543 lines
29 KiB
Plaintext
543 lines
29 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"zhihu 3488\n",
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"weibo 2705\n",
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"douyin 2072\n",
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"hupu 1217\n",
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"toutiao 1058\n",
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"Name: 4, dtype: int64"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"date = '2019-12-20_21'\n",
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"root_dir = '/Users/Leo/Documents/github/GradProj/'\n",
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"example_label_file = root_dir + 'DataSet/result/' + date + '/stream_tag.txt'\n",
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"example_label_df = pd.read_table(example_label_file, sep='\\s+', header=None)\n",
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"example_label_df[3] = 443\n",
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"example_label_df[4].value_counts()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import json"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"ciper_suits = {\n",
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" '1305':0,\n",
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" 'C030':1,\n",
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"\t'C02C':2,\n",
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"\t'C028':3,\n",
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"\t'C024':4,\n",
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"\t'C014':5,\n",
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"\t'C00A':6,\n",
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"\t'00A5':7,\n",
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"\t'00A1':9,\n",
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"\t'009F':10,\n",
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"\t'006A':12,\n",
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"\t'0069':13,\n",
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"\t'0068':14,\n",
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"\t'0039':15,\n",
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"\t'0038':16,\n",
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"\t'0037':17,\n",
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"\t'0036':18,\n",
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"\t'0088':19,\n",
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"\t'0087':20,\n",
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"\t'0086':21,\n",
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"\t'0085':22,\n",
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"\t'C019':23,\n",
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"\t'00A7':24,\n",
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"\t'006D':25,\n",
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"\t'003A':26,\n",
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"\t'0089':27,\n",
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"\t'C032':28,\n",
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"\t'C02E':29,\n",
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"\t'C02A':30,\n",
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"\t'C026':31,\n",
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"\t'C005':33,\n",
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"\t'009D':34,\n",
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"\t'003D':35,\n",
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"\t'0035':36,\n",
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"\t'0084':37,\n",
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"\t'008D':38,\n",
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"\t'C02F':39,\n",
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"\t'C02B':40,\n",
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"\t'C027':41,\n",
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"\t'C013':43,\n",
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"\t'C009':44,\n",
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"\t'0032':54,\n",
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"\t'C018':65,\n",
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"\t'006C':67,\n",
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"\t'0034':68,\n",
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"\t'009B':69,\n",
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"\t'0046':70,\n",
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"\t'C031':71,\n",
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"\t'C02D':72,\n",
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"\t'C029':73,\n",
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"\t'C025':74,\n",
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"\t'C00E':75,\n",
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"\t'C004':76,\n",
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"\t'009C':77,\n",
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"\t'C008':84,\n",
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"\t'0010':87,\n",
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"\t'000D':88,\n",
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"\t'C017':89,\n",
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"\t'001B':90,\n",
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"\t'000A':93,\n",
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"\t'0007':94,\n",
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"\t'008B':95,\n",
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"\t'0021':96,\n",
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"\t'001F':97,\n",
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"\t'0025':98,\n",
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"\t'0023':99,\n",
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"\t'C011':100,\n",
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"\t'C007':101,\n",
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"\t'C016':102,\n",
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"\t'0018':103,\n",
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"\t'C00C':104,\n",
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"\t'C002':105,\n",
