545 lines
20 KiB
Python
545 lines
20 KiB
Python
from nose.tools import assert_equal
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import numpy as np
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from scipy import linalg
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from sklearn.model_selection import train_test_split
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_true
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from sklearn.utils.testing import assert_less
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from sklearn.utils.testing import assert_greater
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from sklearn.utils.testing import assert_raises
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from sklearn.utils.testing import ignore_warnings
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from sklearn.utils.testing import assert_no_warnings, assert_warns
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from sklearn.utils.testing import TempMemmap
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from sklearn.exceptions import ConvergenceWarning
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from sklearn import linear_model, datasets
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from sklearn.linear_model.least_angle import _lars_path_residues
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diabetes = datasets.load_diabetes()
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X, y = diabetes.data, diabetes.target
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# TODO: use another dataset that has multiple drops
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def test_simple():
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# Principle of Lars is to keep covariances tied and decreasing
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# also test verbose output
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from sklearn.externals.six.moves import cStringIO as StringIO
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import sys
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old_stdout = sys.stdout
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try:
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sys.stdout = StringIO()
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alphas_, active, coef_path_ = linear_model.lars_path(
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diabetes.data, diabetes.target, method="lar", verbose=10)
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sys.stdout = old_stdout
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for (i, coef_) in enumerate(coef_path_.T):
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res = y - np.dot(X, coef_)
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cov = np.dot(X.T, res)
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C = np.max(abs(cov))
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eps = 1e-3
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ocur = len(cov[C - eps < abs(cov)])
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if i < X.shape[1]:
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assert_true(ocur == i + 1)
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else:
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# no more than max_pred variables can go into the active set
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assert_true(ocur == X.shape[1])
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finally:
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sys.stdout = old_stdout
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def test_simple_precomputed():
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# The same, with precomputed Gram matrix
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G = np.dot(diabetes.data.T, diabetes.data)
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alphas_, active, coef_path_ = linear_model.lars_path(
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diabetes.data, diabetes.target, Gram=G, method="lar")
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for i, coef_ in enumerate(coef_path_.T):
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res = y - np.dot(X, coef_)
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cov = np.dot(X.T, res)
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C = np.max(abs(cov))
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eps = 1e-3
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ocur = len(cov[C - eps < abs(cov)])
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if i < X.shape[1]:
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assert_true(ocur == i + 1)
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else:
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# no more than max_pred variables can go into the active set
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assert_true(ocur == X.shape[1])
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def test_all_precomputed():
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# Test that lars_path with precomputed Gram and Xy gives the right answer
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X, y = diabetes.data, diabetes.target
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G = np.dot(X.T, X)
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Xy = np.dot(X.T, y)
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for method in 'lar', 'lasso':
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output = linear_model.lars_path(X, y, method=method)
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output_pre = linear_model.lars_path(X, y, Gram=G, Xy=Xy, method=method)
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for expected, got in zip(output, output_pre):
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assert_array_almost_equal(expected, got)
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def test_lars_lstsq():
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# Test that Lars gives least square solution at the end
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# of the path
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X1 = 3 * diabetes.data # use un-normalized dataset
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clf = linear_model.LassoLars(alpha=0.)
