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@andyfaff
Created October 30, 2017 03:59
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benchmarking parser
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{
"cells": [
{
"cell_type": "code",
"execution_count": 195,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import json\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import matplotlib"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open('global-bench-results.json', 'r') as f:\n",
" a = json.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {},
"outputs": [],
"source": [
"problems = list(a.keys())"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {},
"outputs": [],
"source": [
"sucesses_de = [float(a[problem]['DE']['nsuccess']) for problem in problems]\n",
"sucesses_bh = [float(a[problem]['basinh.']['nsuccess']) if 'basinh.' in a[problem] else 0 for problem in problems]\n",
"sucesses_shgo = [float(a[problem]['shgo']['nsuccess']) if 'shgo' in a[problem] else 0 for problem in problems]"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(data=np.c_[sucesses_de, sucesses_bh, sucesses_shgo],\n",
" index=problems, columns=['de', 'bh', 'shgo'])"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"195"
]
},
"execution_count": 203,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(problems)"
]
},
{
"cell_type": "code",
"execution_count": 215,
"metadata": {},
"outputs": [],
"source": [
"de = np.zeros(10)\n",
"bh = np.zeros(10)\n",
"shgo = np.zeros(10)\n",
"\n",
"for i in range(1, 11):\n",
" b = df >= i\n",
" de[i - 1] = b.sum(axis=0)['de']\n",
" bh[i - 1] = b.sum(axis=0)['bh']\n",
" shgo[i - 1] = b.sum(axis=0)['shgo']"
]
},
{
"cell_type": "code",
"execution_count": 221,
"metadata": {},
"outputs": [
{
"data": {
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169dz5ZVXUlZWRkREBC+88EKdPbclBWNqKDm2CaNPzWL0qVms3LyX3Lx83l1QwKcrtpAQ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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ce625f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(np.arange(1, 11), de/195., label='de')\n",
"plt.plot(np.arange(1, 11), bh/195., label='bh')\n",
"plt.plot(np.arange(1, 11), shgo/195., label='shgo')\n",
"plt.ylabel(\"fraction of problems\")\n",
"plt.xlabel(\"at least this number of successes\")\n",
"plt.legend();\n",
"plt.savefig('success.png')"
]
}
],
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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