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()