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"\t'0005':106,\n",
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"\t'0004':107,\n",
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"\t'008A':108,\n",
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"\t'0020':109,\n",
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"\t'0024':110,\n",
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"\t'C010':111,\n",
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"\t'C006':112,\n",
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"\t'C015':113,\n",
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"\t'C00B':114,\n",
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"\t'C001':115,\n",
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"\t'003B':116,\n",
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"\t'0002':117,\n",
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"\t'0001':118,\n",
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" '1301':119,\n",
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"\t'1302':120,\n",
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"\t'1303':121,\n",
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"\t'1304':122\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"extensions = { \n",
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" 0:0, \n",
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" 1:1, \n",
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" 2:2, \n",
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" 3:3, \n",
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" 4:4, \n",
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" 5:5, \n",
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" 6:6, \n",
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" 7:7, \n",
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" 8:8, \n",
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" 9:9, \n",
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" 10:10, \n",
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" 11:11, \n",
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" 12:12, \n",
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" 13:13, \n",
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" 14:14, \n",
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" 15:15, \n",
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" 16:16, \n",
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" 17:17, \n",
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" 18:18, \n",
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" 19:19, \n",
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" 20:20, \n",
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" 21:21, \n",
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" 22:22, \n",
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" 23:23, \n",
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" 24:24, \n",
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" 25:25, \n",
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" 26:26, \n",
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" 27:27, \n",
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" 28:28, \n",
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" 29:29, \n",
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" 30:30, \n",
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" 31:31, \n",
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" 35:32, \n",
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" 65281:33 \n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"#TODO: 加入cipher suites，extensions特征\n",
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"stream_stat_json_file = root_dir + 'DataSet/result/' + date + '/stream_stat.txt'\n",
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"stm2cipherDict = dict()\n",
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"stm2extenDict = dict()\n",
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"with open(stream_stat_json_file) as f:\n",
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" lines = f.readlines()\n",
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" for line in lines:\n",
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" line = json.loads(line)\n",
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" flow_key = (line['sip'], line['sport'], line['dip'], line['dport'])\n",
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" cipher_suites = line['tls']['cipher_suites']\n",
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" extension_list = line['tls']['extensions_list']\n",
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" stm2cipherDict[flow_key] = cipher_suites\n",
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" stm2extenDict[flow_key] = extension_list"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"example_label = {tuple(example_label_df.iloc[i,0:4].values):example_label_df.iloc[i,4] for i in example_label_df.index}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"end\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'\\nkeys = set(example_label.keys()).difference(set(result_key))\\nexample_keys = example_label_df.iloc[:,0:4].values.copy()\\nfor i,value in enumerate(list(example_keys)):\\n #print(tuple(value))\\n if tuple(value) in keys:\\n print(i)\\n'"
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]
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},
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"execution_count": 25,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"example_json_file = root_dir + 'DataSet/result/' + date + '/ssl_stat.txt'\n",
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"example_json_f = open(example_json_file, 'r')\n",
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"#array_shape = (1771,6)\n",
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"result_data = list()\n",
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"result_label = list()\n",
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"result_key = list()\n",
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"i = 0\n",
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"for line in example_json_f.readlines():\n",
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" example_json = ''\n",
