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-rw-r--r--Metrics/Metrics-Calculation/metrics_plot/src/GraphType.py3
-rw-r--r--Metrics/Metrics-Calculation/metrics_plot/src/MPC.pngbin97403 -> 0 bytes
-rw-r--r--Metrics/Metrics-Calculation/metrics_plot/src/Metrics Comparison .ipynb217
-rw-r--r--Metrics/Metrics-Calculation/metrics_plot/src/Node Activity.pngbin88084 -> 0 bytes
-rw-r--r--Metrics/Metrics-Calculation/metrics_plot/src/Out Degree.pngbin70416 -> 0 bytes
-rw-r--r--Metrics/Metrics-Calculation/metrics_plot/src/constants.py8
-rw-r--r--Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance.ipynb121
-rw-r--r--Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance_with_selector.ipynb4415
-rw-r--r--Metrics/Metrics-Calculation/metrics_plot/src/readCSV.py4
-rw-r--r--Metrics/Metrics-Calculation/metrics_plot/src/representative_selector .ipynb262
-rw-r--r--Metrics/Metrics-Calculation/metrics_plot/src/test.py59
11 files changed, 4770 insertions, 319 deletions
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/GraphType.py b/Metrics/Metrics-Calculation/metrics_plot/src/GraphType.py
index b3c9f359..eb35aba3 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/src/GraphType.py
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/GraphType.py
@@ -24,4 +24,5 @@ class GraphStat:
24 def __init__(self, filename): 24 def __init__(self, filename):
25 contents, self.out_d, self.na, self.mpc = reader.getmetrics(filename) 25 contents, self.out_d, self.na, self.mpc = reader.getmetrics(filename)
26 self.num_nodes = np.array(contents[constants.NUMBER_NODES]) 26 self.num_nodes = np.array(contents[constants.NUMBER_NODES])
27 self.id = (contents[constants.STATE_ID])[0] 27 if constants.STATE_ID in contents:
28 self.id = (contents[constants.STATE_ID])[0]
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/src/MPC.png
deleted file mode 100644
index 4f189578..00000000
--- a/Metrics/Metrics-Calculation/metrics_plot/src/MPC.png
+++ /dev/null
Binary files differ
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/Metrics Comparison .ipynb b/Metrics/Metrics-Calculation/metrics_plot/src/Metrics Comparison .ipynb
deleted file mode 100644
index 04af8773..00000000
--- a/Metrics/Metrics-Calculation/metrics_plot/src/Metrics Comparison .ipynb
+++ /dev/null
@@ -1,217 +0,0 @@
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}
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/Node Activity.png b/Metrics/Metrics-Calculation/metrics_plot/src/Node Activity.png
deleted file mode 100644
index add3c0f8..00000000
--- a/Metrics/Metrics-Calculation/metrics_plot/src/Node Activity.png
+++ /dev/null
Binary files differ
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/Out Degree.png b/Metrics/Metrics-Calculation/metrics_plot/src/Out Degree.png
deleted file mode 100644
index 5978c7cb..00000000
--- a/Metrics/Metrics-Calculation/metrics_plot/src/Out Degree.png
+++ /dev/null
Binary files differ
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/constants.py b/Metrics/Metrics-Calculation/metrics_plot/src/constants.py
index 4504030e..58ca7549 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/src/constants.py
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/constants.py
@@ -16,4 +16,10 @@ MPC_COUNT = 'MPCCount'
16 16
17METAMODEL = 'Meta Mode' 17METAMODEL = 'Meta Mode'
18 18
19STATE_ID = 'State Id' \ No newline at end of file 19STATE_ID = 'State Id'
20
21HUMAN_OUT_D_REP = '../statistics/humanOutput\R_20158_run_1.csv'
22
23HUMAN_MPC_REP = '../statistics/humanOutput\R_2015246_run_1.csv'
24
25HUMAN_NA_REP = '../statistics/humanOutput\R_2016176_run_1.csv'
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance.ipynb b/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance.ipynb
index c7bf9817..550e3978 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance.ipynb
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance.ipynb
@@ -16,7 +16,7 @@
16 }, 16 },
17 { 17 {
18 "cell_type": "code", 18 "cell_type": "code",
19 "execution_count": 1, 19 "execution_count": 48,
20 "metadata": {}, 20 "metadata": {},
21 "outputs": [], 21 "outputs": [],
22 "source": [ 22 "source": [
@@ -28,7 +28,8 @@
28 "import ipywidgets as widgets\n", 28 "import ipywidgets as widgets\n",
29 "import matplotlib.pyplot as plt\n", 29 "import matplotlib.pyplot as plt\n",
30 "import random\n", 30 "import random\n",
31 "import numpy as np\n" 31 "import numpy as np\n",
32 "import constants\n"
32 ] 33 ]
33 }, 34 },
34 { 35 {
@@ -47,7 +48,7 @@
47 }, 48 },
48 { 49 {
49 "cell_type": "code", 50 "cell_type": "code",
50 "execution_count": 2, 51 "execution_count": 49,
51 "metadata": {}, 52 "metadata": {},
52 "outputs": [], 53 "outputs": [],
53 "source": [ 54 "source": [
@@ -77,7 +78,7 @@
77 }, 78 },
78 { 79 {
79 "cell_type": "code", 80 "cell_type": "code",
80 "execution_count": 3, 81 "execution_count": 50,
81 "metadata": {}, 82 "metadata": {},
82 "outputs": [], 83 "outputs": [],
83 "source": [ 84 "source": [
@@ -86,26 +87,29 @@
86 " for target in targets:\n", 87 " for target in targets:\n",
87 " value, p = stats.ks_2samp(target, sample)\n", 88 " value, p = stats.ks_2samp(target, sample)\n",
88 " distance += value\n", 89 " distance += value\n",
89 " \n",
90 " distance = distance / len(targets)\n", 90 " distance = distance / len(targets)\n",
