def test_fit_and_predict_rbf(make_whas500, optimizer):
whas500 = make_whas500(to_numeric=True)
ssvm = FastKernelSurvivalSVM(optimizer=optimizer,
kernel='rbf',
tol=2e-6,
max_iter=75,
random_state=0)
ssvm.fit(whas500.x, whas500.y)
assert not ssvm._get_tags()["pairwise"]
assert whas500.x.shape[0] == ssvm.coef_.shape[0]
c = ssvm.score(whas500.x, whas500.y)
assert c >= 0.965
def test_fit_and_predict_linear_precomputed(make_whas500):
whas500 = make_whas500(to_numeric=True)
ssvm = FastKernelSurvivalSVM(optimizer="rbtree",
kernel='precomputed',
random_state=0)
x = numpy.dot(whas500.x, whas500.x.T)
ssvm.fit(x, whas500.y)
assert ssvm._get_tags()["pairwise"]
assert whas500.x.shape[0] == ssvm.coef_.shape[0]
i = numpy.arange(250)
numpy.random.RandomState(0).shuffle(i)
c = ssvm.score(x[i], whas500.y[i])
assert round(abs(c - 0.76923445664157997), 6) == 0
def test_fit_and_predict_hybrid_rbf(make_whas500):
whas500 = make_whas500(to_numeric=True)
ssvm = FastKernelSurvivalSVM(optimizer="rbtree",
rank_ratio=0.5,
kernel="rbf",
max_iter=50,
fit_intercept=True,
random_state=0)
ssvm.fit(whas500.x, whas500.y)
assert not ssvm._get_tags()["pairwise"]
assert abs(5.0289145697617164 - ssvm.intercept_) <= 0.04
pred = ssvm.predict(whas500.x)
rmse = numpy.sqrt(mean_squared_error(whas500.y['lenfol'], pred))
assert abs(880.20361811281487 - rmse) <= 75
def test_fit_and_predict_regression_rbf(make_whas500):
whas500 = make_whas500(to_numeric=True)
ssvm = FastKernelSurvivalSVM(optimizer="rbtree",
rank_ratio=0.0,
kernel="rbf",
tol=1e-6,
max_iter=50,
fit_intercept=True,
random_state=0)
ssvm.fit(whas500.x, whas500.y)
assert not ssvm._get_tags()["pairwise"]
assert round(abs(ssvm.intercept_ - 4.9267218894089533), 7) == 0
pred = ssvm.predict(whas500.x)
rmse = numpy.sqrt(mean_squared_error(whas500.y['lenfol'], pred))
assert round(abs(rmse - 783.525277), 6) == 0
def test_fit_and_predict_clinical_kernel(make_whas500):
whas500 = make_whas500(to_numeric=True)
trans = ClinicalKernelTransform()
trans.fit(whas500.x_data_frame)
ssvm = FastKernelSurvivalSVM(optimizer="rbtree",
kernel=trans.pairwise_kernel,
tol=7e-7,
max_iter=100,
random_state=0)
ssvm.fit(whas500.x, whas500.y)
assert not ssvm._get_tags()["pairwise"]
assert whas500.x.shape[0] == ssvm.coef_.shape[0]
c = ssvm.score(whas500.x, whas500.y)
assert c >= 0.854
def test_fit_and_predict_linear_regression_precomputed(make_whas500):
whas500 = make_whas500(to_numeric=True)
ssvm = FastKernelSurvivalSVM(optimizer="rbtree",
rank_ratio=0.0,
kernel="precomputed",
max_iter=50,
tol=1e-8,
fit_intercept=True,
random_state=0)
x = numpy.dot(whas500.x, whas500.x.T)
ssvm.fit(x, whas500.y)
assert ssvm._get_tags()["pairwise"]
assert round(abs(ssvm.intercept_ - 6.416017539824949), 5) == 0
i = numpy.arange(250)
numpy.random.RandomState(0).shuffle(i)
pred = ssvm.predict(x[i])
rmse = numpy.sqrt(mean_squared_error(whas500.y['lenfol'][i], pred))
assert rmse <= 1342.274550652291 + 0.293