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" try:\n",
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" example_json = json.loads(line)\n",
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" except Exception:\n",
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" continue\n",
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" #标签\n",
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" try:\n",
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" flow_key = (example_json['sip'], example_json['sport'], example_json['dip'], example_json['dport'])\n",
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" result_label.append(example_label[flow_key])\n",
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" result_key.append(flow_key)\n",
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" ciphers = stm2cipherDict[flow_key]\n",
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" extensions_list = stm2extenDict[flow_key]\n",
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" except Exception:\n",
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" continue\n",
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" #print(example_json)\n",
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" san_count = 0\n",
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" cert_length = [0,0,0,0]\n",
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" if 'san' in example_json:\n",
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" san_count = len(example_json['san'].split(';'))\n",
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" cert = example_json['Cert']\n",
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" cert_count = cert['cert_count']\n",
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" if cert_count != 0:\n",
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" cert_length = [c['length'] for c in cert['cert_list']]\n",
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" for i in range(4 - len(cert_length)):\n",
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" cert_length.append(0)\n",
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" result = [san_count, cert_count]\n",
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" result += cert_length\n",
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" #print(len(result))\n",
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" \n",
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" #tls\n",
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" extensions_arr = np.zeros(34, dtype=np.uint8)\n",
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" cipher_suits_arr = np.zeros(123, dtype=np.uint8)\n",
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" for extension in extensions_list:\n",
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" try:\n",
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" extensions_arr[extensions[extension]]=1\n",
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" except Exception:\n",
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" pass\n",
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" for cipher in ciphers:\n",
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" try:\n",
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" cipher = cipher.upper()\n",
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" cipher_suits_arr[ciper_suits[cipher]]=1\n",
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" except Exception:\n",
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" pass\n",
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" result += list(cipher_suits_arr)\n",
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" result += list(extensions_arr)\n",
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" result_data.append(result)\n",
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" i += 1\n",
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" \n",
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"extensions_head = list()\n",
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"for i in range(len(extensions)):\n",
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" extensions_head.append('extension'+str(i))\n",
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"cipher_head = ['cipher'+str(i) for i in range(len(ciper_suits))]\n",
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"base_head = ['san_count', 'cert_count', 'cert_length1', 'cert_length2', 'cert_length3','cert_length4']\n",
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"header = base_head+cipher_head+extensions_head\n",
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"result_df = pd.DataFrame(result_data, columns=header)\n",
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"result_df['label'] = np.array(result_label)\n",
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"\n",
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"print('end')\n",
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"'''\n",
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"keys = set(example_label.keys()).difference(set(result_key))\n",
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"example_keys = example_label_df.iloc[:,0:4].values.copy()\n",
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"for i,value in enumerate(list(example_keys)):\n",
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" #print(tuple(value))\n",
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" if tuple(value) in keys:\n",
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" print(i)\n",
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"'''"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import os\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"164\n",
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" precision recall f1\n",
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"LogisticRegression 0.886296 0.886296 0.886296\n",
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"SVM 0.897751 0.897751 0.897751\n",
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"GaussianNB 0.694103 0.694103 0.694103\n",
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"tree 0.911328 0.911328 0.911328\n",
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"RandomForest 0.905388 0.905388 0.905388\n"
