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summarize_results.py
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summarize_results.py
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_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 import_data as impt
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
def load_logging(filename):
data = dict()
with open(filename) as f:
def is_float(input):
try:
num = float(input)
except ValueError:
return False
return True
for line in f.readlines():
if ':' in line:
key,value = line.strip().split(':', 1)
if value.isdigit():
data[key] = int(value)
elif is_float(value):
data[key] = float(value)
elif value == 'None':
data[key] = None
else:
data[key] = value
else:
pass # deal with bad lines of text here
return data
##### MAIN SETTING
OUT_ITERATION = 5
data_mode = 'SYNTHETIC' #METABRIC, SYNTHETIC
seed = 1234
EVAL_TIMES = [12, 24, 36] # evalution times (for C-index and Brier-Score)
##### IMPORT DATASET
'''
num_Category = max event/censoring time * 1.2 (to make enough time horizon)
num_Event = number of evetns i.e. len(np.unique(label))-1
max_length = maximum number of measurements
x_dim = data dimension including delta (num_features)
mask1, mask2 = used for cause-specific network (FCNet structure)
'''
if data_mode == 'SYNTHETIC':
(x_dim), (data, time, label), (mask1, mask2) = impt.import_dataset_SYNTHETIC(norm_mode = 'standard')
EVAL_TIMES = [12, 24, 36]
elif data_mode == 'METABRIC':
(x_dim), (data, time, label), (mask1, mask2) = impt.import_dataset_METABRIC(norm_mode = 'standard')
EVAL_TIMES = [144, 288, 432]
else:
print('ERROR: DATA_MODE NOT FOUND !!!')
_, num_Event, num_Category = np.shape(mask1) # dim of mask1: [subj, Num_Event, Num_Category]
in_path = data_mode + '/results/'
if not os.path.exists(in_path):
os.makedirs(in_path)
FINAL1 = np.zeros([num_Event, len(EVAL_TIMES), OUT_ITERATION])
FINAL2 = np.zeros([num_Event, len(EVAL_TIMES), OUT_ITERATION])
for out_itr in range(OUT_ITERATION):
in_hypfile = in_path + '/itr_' + str(out_itr) + '/hyperparameters_log.txt'
in_parser = load_logging(in_hypfile)
##### HYPER-PARAMETERS
mb_size = in_parser['mb_size']
iteration = in_parser['iteration']
keep_prob = in_parser['keep_prob']
lr_train = in_parser['lr_train']
h_dim_shared = in_parser['h_dim_shared']
h_dim_CS = in_parser['h_dim_CS']
num_layers_shared = in_parser['num_layers_shared']
num_layers_CS = in_parser['num_layers_CS']
if in_parser['active_fn'] == 'relu':
active_fn = tf.nn.relu
elif in_parser['active_fn'] == 'elu':
active_fn = tf.nn.elu
elif in_parser['active_fn'] == 'tanh':
active_fn = tf.nn.tanh
else:
print('Error!')
