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1{
2 "cells": [
3 {
4 "cell_type": "markdown",
5 "metadata": {},
6 "source": [
7 "## Metric comparison preperation"
8 ]
9 },
10 {
11 "cell_type": "code",
12 "execution_count": 14,
13 "metadata": {},
14 "outputs": [],
15 "source": [
16 "import readCSV as reader\n",
17 "import glob\n",
18 "import random \n",
19 "from sklearn.manifold import MDS\n",
20 "import matplotlib.pyplot as plt\n",
21 "from scipy import stats\n",
22 "import numpy as np"
23 ]
24 },
25 {
26 "cell_type": "code",
27 "execution_count": 15,
28 "metadata": {},
29 "outputs": [],
30 "source": [
31 "def calculateKSMatrix(dists):\n",
32 " dist = []\n",
33 "\n",
34 " for i in range(len(dists)):\n",
35 " dist = dist + dists[i]\n",
36 " matrix = np.empty((len(dist),len(dist)))\n",
37 "\n",
38 " for i in range(len(dist)):\n",
39 " matrix[i,i] = 0\n",
40 " for j in range(i+1, len(dist)):\n",
41 " value, p = stats.ks_2samp(dist[i], dist[j])\n",
42 " matrix[i, j] = value\n",
43 " matrix[j, i] = value\n",
44 " value, p = stats.ks_2samp(dist[j], dist[i])\n",
45 " return matrix\n"
46 ]
47 },
48 {
49 "cell_type": "code",
50 "execution_count": 16,
51 "metadata": {},
52 "outputs": [],
53 "source": [
54 "def calculateMDS(dissimilarities):\n",
55 " embedding = MDS(n_components=2, dissimilarity='precomputed')\n",
56 " trans = embedding.fit_transform(X=dissimilarities)\n",
57 " return trans"
58 ]
59 },
60 {
61 "cell_type": "code",
62 "execution_count": 17,
63 "metadata": {},
64 "outputs": [],
65 "source": [
66 "def plot(names, coords, index = 0, title=''):\n",
67 " half_length = int(coords.shape[0] / len(names))\n",
68 " color = ['blue', 'red', 'green']\n",
69 " graph = plt.figure(index)\n",
70 " plt.title(title)\n",
71 " for i in range(len(names)):\n",
72 " x = (coords[(i*half_length):((i+1)*half_length), 0].tolist())\n",
73 " y = (coords[(i*half_length):((i+1)*half_length), 1].tolist())\n",
74 " plt.plot(x, y, color=color[i], marker='o', label = names[i], linestyle='', alpha=0.7)\n",
75 " plt.legend(loc='upper right')\n",
76 " plt.savefig(fname = title+'.png', dpi=150)\n",
77 " #graph.show()\n"
78 ]
79 },
80 {
81 "cell_type": "markdown",
82 "metadata": {},
83 "source": [
84 "## Read Files\n",
85 "1. define class for metric reading of each graph type"
86 ]
87 },
88 {
89 "cell_type": "code",
90 "execution_count": 18,
91 "metadata": {},
92 "outputs": [],
93 "source": [
94 "class GraphType:\n",
95 " \n",
96 " # init with path contrain files and number of files to read reader is imported from (readCSV)\n",
97 " def __init__(self, path, number):\n",
98 " self.out_ds = []\n",
99 " self.nas = []\n",
100 " self.mpcs = []\n",
101 " models = reader.readmultiplefiles(path, number)\n",
102 " for i in range(len(models)):\n",
103 " out_d, na, mpc = reader.getmetrics(models[i])\n",
104 " self.out_ds.append(out_d)\n",
105 " self.nas.append(na)\n",
106 " self.mpcs.append(mpc)"
107 ]
108 },
109 {
110 "cell_type": "markdown",
111 "metadata": {},
112 "source": [
113 "2. read metrics for each graph type"
114 ]
115 },
116 {
117 "cell_type": "code",
118 "execution_count": 19,
119 "metadata": {},
120 "outputs": [
121 {
122 "ename": "ValueError",
123 "evalue": "too many values to unpack (expected 3)",
124 "output_type": "error",
125 "traceback": [
126 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
127 "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
128 "\u001b[1;32m<ipython-input-19-c45dfc2a26c6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mhuman\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGraphType\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../statistics/humanOutput/'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mviatra30\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGraphType\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../statistics/viatraOutput30/'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mviatra100\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGraphType\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../statistics/viatraOutput100/'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mrandom\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGraphType\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../statistics/randomOutput/'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0malloy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGraphType\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../statistics/alloyOutput/'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
