def test_run_to_run_fit():
diabetes = datasets.load_diabetes()
clf_first = SVR(kernel='linear', C=10.)
clf_first.fit(diabetes.data, diabetes.target)
for _ in range(10):
clf = SVR(kernel='linear', C=10.)
clf.fit(diabetes.data, diabetes.target)
assert_allclose(clf_first.intercept_, clf.intercept_)
assert_allclose(clf_first.support_vectors_, clf.support_vectors_)
assert_allclose(clf_first.dual_coef_, clf.dual_coef_)
def _test_boston_poly_compare_with_sklearn(params):
diabetes = datasets.load_boston()
clf = SVR(kernel='poly', **params)
clf.fit(diabetes.data, diabetes.target)
result = clf.score(diabetes.data, diabetes.target)
clf = SklearnSVR(kernel='poly', **params)
clf.fit(diabetes.data, diabetes.target)
expected = clf.score(diabetes.data, diabetes.target)
assert result > 0.5
assert result > expected - 1e-5
def _test_boston_linear_compare_with_sklearn(C):
diabetes = datasets.load_boston()
clf = SVR(kernel='linear', C=C)
clf.fit(diabetes.data, diabetes.target)
result = clf.score(diabetes.data, diabetes.target)
clf = SklearnSVR(kernel='linear', C=C)
clf.fit(diabetes.data, diabetes.target)
expected = clf.score(diabetes.data, diabetes.target)
assert result > 0.5
assert result > expected - 1e-3
def _test_boston_rbf_compare_with_sklearn(C, gamma):
diabetes = datasets.load_boston()
clf = SVR(kernel='rbf', gamma=gamma, C=C)
clf.fit(diabetes.data, diabetes.target)
result = clf.score(diabetes.data, diabetes.target)
clf = SklearnSVR(kernel='rbf', gamma=gamma, C=C)
clf.fit(diabetes.data, diabetes.target)
expected = clf.score(diabetes.data, diabetes.target)
assert result > 0.4
assert result > expected - 1e-5
def test_pickle():
diabetes = datasets.load_diabetes()
clf = SVR(kernel='rbf', C=10.)
clf.fit(diabetes.data, diabetes.target)
expected = clf.predict(diabetes.data)
import pickle
dump = pickle.dumps(clf)
clf2 = pickle.loads(dump)
assert type(clf2) == clf.__class__
result = clf2.predict(diabetes.data)
assert_array_equal(expected, result)
def _test_diabetes_compare_with_sklearn(kernel):
diabetes = datasets.load_diabetes()
clf_onedal = SVR(kernel=kernel, C=10.)
clf_onedal.fit(diabetes.data, diabetes.target)
result = clf_onedal.score(diabetes.data, diabetes.target)
clf_sklearn = SklearnSVR(kernel=kernel, C=10.)
clf_sklearn.fit(diabetes.data, diabetes.target)
expected = clf_sklearn.score(diabetes.data, diabetes.target)
assert result > expected - 1e-5
assert_allclose(clf_sklearn.intercept_, clf_onedal.intercept_, atol=1e-4)
assert_allclose(clf_sklearn.support_vectors_.shape,
clf_sklearn.support_vectors_.shape)
assert_allclose(clf_sklearn.dual_coef_, clf_onedal.dual_coef_, atol=1e-2)
def test_input_format_for_diabetes():
diabetes = datasets.load_diabetes()
c_contiguous_numpy = np.asanyarray(diabetes.data, dtype='float', order='C')
assert c_contiguous_numpy.flags.c_contiguous
assert not c_contiguous_numpy.flags.f_contiguous
assert not c_contiguous_numpy.flags.fnc
clf = SVR(kernel='linear', C=10.)
clf.fit(c_contiguous_numpy, diabetes.target)
dual_c_contiguous_numpy = clf.dual_coef_
res_c_contiguous_numpy = clf.predict(c_contiguous_numpy)
f_contiguous_numpy = np.asanyarray(diabetes.data, dtype='float', order='F')
assert not f_contiguous_numpy.flags.c_contiguous
assert f_contiguous_numpy.flags.f_contiguous
assert f_contiguous_numpy.flags.fnc
clf = SVR(kernel='linear', C=10.)
clf.fit(f_contiguous_numpy, diabetes.target)
dual_f_contiguous_numpy = clf.dual_coef_
res_f_contiguous_numpy = clf.predict(f_contiguous_numpy)
assert_allclose(dual_c_contiguous_numpy, dual_f_contiguous_numpy)
assert_allclose(res_c_contiguous_numpy, res_f_contiguous_numpy)
def test_sided_sample_weight():
clf = SVR(C=1e-2, kernel='linear')
X = [[-2, 0], [-1, -1], [0, -2], [0, 2], [1, 1], [2, 0]]
Y = [1, 1, 1, 2, 2, 2]
sample_weight = [10., .1, .1, .1, .1, 10]
clf.fit(X, Y, sample_weight=sample_weight)
y_pred = clf.predict([[-1., 1.]])
assert y_pred < 1.5
sample_weight = [1., .1, 10., 10., .1, .1]
clf.fit(X, Y, sample_weight=sample_weight)
y_pred = clf.predict([[-1., 1.]])
assert y_pred > 1.5
sample_weight = [1] * 6
clf.fit(X, Y, sample_weight=sample_weight)
y_pred = clf.predict([[-1., 1.]])
assert y_pred == pytest.approx(1.5)
def test_diabetes_simple():
diabetes = datasets.load_diabetes()
clf = SVR(kernel='linear', C=10.)
clf.fit(diabetes.data, diabetes.target)
assert clf.score(diabetes.data, diabetes.target) > 0.02
