diff options
author | 20001LastOrder <boqi.chen@mail.mcgill.ca> | 2019-05-29 09:55:30 -0400 |
---|---|---|
committer | 20001LastOrder <boqi.chen@mail.mcgill.ca> | 2019-05-29 09:55:30 -0400 |
commit | a1322f1dc5f6ea53712093f75e7c36d01074f669 (patch) | |
tree | 27a6551c7d4a958fa2e924234c8b6a7a2d7604e7 /Metrics/Metrics-Calculation/metrics_plot/src | |
parent | added plot median ks distance line (diff) | |
download | VIATRA-Generator-a1322f1dc5f6ea53712093f75e7c36d01074f669.tar.gz VIATRA-Generator-a1322f1dc5f6ea53712093f75e7c36d01074f669.tar.zst VIATRA-Generator-a1322f1dc5f6ea53712093f75e7c36d01074f669.zip |
metric plot based on chosen rep with k-medoid method
Diffstat (limited to 'Metrics/Metrics-Calculation/metrics_plot/src')
-rw-r--r-- | Metrics/Metrics-Calculation/metrics_plot/src/GraphType.py | 3 | ||||
-rw-r--r-- | Metrics/Metrics-Calculation/metrics_plot/src/MPC.png | bin | 97403 -> 0 bytes | |||
-rw-r--r-- | Metrics/Metrics-Calculation/metrics_plot/src/Metrics Comparison .ipynb | 217 | ||||
-rw-r--r-- | Metrics/Metrics-Calculation/metrics_plot/src/Node Activity.png | bin | 88084 -> 0 bytes | |||
-rw-r--r-- | Metrics/Metrics-Calculation/metrics_plot/src/Out Degree.png | bin | 70416 -> 0 bytes | |||
-rw-r--r-- | Metrics/Metrics-Calculation/metrics_plot/src/constants.py | 8 | ||||
-rw-r--r-- | Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance.ipynb | 121 | ||||
-rw-r--r-- | Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance_with_selector.ipynb | 4415 | ||||
-rw-r--r-- | Metrics/Metrics-Calculation/metrics_plot/src/readCSV.py | 4 | ||||
-rw-r--r-- | Metrics/Metrics-Calculation/metrics_plot/src/representative_selector .ipynb | 262 | ||||
-rw-r--r-- | Metrics/Metrics-Calculation/metrics_plot/src/test.py | 59 |
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 | ||
17 | METAMODEL = 'Meta Mode' | 17 | METAMODEL = 'Meta Mode' |
18 | 18 | ||
19 | STATE_ID = 'State Id' \ No newline at end of file | 19 | STATE_ID = 'State Id' |
20 | |||
21 | HUMAN_OUT_D_REP = '../statistics/humanOutput\R_20158_run_1.csv' | ||
22 | |||
23 | HUMAN_MPC_REP = '../statistics/humanOutput\R_2015246_run_1.csv' | ||
24 | |||
25 | HUMAN_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": [ | ||
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1334 | { | ||
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3205 | { | ||
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3842 | { | ||
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4129 | ] | ||
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 | # |
76 | def readmultiplefiles(dirName, numberOfFiles, shouldShuffle = True): | 76 | def 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 @@ | |||
1 | from sklearn.datasets import load_digits | 1 | from pyclustering.cluster.kmedoids import kmedoids |
2 | from sklearn.manifold import MDS | 2 | from pyclustering.utils import read_sample |
3 | import matplotlib.pyplot as plt | 3 | from pyclustering.samples.definitions import FCPS_SAMPLES |
4 | from scipy import stats | 4 | from pyclustering.utils.metric import distance_metric, type_metric |
5 | import numpy as np | 5 | import matplotlib.pyplot as plt |
6 | 6 | ||
7 | dist = [] | 7 | # metric = distance_metric(type_metric.MINKOWSKI, degree=2) |
8 | # print(metric([1,1], [2,2])) | ||
8 | 9 | ||
9 | for i in range(100): | 10 | # Load list of points for cluster analysis. |
10 | rvs = stats.uniform.rvs(size=500, loc=0., scale=1) | 11 | sample = [[1,1,1], [2,2,2],[3,3,3]] |
11 | dist.append(rvs) | ||
12 | 12 | ||
13 | for i in range(100): | 13 | # Set random initial medoids. |
14 | rvs2 = stats.powerlaw .rvs(1.66, size=500) | 14 | initial_medoids = [1, 1 ,1] |
15 | dist.append(rvs2) | 15 | # Create instance of K-Medoids algorithm. |
16 | kmedoids_instance = kmedoids(sample, initial_medoids) | ||
17 | # Run cluster analysis and obtain results. | ||
18 | kmedoids_instance.process() | ||
19 | centoids = kmedoids_instance.get_medoids() | ||
20 | clusters = kmedoids_instance.get_clusters() | ||
21 | # Show allocated clusters. | ||
22 | for 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 | ||
17 | matrix = np.empty((len(dist),len(dist))) | 30 | plt.plot(sample[centoids[0]][0], sample[centoids[0]][1], 'bo') |
18 | 31 | # plt.plot(sample[centoids[1]][0], sample[centoids[1]][1], 'ro') | |
19 | for i in range(len(dist)): | 32 | plt.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 | |||
26 | embedding = MDS(n_components=2, dissimilarity='precomputed') | ||
27 | trans = embedding.fit_transform(X=matrix) | ||
28 | x = (trans[:100,0]).tolist() | ||
29 | y = (trans[:100,1]).tolist() | ||
30 | |||
31 | x2 = (trans[100:,0]).tolist() | ||
32 | y2 = (trans[100:,1]).tolist() | ||
33 | plt.plot(x, y, 'yo') | ||
34 | plt.plot(x2, y2, 'ro') | ||
35 | plt.show() | ||