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clf.fit(X1, y)
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coef_lstsq = np.linalg.lstsq(X1, y)[0]
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assert_array_almost_equal(clf.coef_, coef_lstsq)
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def test_lasso_gives_lstsq_solution():
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# Test that Lars Lasso gives least square solution at the end
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# of the path
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alphas_, active, coef_path_ = linear_model.lars_path(X, y, method="lasso")
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coef_lstsq = np.linalg.lstsq(X, y)[0]
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assert_array_almost_equal(coef_lstsq, coef_path_[:, -1])
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def test_collinearity():
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# Check that lars_path is robust to collinearity in input
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X = np.array([[3., 3., 1.],
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[2., 2., 0.],
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[1., 1., 0]])
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y = np.array([1., 0., 0])
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f = ignore_warnings
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_, _, coef_path_ = f(linear_model.lars_path)(X, y, alpha_min=0.01)
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assert_true(not np.isnan(coef_path_).any())
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residual = np.dot(X, coef_path_[:, -1]) - y
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assert_less((residual ** 2).sum(), 1.) # just make sure it's bounded
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n_samples = 10
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X = np.random.rand(n_samples, 5)
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y = np.zeros(n_samples)
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_, _, coef_path_ = linear_model.lars_path(X, y, Gram='auto', copy_X=False,
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copy_Gram=False, alpha_min=0.,
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method='lasso', verbose=0,
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max_iter=500)
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assert_array_almost_equal(coef_path_, np.zeros_like(coef_path_))
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def test_no_path():
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# Test that the ``return_path=False`` option returns the correct output
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alphas_, active_, coef_path_ = linear_model.lars_path(
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diabetes.data, diabetes.target, method="lar")
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alpha_, active, coef = linear_model.lars_path(
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diabetes.data, diabetes.target, method="lar", return_path=False)
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assert_array_almost_equal(coef, coef_path_[:, -1])
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assert_true(alpha_ == alphas_[-1])
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def test_no_path_precomputed():
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# Test that the ``return_path=False`` option with Gram remains correct
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G = np.dot(diabetes.data.T, diabetes.data)
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alphas_, active_, coef_path_ = linear_model.lars_path(
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diabetes.data, diabetes.target, method="lar", Gram=G)
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alpha_, active, coef = linear_model.lars_path(
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diabetes.data, diabetes.target, method="lar", Gram=G,
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return_path=False)
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assert_array_almost_equal(coef, coef_path_[:, -1])
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assert_true(alpha_ == alphas_[-1])
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def test_no_path_all_precomputed():
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# Test that the ``return_path=False`` option with Gram and Xy remains
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# correct
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X, y = 3 * diabetes.data, diabetes.target
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G = np.dot(X.T, X)
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Xy = np.dot(X.T, y)
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alphas_, active_, coef_path_ = linear_model.lars_path(
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X, y, method="lasso", Gram=G, Xy=Xy, alpha_min=0.9)
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print("---")
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alpha_, active, coef = linear_model.lars_path(
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X, y, method="lasso", Gram=G, Xy=Xy, alpha_min=0.9, return_path=False)
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assert_array_almost_equal(coef, coef_path_[:, -1])
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assert_true(alpha_ == alphas_[-1])
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def test_singular_matrix():
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# Test when input is a singular matrix
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X1 = np.array([[1, 1.], [1., 1.]])
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y1 = np.array([1, 1])
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alphas, active, coef_path = linear_model.lars_path(X1, y1)
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assert_array_almost_equal(coef_path.T, [[0, 0], [1, 0]])
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def test_rank_deficient_design():
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# consistency test that checks that LARS Lasso is handling rank
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# deficient input data (with n_features < rank) in the same way
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# as coordinate descent Lasso
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y = [5, 0, 5]
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for X in ([[5, 0],
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[0, 5],
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[10, 10]],
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[[10, 10, 0],
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[1e-32, 0, 0],
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[0, 0, 1]],
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):
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# To be able to use the coefs to compute the objective function,
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# we need to turn off normalization
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lars = linear_model.LassoLars(.1, normalize=False)
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coef_lars_ = lars.fit(X, y).coef_
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obj_lars = (1. / (2. * 3.)
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* linalg.norm(y - np.dot(X, coef_lars_)) ** 2
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+ .1 * linalg.norm(coef_lars_, 1))
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coord_descent = linear_model.Lasso(.1, tol=1e-6, normalize=False)
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coef_cd_ = coord_descent.fit(X, y).coef_
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obj_cd = ((1. / (2. * 3.)) * linalg.norm(y - np.dot(X, coef_cd_)) ** 2
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+ .1 * linalg.norm(coef_cd_, 1))
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assert_less(obj_lars, obj_cd * (1. + 1e-8))
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def test_lasso_lars_vs_lasso_cd(verbose=False):
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# Test that LassoLars and Lasso using coordinate descent give the
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# same results.
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X = 3 * diabetes.data
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alphas, _, lasso_path = linear_model.lars_path(X, y, method='lasso')
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lasso_cd = linear_model.Lasso(fit_intercept=False, tol=1e-8)
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for c, a in zip(lasso_path.T, alphas):
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if a == 0:
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continue
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lasso_cd.alpha = a
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lasso_cd.fit(X, y)
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error = linalg.norm(c - lasso_cd.coef_)
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assert_less(error, 0.01)
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# similar test, with the classifiers
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for alpha in np.linspace(1e-2, 1 - 1e-2, 20):
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clf1 = linear_model.LassoLars(alpha=alpha, normalize=False).fit(X, y)
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clf2 = linear_model.Lasso(alpha=alpha, tol=1e-8,
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normalize=False).fit(X, y)
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err = linalg.norm(clf1.coef_ - clf2.coef_)
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assert_less(err, 1e-3)
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# same test, with normalized data
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X = diabetes.data
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alphas, _, lasso_path = linear_model.lars_path(X, y, method='lasso')
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lasso_cd = linear_model.Lasso(fit_intercept=False, normalize=True,
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tol=1e-8)
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for c, a in zip(lasso_path.T, alphas):
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if a == 0:
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continue
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lasso_cd.alpha = a
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lasso_cd.fit(X, y)
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error = linalg.norm(c - lasso_cd.coef_)
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assert_less(error, 0.01)
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def test_lasso_lars_vs_lasso_cd_early_stopping(verbose=False):
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# Test that LassoLars and Lasso using coordinate descent give the
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# same results when early stopping is used.
