def test_fit_dropout(self):
model = ComponentwiseGradientBoostingSurvivalAnalysis(
n_estimators=100,
learning_rate=1.0,
dropout_rate=0.03,
random_state=0)
model.fit(self.x, self.y)
p = model.predict(self.x)
result = concordance_index_censored(self.y['fstat'], self.y['lenfol'],
p)
expected_cindex = numpy.array([0.7772425, 58409, 16740, 0, 119])
assert_array_almost_equal(expected_cindex, numpy.array(result))
coef_index = ['(Intercept)'] + self.columns
expected_coef = pandas.Series(numpy.zeros(15, dtype=float),
index=coef_index)
expected_coef['age'] = 0.275537
expected_coef['hr'] = 0.040048
expected_coef['diasbp'] = -0.029998
expected_coef['bmi'] = -0.138909
expected_coef['sho'] = 3.318941
expected_coef['chf'] = 2.851386
expected_coef['mitype'] = -0.075817
assert_array_almost_equal(expected_coef.values, model.coef_)
def test_fit_subsample(self):
model = ComponentwiseGradientBoostingSurvivalAnalysis(n_estimators=100,
subsample=0.6,
random_state=0)
model.fit(self.x, self.y)
p = model.predict(self.x)
result = concordance_index_censored(self.y['fstat'], self.y['lenfol'],
p)
expected_cindex = numpy.array([0.7750602, 58245, 16904, 0, 119])
assert_array_almost_equal(expected_cindex, numpy.array(result))
coef_index = ['(Intercept)'] + self.columns
expected_coef = pandas.Series(numpy.zeros(15, dtype=float),
index=coef_index)
expected_coef['age'] = 0.041299
expected_coef['hr'] = 0.00487
expected_coef['diasbp'] = -0.003381
expected_coef['bmi'] = -0.017018
expected_coef['sho'] = 0.433685
expected_coef['chf'] = 0.510277
assert_array_almost_equal(expected_coef.values, model.coef_)
self.assertTupleEqual((100, ), model.train_score_.shape)
self.assertTupleEqual((100, ), model.oob_improvement_.shape)
self.assertRaisesRegex(
ValueError, 'Dimensions of X are inconsistent with training data: '
'expected 14 features, but got 2', model.predict, self.x[:, :2])
def test_fit(self):
model = ComponentwiseGradientBoostingSurvivalAnalysis(n_estimators=100)
model.fit(self.x, self.y)
p = model.predict(self.x)
result = concordance_index_censored(self.y['fstat'], self.y['lenfol'],
p)
expected_cindex = numpy.array([0.7755659, 58283, 16866, 0, 119])
assert_array_almost_equal(expected_cindex, numpy.array(result))
coef_index = ['(Intercept)'] + self.columns
expected_coef = pandas.Series(numpy.zeros(15, dtype=float),
index=coef_index)
expected_coef['age'] = 0.040919
expected_coef['hr'] = 0.004977
expected_coef['diasbp'] = -0.003407
expected_coef['bmi'] = -0.017938
expected_coef['sho'] = 0.429904
expected_coef['chf'] = 0.508211
assert_array_almost_equal(expected_coef.values, model.coef_)
self.assertTupleEqual((100, ), model.train_score_.shape)
self.assertRaisesRegex(
ValueError, 'Dimensions of X are inconsistent with training data: '
'expected 14 features, but got 2', model.predict, self.x[:, :2])
def test_fit_dropout(make_whas500):
whas500_data = make_whas500(with_std=False, to_numeric=True)
model = ComponentwiseGradientBoostingSurvivalAnalysis(
n_estimators=100,
learning_rate=1.0,
dropout_rate=0.03,
random_state=0)
model.fit(whas500_data.x, whas500_data.y)
p = model.predict(whas500_data.x)
assert_cindex_almost_equal(whas500_data.y['fstat'],
whas500_data.y['lenfol'], p,
(0.7772425, 58409, 16740, 0, 14))
expected_coef = pandas.Series(numpy.zeros(15, dtype=float),
index=whas500_data.names)
expected_coef['age'] = 0.275537
expected_coef['hr'] = 0.040048
expected_coef['diasbp'] = -0.029998
expected_coef['bmi'] = -0.138909
expected_coef['sho'] = 3.318941
expected_coef['chf'] = 2.851386
expected_coef['mitype'] = -0.075817