91 " return distance\n" 91 " return distance\n"
92 ] 92 ]
93 }, 93 },
94 { 94 {
95 "cell_type": "markdown", 95 "cell_type": "markdown",
96 "source": [
97 "* Find the median ks distance of the same number of nodes"
98 ],
99 "metadata": { 96 "metadata": {
100 "collapsed": false,
101 "pycharm": { 97 "pycharm": {
102 "name": "#%% md\n" 98 "name": "#%% md\n"
103 } 99 }
104 } 100 },
101 "source": [
102 "* Find the median ks distance of the same number of nodes"
103 ]
105 }, 104 },
106 { 105 {
107 "cell_type": "code", 106 "cell_type": "code",
108 "execution_count": null, 107 "execution_count": 51,
108 "metadata": {
109 "pycharm": {
110 "name": "#%%\n"
111 }
112 },
109 "outputs": [], 113 "outputs": [],
110 "source": [ 114 "source": [
111 "def find_median(x, metric_distances):\n", 115 "def find_median(x, metric_distances):\n",
@@ -123,13 +127,7 @@
123 " median_x = np.array(median_x)[order]\n", 127 " median_x = np.array(median_x)[order]\n",
124 " median_y = np.array(y)[order]\n", 128 " median_y = np.array(y)[order]\n",
125 " return median_x, median_y\n" 129 " return median_x, median_y\n"
126 ], 130 ]
127 "metadata": {
128 "collapsed": false,
129 "pycharm": {
130 "name": "#%%\n"
131 }
132 }
133 }, 131 },
134 { 132 {
135 "cell_type": "markdown", 133 "cell_type": "markdown",
@@ -140,7 +138,7 @@
140 }, 138 },
141 { 139 {
142 "cell_type": "code", 140 "cell_type": "code",
143 "execution_count": 4, 141 "execution_count": 52,
144 "metadata": {}, 142 "metadata": {},
145 "outputs": [], 143 "outputs": [],
146 "source": [ 144 "source": [
@@ -171,7 +169,7 @@
171 }, 169 },
172 { 170 {
173 "cell_type": "code", 171 "cell_type": "code",
174 "execution_count": 5, 172 "execution_count": 53,
175 "metadata": {}, 173 "metadata": {},
176 "outputs": [], 174 "outputs": [],
177 "source": [ 175 "source": [
@@ -188,11 +186,11 @@
188 }, 186 },
189 { 187 {
190 "cell_type": "code", 188 "cell_type": "code",
191 "execution_count": 6, 189 "execution_count": 54,
192 "metadata": {}, 190 "metadata": {},
193 "outputs": [], 191 "outputs": [],
194 "source": [ 192 "source": [
195 "human = GraphCollection('../statistics/humanOutput/', 300, 'Human')\n", 193 "human = GraphCollection('../statistics/humanOutput/', 300, 'Human', True)\n",
196 "file_names = reader.readmultiplefiles('../statistics/viatraEvolve/', 1000, False)" 194 "file_names = reader.readmultiplefiles('../statistics/viatraEvolve/', 1000, False)"
197 ] 195 ]
198 }, 196 },
@@ -205,7 +203,7 @@
205 }, 203 },
206 { 204 {
207 "cell_type": "code", 205 "cell_type": "code",
208 "execution_count": 7, 206 "execution_count": 55,
209 "metadata": {}, 207 "metadata": {},
210 "outputs": [], 208 "outputs": [],
211 "source": [ 209 "source": [
@@ -223,13 +221,13 @@
223 }, 221 },
224 { 222 {
225 "cell_type": "code", 223 "cell_type": "code",
226 "execution_count": 8, 224 "execution_count": 56,
227 "metadata": {}, 225 "metadata": {},
228 "outputs": [ 226 "outputs": [
229 { 227 {
230 "data": { 228 "data": {
231 "application/vnd.jupyter.widget-view+json": { 229 "application/vnd.jupyter.widget-view+json": {
232 "model_id": "a42a037c9020429982c906d0b100645b", 230 "model_id": "ca7932bce2a741afaff6b919042c42b0",
233 "version_major": 2, 231 "version_major": 2,
234 "version_minor": 0 232 "version_minor": 0
235 }, 233 },
@@ -275,7 +273,7 @@
275 }, 273 },
276 { 274 {
277 "cell_type": "code", 275 "cell_type": "code",
278 "execution_count": 9, 276 "execution_count": 57,
279 "metadata": {}, 277 "metadata": {},
280 "outputs": [], 278 "outputs": [],
281 "source": [ 279 "source": [
@@ -308,18 +306,18 @@
308 }, 306 },
309 { 307 {
310 "cell_type": "code", 308 "cell_type": "code",
311 "execution_count": 10, 309 "execution_count": 63,
312 "metadata": {}, 310 "metadata": {},
313 "outputs": [ 311 "outputs": [
314 { 312 {
315 "data": { 313 "data": {
316 "application/vnd.jupyter.widget-view+json": { 314 "application/vnd.jupyter.widget-view+json": {
317 "model_id": "248ad5232bb6454589c95c2b92b74db7", 315 "model_id": "08da62cb0c3f4e6e9591c7dc811d27cc",
318 "version_major": 2, 316 "version_major": 2,
319 "version_minor": 0 317 "version_minor": 0
320 }, 318 },
321 "text/plain": [ 319 "text/plain": [
322 "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/trajectori…" 320 "interactive(children=(SelectMultiple(description='Trajectory:', index=(1,), options={'../statistics/trajectori…"
323 ] 321 ]
324 }, 322 },
325 "metadata": {}, 323 "metadata": {},
@@ -331,7 +329,7 @@
331 "<function __main__.plot_out_degree(lines)>" 329 "<function __main__.plot_out_degree(lines)>"
332 ] 330 ]
333 }, 331 },
334 "execution_count": 10, 332 "execution_count": 63,
335 "metadata": {}, 333 "metadata": {},
336 "output_type": "execute_result" 334 "output_type": "execute_result"
337 } 335 }
@@ -351,18 +349,20 @@
351 }, 349 },
352 { 350 {
353 "cell_type": "code", 351 "cell_type": "code",
354 "execution_count": 11, 352 "execution_count": 64,
355 "metadata": {}, 353 "metadata": {
354 "scrolled": true
355 },
356 "outputs": [ 356 "outputs": [
357 { 357 {
358 "data": { 358 "data": {
359 "application/vnd.jupyter.widget-view+json": { 359 "application/vnd.jupyter.widget-view+json": {
360 "model_id": "0df16294cd86434b8f144ff08702d44a", 360 "model_id": "a708f43645a24bd2b15b53ea12c7d88f",
