def unit_timevarying_cox(df, features=[]):
factors = ['user_id', 'start', 'stop', 'event'] + features
df_ = df[factors]
ctv = CoxTimeVaryingFitter()
ctv.fit(df_, id_col='user_id', event_col='event', start_col='start', stop_col='stop', show_progress=True)
ctv.print_summary(3)
plt.show()
"""
Cox Propotional Model using Age, Quickfire Wins, Wins, Highs and Lows to predict Out
"""
from lifelines import CoxTimeVaryingFitter
ctv = CoxTimeVaryingFitter()
ctv.fit(trainData[[
"Age", "QuickfireWins", "Wins", "Highs", "Lows", "Start", "End", "ID",
"Out"
]],
id_col="ID",
event_col="Out",
start_col="Start",
stop_col="End",
show_progress=True)
ctv.print_summary()
#Get Hazards for specific episode
####EDIT THIS TO CHANGE WHICH SEASON/EPISODE YOU WANT TO APPLY THE MODEL TO#####
episode = 5
season = 13
predictData = allSeasonsByEpisode.loc[
(allSeasonsByEpisode["Episode"] == episode)
& (allSeasonsByEpisode["Season"] == season)]
## generate the relative risk with the model and merge it with the existing data
predictData["Partial Hazard"] = ctv.predict_partial_hazard(predictData)
predictData["Log Partial Hazard"] = ctv.predict_log_partial_hazard(predictData)
## clean up the data frame and display the ordered relative risks
predictData = predictData.sort_values(["Partial Hazard"], ascending=[True])
print("Model predictions for season " + str(season) + ", episode " +
# -*- coding: utf-8 -*-
if __name__ == "__main__":
import time
import pandas as pd
from lifelines import CoxTimeVaryingFitter
from lifelines.datasets import load_rossi
from lifelines.utils import to_long_format
df = load_rossi()
df = pd.concat([df] * 20)
df = df.reset_index()
df = to_long_format(df, duration_col="week")
ctv = CoxTimeVaryingFitter()
start_time = time.time()
ctv.fit(df,
id_col="index",
event_col="arrest",
start_col="start",
stop_col="stop")
time_took = time.time() - start_time
print("--- %s seconds ---" % time_took)
ctv.print_summary()