diff options
Diffstat (limited to 'Metrics/Metrics-Calculation/metrics_plot/model comparison')
39 files changed, 38 insertions, 21 deletions
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/src/plot_ks_stats.py b/Metrics/Metrics-Calculation/metrics_plot/model comparison/src/plot_ks_stats.py index af231a98..75fe78eb 100644 --- a/Metrics/Metrics-Calculation/metrics_plot/model comparison/src/plot_ks_stats.py +++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/src/plot_ks_stats.py | |||
@@ -11,27 +11,40 @@ from GraphType import GraphCollection | |||
11 | import DistributionMetrics as metrics | 11 | import DistributionMetrics as metrics |
12 | 12 | ||
13 | def main(): | 13 | def main(): |
14 | domain = 'github' | ||
14 | # read models | 15 | # read models |
15 | alloy = GraphCollection('../input/measurement2/yakindu/Alloy/', 100, 'Alloy') | 16 | alloy = GraphCollection('../input/measurement2/{}/Alloy/'.format(domain), 100, 'All') |
16 | human = GraphCollection('../input/measurement2/yakindu/Human/', 304, 'Human') | 17 | human = GraphCollection('../input/measurement2/{}/Human/'.format(domain), 304, 'Hum') |
17 | base = GraphCollection('../input/measurement2/yakindu/BaseViatra/', 100, 'BaseViatra') | 18 | base = GraphCollection('../input/measurement2/{}/BaseViatra/'.format(domain), 100, 'GS') |
18 | real = GraphCollection('../input/measurement2/yakindu/RealViatra/', 100, 'RealViatra') | 19 | real = GraphCollection('../input/measurement2/{}/RealViatra/'.format(domain), 100, 'Real') |
19 | random = GraphCollection('../input/measurement2/yakindu/Random/', 100, 'Random') | 20 | random = GraphCollection('../input/measurement2/{}/Random/'.format(domain), 100, 'Rand') |
20 | na_rep = GraphCollection('../input/measurement2/yakindu/Human/na_rep/', 1, 'rep') | 21 | na_rep = GraphCollection('../input/measurement2/{}/Human/na_rep/'.format(domain), 1, 'Med') |
21 | mpc_rep = GraphCollection('../input/measurement2/yakindu/Human/mpc_rep/', 1, 'rep') | 22 | mpc_rep = GraphCollection('../input/measurement2/{}/Human/mpc_rep/'.format(domain), 1, 'Med') |
22 | od_rep = GraphCollection('../input/measurement2/yakindu/Human/od_rep/', 1, 'rep') | 23 | od_rep = GraphCollection('../input/measurement2/{}/Human/od_rep/'.format(domain), 1, 'Med') |
23 | 24 | ||
24 | # a hack to make the node type as the same as an exiting model | 25 | # a hack to make the node type as the same as an exiting model |
25 | type_rep = GraphCollection('../input/measurement2/yakindu/Human/od_rep/', 1, 'rep') | 26 | type_rep = GraphCollection('../input/measurement2/{}/Human/od_rep/'.format(domain), 1, 'Med') |
26 | 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}] | 27 | if(domain == 'yakindu'): |
27 | # 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}] | 28 | 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}] |
29 | elif (domain == 'ecore'): | ||
30 | 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}] | ||
31 | elif (domain == 'github'): | ||
32 | type_rep.nts = [{'Project': 0.012636538873420432, 'Commit': 0.5525808524309276, 'User': 0.05847076461769116, 'Issue': 0.12743628185907047, 'PullRequest': 0.07560505461554937, 'IssueEvent': 0.17327050760334123}] | ||
33 | |||
28 | types = sorted(type_rep.nts[0].keys()) | 34 | types = sorted(type_rep.nts[0].keys()) |
29 | 35 | ||
30 | models_to_compare_na = [human, alloy, base, real, random, na_rep] | 36 | model_collections = [human, alloy, random, base, real] |
31 | models_to_compare_mpc = [human, alloy, base, real, random, mpc_rep] | 37 | for model_collection in model_collections: |
32 | models_to_compare_od = [human, alloy, base, real, random, od_rep] | 38 | print(model_collection.name) |
33 | models_to_compare_nt = [human, alloy, base, real, random, type_rep] | 39 | length = len(model_collection.violations) |
40 | percentage = sum(map(lambda v: int(v==0), model_collection.violations)) / length | ||
41 | print(percentage) | ||
