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get_main.py
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'''
This train DeepHit, and outputs the validation performance for random search.
INPUTS:
- DATA = (data, time, label)
- MASK = (mask1, mask2)
- in_parser: dictionary of hyperparameters
- out_itr: the training/testing split indicator
- eval_time: None or a list (e.g. [12, 24, 36]) at which the validation of the network is performed
- MAX_VALUE: maximum validation value
- OUT_ITERATION: total number of training/testing splits
- seed: random seed for training/testing/validation
OUTPUTS:
- the validation performance of the trained network
- save the trained network in the folder directed by "in_parser['out_path'] + '/itr_' + str(out_itr)"
'''
_EPSILON = 1e-08
import numpy as np
import pandas as pd
import tensorflow as tf
import random
import os
# import sys
from termcolor import colored
from tensorflow.contrib.layers import fully_connected as FC_Net
from sklearn.metrics import brier_score_loss
from sklearn.model_selection import train_test_split
import utils_network as utils
from class_DeepHit import Model_DeepHit
from utils_eval import c_index, brier_score, weighted_c_index, weighted_brier_score
##### USER-DEFINED FUNCTIONS
def log(x):
return tf.log(x + 1e-8)
def div(x, y):
return tf.div(x, (y + 1e-8))
def f_get_minibatch(mb_size, x, label, time, mask1, mask2):
idx = range(np.shape(x)[0])
idx = random.sample(idx, mb_size)
x_mb = x[idx, :].astype(np.float32)
k_mb = label[idx, :].astype(np.float32) # censoring(0)/event(1,2,..) label
t_mb = time[idx, :].astype(np.float32)
m1_mb = mask1[idx, :, :].astype(np.float32) #fc_mask
m2_mb = mask2[idx, :].astype(np.float32) #fc_mask
return x_mb, k_mb, t_mb, m1_mb, m2_mb
def get_valid_performance(DATA, MASK, in_parser, out_itr, eval_time=None, MAX_VALUE = -99, OUT_ITERATION=5, seed=1234):
##### DATA & MASK
(data, time, label) = DATA
(mask1, mask2) = MASK
x_dim = np.shape(data)[1]
_, num_Event, num_Category = np.shape(mask1) # dim of mask1: [subj, Num_Event, Num_Category]
ACTIVATION_FN = {'relu': tf.nn.relu, 'elu': tf.nn.elu, 'tanh': tf.nn.tanh}
##### HYPER-PARAMETERS
mb_size = in_parser['mb_size']
iteration = in_parser['iteration']
keep_prob = in_parser['keep_prob']
lr_train = in_parser['lr_train']
alpha = in_parser['alpha'] #for log-likelihood loss
beta = in_parser['beta'] #for ranking loss
gamma = in_parser['gamma'] #for RNN-prediction loss
parameter_name = 'a' + str('%02.0f' %(10*alpha)) + 'b' + str('%02.0f' %(10*beta)) + 'c' + str('%02.0f' %(10*gamma))
initial_W = tf.contrib.layers.xavier_initializer()
##### MAKE DICTIONARIES
# INPUT DIMENSIONS
input_dims = { 'x_dim' : x_dim,
'num_Event' : num_Event,
'num_Category' : num_Category}
# NETWORK HYPER-PARMETERS
network_settings = { 'h_dim_shared' : in_parser['h_dim_shared'],
'num_layers_shared' : in_parser['num_layers_shared'],
'h_dim_CS' : in_parser['h_dim_CS'],
'num_layers_CS' : in_parser['num_layers_CS'],
'active_fn' : ACTIVATION_FN[in_parser['active_fn']],
'initial_W' : initial_W }
file_path_final = in_parser['out_path'] + '/itr_' + str(out_itr)
#change parameters...
if not os.path.exists(file_path_final + '/models/'):
os.makedirs(file_path_final + '/models/')
print (file_path_final + ' (a:' + str(alpha) + ' b:' + str(beta) + ' c:' + str(gamma) + ')' )
##### CREATE DEEPFHT NETWORK
tf.reset_default_graph()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
model = Model_DeepHit(sess, "DeepHit", input_dims, network_settings)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
### TRAINING-TESTING SPLIT
(tr_data,te_data, tr_time,te_time, tr_label,te_label,
tr_mask1,te_mask1, tr_mask2,te_mask2) = train_test_split(data, time, label, mask1, mask2, test_size=0.20, random_state=seed)
(tr_data,va_data, tr_time,va_time, tr_label,va_label,
tr_mask1,va_mask1, tr_mask2,va_mask2) = train_test_split(tr_data, tr_time, tr_label, tr_mask1, tr_mask2, test_size=0.20, random_state=seed)
max_valid = -99
stop_flag = 0
if eval_time is None:
eval_time = [int(np.percentile(tr_time, 25)), int(np.percentile(tr_time, 50)), int(np.percentile(tr_time, 75))]
### TRAINING - MAIN
print( "MAIN TRAINING ...")
print( "EVALUATION TIMES: " + str(eval_time))
avg_loss = 0
for itr in range(iteration):
if stop_flag > 5: #for faster early stopping
break
else:
x_mb, k_mb, t_mb, m1_mb, m2_mb = f_get_minibatch(mb_size, tr_data, tr_label, tr_time, tr_mask1, tr_mask2)
DATA = (x_mb, k_mb, t_mb)
MASK = (m1_mb, m2_mb)
PARAMETERS = (alpha, beta, gamma)
_, loss_curr = model.train(DATA, MASK, PARAMETERS, keep_prob, lr_train)
avg_loss += loss_curr/1000
if (itr+1)%1000 == 0:
print('|| ITR: ' + str('%04d' % (itr + 1)) + ' | Loss: ' + colored(str('%.4f' %(avg_loss)), 'yellow' , attrs=['bold']))
avg_loss = 0
### VALIDATION (based on average C-index of our interest)
if (itr+1)%1000 == 0:
### PREDICTION
pred = model.predict(va_data)
### EVALUATION
va_result1 = np.zeros([num_Event, len(eval_time)])
for t, t_time in enumerate(eval_time):
eval_horizon = int(t_time)
if eval_horizon >= num_Category:
print('ERROR: evaluation horizon is out of range')
va_result1[:, t] = va_result2[:, t] = -1
else:
risk = np.sum(pred[:,:,:(eval_horizon+1)], axis=2) #risk score until eval_time
for k in range(num_Event):
# va_result1[k, t] = c_index(risk[:,k], va_time, (va_label[:,0] == k+1).astype(int), eval_horizon) #-1 for no event (not comparable)
va_result1[k, t] = weighted_c_index(tr_time, (tr_label[:,0] == k+1).astype(int), risk[:,k], va_time, (va_label[:,0] == k+1).astype(int), eval_horizon)
tmp_valid = np.mean(va_result1)
if tmp_valid > max_valid:
stop_flag = 0
max_valid = tmp_valid
print( 'updated.... average c-index = ' + str('%.4f' %(tmp_valid)))
if max_valid > MAX_VALUE:
saver.save(sess, file_path_final + '/models/model_itr_' + str(out_itr))
else:
stop_flag += 1
return max_valid