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direct_learning_subsettarget.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tensorflow as tf
import pandas as pd
import time
import random
import numpy as np
import sys
import argparse
import json
#370 for train, 42 val, 103 test
def get_batch_train(BATCH_SIZE, y_train, x_train, ystatus_train):
while True:
j=0
while (j+1)*BATCH_SIZE <= len(x_train):
x_batch=x_train[int(j*BATCH_SIZE):int((j+1)*BATCH_SIZE),:]
y_batch=y_train[int(j*BATCH_SIZE):int((j+1)*BATCH_SIZE)]
ystatus_batch=ystatus_train[int(j*BATCH_SIZE):int((j+1)*BATCH_SIZE)]
x_batch = np.array(x_batch)
y_batch=np.array(y_batch).reshape((-1,1))
ystatus_batch=np.array(ystatus_batch).reshape((-1,1))
j+=1
yield x_batch, y_batch, ystatus_batch
def get_batch_holdout(BATCH_SIZE, y_holdout, x_holdout, ystatus_holdout):
while True:
j=0
while (j+1)*BATCH_SIZE <= len(x_holdout):
x_batch=x_holdout[int(j*BATCH_SIZE):int((j+1)*BATCH_SIZE),:]
y_batch=y_holdout[int(j*BATCH_SIZE):int((j+1)*BATCH_SIZE)]
ystatus_batch=ystatus_holdout[int(j*BATCH_SIZE):int((j+1)*BATCH_SIZE)]
x_batch = np.array(x_batch)
y_batch=np.array(y_batch).reshape((-1,1))
ystatus_batch=np.array(ystatus_batch).reshape((-1,1))
j+=1
yield x_batch, y_batch, ystatus_batch
def CIndex(pred, ytime_test, ystatus_test):
concord = 0.
total = 0.
N_test = ystatus_test.shape[0]
ystatus_test = np.asarray(ystatus_test, dtype=bool)
theta = pred
for i in range(N_test):
if ystatus_test[i] == 1:
for j in range(N_test):
if ytime_test[j] > ytime_test[i]:
total = total + 1
if theta[j] < theta[i]: concord = concord + 1
elif theta[j] == theta[i]: concord = concord + 0.5
return(concord/total)
def train(y_holdout, x_holdout, ystatus_holdout, y_train, x_train, ystatus_train, checkpoint):
np.set_printoptions(threshold=np.inf)
tf.reset_default_graph()
regularizer = tf.contrib.layers.l2_regularizer(scale=REG_SCALE)
x = tf.placeholder(tf.float32,[None,FEATURE_SIZE], name='input_data')
ystatus=tf.placeholder(tf.float32,[None,1],name='ystatus')
R_matrix= tf.placeholder(tf.float32,[None,None],name='R_matrix')
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
dense_layer1 = tf.layers.dense(inputs=x, units=6000, activation=tf.nn.relu,kernel_regularizer=regularizer)
dense_drop1 = tf.nn.dropout(dense_layer1, keep_prob=keep_prob)
dense_layer2 = tf.layers.dense(inputs=dense_drop1, units=2000, activation=tf.nn.relu,kernel_regularizer=regularizer)
dense_drop2 = tf.nn.dropout(dense_layer2, keep_prob=keep_prob)
dense_layer3 = tf.layers.dense(inputs=dense_drop2, units=200, activation=tf.nn.relu,kernel_regularizer=regularizer)
dense_drop3 = tf.nn.dropout(dense_layer3, keep_prob=keep_prob)
theta = tf.layers.dense(inputs=dense_drop3, units=1, activation=None,use_bias=False,kernel_regularizer=regularizer)
theta=tf.reshape(theta,[-1])
exp_theta=tf.exp(theta)
loss=-tf.reduce_mean(tf.multiply((theta - tf.log(tf.reduce_sum(tf.multiply(exp_theta , R_matrix),axis=1))), tf.reshape(ystatus,[-1])))
l2_loss=tf.losses.get_regularization_loss()
loss=loss+l2_loss
optimizer=tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(loss)
saver = tf.train.Saver()
with tf.Session() as sess:
print('Graph started...')