42 | |||
34 | 43 | ||
44 | models_to_compare_na = [human, alloy, random, base, real, na_rep] | ||
45 | models_to_compare_mpc = [human, alloy, random, base, real, mpc_rep] | ||
46 | models_to_compare_od = [human, alloy, random, base, real, od_rep] | ||
47 | models_to_compare_nt = [human, alloy, random, base, real, type_rep] | ||
35 | for modelCollection in models_to_compare_nt: | 48 | for modelCollection in models_to_compare_nt: |
36 | type_dists = [] | 49 | type_dists = [] |
37 | for nt in modelCollection.nts: | 50 | for nt in modelCollection.nts: |
@@ -43,13 +56,13 @@ def main(): | |||
43 | 56 | ||
44 | 57 | ||
45 | # define output folder | 58 | # define output folder |
46 | outputFolder = '../output/yakindu/' | 59 | outputFolder = '../output/{}/'.format(domain) |
47 | 60 | ||
48 | #calculate metrics | 61 | #calculate metrics |
49 | metricStat(models_to_compare_na, 'Node_Activity', nodeActivity, 0, outputFolder, calculateKSMatrix) | 62 | metricStat(models_to_compare_na, 'Node_Activity', nodeActivity, 0, outputFolder, calculateKSMatrix) |
50 | metricStat(models_to_compare_od, 'Out_Degree', outDegree, 1, outputFolder, calculateKSMatrix) | 63 | metricStat(models_to_compare_od, 'Out_Degree', outDegree, 1, outputFolder, calculateKSMatrix) |
51 | metricStat(models_to_compare_mpc, 'MPC', mpc, 2, outputFolder, calculateKSMatrix) | 64 | metricStat(models_to_compare_mpc, 'MPC', mpc, 2, outputFolder, calculateKSMatrix) |
52 | metricStat(models_to_compare_nt, 'Node_Types', nodeType, 3, outputFolder, calculateManualKSMatrix) | 65 | metricStat(models_to_compare_nt, 'Node_Types', nodeType, 3, outputFolder, calculateManualKSMatrix) |
53 | 66 | ||
54 | def calculateKSMatrix(dists): | 67 | def calculateKSMatrix(dists): |
55 | dist = [] | 68 | dist = [] |
@@ -88,16 +101,17 @@ def calculateMDS(dissimilarities): | |||
88 | return trans | 101 | return trans |
89 | 102 | ||
90 | def plot(graphTypes, coords, title='',index = 0, savePath = ''): | 103 | def plot(graphTypes, coords, title='',index = 0, savePath = ''): |
91 | color = ['blue' , 'm', 'gold', 'green', 'k', 'red'] | 104 | color = ['#377eb8' , '#e41a1c', '#4daf4a', '#984ea3', '#ff7f00', '#ffff33'] |
92 | markers = ['o', 'v','+', 'x', '^', '*'] | 105 | markers = ['o', '+', 'x', '^', 'v', '*'] |
93 | plt.figure(index, figsize=(5, 4)) | 106 | fill_styles = ['full', 'full', 'full', 'none', 'none', 'full'] |
107 | plt.figure(index, figsize=(5, 2)) | ||
94 | # plt.title(title) | 108 | # plt.title(title) |
95 | index = 0 | 109 | index = 0 |
96 | for i in range(len(graphTypes)): | 110 | for i in range(len(graphTypes)): |
97 | x = (coords[index:index+graphTypes[i].size, 0].tolist()) | 111 | x = (coords[index:index+graphTypes[i].size, 0].tolist()) |
98 | y = (coords[index:index+graphTypes[i].size, 1].tolist()) | 112 | y = (coords[index:index+graphTypes[i].size, 1].tolist()) |
99 | index += graphTypes[i].size | 113 | index += graphTypes[i].size |
100 | plt.plot(x, y, color=color[i], marker=markers[i], label = graphTypes[i].name, linestyle='', alpha=0.7) | 114 | plt.plot(x, y, color=color[i], marker=markers[i], label = graphTypes[i].name, linestyle='', alpha=0.7, fillstyle = fill_styles[i]) |
101 | plt.savefig(fname = savePath+'.png', dpi=500) | 115 | plt.savefig(fname = savePath+'.png', dpi=500) |
102 | plt.legend(loc='upper right') | 116 | plt.legend(loc='upper right') |
103 | plt.savefig(fname = savePath+'_lengend.png', dpi=500) | 117 | plt.savefig(fname = savePath+'_lengend.png', dpi=500) |
@@ -137,5 +151,8 @@ def mpc(graphType): | |||
137 | def nodeType(graphType): | 151 | def nodeType(graphType): |
138 | return graphType.nts | 152 | return graphType.nts |
139 | 153 | ||
154 | def tcc(graphType): | ||
155 | return graphType.tccs | ||
156 | |||
140 | if __name__ == '__main__': | 157 | if __name__ == '__main__': |
141 | main() \ No newline at end of file | 158 | main() \ No newline at end of file |