initial_W = tf.contrib.layers.xavier_initializer()
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))
##### 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' : h_dim_shared,
'h_dim_CS' : h_dim_CS,
'num_layers_shared' : num_layers_shared,
'num_layers_CS' : num_layers_CS,
'active_fn' : active_fn,
'initial_W' : initial_W }
# for out_itr in range(OUT_ITERATION):
print ('ITR: ' + str(out_itr+1) + ' DATA MODE: ' + data_mode + ' (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)
##### PREDICTION & EVALUATION
saver.restore(sess, in_path + '/itr_' + str(out_itr) + '/models/model_itr_' + str(out_itr))
### PREDICTION
pred = model.predict(te_data)
### EVALUATION
result1, result2 = np.zeros([num_Event, len(EVAL_TIMES)]), np.zeros([num_Event, len(EVAL_TIMES)])
for t, t_time in enumerate(EVAL_TIMES):
eval_horizon = int(t_time)
if eval_horizon >= num_Category:
print( 'ERROR: evaluation horizon is out of range')
result1[:, t] = result2[:, t] = -1
else:
# calculate F(t | x, Y, t >= t_M) = \sum_{t_M <= \tau < t} P(\tau | x, Y, \tau > t_M)
risk = np.sum(pred[:,:,:(eval_horizon+1)], axis=2) #risk score until EVAL_TIMES
for k in range(num_Event):
# result1[k, t] = c_index(risk[:,k], te_time, (te_label[:,0] == k+1).astype(float), eval_horizon) #-1 for no event (not comparable)
# result2[k, t] = brier_score(risk[:,k], te_time, (te_label[:,0] == k+1).astype(float), eval_horizon) #-1 for no event (not comparable)
result1[k, t] = weighted_c_index(tr_time, (tr_label[:,0] == k+1).astype(int), risk[:,k], te_time, (te_label[:,0] == k+1).astype(int), eval_horizon) #-1 for no event (not comparable)
result2[k, t] = weighted_brier_score(tr_time, (tr_label[:,0] == k+1).astype(int), risk[:,k], te_time, (te_label[:,0] == k+1).astype(int), eval_horizon) #-1 for no event (not comparable)
FINAL1[:, :, out_itr] = result1
FINAL2[:, :, out_itr] = result2
### SAVE RESULTS
row_header = []
for t in range(num_Event):
row_header.append('Event_' + str(t+1))
col_header1 = []
col_header2 = []
for t in EVAL_TIMES:
col_header1.append(str(t) + 'yr c_index')
col_header2.append(str(t) + 'yr B_score')
# c-index result
df1 = pd.DataFrame(result1, index = row_header, columns=col_header1)
df1.to_csv(in_path + '/result_CINDEX_itr' + str(out_itr) + '.csv')
# brier-score result
df2 = pd.DataFrame(result2, index = row_header, columns=col_header2)
df2.to_csv(in_path + '/result_BRIER_itr' + str(out_itr) + '.csv')
### PRINT RESULTS
print('========================================================')
print('ITR: ' + str(out_itr+1) + ' DATA MODE: ' + data_mode + ' (a:' + str(alpha) + ' b:' + str(beta) + ' c:' + str(gamma) + ')' )
print('SharedNet Parameters: ' + 'h_dim_shared = '+str(h_dim_shared) + ' num_layers_shared = '+str(num_layers_shared) + 'Non-Linearity: ' + str(active_fn))
print('CSNet Parameters: ' + 'h_dim_CS = '+str(h_dim_CS) + ' num_layers_CS = '+str(num_layers_CS) + 'Non-Linearity: ' + str(active_fn))
print('--------------------------------------------------------')
print('- C-INDEX: ')
print(df1)
print('--------------------------------------------------------')
print('- BRIER-SCORE: ')
print(df2)
print('========================================================')
### FINAL MEAN/STD
# c-index result
df1_mean = pd.DataFrame(np.mean(FINAL1, axis=2), index = row_header, columns=col_header1)
df1_std = pd.DataFrame(np.std(FINAL1, axis=2), index = row_header, columns=col_header1)
df1_mean.to_csv(in_path + '/result_CINDEX_FINAL_MEAN.csv')
df1_std.to_csv(in_path + '/result_CINDEX_FINAL_STD.csv')
# brier-score result
df2_mean = pd.DataFrame(np.mean(FINAL2, axis=2), index = row_header, columns=col_header2)
df2_std = pd.DataFrame(np.std(FINAL2, axis=2), index = row_header, columns=col_header2)
df2_mean.to_csv(in_path + '/result_BRIER_FINAL_MEAN.csv')
df2_std.to_csv(in_path + '/result_BRIER_FINAL_STD.csv')
### PRINT RESULTS
print('========================================================')
print('- FINAL C-INDEX: ')
print(df1_mean)
print('--------------------------------------------------------')
print('- FINAL BRIER-SCORE: ')
print(df2_mean)
print('========================================================')