361 "version_major": 2, 361 "version_major": 2,
362 "version_minor": 0 362 "version_minor": 0
363 }, 363 },
364 "text/plain": [ 364 "text/plain": [
365 "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/trajectori…" 365 "interactive(children=(SelectMultiple(description='Trajectory:', index=(1,), options={'../statistics/trajectori…"
366 ] 366 ]
367 }, 367 },
368 "metadata": {}, 368 "metadata": {},
@@ -371,18 +371,18 @@
371 { 371 {
372 "data": { 372 "data": {
373 "text/plain": [ 373 "text/plain": [
374 "<function __main__.plot_out_degree(lines)>" 374 "<function __main__.plot_na(lines)>"
375 ] 375 ]
376 }, 376 },
377 "execution_count": 11, 377 "execution_count": 64,
378 "metadata": {}, 378 "metadata": {},
379 "output_type": "execute_result" 379 "output_type": "execute_result"
380 } 380 }
381 ], 381 ],
382 "source": [ 382 "source": [
383 "def plot_out_degree(lines):\n", 383 "def plot_na(lines):\n",
384 " plot(info_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity')\n", 384 " plot(info_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity')\n",
385 "interact(plot_out_degree, lines=w)" 385 "interact(plot_na, lines=w)"
386 ] 386 ]
387 }, 387 },
388 { 388 {
@@ -394,18 +394,25 @@
394 }, 394 },
395 { 395 {
396 "cell_type": "code", 396 "cell_type": "code",
397 "execution_count": 12, 397 "execution_count": null,
398 "metadata": {},
399 "outputs": [],
400 "source": []
401 },
402 {
403 "cell_type": "code",
404 "execution_count": 65,
398 "metadata": {}, 405 "metadata": {},
399 "outputs": [ 406 "outputs": [
400 { 407 {
401 "data": { 408 "data": {
402 "application/vnd.jupyter.widget-view+json": { 409 "application/vnd.jupyter.widget-view+json": {
403 "model_id": "b4e76d41b3d644808e47e3d1d7aaf1a7", 410 "model_id": "124a0cb0ebfb4225bf4ced24c09032f7",
404 "version_major": 2, 411 "version_major": 2,
405 "version_minor": 0 412 "version_minor": 0
406 }, 413 },
407 "text/plain": [ 414 "text/plain": [
408 "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/trajectori…" 415 "interactive(children=(SelectMultiple(description='Trajectory:', index=(1,), options={'../statistics/trajectori…"
409 ] 416 ]
410 }, 417 },
411 "metadata": {}, 418 "metadata": {},
@@ -417,7 +424,7 @@
417 "<function __main__.plot_out_degree(lines)>" 424 "<function __main__.plot_out_degree(lines)>"
418 ] 425 ]
419 }, 426 },
420 "execution_count": 12, 427 "execution_count": 65,
421 "metadata": {}, 428 "metadata": {},
422 "output_type": "execute_result" 429 "output_type": "execute_result"
423 } 430 }
@@ -430,35 +437,15 @@
430 }, 437 },
431 { 438 {
432 "cell_type": "code", 439 "cell_type": "code",
433 "execution_count": 42, 440 "execution_count": 19,
434 "metadata": {}, 441 "metadata": {},
435 "outputs": [ 442 "outputs": [],
436 {
437 "name": "stdout",
438 "output_type": "stream",
439 "text": [
440 "../statistics/viatraEvolve\\state_735.csv\n"
441 ]
442 }
443 ],
444 "source": [ 443 "source": [
445 "for name in file_names:\n", 444 "for name in file_names:\n",
446 " contents = reader.readcsvfile(name)\n", 445 " contents = reader.readcsvfile(name)\n",
447 " if(contents['State Id'][0] == 1032396643):\n", 446 " if(contents['State Id'][0] == 1032396643):\n",
448 " print(name)" 447 " print(name)"
449 ] 448 ]
450 },
451 {
452 "cell_type": "code",
453 "execution_count": null,
454 "metadata": {},
455 "outputs": [],
456 "source": []
457 },
458 {
459 "cell_type": "markdown",
460 "metadata": {},
461 "source": []
462 } 449 }
463 ], 450 ],
464 "metadata": { 451 "metadata": {
@@ -482,13 +469,13 @@
482 "pycharm": { 469 "pycharm": {
483 "stem_cell": { 470 "stem_cell": {
484 "cell_type": "raw", 471 "cell_type": "raw",
485 "source": [],
486 "metadata": { 472 "metadata": {
487 "collapsed": false 473 "collapsed": false
488 } 474 },
475 "source": []
489 } 476 }
490 } 477 }
491 }, 478 },
492 "nbformat": 4, 479 "nbformat": 4,
493 "nbformat_minor": 2 480 "nbformat_minor": 2
494} \ No newline at end of file 481}
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance_with_selector.ipynb b/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance_with_selector.ipynb
new file mode 100644
index 00000000..66189291
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance_with_selector.ipynb
@@ -0,0 +1,4415 @@
1{
2 "cells": [
3 {
4 "cell_type": "markdown",
5 "metadata": {},
6 "source": [
7 "# Measuremments with Representative"
8 ]
9 },
10 {
11 "cell_type": "markdown",
12 "metadata": {},
13 "source": [
14 "### Imports"
15 ]
16 },
17 {
18 "cell_type": "code",
19 "execution_count": 1,
20 "metadata": {},
21 "outputs": [],
22 "source": [
23 "from GraphType import GraphStat\n",
24 "from GraphType import GraphCollection\n",
25 "from scipy import stats\n",
26 "from ipywidgets import interact, fixed, interactive\n",
27 "import readCSV as reader\n",
28 "import ipywidgets as widgets\n",
29 "import matplotlib.pyplot as plt\n",
30 "import random\n",
31 "import numpy as np\n",
32 "import constants\n"
33 ]
34 },
35 {
36 "cell_type": "markdown",
37 "metadata": {},
38 "source": [
39 "### Classes"
40 ]
41 },
42 {
43 "cell_type": "markdown",
44 "metadata": {},
45 "source": [
46 "* Record the distances of different metrics using a representative"
47 ]
48 },
49 {
50 "cell_type": "code",
51 "execution_count": 63,
