torchlife 0.0.2

Deep Learning for Survival Analysis

TorchLife

Survival Analysis using pytorch

This library takes a deep learning approach to Survival Analysis.

Install

pip install torchlife

How to use

We need a dataframe that has a column named 't' indicating time, and 'e' indicating a death event.

import pandas as pd
import numpy as np
url = "https://raw.githubusercontent.com/CamDavidsonPilon/lifelines/master/lifelines/datasets/rossi.csv"
df = pd.read_csv(url)
df.rename(columns={'week':'t', 'arrest':'e'}, inplace=True)

df.head()

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	t	e	fin	age	race	wexp	mar	paro	prio
0	20	1	0	27	1	0	0	1	3
1	17	1	0	18	1	0	0	1	8
2	25	1	0	19	0	1	0	1	13
3	52	0	1	23	1	1	1	1	1
4	52	0	0	19	0	1	0	1	3

from torchlife.model import ModelHazard

model = ModelHazard('cox', lr=0.5)
model.fit(df)
λ, S = model.predict(df)

epoch	train_loss	valid_loss	time
0	6.993955	10.741218	00:00
1	8.774823	14.736155	00:00
2	9.991431	16.564432	00:00
3	10.995527	17.174604	00:00
4	11.723181	16.920387	00:00
5	12.060142	15.983603	00:00
6	12.174074	14.553919	00:00
7	12.038597	12.683950	00:00
8	11.702325	10.452137	00:00
9	11.218502	7.981377	00:00
10	10.570101	5.209520	00:00
11	9.859859	4.039678	00:00
12	9.155064	3.643379	00:00
13	8.514476	2.742133	00:00
14	7.915660	3.074418	00:00
15	7.413548	2.585245	00:00
16	6.967895	2.710384	00:00
17	6.569957	2.544009	00:00
18	6.215098	2.433515	00:00
19	5.880322	2.342750	00:00

Let's plot the survival function for the 4th element in the dataframe:

x = df.drop(['t', 'e'], axis=1).iloc[2]
t = np.arange(df['t'].max())
model.plot_survival_function(t, x)