def test_coxtv_plotting(self, block): df = load_stanford_heart_transplants() ctv = CoxTimeVaryingFitter() ctv.fit(df, id_col="id", event_col="event") ctv.plot(fmt="o") self.plt.title("test_coxtv_plotting") self.plt.show(block=block)
def test_coxtv_plotting_with_subset_of_columns(self, block): df = load_stanford_heart_transplants() ctv = CoxTimeVaryingFitter() ctv.fit(df, id_col="id", event_col="event") ctv.plot(columns=["age", "year"]) self.plt.title("test_coxtv_plotting_with_subset_of_columns") self.plt.show(block=block)
def test_coxtv_plotting_with_subset_of_columns_and_standardized( self, block): df = load_stanford_heart_transplants() ctv = CoxTimeVaryingFitter() ctv.fit(df, id_col='id', event_col='event') ctv.plot(True, columns=['age', 'year']) self.plt.title( 'test_coxtv_plotting_with_subset_of_columns_and_standardized') self.plt.show(block=block)
if __name__ == "__main__": import time import pandas as pd from lifelines.estimation import CoxTimeVaryingFitter from lifelines.datasets import load_stanford_heart_transplants dfcv = load_stanford_heart_transplants() dfcv = pd.concat([dfcv] * 50) ctv = CoxTimeVaryingFitter() start_time = time.time() ctv.fit(dfcv, id_col="id", event_col="event", start_col='start', stop_col='stop') time_took = (time.time() - start_time) print("--- %s seconds ---" % time_took) ctv.print_summary()