def test_libsvm_iris():
# Check consistency on dataset iris.
# shuffle the dataset so that labels are not ordered
for k in ('linear', 'rbf'):
clf = svm.SVC(kernel=k).fit(iris.data, iris.target)
assert np.mean(clf.predict(iris.data) == iris.target) > 0.9
assert hasattr(clf, "coef_") == (k == 'linear')
assert_array_equal(clf.classes_, np.sort(clf.classes_))
# check also the low-level API
model = _libsvm.fit(iris.data, iris.target.astype(np.float64))
pred = _libsvm.predict(iris.data, *model)
assert np.mean(pred == iris.target) > .95
model = _libsvm.fit(iris.data, iris.target.astype(np.float64),
kernel='linear')
pred = _libsvm.predict(iris.data, *model, kernel='linear')
assert np.mean(pred == iris.target) > .95
pred = _libsvm.cross_validation(iris.data,
iris.target.astype(np.float64), 5,
kernel='linear',
random_seed=0)
assert np.mean(pred == iris.target) > .95
# If random_seed >= 0, the libsvm rng is seeded (by calling `srand`), hence
# we should get deterministic results (assuming that there is no other
# thread calling this wrapper calling `srand` concurrently).
pred2 = _libsvm.cross_validation(iris.data,
iris.target.astype(np.float64), 5,
kernel='linear',
random_seed=0)
assert_array_equal(pred, pred2)
def test_unicode_kernel():
# Test that a unicode kernel name does not cause a TypeError
clf = svm.SVC(kernel="linear", probability=True)
clf.fit(X, Y)
clf.predict_proba(T)
_libsvm.cross_validation(
iris.data, iris.target.astype(np.float64), 5, kernel="linear", random_seed=0
)
def test_libsvm_iris():
# Check consistency on dataset iris.
# shuffle the dataset so that labels are not ordered
for k in ("linear", "rbf"):
clf = svm.SVC(kernel=k).fit(iris.data, iris.target)
assert np.mean(clf.predict(iris.data) == iris.target) > 0.9
assert hasattr(clf, "coef_") == (k == "linear")
assert_array_equal(clf.classes_, np.sort(clf.classes_))
# check also the low-level API
# We unpack the values to create a dictionary with some of the return values
# from Libsvm's fit.
(
libsvm_support,
libsvm_support_vectors,
libsvm_n_class_SV,
libsvm_sv_coef,
libsvm_intercept,
libsvm_probA,
libsvm_probB,
# libsvm_fit_status and libsvm_n_iter won't be used below.
libsvm_fit_status,
libsvm_n_iter,
) = _libsvm.fit(iris.data, iris.target.astype(np.float64))
model_params = {
"support": libsvm_support,
"SV": libsvm_support_vectors,
"nSV": libsvm_n_class_SV,
"sv_coef": libsvm_sv_coef,
"intercept": libsvm_intercept,
"probA": libsvm_probA,
"probB": libsvm_probB,
}
pred = _libsvm.predict(iris.data, **model_params)
assert np.mean(pred == iris.target) > 0.95
# We unpack the values to create a dictionary with some of the return values
# from Libsvm's fit.
(
libsvm_support,
libsvm_support_vectors,
libsvm_n_class_SV,
libsvm_sv_coef,
libsvm_intercept,
libsvm_probA,
libsvm_probB,
# libsvm_fit_status and libsvm_n_iter won't be used below.
libsvm_fit_status,
libsvm_n_iter,
) = _libsvm.fit(iris.data, iris.target.astype(np.float64), kernel="linear")
model_params = {
"support": libsvm_support,
"SV": libsvm_support_vectors,
"nSV": libsvm_n_class_SV,
"sv_coef": libsvm_sv_coef,
"intercept": libsvm_intercept,
"probA": libsvm_probA,
"probB": libsvm_probB,
}
pred = _libsvm.predict(iris.data, **model_params, kernel="linear")
assert np.mean(pred == iris.target) > 0.95
pred = _libsvm.cross_validation(
iris.data, iris.target.astype(np.float64), 5, kernel="linear", random_seed=0
)
assert np.mean(pred == iris.target) > 0.95
# If random_seed >= 0, the libsvm rng is seeded (by calling `srand`), hence
# we should get deterministic results (assuming that there is no other
# thread calling this wrapper calling `srand` concurrently).
pred2 = _libsvm.cross_validation(
iris.data, iris.target.astype(np.float64), 5, kernel="linear", random_seed=0
)
assert_array_equal(pred, pred2)
