/
analysis.py
executable file
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/
analysis.py
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# this file is used to plot images
from main import *
args = Args()
print(args.graph_type, args.note)
# epoch = 16000
epoch = 3000
sample_time = 3
def find_nearest_idx(array,value):
idx = (np.abs(array-value)).argmin()
return idx
# for baseline model
for num_layers in range(4,5):
# give file name and figure name
fname_real = args.graph_save_path + args.fname_real + str(0)
fname_pred = args.graph_save_path + args.fname_pred + str(epoch) +'_'+str(sample_time)
figname = args.figure_save_path + args.fname + str(epoch) +'_'+str(sample_time)
# fname_real = args.graph_save_path + args.note + '_' + args.graph_type + '_' + str(args.graph_node_num) + '_' + \
# str(epoch) + '_real_' + str(True) + '_' + str(num_layers)
# fname_pred = args.graph_save_path + args.note + '_' + args.graph_type + '_' + str(args.graph_node_num) + '_' + \
# str(epoch) + '_pred_' + str(True) + '_' + str(num_layers)
# figname = args.figure_save_path + args.note + '_' + args.graph_type + '_' + str(args.graph_node_num) + '_' + \
# str(epoch) + '_' + str(num_layers)
print(fname_real)
print(fname_pred)
# load data
graph_real_list = load_graph_list(fname_real + '.dat')
shuffle(graph_real_list)
graph_pred_list_raw = load_graph_list(fname_pred + '.dat')
graph_real_len_list = np.array([len(graph_real_list[i]) for i in range(len(graph_real_list))])
graph_pred_len_list_raw = np.array([len(graph_pred_list_raw[i]) for i in range(len(graph_pred_list_raw))])
graph_pred_list = graph_pred_list_raw
graph_pred_len_list = graph_pred_len_list_raw
# # select samples
# graph_pred_list = []
# graph_pred_len_list = []
# for value in graph_real_len_list:
# pred_idx = find_nearest_idx(graph_pred_len_list_raw, value)
# graph_pred_list.append(graph_pred_list_raw[pred_idx])
# graph_pred_len_list.append(graph_pred_len_list_raw[pred_idx])
# # delete
# graph_pred_len_list_raw=np.delete(graph_pred_len_list_raw, pred_idx)
# del graph_pred_list_raw[pred_idx]
# if len(graph_pred_list)==200:
# break
# graph_pred_len_list = np.array(graph_pred_len_list)
# # select pred data within certain range
# len_min = np.amin(graph_real_len_list)
# len_max = np.amax(graph_real_len_list)
# pred_index = np.where((graph_pred_len_list>=len_min)&(graph_pred_len_list<=len_max))
# # print(pred_index[0])
# graph_pred_list = [graph_pred_list[i] for i in pred_index[0]]
# graph_pred_len_list = graph_pred_len_list[pred_index[0]]
# real_order = np.argsort(graph_real_len_list)
# pred_order = np.argsort(graph_pred_len_list)
real_order = np.argsort(graph_real_len_list)[::-1]
pred_order = np.argsort(graph_pred_len_list)[::-1]
# print(real_order)
# print(pred_order)
graph_real_list = [graph_real_list[i] for i in real_order]
graph_pred_list = [graph_pred_list[i] for i in pred_order]
# shuffle(graph_real_list)
# shuffle(graph_pred_list)
print('real average nodes', sum([graph_real_list[i].number_of_nodes() for i in range(len(graph_real_list))])/len(graph_real_list))
print('pred average nodes', sum([graph_pred_list[i].number_of_nodes() for i in range(len(graph_pred_list))])/len(graph_pred_list))
print('num of real graphs', len(graph_real_list))
print('num of pred graphs', len(graph_pred_list))
# # draw all graphs
# for iter in range(8):
# print('iter', iter)
# graph_list = []
# for i in range(8):
# index = 8 * iter + i
# # graph_real_list[index].remove_nodes_from(list(nx.isolates(graph_real_list[index])))
# # graph_pred_list[index].remove_nodes_from(list(nx.isolates(graph_pred_list[index])))
# graph_list.append(graph_real_list[index])
# graph_list.append(graph_pred_list[index])
# print('real', graph_real_list[index].number_of_nodes())
# print('pred', graph_pred_list[index].number_of_nodes())
#
# draw_graph_list(graph_list, row=4, col=4, fname=figname + '_' + str(iter))
# draw all graphs
for iter in range(8):
print('iter', iter)
graph_list = []
for i in range(8):
index = 32 * iter + i
# graph_real_list[index].remove_nodes_from(list(nx.isolates(graph_real_list[index])))
# graph_pred_list[index].remove_nodes_from(list(nx.isolates(graph_pred_list[index])))
# graph_list.append(graph_real_list[index])
graph_list.append(graph_pred_list[index])
# print('real', graph_real_list[index].number_of_nodes())
print('pred', graph_pred_list[index].number_of_nodes())
draw_graph_list(graph_list, row=4, col=4, fname=figname + '_' + str(iter)+'_pred')
# draw all graphs
for iter in range(8):
print('iter', iter)
graph_list = []
for i in range(8):
index = 16 * iter + i
# graph_real_list[index].remove_nodes_from(list(nx.isolates(graph_real_list[index])))
# graph_pred_list[index].remove_nodes_from(list(nx.isolates(graph_pred_list[index])))
graph_list.append(graph_real_list[index])
# graph_list.append(graph_pred_list[index])
print('real', graph_real_list[index].number_of_nodes())
