def test_max_iter(gbsg2):
x, y = gbsg2
x = scale(x)
m = MinlipSurvivalAnalysis(solver="cvxopt", alpha=1, kernel="polynomial",
degree=2, pairs="next", max_iter=5)
with pytest.warns(ConvergenceWarning,
match=r"cvxopt solver did not converge: unknown \(duality gap = [.0-9]+\)"):
m.fit(x, y)
def test_breast_cancer_cvxpy(gbsg2):
x, y = gbsg2
m = MinlipSurvivalAnalysis(solver="cvxpy", alpha=1, pairs="next")
m.fit(x, y)
assert (1, x.shape[0]) == m.coef_.shape
p = m.predict(x)
assert_cindex_almost_equal(y['cens'], y['time'], p,
(0.59576770470121443, 79280, 53792, 0, 32))
def test_breast_cancer_osqp(gbsg2):
x, y = gbsg2
x = scale(x)
m = MinlipSurvivalAnalysis(solver="osqp", alpha=1, pairs="next")
m.fit(x, y)
assert (1, x.shape[0]) == m.coef_.shape
p = m.predict(x)
assert_cindex_almost_equal(y['cens'], y['time'], p,
(0.599066670674522, 79719, 53353, 0, 42))
def test_breast_cancer_ecos(gbsg2):
x, y = gbsg2
x = scale(x)
m = MinlipSurvivalAnalysis(solver="ecos", alpha=1, pairs="next")
m.fit(x, y)
assert (1, x.shape[0]) == m.coef_.shape
p = m.predict(x)
assert_cindex_almost_equal(y['cens'], y['time'], p,
(0.5990741854033906, 79720, 53352, 0, 42))
def test_breast_cancer_cvxpy(self):
m = MinlipSurvivalAnalysis(solver="cvxpy", alpha=1, pairs="next")
m.fit(self.x.values, self.y)
self.assertTupleEqual((1, self.x.shape[0]), m.coef_.shape)
p = m.predict(self.x.values)
v = concordance_index_censored(self.y['cens'], self.y['time'], p)
expected = numpy.array([0.59576770470121443, 79280, 53792, 0, 32])
assert_array_almost_equal(expected, v)
def test_breast_cancer_rbf_cvxpy(gbsg2):
x, y = gbsg2
x = scale(x)
m = MinlipSurvivalAnalysis(solver="cvxpy", alpha=1, kernel="rbf",
gamma=1./8, pairs="next", max_iter=1000)
m.fit(x, y)
assert (1, x.shape[0]) == m.coef_.shape
p = m.predict(x)
assert_cindex_almost_equal(y['cens'], y['time'], p,
(0.6105867500300589, 81252, 51820, 0, 42))
def test_max_iter(self):
x = scale(self.x.values)
m = MinlipSurvivalAnalysis(solver="cvxopt", alpha=1, kernel="polynomial",
degree=2, pairs="next", max_iter=5)
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
m.fit(x, self.y)
self.assertEqual(len(w), 1)
self.assertTrue(issubclass(w[0].category, ConvergenceWarning))
self.assertRegex(str(w[0].message),
r"cvxopt solver did not converge: unknown \(duality gap = [.0-9]+\)")
def test_max_iter(gbsg2):
x, y = gbsg2
x = scale(x)
m = MinlipSurvivalAnalysis(solver="osqp",
alpha=1,
kernel="polynomial",
degree=2,
pairs="next",
max_iter=5)
with pytest.warns(
ConvergenceWarning,
match=
r"OSQP solver did not converge: maximum iterations reached"):
m.fit(x, y)
def test_breast_cancer_rbf_osqp(gbsg2):
x, y = gbsg2
x = scale(x)
m = MinlipSurvivalAnalysis(solver="osqp",
alpha=1,
kernel="rbf",
gamma=1. / 8,
pairs="next",
max_iter=1000)
m.fit(x, y)
assert (1, x.shape[0]) == m.coef_.shape
p = m.predict(x)
assert_cindex_almost_equal(y['cens'], y['time'], p,
(0.6106168089455333, 81256, 51816, 0, 42))
def test_breast_cancer_rbf_cvxpy(self):
x = scale(self.x.values)
m = MinlipSurvivalAnalysis(solver="cvxpy", alpha=1, kernel="rbf",
gamma=32, pairs="next", max_iter=1000)
m.fit(x, self.y)
self.assertTupleEqual((1, self.x.shape[0]), m.coef_.shape)
p = m.predict(x)
v = concordance_index_censored(self.y['cens'], self.y['time'], p)
self.assertGreaterEqual(v[0], 0.6402849585186966)
self.assertGreaterEqual(v[1], 85203)
self.assertLessEqual(v[2], 47869)
self.assertEqual(0, v[3])
self.assertEqual(32, v[4])
def test_kernel_precomputed(gbsg2):
x, y = gbsg2
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils.metaestimators import _safe_split
m = MinlipSurvivalAnalysis(kernel="precomputed", solver="ecos")
K = pairwise_kernels(x, metric="rbf", gamma=1. / 32)
train_idx = numpy.arange(50, x.shape[0])
test_idx = numpy.arange(50)
X_fit, y_fit = _safe_split(m, K, y, train_idx)
X_test, y_test = _safe_split(m, K, y, test_idx, train_idx)
m.fit(X_fit, y_fit)
p = m.predict(X_test)
assert_cindex_almost_equal(y_test['cens'], y_test['time'], p,
(0.626514131897712, 457, 269, 17, 0))
def test_kernel_precomputed(self):
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils.metaestimators import _safe_split
m = MinlipSurvivalAnalysis(kernel="precomputed", solver="cvxpy")
K = pairwise_kernels(self.x, metric="rbf")
train_idx = numpy.arange(50, self.x.shape[0])
test_idx = numpy.arange(50)
X_fit, y_fit = _safe_split(m, K, self.y, train_idx)
X_test, y_test = _safe_split(m, K, self.y, test_idx, train_idx)
m.fit(X_fit, y_fit)
p = m.predict(X_test)
v = concordance_index_censored(y_test['cens'], y_test['time'], p)
expected = numpy.array([0.508748, 378, 365, 0, 0])
assert_array_almost_equal(expected, v)
def test_kernel_precomputed(gbsg2):
x, y = gbsg2
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils.metaestimators import _safe_split
m = MinlipSurvivalAnalysis(kernel="precomputed",
solver="osqp",
max_iter=25000)
xt = scale(x)
K = pairwise_kernels(xt, metric="rbf", gamma=0.1)
train_idx = numpy.arange(200, x.shape[0])
test_idx = numpy.arange(200)
X_fit, y_fit = _safe_split(m, K, y, train_idx)
X_test, y_test = _safe_split(m, K, y, test_idx, train_idx)
m.fit(X_fit, y_fit)
p = m.predict(X_test)
assert_cindex_almost_equal(y_test['cens'], y_test['time'], p,
(0.6518928901200369, 8472, 4524, 0, 3))
def test_unknown_solver(gbsg2):
x, y = gbsg2
m = MinlipSurvivalAnalysis(solver=None)
with pytest.raises(ValueError, match="unknown solver: None"):
m.fit(x, y)
m.set_params(solver="i don't know")
with pytest.raises(ValueError, match="unknown solver: i don't know"):
m.fit(x, y)
m.set_params(solver=[('why', 'are'), ('you', 'doing this')])
with pytest.raises(ValueError,
match=r"unknown solver: \[\('why', 'are'\), \('you', 'doing this'\)\]"):
m.fit(x, y)