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# (test : before, in the middle, and in the last part of the path)
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alphas_min = [10, 0.9, 1e-4]
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for alphas_min in alphas_min:
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alphas, _, lasso_path = linear_model.lars_path(X, y, method='lasso',
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alpha_min=0.9)
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lasso_cd = linear_model.Lasso(fit_intercept=False, tol=1e-8)
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lasso_cd.alpha = alphas[-1]
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lasso_cd.fit(X, y)
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error = linalg.norm(lasso_path[:, -1] - lasso_cd.coef_)
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assert_less(error, 0.01)
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alphas_min = [10, 0.9, 1e-4]
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# same test, with normalization
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for alphas_min in alphas_min:
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alphas, _, lasso_path = linear_model.lars_path(X, y, method='lasso',
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alpha_min=0.9)
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lasso_cd = linear_model.Lasso(fit_intercept=True, normalize=True,
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tol=1e-8)
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lasso_cd.alpha = alphas[-1]
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lasso_cd.fit(X, y)
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error = linalg.norm(lasso_path[:, -1] - lasso_cd.coef_)
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assert_less(error, 0.01)
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def test_lasso_lars_path_length():
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# Test that the path length of the LassoLars is right
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lasso = linear_model.LassoLars()
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lasso.fit(X, y)
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lasso2 = linear_model.LassoLars(alpha=lasso.alphas_[2])
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lasso2.fit(X, y)
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assert_array_almost_equal(lasso.alphas_[:3], lasso2.alphas_)
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# Also check that the sequence of alphas is always decreasing
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assert_true(np.all(np.diff(lasso.alphas_) < 0))
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def test_lasso_lars_vs_lasso_cd_ill_conditioned():
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# Test lasso lars on a very ill-conditioned design, and check that
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# it does not blow up, and stays somewhat close to a solution given
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# by the coordinate descent solver
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# Also test that lasso_path (using lars_path output style) gives
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# the same result as lars_path and previous lasso output style
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# under these conditions.
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rng = np.random.RandomState(42)
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# Generate data
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n, m = 70, 100
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k = 5
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X = rng.randn(n, m)
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w = np.zeros((m, 1))
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i = np.arange(0, m)
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rng.shuffle(i)
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supp = i[:k]
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w[supp] = np.sign(rng.randn(k, 1)) * (rng.rand(k, 1) + 1)
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y = np.dot(X, w)
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sigma = 0.2
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y += sigma * rng.rand(*y.shape)
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y = y.squeeze()
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lars_alphas, _, lars_coef = linear_model.lars_path(X, y, method='lasso')
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_, lasso_coef2, _ = linear_model.lasso_path(X, y,
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alphas=lars_alphas,
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tol=1e-6,
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fit_intercept=False)
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assert_array_almost_equal(lars_coef, lasso_coef2, decimal=1)
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def test_lasso_lars_vs_lasso_cd_ill_conditioned2():
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# Create an ill-conditioned situation in which the LARS has to go
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# far in the path to converge, and check that LARS and coordinate
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# descent give the same answers
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# Note it used to be the case that Lars had to use the drop for good
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# strategy for this but this is no longer the case with the
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# equality_tolerance checks
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X = [[1e20, 1e20, 0],
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[-1e-32, 0, 0],
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[1, 1, 1]]
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y = [10, 10, 1]
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alpha = .0001
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def objective_function(coef):
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return (1. / (2. * len(X)) * linalg.norm(y - np.dot(X, coef)) ** 2
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+ alpha * linalg.norm(coef, 1))
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lars = linear_model.LassoLars(alpha=alpha, normalize=False)
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assert_warns(ConvergenceWarning, lars.fit, X, y)
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lars_coef_ = lars.coef_
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lars_obj = objective_function(lars_coef_)