# print('pred', graph_pred_list[index].number_of_nodes())
draw_graph_list(graph_list, row=4, col=4, fname=figname + '_' + str(iter)+'_real')
#
# # for new model
# elif args.note == 'GraphRNN_structure' and args.is_flexible==False:
# for num_layers in range(4,5):
# # give file name and figure name
# # fname_real = args.graph_save_path + args.note + '_' + args.graph_type + '_' + str(args.graph_node_num) + '_' + \
# # str(epoch) + '_real_bptt_' + str(args.bptt)+'_'+str(num_layers)+'_dilation_'+str(args.is_dilation)+'_flexible_'+str(args.is_flexible)+'_bn_'+str(args.is_bn)+'_lr_'+str(args.lr)
# # fname_pred = args.graph_save_path + args.note + '_' + args.graph_type + '_' + str(args.graph_node_num) + '_' + \
# # str(epoch) + '_pred_bptt_' + str(args.bptt)+'_'+str(num_layers)+'_dilation_'+str(args.is_dilation)+'_flexible_'+str(args.is_flexible)+'_bn_'+str(args.is_bn)+'_lr_'+str(args.lr)
#
# fname_pred = args.graph_save_path + args.note + '_' + args.graph_type + '_' + \
# str(epoch) + '_pred_' + str(args.num_layers) + '_' + str(args.bptt)+ '_' + str(args.bptt_len) + '_' + str(args.hidden_size)
# fname_real = args.graph_save_path + args.note + '_' + args.graph_type + '_' + \
# str(epoch) + '_real_' + str(args.num_layers) + '_' + str(args.bptt)+ '_' + str(args.bptt_len) + '_' + str(args.hidden_size)
# figname = args.figure_save_path + args.note + '_' + args.graph_type + '_' + \
# str(epoch) + '_pred_' + str(args.num_layers) + '_' + str(args.bptt)+ '_' + str(args.bptt_len) + '_' + str(args.hidden_size)
# print(fname_real)
# # load data
# graph_real_list = load_graph_list(fname_real+'.dat')
# graph_pred_list = load_graph_list(fname_pred+'.dat')
#
# graph_real_len_list = np.array([len(graph_real_list[i]) for i in range(len(graph_real_list))])
# graph_pred_len_list = np.array([len(graph_pred_list[i]) for i in range(len(graph_pred_list))])
# real_order = np.argsort(graph_real_len_list)[::-1]
# pred_order = np.argsort(graph_pred_len_list)[::-1]
# # print(real_order)
# # print(pred_order)
# graph_real_list = [graph_real_list[i] for i in real_order]
# graph_pred_list = [graph_pred_list[i] for i in pred_order]
#
# shuffle(graph_pred_list)
#
#
# print('real average nodes',
# sum([graph_real_list[i].number_of_nodes() for i in range(len(graph_real_list))]) / len(graph_real_list))
# print('pred average nodes',
# sum([graph_pred_list[i].number_of_nodes() for i in range(len(graph_pred_list))]) / len(graph_pred_list))
# print('num of graphs', len(graph_real_list))
#
# # draw all graphs
# for iter in range(2):
# print('iter', iter)
# graph_list = []
# for i in range(8):
# index = 8*iter + i
# graph_real_list[index].remove_nodes_from(nx.isolates(graph_real_list[index]))
# graph_pred_list[index].remove_nodes_from(nx.isolates(graph_pred_list[index]))
# graph_list.append(graph_real_list[index])
# graph_list.append(graph_pred_list[index])
# print('real', graph_real_list[index].number_of_nodes())
# print('pred', graph_pred_list[index].number_of_nodes())
# draw_graph_list(graph_list, row=4, col=4, fname=figname+'_'+str(iter))
#
#
# # for new model
# elif args.note == 'GraphRNN_structure' and args.is_flexible==True:
# for num_layers in range(4,5):
# graph_real_list = []
# graph_pred_list = []
# epoch_end = 30000
# for epoch in [epoch_end-500*(8-i) for i in range(8)]:
# # give file name and figure name
# fname_real = args.graph_save_path + args.note + '_' + args.graph_type + '_' + str(args.graph_node_num) + '_' + \
# str(epoch) + '_real_bptt_' + str(args.bptt)+'_'+str(num_layers)+'_dilation_'+str(args.is_dilation)+'_flexible_'+str(args.is_flexible)+'_bn_'+str(args.is_bn)+'_lr_'+str(args.lr)
# fname_pred = args.graph_save_path + args.note + '_' + args.graph_type + '_' + str(args.graph_node_num) + '_' + \
# str(epoch) + '_pred_bptt_' + str(args.bptt)+'_'+str(num_layers)+'_dilation_'+str(args.is_dilation)+'_flexible_'+str(args.is_flexible)+'_bn_'+str(args.is_bn)+'_lr_'+str(args.lr)
#
# # load data
# graph_real_list += load_graph_list(fname_real+'.dat')
# graph_pred_list += load_graph_list(fname_pred+'.dat')
# print('num of graphs', len(graph_real_list))
#
# figname = args.figure_save_path + args.note + '_' + args.graph_type + '_' + str(args.graph_node_num) + '_' + \
# str(epoch) + str(args.sample_when_validate) + '_' + str(num_layers) + '_dilation_' + str(args.is_dilation) + '_flexible_' + str(args.is_flexible) + '_bn_' + str(args.is_bn) + '_lr_' + str(args.lr)
#
# # draw all graphs
# for iter in range(1):
# print('iter', iter)
# graph_list = []
# for i in range(8):
# index = 8*iter + i
# graph_real_list[index].remove_nodes_from(nx.isolates(graph_real_list[index]))
# graph_pred_list[index].remove_nodes_from(nx.isolates(graph_pred_list[index]))
# graph_list.append(graph_real_list[index])
# graph_list.append(graph_pred_list[index])
# draw_graph_list(graph_list, row=4, col=4, fname=figname+'_'+str(iter))