52 "metadata": {},
53 "outputs": [],
54 "source": [
55 "class GraphDistanceWithRep:\n",
56 " #init with a graph stat and a collection of graph stats\n",
57 " def __init__(self, graphStat, rep):\n",
58 " self.graph = graphStat\n",
59 " self.rep = rep\n",
60 " self.out_d_distance, _ = stats.ks_2samp(graphStat.out_d, rep.out_d)\n",
61 " self.na_distance,_ = stats.ks_2samp(graphStat.na, rep.na)\n",
62 " self.mpc_distance,_ = stats.ks_2samp(graphStat.mpc, rep.mpc)\n",
63 " print(self.mpc_distance)"
64 ]
65 },
66 {
67 "cell_type": "markdown",
68 "metadata": {},
69 "source": [
70 "### Methods\n"
71 ]
72 },
73 {
74 "cell_type": "markdown",
75 "metadata": {},
76 "source": [
77 "* Find the median ks distance of the same number of nodes"
78 ]
79 },
80 {
81 "cell_type": "code",
82 "execution_count": 3,
83 "metadata": {},
84 "outputs": [],
85 "source": [
86 "def find_median(x, metric_distances):\n",
87 " distance_dic = {}\n",
88 " for index, num_of_nodes in enumerate(x):\n",
89 " if num_of_nodes[0] not in distance_dic:\n",
90 " distance_dic[num_of_nodes[0]] = []\n",
91 " distance_dic[num_of_nodes[0]].append(metric_distances[index])\n",
92 " median_x = []\n",
93 " y = []\n",
94 " for num_of_nodes, distances in distance_dic.items():\n",
95 " median_x.append(num_of_nodes)\n",
96 " y.append(np.median(distances))\n",
97 " order = np.argsort(median_x)\n",
98 " median_x = np.array(median_x)[order]\n",
99 " median_y = np.array(y)[order]\n",
100 " return median_x, median_y\n"
101 ]
102 },
103 {
104 "cell_type": "markdown",
105 "metadata": {},
106 "source": [
107 "* Plot Diagram"
108 ]
109 },
110 {
111 "cell_type": "code",
112 "execution_count": 4,
113 "metadata": {},
114 "outputs": [],
115 "source": [
116 "# metric_selector: GraphDistance -> float\n",
117 "def plot(infos, lines, id, metric_selector,colors, title):\n",
118 " metric_distances = retrive_info_from_list(metric_selector, list(infos.values()))\n",
119 " x = retrive_info_from_list(lambda a : a.graph.num_nodes, list(infos.values()))\n",
120 " graph = plt.figure(id,figsize=(18, 10))\n",
121 " plt.title(title)\n",
122 " plt.plot(x, metric_distances, color='red', linestyle='', marker='o',alpha=0.7)\n",
123 " #plot ks distance median\n",
124 " median_x, median_y = find_median(x, metric_distances)\n",
125 " plt.plot(median_x, median_y, color='black',marker='o')\n",
126 " for i in range(0, len(lines)):\n",
127 " line_infos = retrive_info_from_list(lambda a: infos[a], lines[i])\n",
128 " line_y = retrive_info_from_list(metric_selector, line_infos)\n",
129 " line_x = retrive_info_from_list(lambda a : a.graph.num_nodes, line_infos)\n",
130 " plt.plot(line_x, line_y, marker='o', color=colors[i])\n",
131 " #graph.show()"
132 ]
133 },
134 {
135 "cell_type": "markdown",
136 "metadata": {},
137 "source": [
138 "* Retrieve information from a list "
139 ]
140 },
141 {
142 "cell_type": "code",
143 "execution_count": 5,
144 "metadata": {},
145 "outputs": [],
146 "source": [
147 "def retrive_info_from_list(selector, distances):\n",
148 " return list(map(selector, distances))"
149 ]
150 },
151 {
152 "cell_type": "code",
153 "execution_count": 16,
154 "metadata": {},
155 "outputs": [],
156 "source": [
157 "def readStats(path, numModels):\n",
158 " names = reader.readmultiplefiles(path, numModels, False)\n",
159 " stats = []\n",
160 " for name in names:\n",
161 " stats.append(GraphStat(name))\n",
162 " return stats"
163 ]
164 },
165 {
166 "cell_type": "code",
167 "execution_count": 21,
168 "metadata": {},
169 "outputs": [],
170 "source": [
171 "def calDistanceDic(stats, rep):\n",
172 " dic = {}\n",
173 " for info in stats:\n",
174 " info = GraphDistanceWithRep(info, rep)\n",
175 " dic[info.graph.id] = info\n",
176 " return dic"
177 ]
178 },
179 {
180 "cell_type": "markdown",
181 "metadata": {},
182 "source": [
183 "## Read Models"
184 ]
185 },
186 {
187 "cell_type": "code",
188 "execution_count": 94,
189 "metadata": {},
190 "outputs": [
191 {
192 "name": "stdout",
193 "output_type": "stream",
194 "text": [
195 "[0.0, 0.216, 0.216, 0.45, 0.768, 0.8, 0.8, 0.8, 0.89751, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.91349, 0.91349, 0.9241, 0.9241, 0.93061, 0.93061, 0.93061, 0.93061, 0.93061, 0.93061, 0.93061, 0.93728, 0.96, 0.96, 0.975, 1.0]\n",
196 "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.14933, 0.23704, 0.45, 0.45, 0.53333, 0.6, 0.6, 0.6, 0.768, 0.768, 0.768, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.85289, 0.86667, 0.88533, 0.88889, 0.88889, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.91349, 0.93061, 0.93223, 0.93728, 0.94815, 0.94815, 0.94815, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96667]\n",
197 "Ks_2sampResult(statistic=0.8904894133981257, pvalue=1.5238394791093816e-27)\n",
198 "[0.0, 0.0, 0.0, 0.0, 0.0, 0.14911, 0.64286, 0.672, 0.672, 0.7, 0.72071, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.7875, 0.78943, 0.84, 0.84]\n",
199 "92\n"
200 ]
201 }
202 ],
203 "source": [
204 "### Read Models\n",
205 "#read representative\n",
206 "human_rep = GraphStat(constants.HUMAN_OUT_D_REP)\n",
207 "human_na = GraphStat(constants.HUMAN_NA_REP)\n",
208 "human_mpc = GraphStat(constants.HUMAN_MPC_REP)\n",
209 "\n",
210 "# assign rep distributions to human_rep\n",
