Uploaded Test files

venv/Lib/site-packages/sklearn/svm/tests/test_bounds.py

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