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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
alloy = GraphCollection('../input/measurement2/yakindu/Alloy/', 100, 'Alloy')
human = GraphCollection('../input/measurement2/yakindu/Human/', 304, 'Human')
base = GraphCollection('../input/measurement2/yakindu/BaseViatra/', 100, 'BaseViatra')
real = GraphCollection('../input/measurement2/yakindu/RealViatra/', 100, 'RealViatra')
random = GraphCollection('../input/measurement2/yakindu/Random/', 100, 'Random')
na_rep = GraphCollection('../input/measurement2/yakindu/Human/na_rep/', 1, 'rep')
mpc_rep = GraphCollection('../input/measurement2/yakindu/Human/mpc_rep/', 1, 'rep')
od_rep = GraphCollection('../input/measurement2/yakindu/Human/od_rep/', 1, 'rep')
# a hack to make the node type as the same as an exiting model
type_rep = GraphCollection('../input/measurement2/yakindu/Human/od_rep/', 1, 'rep')
type_rep.nts = [{'Entry': 0.04257802080554814, 'Choice': 0.1267671379034409, 'State': 0.1596092291277674, 'Transition': 0.6138636969858629, 'Statechart': 0.010136036276340358, 'Region': 0.04467858095492131, 'Exit': 0.0018338223526273673, 'FinalState': 0.0005334755934915977}]
# type_rep.nts = [{'EAttribute': 0.23539778449144008, 'EClass': 0.30996978851963747, 'EReference': 0.33081570996978854, 'EPackage': 0.012789526686807653, 'EAnnotation': 0.002517623363544813, 'EEnumLiteral': 0.07275931520644502, 'EEnum': 0.013645518630412891, 'EDataType': 0.004028197381671702, 'EParameter': 0.005941591137965764, 'EGenericType': 0.002014098690835851, 'EOperation': 0.009415911379657605, 'ETypeParameter': 0.0007049345417925478}]
types = sorted(type_rep.nts[0].keys())
models_to_compare_na = [human, alloy, base, real, random, na_rep]
models_to_compare_mpc = [human, alloy, base, real, random, mpc_rep]
models_to_compare_od = [human, alloy, base, real, random, od_rep]
models_to_compare_nt = [human, alloy, base, real, random, type_rep]
for modelCollection in models_to_compare_nt:
type_dists = []
for nt in modelCollection.nts:
type_dist = []
for key in types:
type_dist.append(nt.get(key, 0.0))
type_dists.append(type_dist)
modelCollection.nts = type_dists
# define output folder
outputFolder = '../output/yakindu/'
#calculate metrics
metricStat(models_to_compare_na, 'Node_Activity', nodeActivity, 0, outputFolder, calculateKSMatrix)
metricStat(models_to_compare_od, 'Out_Degree', outDegree, 1, outputFolder, calculateKSMatrix)
metricStat(models_to_compare_mpc, 'MPC', mpc, 2, outputFolder, calculateKSMatrix)
metricStat(models_to_compare_nt, 'Node_Types', nodeType, 3, outputFolder, calculateManualKSMatrix)
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 calculateManualKSMatrix(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 = metrics.manual_ks(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' , 'm', 'gold', 'green', 'k', 'red']
markers = ['o', 'v','+', 'x', '^', '*']
plt.figure(index, figsize=(5, 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=markers[i], label = graphTypes[i].name, linestyle='', alpha=0.7)
plt.savefig(fname = savePath+'.png', dpi=500)
plt.legend(loc='upper right')
plt.savefig(fname = savePath+'_lengend.png', dpi=500)
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, matrix_calculator):
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(matrix_calculator(metrics))
plot(graphTypes, out_d_coords, metricName, graphIndex,outputFolder + '/'+ metricName)
def nodeActivity(graphType):
return graphType.nas
def outDegree(graphType):
return graphType.out_ds
def mpc(graphType):
return graphType.mpcs
def nodeType(graphType):
return graphType.nts
if __name__ == '__main__':
main()
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