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main.py
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main.py
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# -*- coding: utf-8 -*-
import pickle as pkl
import tensorflow as tf
import pandas as pd
import numpy as np
import math
import os
import numpy.linalg as la
from input_data import preprocess_data,load_sz_data,load_los_data
from tgcn import tgcnCell
#from gru import GRUCell
from visualization import plot_result,plot_error
from sklearn.metrics import mean_squared_error,mean_absolute_error
#import matplotlib.pyplot as plt
import time
time_start = time.time()
###### Settings ######
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_float('learning_rate', 0.001, 'Initial learning rate.')
flags.DEFINE_integer('training_epoch', 1, 'Number of epochs to train.')
flags.DEFINE_integer('gru_units', 64, 'hidden units of gru.')
flags.DEFINE_integer('seq_len',12 , ' time length of inputs.')
flags.DEFINE_integer('pre_len', 3, 'time length of prediction.')
flags.DEFINE_float('train_rate', 0.8, 'rate of training set.')
flags.DEFINE_integer('batch_size', 32, 'batch size.')
flags.DEFINE_string('dataset', 'los', 'sz or los.')
flags.DEFINE_string('model_name', 'tgcn', 'tgcn')
model_name = FLAGS.model_name
data_name = FLAGS.dataset
train_rate = FLAGS.train_rate
seq_len = FLAGS.seq_len
output_dim = pre_len = FLAGS.pre_len
batch_size = FLAGS.batch_size
lr = FLAGS.learning_rate
training_epoch = FLAGS.training_epoch
gru_units = FLAGS.gru_units
###### load data ######
if data_name == 'sz':
data, adj = load_sz_data('sz')
if data_name == 'los':
data, adj = load_los_data('los')
time_len = data.shape[0]
num_nodes = data.shape[1]
data1 =np.mat(data,dtype=np.float32)
#### normalization
max_value = np.max(data1)
data1 = data1/max_value
trainX, trainY, testX, testY = preprocess_data(data1, time_len, train_rate, seq_len, pre_len)
totalbatch = int(trainX.shape[0]/batch_size)
training_data_count = len(trainX)
def TGCN(_X, _weights, _biases):
###
cell_1 = tgcnCell(gru_units, adj, num_nodes=num_nodes)
cell = tf.nn.rnn_cell.MultiRNNCell([cell_1], state_is_tuple=True)
_X = tf.unstack(_X, axis=1)
outputs, states = tf.nn.static_rnn(cell, _X, dtype=tf.float32)
m = []
for i in outputs:
o = tf.reshape(i,shape=[-1,num_nodes,gru_units])
o = tf.reshape(o,shape=[-1,gru_units])
m.append(o)
last_output = m[-1]
output = tf.matmul(last_output, _weights['out']) + _biases['out']
output = tf.reshape(output,shape=[-1,num_nodes,pre_len])
output = tf.transpose(output, perm=[0,2,1])
output = tf.reshape(output, shape=[-1,num_nodes])
return output, m, states
###### placeholders ######
inputs = tf.placeholder(tf.float32, shape=[None, seq_len, num_nodes])
labels = tf.placeholder(tf.float32, shape=[None, pre_len, num_nodes])
# Graph weights
weights = {
'out': tf.Variable(tf.random_normal([gru_units, pre_len], mean=1.0), name='weight_o')}
biases = {
'out': tf.Variable(tf.random_normal([pre_len]),name='bias_o')}
if model_name == 'tgcn':
pred,ttts,ttto = TGCN(inputs, weights, biases)
y_pred = pred
###### optimizer ######
lambda_loss = 0.0015
Lreg = lambda_loss * sum(tf.nn.l2_loss(tf_var) for tf_var in tf.trainable_variables())