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coord_descent = linear_model.Lasso(alpha=alpha, tol=1e-4, normalize=False)
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cd_coef_ = coord_descent.fit(X, y).coef_
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cd_obj = objective_function(cd_coef_)
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assert_less(lars_obj, cd_obj * (1. + 1e-8))
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def test_lars_add_features():
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# assure that at least some features get added if necessary
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# test for 6d2b4c
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# Hilbert matrix
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n = 5
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H = 1. / (np.arange(1, n + 1) + np.arange(n)[:, np.newaxis])
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clf = linear_model.Lars(fit_intercept=False).fit(
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H, np.arange(n))
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assert_true(np.all(np.isfinite(clf.coef_)))
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def test_lars_n_nonzero_coefs(verbose=False):
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lars = linear_model.Lars(n_nonzero_coefs=6, verbose=verbose)
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lars.fit(X, y)
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assert_equal(len(lars.coef_.nonzero()[0]), 6)
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# The path should be of length 6 + 1 in a Lars going down to 6
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# non-zero coefs
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assert_equal(len(lars.alphas_), 7)
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@ignore_warnings
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def test_multitarget():
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# Assure that estimators receiving multidimensional y do the right thing
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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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estimator.fit(X, Y)
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Y_pred = estimator.predict(X)
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Y_dec = assert_warns(DeprecationWarning, estimator.decision_function, X)
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assert_array_almost_equal(Y_pred, Y_dec)
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alphas, active, coef, path = (estimator.alphas_, estimator.active_,
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estimator.coef_, estimator.coef_path_)
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for k in range(n_targets):
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estimator.fit(X, Y[:, k])
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y_pred = estimator.predict(X)
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assert_array_almost_equal(alphas[k], estimator.alphas_)
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assert_array_almost_equal(active[k], estimator.active_)
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assert_array_almost_equal(coef[k], estimator.coef_)
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assert_array_almost_equal(path[k], estimator.coef_path_)
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assert_array_almost_equal(Y_pred[:, k], y_pred)
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def test_lars_cv():
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# Test the LassoLarsCV object by checking that the optimal alpha
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# increases as the number of samples increases.
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# This property is not actually guaranteed in general and is just a
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# property of the given dataset, with the given steps chosen.
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old_alpha = 0
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lars_cv = linear_model.LassoLarsCV()
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for length in (400, 200, 100):
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X = diabetes.data[:length]
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y = diabetes.target[:length]
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lars_cv.fit(X, y)
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np.testing.assert_array_less(old_alpha, lars_cv.alpha_)
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old_alpha = lars_cv.alpha_
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def test_lasso_lars_ic():
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# Test the LassoLarsIC object by checking that
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# - some good features are selected.
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# - alpha_bic > alpha_aic
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# - n_nonzero_bic < n_nonzero_aic
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lars_bic = linear_model.LassoLarsIC('bic')
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lars_aic = linear_model.LassoLarsIC('aic')
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rng = np.random.RandomState(42)
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X = diabetes.data
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y = diabetes.target
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X = np.c_[X, rng.randn(X.shape[0], 4)] # add 4 bad features
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lars_bic.fit(X, y)
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lars_aic.fit(X, y)
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nonzero_bic = np.where(lars_bic.coef_)[0]
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nonzero_aic = np.where(lars_aic.coef_)[0]
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assert_greater(lars_bic.alpha_, lars_aic.alpha_)
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assert_less(len(nonzero_bic), len(nonzero_aic))
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assert_less(np.max(nonzero_bic), diabetes.data.shape[1])
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# test error on unknown IC
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lars_broken = linear_model.LassoLarsIC('<unknown>')
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assert_raises(ValueError, lars_broken.fit, X, y)
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def test_no_warning_for_zero_mse():
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# LassoLarsIC should not warn for log of zero MSE.