print('NUM_TRAIN_STEPS',NUM_TRAIN_STEPS)
print('NUM_EPOCHES',NUM_EPOCHES)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
for ep in range(NUM_EPOCHES):
batch_gen_train=get_batch_train(BATCH_SIZE,y_train, x_train, ystatus_train)
batch_gen_holdout=get_batch_holdout(BATCH_SIZE,y_holdout, x_holdout, ystatus_holdout)
total_loss_train=0.0
total_loss_holdout=0.0
for step in range(NUM_TRAIN_STEPS):
batch_x_train, batch_y_train, batch_ystatus_train = next(batch_gen_train)
batch_x_holdout, batch_y_holdout,batch_ystatus_holdout = next(batch_gen_holdout)
print(batch_x_train.shape)
print(batch_x_holdout.shape)
R_matrix_train = np.zeros([batch_y_train.shape[0], batch_y_train.shape[0]], dtype=int)
for i in range(batch_y_train.shape[0]):
for j in range(batch_y_train.shape[0]):
R_matrix_train[i,j] = batch_y_train[j] >= batch_y_train[i]
R_matrix_holdout = np.zeros([batch_y_holdout.shape[0], batch_y_holdout.shape[0]], dtype=int)
for i in range(batch_y_holdout.shape[0]):
for j in range(batch_y_holdout.shape[0]):
R_matrix_holdout[i,j] = batch_y_holdout[j] >= batch_y_holdout[i]
loss_batch_train, _ = sess.run([loss, optimizer], feed_dict={x: batch_x_train,
ystatus: batch_ystatus_train,
R_matrix: R_matrix_train,
keep_prob:KEEP_PROB})
pred_batch_holdout, loss_batch_holdout= sess.run([theta,loss], feed_dict={x: batch_x_holdout, ystatus: batch_ystatus_holdout,R_matrix: R_matrix_holdout,keep_prob:1})
cind=CIndex(pred_batch_holdout, batch_y_holdout, np.asarray(batch_ystatus_holdout))
total_loss_train += loss_batch_train
total_loss_holdout += loss_batch_holdout
if (step+1) % EVA_STEP == 0: # print loss every EVA_STEP
print('Average train loss at Epoch %d and Step %d is: %f' %(ep, step, total_loss_train/EVA_STEP),';',
'Holdout loss is: %f' %(total_loss_holdout/EVA_STEP))
total_loss_train=0.0
total_loss_holdout=0.0
print('test CI:', cind)
save_path = saver.save(sess, checkpoint)
print(("Model saved in file: %s" % save_path))
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='config.json', help='configuration json file')
if __name__ == '__main__':
args = parser.parse_args()
with open(args.config) as f:
config = json.load(f)
FEATURE_SIZE= config['feature_size']
BATCH_SIZE=config['batch_size']
LEARNING_RATE=config['lr']
NUM_EPOCHES=config['num_epo']
KEEP_PROB=config['keep_prob']
REG_SCALE=config['reg_scale']
SELECT_SIZE=config['select_size']
model_path=config['model_path']
x_train = np.loadtxt(fname=config['train_feature'],delimiter=",",skiprows=1)
y_train = np.loadtxt(fname=config['train_time'],delimiter=",",skiprows=1)
ystatus_train = np.loadtxt(fname=config['train_status'],delimiter=",",skiprows=1)
x_val = np.loadtxt(fname=config['val_feature'],delimiter=",",skiprows=1)
y_val = np.loadtxt(fname=config['val_time'],delimiter=",",skiprows=1)
ystatus_val = np.loadtxt(fname=config['val_status'],delimiter=",",skiprows=1)
NUM_TRAIN_STEPS=int(SELECT_SIZE/BATCH_SIZE)
EVA_STEP=1
START=1
END= 2
for i in range(START, END):
random.seed(i)
smp_ind=random.sample(range(x_train.shape[0]),SELECT_SIZE)
x_tr_smp = x_train[smp_ind,]
y_tr_smp= y_train[smp_ind,]
ystatus_tr_smp = ystatus_train[smp_ind,]
print(x_tr_smp.shape)
CHECKPOINT_FILE=model_path+'directlearning_samplesize'+str(SELECT_SIZE)+'_dropout'+str(KEEP_PROB)+'_reg'+str(REG_SCALE)+'_batch'+str(BATCH_SIZE)+'_epo'+str(NUM_EPOCHES)+'_dup'+str(i)+'.ckpt'
train(y_val, x_val, ystatus_val, y_tr_smp, x_tr_smp, ystatus_tr_smp, checkpoint=CHECKPOINT_FILE)