129 "\u001b[1;32m<ipython-input-18-556621ada738>\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, path, number)\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mmodels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadmultiplefiles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumber\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mout_d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mna\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmpc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetmetrics\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mout_ds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_d\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mna\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
130 "\u001b[1;31mValueError\u001b[0m: too many values to unpack (expected 3)"
131 ]
132 }
133 ],
134 "source": [
135 "human = GraphType('../statistics/humanOutput/', 300)\n",
136 "viatra30 = GraphType('../statistics/viatraOutput30/', 300)\n",
137 "viatra100 = GraphType('../statistics/viatraOutput100/', 300)\n",
138 "random = GraphType('../statistics/randomOutput/', 300)\n",
139 "alloy = GraphType('../statistics/alloyOutput/', 300)"
140 ]
141 },
142 {
143 "cell_type": "markdown",
144 "metadata": {},
145 "source": [
146 "* outdegree comparison for human, Viatra30, and alloy"
147 ]
148 },
149 {
150 "cell_type": "code",
151 "execution_count": 20,
152 "metadata": {},
153 "outputs": [
154 {
155 "ename": "NameError",
156 "evalue": "name 'viatra30' is not defined",
157 "output_type": "error",
158 "traceback": [
159 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
160 "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
161 "\u001b[1;32m<ipython-input-20-5692e29d4679>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mout_d_coords\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalculateMDS\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcalculateKSMatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mviatra30\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mout_ds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malloy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mout_ds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhuman\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mout_ds\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Viatra (30 nodes)'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Alloy (30 nodes)'\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;34m'Human'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout_d_coords\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Out Degree'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
162 "\u001b[1;31mNameError\u001b[0m: name 'viatra30' is not defined"
163 ]
164 }
165 ],
166 "source": [
167 "out_d_coords = calculateMDS(calculateKSMatrix([viatra30.out_ds, alloy.out_ds, human.out_ds]))\n",
168 "plot(['Viatra (30 nodes)', 'Alloy (30 nodes)' , 'Human'], out_d_coords,0, 'Out Degree')"
169 ]
170 },
171 {
172 "cell_type": "markdown",
173 "metadata": {},
174 "source": [
175 "* outdegree comparison for human, Viatra30, and alloy"
176 ]
177 },
178 {
179 "cell_type": "code",
180 "execution_count": null,
181 "metadata": {},
182 "outputs": [],
183 "source": [
184 "out_d_coords = calculateMDS(calculateKSMatrix([viatra30.nas, alloy.nas, human.nas]))\n",
185 "plot(['Viatra (30 nodes)', 'Alloy (30 nodes)' , 'Human'], out_d_coords,0, 'Node Activity')"
186 ]
187 },
188 {
189 "cell_type": "code",
190 "execution_count": null,
191 "metadata": {},
192 "outputs": [],
193 "source": []
194 }
195 ],
196 "metadata": {
197 "kernelspec": {
198 "display_name": "Python 3",
199 "language": "python",
200 "name": "python3"
201 },
202 "language_info": {
203 "codemirror_mode": {
204 "name": "ipython",
205 "version": 3
206 },
207 "file_extension": ".py",
208 "mimetype": "text/x-python",
209 "name": "python",
210 "nbconvert_exporter": "python",
211 "pygments_lexer": "ipython3",
212 "version": "3.7.3"
213 }
214 },
215 "nbformat": 4,
216 "nbformat_minor": 2
217}
generated by cgit v1.2.3-54-g00ecf (git 2.45.2) at 2024-07-09 17:02:41 +0000