8000 TST: Added a test as proof of concept. · scikit-learn/scikit-learn@bc94923 · GitHub
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TST: Added a test as proof of concept.
Added a test to show that the number of passes made are smaller for stopping="objective" even on a small dataset.
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‎sklearn/linear_model/coordinate_descent.py

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@@ -492,7 +492,6 @@ def enet_path(X, y, l1_ratio=0.5, eps=1e-3, n_alphas=100, alphas=None,
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coef_ = np.asfortranarray(coef_init)
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for i, alpha in enumerate(alphas):
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#print alpha
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l1_reg = alpha * l1_ratio * n_samples
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l2_reg = alpha * (1.0 - l1_ratio) * n_samples
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if not multi_output and sparse.isspmatrix(X):

‎sklearn/linear_model/tests/test_coordinate_descent.py

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@@ -8,7 +8,7 @@
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from scipy import interpolate, sparse
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from copy import deepcopy
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from sklearn.datasets import load_boston
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from sklearn.datasets import load_boston, make_regression
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_almost_equal
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from sklearn.utils.testing import assert_equal
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from sklearn.linear_model.coordinate_descent import Lasso, \
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LassoCV, ElasticNet, ElasticNetCV, MultiTaskLasso, MultiTaskElasticNet, \
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MultiTaskElasticNetCV, MultiTaskLassoCV, lasso_path
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MultiTaskElasticNetCV, MultiTaskLassoCV, lasso_path, enet_path
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from sklearn.linear_model import LassoLarsCV, lars_path
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@@ -574,6 +574,30 @@ def test_deprection_precompute_enet():
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assert_warns(DeprecationWarning, clf.fit, X, y)
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def test_n_passes_stopping_criteria():
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"""Test the number of passes for the stopping condition.
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Ideally, when stopping is set to 'objective' lesser number of passes
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should be made on the data.
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"""
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X, y = make_regression()
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models = enet_path(X, y, precompute=False, return_n_iter=True, tol=1e-7,
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stopping="objective")
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n_iters_obj = sum(models[-1])
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models = enet_path(X, y, precompute=False, return_n_iter=True, tol=1e-4,
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stopping="dual_gap")
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n_iters_dual = sum(models[-1])
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assert_greater(n_iters_dual, n_iters_obj)
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models = lasso_path(X, y, precompute=False, return_n_iter=True, tol=1e-7,
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stopping="objective")
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n_iters_obj = sum(models[-1])
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models = lasso_path(X, y, precompute=False, return_n_iter=True, tol=1e-4,
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stopping="dual_gap")
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n_iters_dual = sum(models[-1])
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assert_greater(n_iters_dual, n_iters_obj)
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if __name__ == '__main__':
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import nose
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nose.runmodule()

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