211 "human_rep.na = human_na.na\n",
212 "human_rep.mpc = human_mpc.mpc\n",
213 "print(human_mpc.mpc)\n",
214 "# Read generated models\n",
215 "viatra_no_con_stats = readStats('../statistics/viatraEvolve/', 1000)\n",
216 "print(viatra_no_con_stats[350].mpc)\n",
217 "print(stats.ks_2samp(viatra_con_stats[300].mpc, human_mpc.mpc))\n",
218 "viatra_con_stats = readStats('../statistics/viatra_con_output/',3100)\n",
219 "print(viatra_con_stats[100].mpc)\n",
220 "print(len(viatra_con_stats[30].mpc))"
221 ]
222 },
223 {
224 "cell_type": "markdown",
225 "metadata": {},
226 "source": [
227 "## calculate distribution distantces"
228 ]
229 },
230 {
231 "cell_type": "code",
232 "execution_count": 88,
233 "metadata": {},
234 "outputs": [
235 {
236 "name": "stdout",
237 "output_type": "stream",
238 "text": [
239 "0.9850746268656716\n",
240 "0.9850746268656716\n",
241 "0.7805970149253731\n",
242 "0.4332285938727416\n",
243 "0.43903857336693153\n",
244 "0.41306454739290555\n",
245 "0.41905855338691156\n",
246 "0.4062380405663988\n",
247 "0.41224258454562623\n",
248 "0.4180970149253731\n",
249 "0.42380689146858297\n",
250 "0.4293775027302512\n",
251 "0.43481388239525265\n",
252 "0.6987788331071914\n",
253 "0.44012082444918266\n",
254 "0.4282160625444208\n",
255 "0.4335381913959614\n",
256 "0.4217734855136084\n",
257 "0.42710864283234984\n",
258 "0.4438154057299708\n",
259 "0.4323211528564076\n",
260 "0.4374151967435549\n",
261 "0.4423947677343619\n",
262 "0.4472636815920398\n",
263 "0.7139303482587065\n",
264 "0.4520255863539445\n",
265 "0.44103657536493357\n",
266 "0.4458144062297209\n",
267 "0.45048948804365274\n",
268 "0.45506510003175615\n",
269 "0.4595443833464258\n",
270 "0.44901806755695206\n",
271 "0.45351368159203975\n",
272 "0.4430970149253731\n",
273 "0.4326803482587065\n",
274 "0.568407960199005\n",
275 "0.4372980458532082\n",
276 "0.44182150472129145\n",
277 "0.4316174230886384\n",
278 "0.4361525704809287\n",
279 "0.4405970149253732\n",
280 "0.9850746268656716\n",
281 "0.9850746268656716\n",
282 "0.9402985074626866\n",
283 "0.9402985074626866\n",
284 "0.8805970149253731\n",
285 "0.600459242250287\n",
286 "0.8805970149253731\n",
287 "0.8805970149253731\n",
288 "0.8805970149253731\n",
289 "0.8805970149253731\n",
290 "0.8805970149253731\n",
291 "0.8805970149253731\n",
292 "0.8805970149253731\n",
293 "0.7139303482587065\n",
294 "0.7267508610792193\n",
295 "0.6498277841561424\n",
296 "0.6279317697228145\n",
297 "0.6663113006396588\n",
298 "0.6805970149253732\n",
299 "0.6930970149253731\n",
300 "0.6305970149253731\n",
301 "0.6453028972783142\n",
302 "0.6583747927031509\n",
303 "0.6583747927031509\n",
304 "0.5472636815920398\n",
305 "0.5648075412411626\n",
306 "0.4595443833464258\n",
307 "0.6139303482587064\n",
308 "0.4805970149253731\n",
309 "0.4996446339729922\n",
310 "0.5169606512890095\n",
311 "0.42605156037991854\n",
312 "0.4458144062297209\n",
313 "0.4023361453601557\n",
314 "0.42226368159203975\n",
315 "0.4405970149253732\n",
316 "0.45752009184845\n",
317 "0.4731896075179657\n",
318 "0.5472636815920398\n",
319 "0.4361525704809287\n",
320 "0.3991155334438916\n",
321 "0.4520255863539445\n",
322 "0.4668039114770972\n",
323 "0.4472636815920398\n",
324 "0.46124217621569574\n",
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4130 }
4131 ],
4132 "source": [
4133 "viatra_no_con_dic = calDistanceDic(viatra_no_con_stats, human_rep)\n",
4134 "viatra_con_dic = calDistanceDic(viatra_con_stats, human_rep)"
4135 ]
4136 },
4137 {
4138 "cell_type": "code",
4139 "execution_count": 66,
4140 "metadata": {},
4141 "outputs": [],
4142 "source": [
4143 "filenames = reader.readmultiplefiles('../statistics/trajectories/', 10, False)\n",
4144 "trajectories = {}\n",
4145 "for name in filenames:\n",
4146 " trajectories[name] = reader.readTrajectory(name)\n",
4147 "\n",
4148 "w = widgets.SelectMultiple(\n",
4149 " options = trajectories,\n",
4150 " value = [trajectories[filenames[0]]],\n",
4151 " description='Trajectory:',\n",
4152 " disabled=False,\n",
4153 ")\n",
4154 "\n",
4155 "#generate random color for each line\n",
4156 "colors = []\n",
4157 "\n",
4158 "for i in range(0, len(trajectories)):\n",
4159 " color = \"#%06x\" % random.randint(0, 0xFFFFFF)\n",
4160 " colors.append(color)"
4161 ]
4162 },
4163 {
4164 "cell_type": "code",
4165 "execution_count": 67,
4166 "metadata": {},
4167 "outputs": [
4168 {
4169 "data": {
4170 "application/vnd.jupyter.widget-view+json": {
4171 "model_id": "75477310ae014aa797b401708d3c1388",
4172 "version_major": 2,
4173 "version_minor": 0
4174 },
4175 "text/plain": [
4176 "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/trajectori…"
4177 ]
4178 },
4179 "metadata": {},
4180 "output_type": "display_data"
4181 },
4182 {
4183 "data": {
4184 "text/plain": [
4185 "<function __main__.plot_out_degree(lines)>"
4186 ]
4187 },
4188 "execution_count": 67,
4189 "metadata": {},
4190 "output_type": "execute_result"
4191 }
4192 ],
4193 "source": [
4194 "def plot_out_degree(lines):\n",
4195 " plot(viatra_no_con_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out degree')\n",
4196 "interact(plot_out_degree, lines=w)"
4197 ]
4198 },
4199 {
4200 "cell_type": "code",
4201 "execution_count": 68,
4202 "metadata": {},
4203 "outputs": [
4204 {
