def test_aaf_panel_dataset_with_no_censorship(self): panel_dataset = load_panel_test() aaf = AalenAdditiveFitter() aaf.fit(panel_dataset, id_col='id', duration_col='t') expected = pd.Series([True] * 9, index=range(1, 10)) expected.index.name = 'id' assert_series_equal(aaf.event_observed, expected)
def test_aaf_panel_dataset(self): matplotlib = pytest.importorskip("matplotlib") from matplotlib import pyplot as plt panel_dataset = load_panel_test() aaf = AalenAdditiveFitter() aaf.fit(panel_dataset, id_col='id', duration_col='t', event_col='E') aaf.plot()
def test_using_a_custom_timeline_in_varying_fitting(self): panel_dataset = load_panel_test() aaf = AalenAdditiveFitter() timeline = np.arange(10) aaf.fit(panel_dataset, id_col='id', duration_col='t', timeline=timeline) npt.assert_array_equal(aaf.hazards_.index.values, timeline) npt.assert_array_equal(aaf.cumulative_hazards_.index.values, timeline) npt.assert_array_equal(aaf.variance_.index.values, timeline) npt.assert_array_equal(aaf.timeline, timeline)
def test_aaf_panel_dataset(self, block): panel_dataset = load_panel_test() aaf = AalenAdditiveFitter() aaf.fit(panel_dataset, id_col="id", duration_col="t", event_col="E") aaf.plot() self.plt.title("test_aaf_panel_dataset") self.plt.show(block=block) return
def test_aaf_panel_dataset(self, block): panel_dataset = load_panel_test() aaf = AalenAdditiveFitter() aaf.fit(panel_dataset, id_col='id', duration_col='t', event_col='E') aaf.plot() self.plt.title("test_aaf_panel_dataset") self.plt.show(block=block) return