# -*- coding: utf-8 -*-
# weibull aft
if __name__ == "__main__":
import pandas as pd
import time
import numpy as np
from lifelines import WeibullAFTFitter
from lifelines.datasets import load_rossi
df = load_rossi()
df = pd.concat([df] * 1)
# df["start"] = df["week"]
# df["stop"] = np.where(df["arrest"], df["start"], np.inf)
# df = df.drop("week", axis=1)
wp = WeibullAFTFitter()
start_time = time.time()
wp.fit_right_censoring(df, "week", event_col="arrest")
print("--- %s seconds ---" % (time.time() - start_time))
wp.print_summary()
print(wp.score(df, scoring_method="log_likelihood"))
print(wp.score(df, scoring_method="concordance_index"))
# -*- coding: utf-8 -*-
# weibull aft
if __name__ == "__main__":
import pandas as pd
import time
import numpy as np
from lifelines import WeibullAFTFitter
from lifelines.datasets import load_rossi
df = load_rossi()
df = pd.concat([df] * 1)
df["start"] = df["week"]
df["stop"] = np.where(df["arrest"], df["start"], np.inf)
df = df.drop("week", axis=1)
wp = WeibullAFTFitter()
start_time = time.time()
print(df.head())
wp.fit_interval_censoring(df,
start_col="start",
stop_col="stop",
event_col="arrest")
print("--- %s seconds ---" % (time.time() - start_time))
wp.print_summary()
wp.fit_right_censoring(load_rossi(), "week", event_col="arrest")
wp.print_summary()