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y = np.arange(10, dtype=float)
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X = y.reshape(-1, 1)
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lars = linear_model.LassoLarsIC(normalize=False)
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assert_no_warnings(lars.fit, X, y)
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assert_true(np.any(np.isinf(lars.criterion_)))
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def test_lars_path_readonly_data():
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# When using automated memory mapping on large input, the
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# fold data is in read-only mode
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# This is a non-regression test for:
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# https://github.com/scikit-learn/scikit-learn/issues/4597
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splitted_data = train_test_split(X, y, random_state=42)
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with TempMemmap(splitted_data) as (X_train, X_test, y_train, y_test):
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# The following should not fail despite copy=False
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_lars_path_residues(X_train, y_train, X_test, y_test, copy=False)
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|
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def test_lars_path_positive_constraint():
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# this is the main test for the positive parameter on the lars_path method
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# the estimator classes just make use of this function
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# we do the test on the diabetes dataset
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|
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# ensure that we get negative coefficients when positive=False
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# and all positive when positive=True
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# for method 'lar' (default) and lasso
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for method in ['lar', 'lasso']:
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alpha, active, coefs = \
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linear_model.lars_path(diabetes['data'], diabetes['target'],
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return_path=True, method=method,
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positive=False)
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assert_true(coefs.min() < 0)
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|
|
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alpha, active, coefs = \
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linear_model.lars_path(diabetes['data'], diabetes['target'],
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return_path=True, method=method,
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positive=True)
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assert_true(coefs.min() >= 0)
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|
|
|
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# now we gonna test the positive option for all estimator classes
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|
|
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default_parameter = {'fit_intercept': False}
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|
|
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estimator_parameter_map = {'Lars': {'n_nonzero_coefs': 5},
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'LassoLars': {'alpha': 0.1},
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'LarsCV': {},
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|
'LassoLarsCV': {},
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|
'LassoLarsIC': {}}
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|
|
|
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def test_estimatorclasses_positive_constraint():
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# testing the transmissibility for the positive option of all estimator
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|
# classes in this same function here
|
|
|
|
for estname in estimator_parameter_map:
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|
params = default_parameter.copy()
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|
params.update(estimator_parameter_map[estname])
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|
estimator = getattr(linear_model, estname)(positive=False, **params)
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|
estimator.fit(diabetes['data'], diabetes['target'])
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|
assert_true(estimator.coef_.min() < 0)
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|
estimator = getattr(linear_model, estname)(positive=True, **params)
|
|
estimator.fit(diabetes['data'], diabetes['target'])
|
|
assert_true(min(estimator.coef_) >= 0)
|
|
|
|
|
|
def test_lasso_lars_vs_lasso_cd_positive(verbose=False):
|
|
# Test that LassoLars and Lasso using coordinate descent give the
|
|
# same results when using the positive option
|
|
|
|
# This test is basically a copy of the above with additional positive
|
|
# option. However for the middle part, the comparison of coefficient values
|
|
# for a range of alphas, we had to make an adaptations. See below.
|
|
|
|
# not normalized data
|
|
X = 3 * diabetes.data
|
|
|
|
alphas, _, lasso_path = linear_model.lars_path(X, y, method='lasso',
|
|
positive=True)
|
|
lasso_cd = linear_model.Lasso(fit_intercept=False, tol=1e-8, positive=True)
|
|
for c, a in zip(lasso_path.T, alphas):
|
|
if a == 0:
|
|
continue
|
|
lasso_cd.alpha = a
|
|
lasso_cd.fit(X, y)
|
|
error = linalg.norm(c - lasso_cd.coef_)
|
|
assert_less(error, 0.01)
|
|
|
|
# The range of alphas chosen for coefficient comparison here is restricted
|
|
# as compared with the above test without the positive option. This is due
|
|
# to the circumstance that the Lars-Lasso algorithm does not converge to
|
|
# the least-squares-solution for small alphas, see 'Least Angle Regression'
|
|
# by Efron et al 2004. The coefficients are typically in congruence up to
|
|
# the smallest alpha reached by the Lars-Lasso algorithm and start to
|
|
# diverge thereafter. See
|
|
# https://gist.github.com/michigraber/7e7d7c75eca694c7a6ff
|
|
|
|
for alpha in np.linspace(6e-1, 1 - 1e-2, 20):
|
|
clf1 = linear_model.LassoLars(fit_intercept=False, alpha=alpha,
|
|
normalize=False, positive=True).fit(X, y)
|
|
clf2 = linear_model.Lasso(fit_intercept=False, alpha=alpha, tol=1e-8,
|
|
normalize=False, positive=True).fit(X, y)
|
|
err = linalg.norm(clf1.coef_ - clf2.coef_)
|
|
assert_less(err, 1e-3)
|
|
|
|
# normalized data
|
|
X = diabetes.data
|
|
alphas, _, lasso_path = linear_model.lars_path(X, y, method='lasso',
|
|
positive=True)
|
|
lasso_cd = linear_model.Lasso(fit_intercept=False, normalize=True,
|
|
tol=1e-8, positive=True)
|
|
for c, a in zip(lasso_path.T[:-1], alphas[:-1]): # don't include alpha=0
|
|
lasso_cd.alpha = a
|
|
lasso_cd.fit(X, y)
|
|
error = linalg.norm(c - lasso_cd.coef_)
|
|
assert_less(error, 0.01)
|