sklearn/linear_model/least_angle.py

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

‎sklearn/linear_model/tests/test_least_angle.py

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

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