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Iraj 3 years ago
parent
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806c56d257
100 changed files with 1857 additions and 603647 deletions
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MissingNodes_SrcCode2014/DumpDataset.mlx View File


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MissingNodes_SrcCode2014/MissingNodes_S8b.m View File

@@ -172,6 +172,7 @@ function [ rand_score, purity, p_triads, missing_nodes_mapping, removed_nodes] =
fprintf('testsalam%i', num_missing_nodes);
[data, attData, missing_nodes_mapping] = RemoveRandomNodesWithImages( original_data, original_attData, totalAttNum, num_missing_nodes, missing_nodes_mapping, numImagesProfiles );
else
%Sigal 13.10.13 - TODO - add option to pre selected missing node
%Sigal - 23.1.14 - %%%%TODO - add option for images

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MissingNodes_SrcCode2014/RemoveRandomNodesWithImages.m View File

@@ -153,6 +153,7 @@ function [missing_nodes] = ChooseMissingNodes(num_nodes_to_remove, data, attData
for i=1:num_nodes_to_remove
valid_nodes = find(missing_nodes_all_neighbors~=1);
<<<<<<< HEAD
if(size(valid_nodes,2) < 1)
fprintf('Full Graph')
end
@@ -160,6 +161,12 @@ function [missing_nodes] = ChooseMissingNodes(num_nodes_to_remove, data, attData
inx = ceil(rand(1)*size(valid_nodes,2));
node = valid_nodes(inx);
=======
%inx = ceil(rand(1)*size(valid_nodes,2));
inx = size(data,2)-1;
%node = valid_nodes(inx);
node = size(data,2);
>>>>>>> 3750fcfc8ca443a312af79261f66978da7181c08
%sigal - 23.1.14 - choose image profile
profile = ceil(rand(1)*size(imagesProfiles,2));
newNode = [node;imagesProfiles(profile)];

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MissingNodes_SrcCode2014/RemoveRandomNodesWithImages.mlx View File


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MissingNodes_SrcCode2014/RunExpSrv8s.m View File

@@ -82,8 +82,13 @@ ds_GridTrain = [45];
% fprintf('RunExpSrv8s:Invalid netSize %d.\n',netSize);
% return;
% end
<<<<<<< HEAD
ds_str = 'in/';
num_missing_nodes_arr = [2];
=======
ds_str = 'graph_production/produced_graphs/';
num_missing_nodes_arr = [1];
>>>>>>> 3750fcfc8ca443a312af79261f66978da7181c08
rootDir = '../';
@@ -193,12 +198,38 @@ LogMsg(sprintf('%s Start RunExpSrv8s RunExperiment (random %d, addMissingAtt %.2
if(iter == 0)
copyfile('../output/graphed_0.mat',file_name);
end
<<<<<<< HEAD
LogMsg(sprintf('Results for file %s,iter %d at %s',file,iter,out_file));
%fprintf('Completed RunExperiment cycle - results at %s.\n',out_file);
end
catch
fprintf('An Error Occured!!!!!')
=======
end
% run algorithm (file load is done internaly)
[rand_score,purity,p_triads,missing_nodes_mapping,removed_nodes] = MissingNodes_S8b(datasetDir, file, ...
attributes, attUpperRange, attWeight, addMissingAtt, normFactorVec, affinities, num_missing_nodes_arr, attAffinityThreshold, ...
imagesData, numImagesProfiles, imgMissProb, imgSimType, imgSimProbDiff, percentKnownPHsVec, dumpGED, dump_data_dir, iter);
%[rand_score,purity,p_triads,missing_nodes_mapping,removed_nodes] = MissingNodes_Sparse(datasetDir, file, affinities, 1);
% dump graph data for KronEM runs
if dumpKronEM == 1
DumpDataset(datasetDir, file, iter, removed_nodes, dumpFilePath);
end
% save results
out_file = sprintf('%sres_%s_%s.mat', results_dir, file, date_now);
save(out_file);
file_name = sprintf('%s%s','D:\Uni\sharif\Project\SAMI\Code\SAMI\output\mine\',file);
if(iter == 1)
copyfile('D:\Uni\sharif\Project\SAMI\Code\SAMI\output\graphed_1.mat',file_name);
end
LogMsg(sprintf('Results for file %s,iter %d at %s',file,iter,out_file));
%fprintf('Completed RunExperiment cycle - results at %s.\n',out_file);
>>>>>>> 3750fcfc8ca443a312af79261f66978da7181c08
end
% beep;

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README.md View File

@@ -1,30 +1,12 @@
The "src" folder is the source code of SAMI paper which has seen some minor changes
The "MissingNodes_SrcCode2014" folder is the source code of SAMI paper which has seen some minor changes
from the original received version.
The "codes" folder is the folder containing the following files:
1.adjlisttext_tonxgraph.py: gets an adj. list as input file and gives out the visualized
graph
2.mat_tonxgraph.py: gets a .mat file as input file and gives out the visualized graph
3.mae.py: computes mae of two graphs given their .mat adj matrix file

For using the source code you must pay attention to two files: "RunExpSrv8s" and
"MissingNodes_S8b". The first one is the function which sets up the files and
calls the main function i.e. "MissingNodes_S8b".
**Running the project**
For running the source code you must use a command like "RunExpSrv8s('0','0','1')" where first parameter shows start of iteration,
second parameter shows end of iteration and last param is an arbitrary number.

**Input**
You must place all input files in a folder named "in" in root directory of project.

For having your own dataset run on this code you should know the below variables:

*RUNEXPSRV_8S.m
1. num_missing_nodes_arr: number of randomly removed nodes on each iteration
2. ds_*: array of node numbers of your input arr
3. ds_str: dataset path as a string
4. root_dir
5. file_prefix: file name prefix of the input files

*MISSINGNODES_S8B
1. k: filename postfix for saving
2. clusters_out: true and predicted graph saved as .mat file
3. graphs_out: 4 graphs including {original, predicted, predicted permuted,
original permuted} as .mat file


*grid dataset is run on the src code as an example
**Calcuating MAE**
For calculating the MEA you must first run the project to get the output then run the python code named "calc_mae.py"

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import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import scipy.io as sp
import os

path = 'output\mine'
missing_node_count = 2
mae_list = list()
for filename in os.listdir(path):
adj_matrix_orig = sp.loadmat(path + '\\' + filename)['remapped_data'].A
adj_matrix_p = sp.loadmat(path + '\\' + filename)['out_data'].A
mae = np.sum(abs(adj_matrix_orig - adj_matrix_p))
mae = mae / (adj_matrix_p.shape[0] * 2 * missing_node_count)
mae_list.append(mae)

print(np.mean(mae_list))

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