Deep Cox proportional risk model and survival analysis implemented by tensorflow.
DeepSurv, a package of Deep Cox proportional risk model, is open-source on Github. But our works may shine in:
- Evaluating variable importance in deep neural network.
- Identifying ties of death time in your survival data, which means different loss function and estimator for survival function (
BresloworEfronapproximation). - Providing survival function estimated by three optional algorithm.
- Tuning hyperparameters of DNN using scientific method - Bayesian Hyperparameters Optimization.
The project is based on the research of Breast Cancer. The paper about this project has been submitted to IEEE JBHI. We will update status here once paper published !
Download TFDeepSurv package and install from the directory (Python version : 3.x):
git clone https://github.com/liupei101/TFDeepSurv.git
cd TFDeepSurv
pip install .### import package
from tfdeepsurv import dsl
from tfdeepsurv.dataset import SimulatedData
### generate simulated data
# data configuration:
# hazard ratio = 2000
# number of features = 10
# number of valid features = 2
data_generator = SimulatedData(2000, num_var=2, num_features=10)
# training dataset:
# number of rows = 2000
# random seed = 1
train_data = data_generator.generate_data(2000, seed=1)
# test dataset :
# number of rows = 800
# random seed = 1
test_data = data_generator.generate_data(800, seed=1)import matplotlib.pyplot as plt
from lifelines import KaplanMeierFitter
from lifelines.plotting import add_at_risk_counts
### Visualize survival status
fig, ax = plt.subplots(figsize=(8, 6))
l_kmf = []
# training set
kmf = KaplanMeierFitter()
kmf.fit(train_data['t'], event_observed=train_data['e'], label='Training Set')
kmf.survival_function_.plot(ax=ax)
l_kmf.append(kmf)
# test set
kmf = KaplanMeierFitter()
kmf.fit(test_data['t'], event_observed=test_data['e'], label='Test Set')
kmf.survival_function_.plot(ax=ax)
l_kmf.append(kmf)
#
plt.ylim(0, 1.01)
plt.xlabel("Time")
plt.ylabel("Survival rate")
plt.title("Survival Curve")
plt.legend(loc="best", title="Dataset")
add_at_risk_counts(*l_kmf, ax=ax)
plt.show()result :
input_nodes = 10
output_nodes = 1
train_X = train_data['x']
train_y = {'e': train_data['e'], 't': train_data['t']}
# the arguments of dsnn is obtained by Bayesian Hyperparameters Tuning
model = dsl.dsnn(
train_X, train_y,
input_nodes, [6, 3], output_nodes,
learning_rate=0.7,
learning_rate_decay=1.0,
activation='relu',
L1_reg=3.4e-5,
L2_reg=8.8e-5,
optimizer='adam',
dropout_keep_prob=1.0
)
# Get the type of ties (three types)
# 'noties', 'breslow' when ties occur or 'efron' when ties occur frequently
print(model.get_ties())# Plot curve of loss and CI on train data
model.train(num_epoch=1900, iteration=100,
plot_train_loss=True, plot_train_ci=True)result :
-------------------------------------------------
training steps 1:
loss = 7.07988.
CI = 0.494411.
-------------------------------------------------
training steps 101:
loss = 7.0797.
CI = 0.524628.
-------------------------------------------------
training steps 201:
loss = 7.06293.
CI = 0.569339.
...
...
...
-------------------------------------------------
training steps 1801:
loss = 6.27862.
CI = 0.823937.
Curve of loss and CI:
| Loss Value | CI |
|---|---|
 |
 |
test_X = test_data['x']
test_y = {'e': test_data['e'], 't': test_data['t']}
print("CI on train set: %g" % model.score(train_X, train_y))
print("CI on test set: %g" % model.score(test_X, test_y))result :
CI on train set: 0.823772
CI on test set: 0.812503
model.get_vip_byweights()result:
0th feature score : 1.
1th feature score : 0.149105.
2th feature score : -0.126712.
3th feature score : 0.033377.
4th feature score : 0.123096.
5th feature score : 0.0321232.
6th feature score : 0.101529.
7th feature score : -0.0707392.
8th feature score : -0.0415884.
9th feature score : 0.0439712.
# optional algo: 'wwe', 'bls' or 'kp', the algorithm for estimating survival function
model.survival_function(test_X[0:3], algo="wwe")result:
The procedure on real-world data is similar with the described on simulated data. One we need to notice is data preparation. This package provides functions for loading standard dataset for traning or testing.
# import package
from tfdeepsurv import dsl
from tfdeepsurv.utils import load_data
# Notice: the object train_X or test_X returned from function load_data is numpy.array.
# the object train_y or test_y returned from function load_data is dict like {'e': numpy.array,'t': numpy.array}.
# You can load training data and testing data, respectively
train_X, train_y = load_data('train.csv', excluded_col=['ID'], surv_col={'e': 'event', 't': 'time'})
test_X, test_y = load_data('test.csv', excluded_col=['ID'], surv_col={'e': 'event', 't': 'time'})
# Or load full data, then split it into training and testing set (=8:2).
train_X, train_y, test_X, test_y = load_data('full_data.csv', excluded_col=['ID'], surv_col={'e': 'event', 't': 'time'}, split_ratio=0.8)This is the same as doing in simulated data.
We provide tools for hyperparameters tuning (Bayesian Hyperparameters Optimization) in deep neural network, which is automatic in searching optimal hyperparameters of DNN.
For more usage of Bayesian Hyperparameters Optimization, you can refer to here
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