76 lines
2.3 KiB
Python
76 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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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.logistic import LogisticRegression
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from sklearn.utils.testing import assert_true, raises
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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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def test_l1_min_c():
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losses = ['squared_hinge', 'log']
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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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for loss in losses:
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for X_label, X in Xs.items():
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for Y_label, Y in Ys.items():
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for intercept_label, intercept_params in intercepts.items():
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check = lambda: check_l1_min_c(X, Y, loss,
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**intercept_params)
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check.description = ('Test l1_min_c loss=%r %s %s %s' %
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(loss, X_label, Y_label,
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intercept_label))
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yield check
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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, "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, fit_intercept, intercept_scaling)
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clf = {
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'log': LogisticRegression(penalty='l1'),
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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_true((np.asarray(clf.coef_) == 0).all())
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assert_true((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_true((np.asarray(clf.coef_) != 0).any() or
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(np.asarray(clf.intercept_) != 0).any())
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@raises(ValueError)
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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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l1_min_c(X, y)
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@raises(ValueError)
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def test_unsupported_loss():
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l1_min_c(dense_X, Y1, 'l1')
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