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]
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},
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{
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"data": {
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"image/png": 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mLVFOuHcHXivZrk/3ldoV2FXSk5KmSDqiUgWamVnLNdstA6iJfdHE8/QBDgV6AI9L2iMi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\nh7uZWQ453M3Mcuj/Azvm9WexyMnIAAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x1110cfe80>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.linear_model import LogisticRegression\n",
|
||
"from sklearn.svm import SVC\n",
|
||
"from sklearn.naive_bayes import GaussianNB\n",
|
||
"from sklearn import tree\n",
|
||
"from sklearn.ensemble import RandomForestClassifier\n",
|
||
"from sklearn.metrics import f1_score,recall_score,precision_score\n",
|
||
"import random\n",
|
||
"examples = result_df.values.copy()\n",
|
||
"print(len(examples[0]))\n",
|
||
"score_df = pd.DataFrame(np.zeros((5,3)),index = ['LogisticRegression', 'SVM', 'GaussianNB', 'tree', 'RandomForest'], \\\n",
|
||
" columns = ['precision', 'recall', 'f1'])\n",
|
||
"#def a():\n",
|
||
"f1_score_list = list()\n",
|
||
"recall_score_list = list()\n",
|
||
"precision_score_list = list()\n",
|
||
"for i in range(1):\n",
|
||
" np.random.shuffle(examples)\n",
|
||
" examples_train = examples[:int(len(examples)*0.75)]\n",
|
||
" examples_test = examples[int(len(examples)*0.75):]\n",
|
||
" x_train = examples_train[:,0:-1]\n",
|
||
" y_train = examples_train[:,-1]\n",
|
||
" x_test = examples_test[:,0:-1]\n",
|
||
" y_test = examples_test[:,-1]\n",
|
||
" classifer = LogisticRegression()\n",
|
||
" classifer.fit(x_train, y_train)\n",
|
||
" y_pred = classifer.predict(x_test)\n",
|
||
" f1_score_list.append(f1_score(y_test, y_pred, average='micro'))\n",
|
||
" recall_score_list.append(recall_score(y_test, y_pred, average='micro'))\n",
|
||
" precision_score_list.append(precision_score(y_test, y_pred, average='micro'))\n",
|
||
"scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n",
|
||
"score_df.loc['LogisticRegression'] = scores\n",
|
||
"\n",
|
||
"f1_score_list = list()\n",
|
||
"recall_score_list = list()\n",
|
||
"precision_score_list = list()\n",
|
||
"for i in range(1):\n",
|
||
" #np.random.shuffle(examples)\n",
|
||
" examples_train = examples[:int(len(examples)*0.75)]\n",
|
||
" examples_test = examples[int(len(examples)*0.75):]\n",
|
||
" x_train = examples_train[:,0:-1]\n",
|
||
" y_train = examples_train[:,-1]\n",
|
||
" x_test = examples_test[:,0:-1]\n",
|
||
" y_test = examples_test[:,-1]\n",
|
||
" classifer = SVC()\n",
|
||
" classifer.fit(x_train, y_train)\n",
|
||
" y_pred = classifer.predict(x_test)\n",
|
||
" f1_score_list.append(f1_score(y_test, y_pred, average='micro'))\n",
|
||
" recall_score_list.append(recall_score(y_test, y_pred, average='micro'))\n",
|
||
" precision_score_list.append(precision_score(y_test, y_pred, average='micro'))\n",
|
||
"scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n",
|
||
"score_df.loc['SVM'] = scores\n",
|
||
"\n",
|
||
"f1_score_list = list()\n",
|
||
"recall_score_list = list()\n",
|
||
"precision_score_list = list()\n",
|
||
"for i in range(1):\n",
|
||
" #np.random.shuffle(examples)\n",
|
||
" examples_train = examples[:int(len(examples)*0.75)]\n",
|
||
" examples_test = examples[int(len(examples)*0.75):]\n",
|
||
" x_train = examples_train[:,0:-1]\n",
|
||
" y_train = examples_train[:,-1]\n",
|
||
" x_test = examples_test[:,0:-1]\n",
|
||
" y_test = examples_test[:,-1]\n",
|
||
" classifer = GaussianNB()\n",
|
||
" classifer.fit(x_train, y_train)\n",
|
||
" y_pred = classifer.predict(x_test)\n",
|
||
" f1_score_list.append(f1_score(y_test, y_pred, average='micro'))\n",
|
||
" recall_score_list.append(recall_score(y_test, y_pred, average='micro'))\n",
|
||
" precision_score_list.append(precision_score(y_test, y_pred, average='micro'))\n",
|
||
"scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n",
|
||
"score_df.loc['GaussianNB'] = scores\n",
|
||
"\n",
|
||
"f1_score_list = list()\n",
|
||
"recall_score_list = list()\n",
|
||
"precision_score_list = list()\n",
|
||
"for i in range(1):\n",
|
||
" #np.random.shuffle(examples)\n",
|
||
" examples_train = examples[:int(len(examples)*0.75)]\n",
|
||
" examples_test = examples[int(len(examples)*0.75):]\n",
|
||
" x_train = examples_train[:,0:-1]\n",
|
||
" y_train = examples_train[:,-1]\n",
|
||
" x_test = examples_test[:,0:-1]\n",
|
||
" y_test = examples_test[:,-1]\n",
|
||
" classifer = tree.DecisionTreeClassifier()\n",
|
||
" classifer.fit(x_train, y_train)\n",
|
||
" y_pred = classifer.predict(x_test)\n",
|
||
" f1_score_list.append(f1_score(y_test, y_pred, average='micro'))\n",
|
||
" recall_score_list.append(recall_score(y_test, y_pred, average='micro'))\n",
|
||
" precision_score_list.append(precision_score(y_test, y_pred, average='micro'))\n",
|
||
"scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n",
|
||
"score_df.loc['tree'] = scores\n",
|
||
"\n",
|
||
"f1_score_list = list()\n",
|
||
"recall_score_list = list()\n",
|
||
"precision_score_list = list()\n",
|
||
"for i in range(1):\n",
|
||
" #np.random.shuffle(examples)\n",
|
||
" examples_train = examples[:int(len(examples)*0.75)]\n",
|
||
" examples_test = examples[int(len(examples)*0.75):]\n",
|
||
" x_train = examples_train[:,0:-1]\n",
|
||
" y_train = examples_train[:,-1]\n",
|
||
" x_test = examples_test[:,0:-1]\n",
|
||
" y_test = examples_test[:,-1]\n",
|
||
" classifer = RandomForestClassifier()\n",
|
||
" classifer.fit(x_train, y_train)\n",
|
||
" y_pred = classifer.predict(x_test)\n",
|
||
" f1_score_list.append(f1_score(y_test, y_pred, average='micro'))\n",
|
||
" recall_score_list.append(recall_score(y_test, y_pred, average='micro'))\n",
|
||
" precision_score_list.append(precision_score(y_test, y_pred, average='micro'))\n",
|
||
" \n",
|
||
"scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n",
|
||
"score_df.loc['RandomForest'] = scores\n",
|
||
"print(score_df)\n",
|
||
"ax = score_df.plot.bar(title='ssl-fingerprint')\n",
|
||
"fig = ax.get_figure()\n",
|
||
"#fig.savefig('../figure/ssl.svg')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|