assert_array_almost_equal(expected_coef.values, model.coef_)
def test_squared_loss(make_whas500):
whas500_data = make_whas500(with_std=False, to_numeric=True)
model = ComponentwiseGradientBoostingSurvivalAnalysis(loss="squared",
n_estimators=100,
random_state=0)
model.fit(whas500_data.x, whas500_data.y)
time_predicted = model.predict(whas500_data.x)
time_true = whas500_data.y["lenfol"]
event_true = whas500_data.y["fstat"]
rmse_all = numpy.sqrt(mean_squared_error(time_true, time_predicted))
assert round(abs(rmse_all - 793.6256945839657), 7) == 0
rmse_uncensored = numpy.sqrt(
mean_squared_error(time_true[event_true],
time_predicted[event_true]))
assert round(abs(rmse_uncensored - 542.83358120153525), 7) == 0
cindex = model.score(whas500_data.x, whas500_data.y)
assert round(abs(cindex - 0.7777082862), 7) == 0
with pytest.raises(
ValueError,
match="`fit` must be called with the loss option set to 'coxph'"
):
model.predict_survival_function(whas500_data.x)
with pytest.raises(
ValueError,
match="`fit` must be called with the loss option set to 'coxph'"
):
model.predict_cumulative_hazard_function(whas500_data.x)
def test_squared_loss(self):
model = ComponentwiseGradientBoostingSurvivalAnalysis(loss="squared", n_estimators=100, random_state=0)
model.fit(self.x, self.y)
time_predicted = model.predict(self.x)
time_true = self.y["lenfol"]
event_true = self.y["fstat"]
rmse_all = numpy.sqrt(mean_squared_error(time_true, time_predicted))
self.assertAlmostEqual(rmse_all, 793.6256945839657)
rmse_uncensored = numpy.sqrt(mean_squared_error(time_true[event_true], time_predicted[event_true]))
self.assertAlmostEqual(rmse_uncensored, 542.83358120153525)
def test_ipcwls_loss(self):
model = ComponentwiseGradientBoostingSurvivalAnalysis(loss="ipcwls", n_estimators=100, random_state=0)
model.fit(self.x, self.y)
time_predicted = model.predict(self.x)
time_true = self.y["lenfol"]
event_true = self.y["fstat"]
rmse_all = numpy.sqrt(mean_squared_error(time_true, time_predicted))
self.assertAlmostEqual(rmse_all, 806.283308322)
rmse_uncensored = numpy.sqrt(mean_squared_error(time_true[event_true], time_predicted[event_true]))
self.assertAlmostEqual(rmse_uncensored, 542.884585289)
def test_squared_loss(make_whas500):
whas500_data = make_whas500(with_std=False, to_numeric=True)
model = ComponentwiseGradientBoostingSurvivalAnalysis(loss="squared", n_estimators=100, random_state=0)
model.fit(whas500_data.x, whas500_data.y)
time_predicted = model.predict(whas500_data.x)
time_true = whas500_data.y["lenfol"]
event_true = whas500_data.y["fstat"]
rmse_all = numpy.sqrt(mean_squared_error(time_true, time_predicted))
assert round(abs(rmse_all - 793.6256945839657), 7) == 0
rmse_uncensored = numpy.sqrt(mean_squared_error(time_true[event_true], time_predicted[event_true]))
assert round(abs(rmse_uncensored - 542.83358120153525), 7) == 0
def test_ipcwls_loss(make_whas500):
whas500_data = make_whas500(with_std=False, to_numeric=True)
model = ComponentwiseGradientBoostingSurvivalAnalysis(loss="ipcwls", n_estimators=100, random_state=0)
model.fit(whas500_data.x, whas500_data.y)
time_predicted = model.predict(whas500_data.x)
time_true = whas500_data.y["lenfol"]
event_true = whas500_data.y["fstat"]
rmse_all = numpy.sqrt(mean_squared_error(time_true, time_predicted))
assert round(abs(rmse_all - 806.283308322), 7) == 0
rmse_uncensored = numpy.sqrt(mean_squared_error(time_true[event_true], time_predicted[event_true]))
assert round(abs(rmse_uncensored - 542.884585289), 7) == 0
cindex = model.score(whas500_data.x, whas500_data.y)
assert round(abs(cindex - 0.7773356931), 7) == 0