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import os, sys
lib_path = os.path.abspath(os.path.join('..', '..', 'utils'))
sys.path.append(lib_path)
import glob
import random
from sklearn.manifold import MDS
import matplotlib.pyplot as plt
from scipy import stats
import numpy as np
from GraphType import GraphCollection
import DistributionMetrics as metrics
def main():
# read models
human = GraphCollection('../input/humanOutput/', 500, 'Human')
# viatra30 = GraphCollection('../input/viatraOutput30/', 500,'Viatra (30 nodes)')
# viatra60 = GraphCollection('../input/viatraOutput60/', 500, 'Viatra (60 nodes)')
viatra100 = GraphCollection('../input/viatraOutput100/', 500, 'Viatra (100 nodes)')
viatra100R = GraphCollection('../input/realisticViatraOutput_container/', 500, 'Realistic Viatra containers (100 nodes)')
viatra100C = GraphCollection('../input/viatraOutput100C/', 500, 'Realistic Viatra consistent (100 nodes)')
viatra100EE = GraphCollection('../input/realisticViatra_excludeExit/', 500, 'Realistic Viatra no Exit (100 nodes)')
# random = GraphCollection('../input/randomOutput/', 500, 'Random')
# alloy = GraphCollection('../input/alloyOutput/', 500, 'Alloy (30 nodes)')
# realistic_viatra = GraphCollection('../input/viatra_output_consistent_100/', 50, 'Realistic Viatra With Some Constraints (100 nodes)')
# human100 = GraphCollection('../input/human_output_100/', 304, 'Human')
models_to_compare = [human, viatra100EE,viatra100C]
# define output folder
outputFolder = '../output/'
#calculate metrics
metricStat(models_to_compare, 'Node Activity', nodeActivity, 0, outputFolder)
metricStat(models_to_compare, 'Out Degree', outDegree, 1, outputFolder)
metricStat(models_to_compare, 'MPC', mpc, 2, outputFolder)
def calculateKSMatrix(dists):
dist = []
for i in range(len(dists)):
dist = dist + dists[i]
matrix = np.empty((len(dist),len(dist)))
for i in range(len(dist)):
matrix[i,i] = 0
for j in range(i+1, len(dist)):
value, p = metrics.ks_distance(dist[i], dist[j])
matrix[i, j] = value
matrix[j, i] = value
return matrix
def calculateMDS(dissimilarities):
embedding = MDS(n_components=2, dissimilarity='precomputed')
trans = embedding.fit_transform(X=dissimilarities)
return trans
def plot(graphTypes, coords, title='',index = 0, savePath = ''):
color = ['blue', 'red', 'green', 'yellow']
plt.figure(index, figsize=(7, 4))
plt.title(title)
index = 0
for i in range(len(graphTypes)):
x = (coords[index:index+graphTypes[i].size, 0].tolist())
y = (coords[index:index+graphTypes[i].size, 1].tolist())
index += graphTypes[i].size
plt.plot(x, y, color=color[i], marker='o', label = graphTypes[i].name, linestyle='', alpha=0.7)
plt.legend(loc='upper right')
plt.savefig(fname = savePath, dpi=150)
#graph.show()
def mkdir_p(mypath):
'''Creates a directory. equivalent to using mkdir -p on the command line'''
from errno import EEXIST
from os import makedirs,path
try:
makedirs(mypath)
except OSError as exc: # Python >2.5
if exc.errno == EEXIST and path.isdir(mypath):
pass
else: raise
def metricStat(graphTypes, metricName, metric, graphIndex, outputFolder):
metrics = []
for graph in graphTypes:
metrics.append(metric(graph))
outputFolder = outputFolder + graph.name + '-'
print('calculate' + metricName +' for ' + outputFolder)
mkdir_p(outputFolder)
out_d_coords = calculateMDS(calculateKSMatrix(metrics))
plot(graphTypes, out_d_coords, metricName, graphIndex,outputFolder + '/'+ metricName+'.png')
def nodeActivity(graphType):
return graphType.nas
def outDegree(graphType):
return graphType.out_ds
def mpc(graphType):
return graphType.mpcs
if __name__ == '__main__':
main()
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