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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Metric comparison preperation"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import readCSV as reader\n",
"import glob\n",
"import random \n",
"from sklearn.manifold import MDS\n",
"import matplotlib.pyplot as plt\n",
"from scipy import stats\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def calculateKSMatrix(dists):\n",
" dist = []\n",
"\n",
" for i in range(len(dists)):\n",
" dist = dist + dists[i]\n",
" matrix = np.empty((len(dist),len(dist)))\n",
"\n",
" for i in range(len(dist)):\n",
" matrix[i,i] = 0\n",
" for j in range(i+1, len(dist)):\n",
" value, p = stats.ks_2samp(dist[i], dist[j])\n",
" matrix[i, j] = value\n",
" matrix[j, i] = value\n",
" value, p = stats.ks_2samp(dist[j], dist[i])\n",
" return matrix\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def calculateMDS(dissimilarities):\n",
" embedding = MDS(n_components=2, dissimilarity='precomputed')\n",
" trans = embedding.fit_transform(X=dissimilarities)\n",
" return trans"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def plot(names, coords, index = 0, title=''):\n",
" half_length = int(coords.shape[0] / len(names))\n",
" color = ['blue', 'red', 'green']\n",
" graph = plt.figure(index)\n",
" plt.title(title)\n",
" for i in range(len(names)):\n",
" x = (coords[(i*half_length):((i+1)*half_length), 0].tolist())\n",
" y = (coords[(i*half_length):((i+1)*half_length), 1].tolist())\n",
" plt.plot(x, y, color=color[i], marker='o', label = names[i], linestyle='', alpha=0.7)\n",
" plt.legend(loc='upper right')\n",
" plt.savefig(fname = title+'.png', dpi=150)\n",
" #graph.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read Files\n",
"1. define class for metric reading of each graph type"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"class GraphType:\n",
" \n",
" # init with path contrain files and number of files to read reader is imported from (readCSV)\n",
" def __init__(self, path, number):\n",
" self.out_ds = []\n",
" self.nas = []\n",
" self.mpcs = []\n",
" models = reader.readmultiplefiles(path, number)\n",
" for i in range(len(models)):\n",
" out_d, na, mpc = reader.getmetrics(models[i])\n",
" self.out_ds.append(out_d)\n",
" self.nas.append(na)\n",
" self.mpcs.append(mpc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. read metrics for each graph type"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "too many values to unpack (expected 3)",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\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",
"\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",
"\u001b[1;31mValueError\u001b[0m: too many values to unpack (expected 3)"
]
}
],
"source": [
"human = GraphType('../statistics/humanOutput/', 300)\n",
"viatra30 = GraphType('../statistics/viatraOutput30/', 300)\n",
"viatra100 = GraphType('../statistics/viatraOutput100/', 300)\n",
"random = GraphType('../statistics/randomOutput/', 300)\n",
"alloy = GraphType('../statistics/alloyOutput/', 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* outdegree comparison for human, Viatra30, and alloy"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'viatra30' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\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",
"\u001b[1;31mNameError\u001b[0m: name 'viatra30' is not defined"
]
}
],
"source": [
"out_d_coords = calculateMDS(calculateKSMatrix([viatra30.out_ds, alloy.out_ds, human.out_ds]))\n",
"plot(['Viatra (30 nodes)', 'Alloy (30 nodes)' , 'Human'], out_d_coords,0, 'Out Degree')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* outdegree comparison for human, Viatra30, and alloy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"out_d_coords = calculateMDS(calculateKSMatrix([viatra30.nas, alloy.nas, human.nas]))\n",
"plot(['Viatra (30 nodes)', 'Alloy (30 nodes)' , 'Human'], out_d_coords,0, 'Node Activity')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