4205 "data": {
4206 "application/vnd.jupyter.widget-view+json": {
4207 "model_id": "8944902bb0c44eb2b94cfdd1f8f98332",
4208 "version_major": 2,
4209 "version_minor": 0
4210 },
4211 "text/plain": [
4212 "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/trajectori…"
4213 ]
4214 },
4215 "metadata": {},
4216 "output_type": "display_data"
4217 },
4218 {
4219 "data": {
4220 "text/plain": [
4221 "<function __main__.plot_out_na(lines)>"
4222 ]
4223 },
4224 "execution_count": 68,
4225 "metadata": {},
4226 "output_type": "execute_result"
4227 }
4228 ],
4229 "source": [
4230 "def plot_out_na(lines):\n",
4231 " plot(viatra_no_con_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity')\n",
4232 "interact(plot_out_na, lines=w)"
4233 ]
4234 },
4235 {
4236 "cell_type": "code",
4237 "execution_count": 69,
4238 "metadata": {},
4239 "outputs": [
4240 {
4241 "data": {
4242 "application/vnd.jupyter.widget-view+json": {
4243 "model_id": "3084ff17e14447058f88cf2fb64d4595",
4244 "version_major": 2,
4245 "version_minor": 0
4246 },
4247 "text/plain": [
4248 "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/trajectori…"
4249 ]
4250 },
4251 "metadata": {},
4252 "output_type": "display_data"
4253 },
4254 {
4255 "data": {
4256 "text/plain": [
4257 "<function __main__.plot_out_mpc(lines)>"
4258 ]
4259 },
4260 "execution_count": 69,
4261 "metadata": {},
4262 "output_type": "execute_result"
4263 }
4264 ],
4265 "source": [
4266 "def plot_out_mpc(lines):\n",
4267 " plot(viatra_no_con_dic, lines, 0, lambda a: a.mpc_distance, colors, 'MPC')\n",
4268 "interact(plot_out_mpc, lines=w)"
4269 ]
4270 },
4271 {
4272 "cell_type": "code",
4273 "execution_count": 70,
4274 "metadata": {},
4275 "outputs": [
4276 {
4277 "data": {
4278 "application/vnd.jupyter.widget-view+json": {
4279 "model_id": "9c21e79436ce422ea276fa0654a8bc83",
4280 "version_major": 2,
4281 "version_minor": 0
4282 },
4283 "text/plain": [
4284 "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
4285 ]
4286 },
4287 "metadata": {},
4288 "output_type": "display_data"
4289 },
4290 {
4291 "data": {
4292 "text/plain": [
4293 "<function __main__.plot_out_degree(lines)>"
4294 ]
4295 },
4296 "execution_count": 70,
4297 "metadata": {},
4298 "output_type": "execute_result"
4299 }
4300 ],
4301 "source": [
4302 "def plot_out_degree(lines):\n",
4303 " plot(viatra_con_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out degree')\n",
4304 "interact(plot_out_degree, lines=[[]])"
4305 ]
4306 },
4307 {
4308 "cell_type": "code",
4309 "execution_count": 71,
4310 "metadata": {},
4311 "outputs": [
4312 {
4313 "data": {
4314 "application/vnd.jupyter.widget-view+json": {
4315 "model_id": "b27c9ac0d1ac418fbc481eaaf81aec1b",
4316 "version_major": 2,
4317 "version_minor": 0
4318 },
4319 "text/plain": [
4320 "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
4321 ]
4322 },
4323 "metadata": {},
4324 "output_type": "display_data"
4325 },
4326 {
4327 "data": {
4328 "text/plain": [
4329 "<function __main__.plot_na(lines)>"
4330 ]
4331 },
4332 "execution_count": 71,
4333 "metadata": {},
4334 "output_type": "execute_result"
4335 }
4336 ],
4337 "source": [
4338 "def plot_na(lines):\n",
4339 " plot(viatra_con_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity')\n",
4340 "interact(plot_na, lines=[[]])"
4341 ]
4342 },
4343 {
4344 "cell_type": "code",
4345 "execution_count": 72,
4346 "metadata": {},
4347 "outputs": [
4348 {
4349 "data": {
4350 "application/vnd.jupyter.widget-view+json": {
4351 "model_id": "88a4e2356bdd45a4a51c9c5b71b8896b",
4352 "version_major": 2,
4353 "version_minor": 0
4354 },
4355 "text/plain": [
4356 "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
4357 ]
4358 },
4359 "metadata": {},
4360 "output_type": "display_data"
4361 },
4362 {
4363 "data": {
4364 "text/plain": [
4365 "<function __main__.plot_mpc(lines)>"
4366 ]
4367 },
4368 "execution_count": 72,
4369 "metadata": {},
4370 "output_type": "execute_result"
4371 }
4372 ],
4373 "source": [
4374 "def plot_mpc(lines):\n",
4375 " plot(viatra_con_dic, lines, 0, lambda a: a.mpc_distance, colors, 'MPC')\n",
4376 "interact(plot_mpc, lines=[[]])"
4377 ]
4378 },
4379 {
4380 "cell_type": "code",
4381 "execution_count": null,
4382 "metadata": {},
4383 "outputs": [],
4384 "source": []
4385 },
4386 {
4387 "cell_type": "code",
4388 "execution_count": null,
4389 "metadata": {},
4390 "outputs": [],
4391 "source": []
4392 }
4393 ],
4394 "metadata": {
4395 "kernelspec": {
4396 "display_name": "Python 3",
4397 "language": "python",
4398 "name": "python3"
4399 },
4400 "language_info": {
4401 "codemirror_mode": {
4402 "name": "ipython",
4403 "version": 3
4404 },
4405 "file_extension": ".py",
4406 "mimetype": "text/x-python",
4407 "name": "python",
4408 "nbconvert_exporter": "python",
4409 "pygments_lexer": "ipython3",
4410 "version": "3.7.3"
4411 }
4412 },
4413 "nbformat": 4,
4414 "nbformat_minor": 2
4415}
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/readCSV.py b/Metrics/Metrics-Calculation/metrics_plot/src/readCSV.py
index 8627ad4a..e0402519 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/src/readCSV.py
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/readCSV.py
@@ -73,12 +73,12 @@ def getmetrics(filename):
73# 73#
