1 files changed, 98 insertions, 0 deletions

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
new file mode 100644
index 00000000..2f39ca93
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/src/plot_ks_stats.py
@@ -0,0 +1,98 @@
+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
+
+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)')
+    # random = GraphCollection('../input/randomOutput/', 500, 'Random')
+    # alloy = GraphCollection('../input/alloyOutput/', 500, 'Alloy (30 nodes)')
+
+    models_to_compare = [human, viatra30]
+    
+    # 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 = stats.ks_2samp(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 = ''):
+    half_length = int(coords.shape[0] / len(graphTypes))
+    color = ['blue', 'red', 'green', 'yellow']
+    plt.figure(index, figsize=(7, 4))
+    plt.title(title)
+    for i in range(len(graphTypes)):
+        x = (coords[(i*half_length):((i+1)*half_length), 0].tolist())
+        y = (coords[(i*half_length):((i+1)*half_length), 1].tolist())
+        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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