77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
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import numpy as np
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from scipy import sparse as sp
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import pytest
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from sklearn.svm._bounds import l1_min_c
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from sklearn.svm import LinearSVC
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from sklearn.linear_model import LogisticRegression
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from sklearn.utils._testing import assert_raise_message
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dense_X = [[-1, 0], [0, 1], [1, 1], [1, 1]]
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sparse_X = sp.csr_matrix(dense_X)
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Y1 = [0, 1, 1, 1]
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Y2 = [2, 1, 0, 0]
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@pytest.mark.parametrize('loss', ['squared_hinge', 'log'])
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@pytest.mark.parametrize('X_label', ['sparse', 'dense'])
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@pytest.mark.parametrize('Y_label', ['two-classes', 'multi-class'])
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@pytest.mark.parametrize('intercept_label', ['no-intercept', 'fit-intercept'])
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def test_l1_min_c(loss, X_label, Y_label, intercept_label):
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Xs = {'sparse': sparse_X, 'dense': dense_X}
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Ys = {'two-classes': Y1, 'multi-class': Y2}
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intercepts = {'no-intercept': {'fit_intercept': False},
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'fit-intercept': {'fit_intercept': True,
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'intercept_scaling': 10}}
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X = Xs[X_label]
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Y = Ys[Y_label]
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intercept_params = intercepts[intercept_label]
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check_l1_min_c(X, Y, loss, **intercept_params)
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def test_l1_min_c_l2_loss():
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# loss='l2' should raise ValueError
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assert_raise_message(ValueError, "loss type not in",
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l1_min_c, dense_X, Y1, loss="l2")
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def check_l1_min_c(X, y, loss, fit_intercept=True, intercept_scaling=None):
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min_c = l1_min_c(X, y, loss=loss, fit_intercept=fit_intercept,
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intercept_scaling=intercept_scaling)
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clf = {
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'log': LogisticRegression(penalty='l1', solver='liblinear'),
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'squared_hinge': LinearSVC(loss='squared_hinge',
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penalty='l1', dual=False),
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}[loss]
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clf.fit_intercept = fit_intercept
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clf.intercept_scaling = intercept_scaling
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clf.C = min_c
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clf.fit(X, y)
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assert (np.asarray(clf.coef_) == 0).all()
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assert (np.asarray(clf.intercept_) == 0).all()
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clf.C = min_c * 1.01
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clf.fit(X, y)
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assert ((np.asarray(clf.coef_) != 0).any() or
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(np.asarray(clf.intercept_) != 0).any())
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def test_ill_posed_min_c():
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X = [[0, 0], [0, 0]]
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y = [0, 1]
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with pytest.raises(ValueError):
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l1_min_c(X, y)
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def test_unsupported_loss():
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with pytest.raises(ValueError):
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l1_min_c(dense_X, Y1, loss='l1')
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