74# read number of files in the given path RANDOMLY 74# read number of files in the given path RANDOMLY
75# 75#
76def readmultiplefiles(dirName, numberOfFiles, shouldShuffle = True): 76def readmultiplefiles(dirName, maxNumberOfFiles, shouldShuffle = True):
77 list_of_files = glob.glob(dirName + '*.csv') # create the list of file 77 list_of_files = glob.glob(dirName + '*.csv') # create the list of file
78 if shouldShuffle: 78 if shouldShuffle:
79 random.shuffle(list_of_files) 79 random.shuffle(list_of_files)
80 #if the number of files is out of bound then just give the whole list 80 #if the number of files is out of bound then just give the whole list
81 file_names = list_of_files[:numberOfFiles] if numberOfFiles > len(list_of_files) else list_of_files 81 file_names = list_of_files[:maxNumberOfFiles]
82 # print(file_names) 82 # print(file_names)
83 return file_names 83 return file_names
84 84
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/representative_selector .ipynb b/Metrics/Metrics-Calculation/metrics_plot/src/representative_selector .ipynb
new file mode 100644
index 00000000..4886c215
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/representative_selector .ipynb
@@ -0,0 +1,262 @@
1{
2 "cells": [
3 {
4 "cell_type": "markdown",
5 "metadata": {},
6 "source": [
7 "## Use K-medoid algorithm to find the suitable human model representitives"
8 ]
9 },
10 {
11 "cell_type": "markdown",
12 "metadata": {},
13 "source": [
14 "### Imports"
15 ]
16 },
17 {
18 "cell_type": "code",
19 "execution_count": 1,
20 "metadata": {},
21 "outputs": [],
22 "source": [
23 "from GraphType import GraphStat\n",
24 "import readCSV as reader\n",
25 "from scipy import stats\n",
26 "from ipywidgets import interact, fixed, interactive\n",
27 "import ipywidgets as widgets\n",
28 "from pyclustering.cluster.kmedoids import kmedoids\n",
29 "from pyclustering.utils.metric import distance_metric, type_metric\n",
30 "import random"
31 ]
32 },
33 {
34 "cell_type": "markdown",
35 "metadata": {},
36 "source": [
37 "### Define a new distance metric"
38 ]
39 },
40 {
41 "cell_type": "code",
42 "execution_count": 2,
43 "metadata": {},
44 "outputs": [],
45 "source": [
46 "def ks_value(dest1, dest2):\n",
47 " value, p = stats.ks_2samp(dest1, dest2)\n",
48 " return value\n",
49 "\n",
50 "\n",
51 "ks_metric = distance_metric(type_metric.USER_DEFINED, func=ks_value)"
52 ]
53 },
54 {
55 "cell_type": "markdown",
56 "metadata": {},
57 "source": [
58 "### Read Human Models"
59 ]
60 },
61 {
62 "cell_type": "code",
63 "execution_count": 3,
64 "metadata": {},
65 "outputs": [
66 {
67 "data": {
68 "text/plain": [
69 "1253"
70 ]
71 },
72 "execution_count": 3,
73 "metadata": {},
74 "output_type": "execute_result"
75 }
76 ],
77 "source": [
78 "# Progress Widge\n",
79 "w = widgets.FloatProgress(\n",
80 " value=0,\n",
81 " min=0,\n",
82 " max=1.0,\n",
83 " step=0.1,\n",
84 " description='Loading Files...:',\n",
85 " bar_style='info',\n",
86 " orientation='horizontal'\n",
87 ")\n",
88 "\n",
89 "\n",
90 "humanFiles = reader.readmultiplefiles('../statistics/humanOutput/', 1300, False)\n",
91 "modelToFileName = {}\n",
92 "for name in humanFiles:\n",
93 " modelToFileName[GraphStat(name)] = name\n",
94 "\n",
95 "models = list(modelToFileName.keys())\n",
96 "len(humanFiles)"
97 ]
98 },
99 {
100 "cell_type": "markdown",
101 "metadata": {},
102 "source": [
103 "### Find Representative by K-medroid for different dists on GraphStat"
104 ]
105 },
106 {
107 "cell_type": "markdown",
108 "metadata": {},
109 "source": [
110 "* Returns the index of the representative"
111 ]
112 },
113 {
114 "cell_type": "code",
115 "execution_count": 7,
116 "metadata": {},
117 "outputs": [],
118 "source": [
119 "def findRep(graphStats, func):\n",
120 " out_ds = list(map(func, models))\n",
121 "\n",
122 " #choose a random starting point\n",
123 " start_index = random.randint(0, len(out_ds))\n",
124 "\n",
125 " # start with one initial metrid [start_index]\n",
126 " outdegree_kmedoid = kmedoids(out_ds, [start_index], metric=ks_metric)\n",
127 "\n",
128 " outdegree_kmedoid.process()\n",
129 " centoids = outdegree_kmedoid.get_medoids()\n",
130 " return centoids[0]"
131 ]
132 },
133 {
134 "cell_type": "markdown",
135 "metadata": {},
136 "source": [
137 "### Find representative for out degree"
138 ]
139 },
140 {
141 "cell_type": "code",
142 "execution_count": 8,
143 "metadata": {},
144 "outputs": [
145 {
146 "name": "stdout",
147 "output_type": "stream",
148 "text": [
149 "../statistics/humanOutput\\R_20158_run_1.csv\n",
150 "../statistics/humanOutput\\R_20158_run_1.csv\n"
151 ]
152 }
153 ],
154 "source": [
155 "od_rep_index = findRep(models, lambda m: m.out_d)\n",
156 "print(list(modelToFileName.values())[od_rep_index])\n",
157 "od_rep_model = models[od_rep_index]\n",
158 "print(modelToFileName[od_rep_model])\n"
159 ]
160 },
161 {
162 "cell_type": "markdown",
163 "metadata": {},
164 "source": [
165 "### Find Representative for node activities"
166 ]
167 },
168 {
169 "cell_type": "code",
170 "execution_count": 9,
171 "metadata": {},
172 "outputs": [
173 {
174 "ename": "NameError",
175 "evalue": "name 'na_rep_index' is not defined",
176 "output_type": "error",
177 "traceback": [
178 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