label = tf.reshape(labels, [-1,num_nodes])
##loss
loss = tf.reduce_mean(tf.nn.l2_loss(y_pred-label) + Lreg)
##rmse
error = tf.sqrt(tf.reduce_mean(tf.square(y_pred-label)))
optimizer = tf.train.AdamOptimizer(lr).minimize(loss)
###### Initialize session ######
variables = tf.global_variables()
saver = tf.train.Saver(tf.global_variables())
#sess = tf.Session()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
sess.run(tf.global_variables_initializer())
out = 'out/%s'%(model_name)
#out = 'out/%s_%s'%(model_name,'perturbation')
path1 = '%s_%s_lr%r_batch%r_unit%r_seq%r_pre%r_epoch%r'%(model_name,data_name,lr,batch_size,gru_units,seq_len,pre_len,training_epoch)
path = os.path.join(out,path1)
if not os.path.exists(path):
os.makedirs(path)
###### evaluation ######
def evaluation(a,b):
rmse = math.sqrt(mean_squared_error(a,b))
mae = mean_absolute_error(a, b)
F_norm = la.norm(a-b,'fro')/la.norm(a,'fro')
r2 = 1-((a-b)**2).sum()/((a-a.mean())**2).sum()
var = 1-(np.var(a-b))/np.var(a)
return rmse, mae, 1-F_norm, r2, var
x_axe,batch_loss,batch_rmse,batch_pred = [], [], [], []
test_loss,test_rmse,test_mae,test_acc,test_r2,test_var,test_pred = [],[],[],[],[],[],[]
for epoch in range(training_epoch):
for m in range(totalbatch):
mini_batch = trainX[m * batch_size : (m+1) * batch_size]
mini_label = trainY[m * batch_size : (m+1) * batch_size]
_, loss1, rmse1, train_output = sess.run([optimizer, loss, error, y_pred],
feed_dict = {inputs:mini_batch, labels:mini_label})
batch_loss.append(loss1)
batch_rmse.append(rmse1 * max_value)
# Test completely at every epoch
loss2, rmse2, test_output = sess.run([loss, error, y_pred],
feed_dict = {inputs:testX, labels:testY})
test_label = np.reshape(testY,[-1,num_nodes])
rmse, mae, acc, r2_score, var_score = evaluation(test_label, test_output)
test_label1 = test_label * max_value
test_output1 = test_output * max_value
test_loss.append(loss2)
test_rmse.append(rmse * max_value)
test_mae.append(mae * max_value)
test_acc.append(acc)
test_r2.append(r2_score)
test_var.append(var_score)
test_pred.append(test_output1)
print('Iter:{}'.format(epoch),
'train_rmse:{:.4}'.format(batch_rmse[-1]),
'test_loss:{:.4}'.format(loss2),
'test_rmse:{:.4}'.format(rmse),
'test_acc:{:.4}'.format(acc))
if (epoch % 500 == 0):
saver.save(sess, path+'/model_100/TGCN_pre_%r'%epoch, global_step = epoch)
time_end = time.time()
print(time_end-time_start,'s')
############## visualization ###############
b = int(len(batch_rmse)/totalbatch)
batch_rmse1 = [i for i in batch_rmse]
train_rmse = [(sum(batch_rmse1[i*totalbatch:(i+1)*totalbatch])/totalbatch) for i in range(b)]
batch_loss1 = [i for i in batch_loss]
train_loss = [(sum(batch_loss1[i*totalbatch:(i+1)*totalbatch])/totalbatch) for i in range(b)]
index = test_rmse.index(np.min(test_rmse))
test_result = test_pred[index]
var = pd.DataFrame(test_result)
var.to_csv(path+'/test_result.csv',index = False,header = False)
#plot_result(test_result,test_label1,path)
#plot_error(train_rmse,train_loss,test_rmse,test_acc,test_mae,path)
print('min_rmse:%r'%(np.min(test_rmse)),
'min_mae:%r'%(test_mae[index]),
'max_acc:%r'%(test_acc[index]),
'r2:%r'%(test_r2[index]),
'var:%r'%test_var[index])