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FIX : broken MultiTaskLasso with warm_start=True #12853

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FIX : broken MultiTaskLasso with warm_start=True #12853

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2 changes: 1 addition & 1 deletion sklearn/linear_model/coordinate_descent.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1796,7 +1796,7 @@ def fit(self, X, y):
X, y, X_offset, y_offset, X_scale = _preprocess_data(
X, y, self.fit_intercept, self.normalize, copy=False)

if not self.warm_start or self.coef_ is None:
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we were trying to access a fit param before actually setting it.

if not self.warm_start or not hasattr(self, "coef_"):
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@jnothman shall I replace not hasattr(self, "coef_") by a getattr and we're good?

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I'm okay with it either way, actually. I just never know how flexible we should make these kinds of changes... I mean if someone's gone and changed their code now to set coef_ = None so that it works, using getattr would make sure it continues to work in the next release... but it's also hacky.

self.coef_ = np.zeros((n_tasks, n_features), dtype=X.dtype.type,
order='F')

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8 changes: 8 additions & 0 deletions sklearn/linear_model/tests/test_coordinate_descent.py
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Expand Up @@ -818,3 +818,11 @@ def test_coef_shape_not_zero():
est_no_intercept = Lasso(fit_intercept=False)
est_no_intercept.fit(np.c_[np.ones(3)], np.ones(3))
assert est_no_intercept.coef_.shape == (1,)


def test_multi_task_lasso_warm_start():
X = np.array([[1, 2, 4, 5, 8], [3, 5, 7, 7, 8]]).T
y = np.array([12, 10, 11, 21, 5])[:, np.newaxis]

est = MultiTaskLasso(warm_start=True)
est.fit(X, y)