179 "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
180 "\u001b[1;32m<ipython-input-9-7899480190c8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mna_rp_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfindRep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mm\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\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodelToFileName\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mna_rep_index\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 3\u001b[0m \u001b[0mna_rep_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mna_rep_index\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[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodelToFileName\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mna_rep_model\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",
181 "\u001b[1;31mNameError\u001b[0m: name 'na_rep_index' is not defined"
182 ]
183 }
184 ],
185 "source": [
186 "na_rep_index = findRep(models, lambda m: m.na)\n",
187 "print(list(modelToFileName.values())[na_rep_index])\n",
188 "na_rep_model = models[na_rep_index]\n",
189 "print(modelToFileName[na_rep_model])"
190 ]
191 },
192 {
193 "cell_type": "code",
194 "execution_count": 11,
195 "metadata": {},
196 "outputs": [
197 {
198 "name": "stdout",
199 "output_type": "stream",
200 "text": [
201 "../statistics/humanOutput\\R_2016176_run_1.csv\n",
202 "../statistics/humanOutput\\R_2016176_run_1.csv\n"
203 ]
204 }
205 ],
206 "source": [
207 "print(list(modelToFileName.values())[na_rp_index])\n",
208 "na_rep_model = models[na_rp_index]\n",
209 "print(modelToFileName[na_rep_model])"
210 ]
211 },
212 {
213 "cell_type": "markdown",
214 "metadata": {},
215 "source": [
216 "### Find Representative for MPC"
217 ]
218 },
219 {
220 "cell_type": "code",
221 "execution_count": 12,
222 "metadata": {},
223 "outputs": [
224 {
225 "name": "stdout",
226 "output_type": "stream",
227 "text": [
228 "../statistics/humanOutput\\R_2015246_run_1.csv\n",
229 "../statistics/humanOutput\\R_2015246_run_1.csv\n"
230 ]
231 }
232 ],
233 "source": [
234 "mpc_rep_index = findRep(models, lambda m: m.mpc)\n",
235 "print(list(modelToFileName.values())[mpc_rep_index])\n",
236 "mpc_rep_model = models[mpc_rep_index]\n",
237 "print(modelToFileName[mpc_rep_model])"
238 ]
239 }
240 ],
241 "metadata": {
242 "kernelspec": {
243 "display_name": "Python 3",
244 "language": "python",
245 "name": "python3"
246 },
247 "language_info": {
248 "codemirror_mode": {
249 "name": "ipython",
250 "version": 3
251 },
252 "file_extension": ".py",
253 "mimetype": "text/x-python",
254 "name": "python",
255 "nbconvert_exporter": "python",
256 "pygments_lexer": "ipython3",
257 "version": "3.7.3"
258 }
259 },
260 "nbformat": 4,
261 "nbformat_minor": 2
262}
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/test.py b/Metrics/Metrics-Calculation/metrics_plot/src/test.py
index d1aae53a..0212cc2a 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/src/test.py
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/test.py
@@ -1,35 +1,32 @@
1from sklearn.datasets import load_digits 1from pyclustering.cluster.kmedoids import kmedoids
2from sklearn.manifold import MDS 2from pyclustering.utils import read_sample
3import matplotlib.pyplot as plt 3from pyclustering.samples.definitions import FCPS_SAMPLES
4from scipy import stats 4from pyclustering.utils.metric import distance_metric, type_metric
5import numpy as np 5import matplotlib.pyplot as plt
6 6
7dist = [] 7# metric = distance_metric(type_metric.MINKOWSKI, degree=2)
8# print(metric([1,1], [2,2]))
8 9
9for i in range(100): 10# Load list of points for cluster analysis.
10 rvs = stats.uniform.rvs(size=500, loc=0., scale=1) 11sample = [[1,1,1], [2,2,2],[3,3,3]]
11 dist.append(rvs)
12 12
13for i in range(100): 13# Set random initial medoids.
14 rvs2 = stats.powerlaw .rvs(1.66, size=500) 14initial_medoids = [1, 1 ,1]
15 dist.append(rvs2) 15# Create instance of K-Medoids algorithm.
16kmedoids_instance = kmedoids(sample, initial_medoids)
17# Run cluster analysis and obtain results.
18kmedoids_instance.process()
19centoids = kmedoids_instance.get_medoids()
20clusters = kmedoids_instance.get_clusters()
21# Show allocated clusters.
22for cluster_id in range(len(clusters)):
23 for index in clusters[cluster_id]:
24 if(cluster_id == 0):
25 plt.plot(sample[index][0], sample[index][1], 'ro')
26 print(sample[index][0])
27 else:
28 plt.plot(sample[index][0], sample[index][1], 'bo')
16 29
17matrix = np.empty((len(dist),len(dist))) 30plt.plot(sample[centoids[0]][0], sample[centoids[0]][1], 'bo')
18 31# plt.plot(sample[centoids[1]][0], sample[centoids[1]][1], 'ro')
19for i in range(len(dist)): 32plt.show() \ No newline at end of file
20 matrix[i,i] = 0
21 for j in range(i+1, len(dist)):
22 value, p = stats.ks_2samp(dist[i], dist[j])
23 matrix[i, j] = value
24 matrix[j, i] = value
25
26embedding = MDS(n_components=2, dissimilarity='precomputed')
27trans = embedding.fit_transform(X=matrix)
28x = (trans[:100,0]).tolist()
29y = (trans[:100,1]).tolist()
30
31x2 = (trans[100:,0]).tolist()
32y2 = (trans[100:,1]).tolist()
33plt.plot(x, y, 'yo')
34plt.plot(x2, y2, 'ro')
35plt.show()
generated by cgit v1.2.3-54-g00ecf (git 2.45.2) at 2024-07-03 14:45:05 +0000