def test_unnormalize():
df = load_larynx()
m = df.mean(0)
s = df.std(0)
ndf = utils.normalize(df)
npt.assert_almost_equal(df.values, utils.unnormalize(ndf, m, s).values)
def test_unnormalize():
df = load_larynx()
m = df.mean(0)
s = df.std(0)
ndf = utils.normalize(df)
npt.assert_almost_equal(df.values, utils.unnormalize(ndf, m, s).values)
def test_p_value_against_Survival_Analysis_by_John_Klein_and_Melvin_Moeschberger(self):
# see table 8.1 in Survival Analysis by John P. Klein and Melvin L. Moeschberger, Second Edition
df = load_larynx()
cf = CoxPHFitter()
cf.fit(df, duration_col='time', event_col='death')
# p-values
actual_p = cf._compute_p_values()
expected_p = np.array([0.1847, 0.7644, 0.0730, 0.00])
npt.assert_array_almost_equal(actual_p, expected_p, decimal=2)
def test_se_against_Survival_Analysis_by_John_Klein_and_Melvin_Moeschberger(self):
# see table 8.1 in Survival Analysis by John P. Klein and Melvin L. Moeschberger, Second Edition
df = load_larynx()
cf = CoxPHFitter()
cf.fit(df, duration_col='time', event_col='death')
# standard errors
actual_se = cf._compute_standard_errors().values
expected_se = np.array([[0.0143, 0.4623, 0.3561, 0.4222]])
npt.assert_array_almost_equal(actual_se, expected_se, decimal=2)
def test_p_value_against_Survival_Analysis_by_John_Klein_and_Melvin_Moeschberger(self):
# see table 8.1 in Survival Analysis by John P. Klein and Melvin L. Moeschberger, Second Edition
df = load_larynx()
cf = CoxPHFitter()
cf.fit(df, duration_col='time', event_col='death')
# p-values
actual_p = cf._compute_p_values()
expected_p = np.array([0.1847, 0.7644, 0.0730, 0.00])
npt.assert_array_almost_equal(actual_p, expected_p, decimal=2)
def test_se_against_Survival_Analysis_by_John_Klein_and_Melvin_Moeschberger(self):
# see table 8.1 in Survival Analysis by John P. Klein and Melvin L. Moeschberger, Second Edition
df = load_larynx()
cf = CoxPHFitter(normalize=False)
cf.fit(df, duration_col='time', event_col='death')
# standard errors
actual_se = cf._compute_standard_errors().values
expected_se = np.array([[0.0143, 0.4623, 0.3561, 0.4222]])
npt.assert_array_almost_equal(actual_se, expected_se, decimal=2)
def test_normalize():
df = load_larynx()
n, d = df.shape
npt.assert_almost_equal(utils.normalize(df).mean(0).values, np.zeros(d))
npt.assert_almost_equal(utils.normalize(df).std(0).values, np.ones(d))
def test_normalize():
df = load_larynx()
n, d = df.shape
npt.assert_almost_equal(utils.normalize(df).mean(0).values, np.zeros(d))
npt.assert_almost_equal(utils.normalize(df).std(0).values, np.ones(d))
