543 lines
29 KiB
Plaintext
543 lines
29 KiB
Plaintext
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{
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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'00A3':8,\n",
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"\t'00A1':9,\n",
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"\t'009F':10,\n",
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"\t'006B':11,\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'C032':28,\n",
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"\t'C026':31,\n",
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"\t'C00F':32,\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'009E':48,\n",
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"\t'0067':49,\n",
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"\t'0040':50,\n",
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"\t'003F':51,\n",
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"\t'003E':52,\n",
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"\t'0033':53,\n",
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"\t'0032':54,\n",
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"\t'0031':55,\n",
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"\t'0030':56,\n",
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"\t'009A':57,\n",
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"\t'0099':58,\n",
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"\t'0098':59,\n",
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"\t'0097':60,\n",
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"\t'0045':61,\n",
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"\t'0044':62,\n",
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"\t'0043':63,\n",
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"\t'0042':64,\n",
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"\t'C018':65,\n",
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"\t'00A6':66,\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'003C':78,\n",
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"\t'002F':79,\n",
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"\t'0096':80,\n",
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"\t'0041':81,\n",
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"\t'008C':82,\n",
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"\t'C012':83,\n",
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"\t'C008':84,\n",
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"\t'0016':85,\n",
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"\t'0013':86,\n",
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"\t'0010':87,\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",
|
|||
|
|
"for i in range(len(extensions)):\n",
|
|||
|
|
" extensions_head.append('extension'+str(i))\n",
|
|||
|
|
"cipher_head = ['cipher'+str(i) for i in range(len(ciper_suits))]\n",
|
|||
|
|
"base_head = ['san_count', 'cert_count', 'cert_length1', 'cert_length2', 'cert_length3','cert_length4']\n",
|
|||
|
|
"header = base_head+cipher_head+extensions_head\n",
|
|||
|
|
"result_df = pd.DataFrame(result_data, columns=header)\n",
|
|||
|
|
"result_df['label'] = np.array(result_label)\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('end')\n",
|
|||
|
|
"'''\n",
|
|||
|
|
"keys = set(example_label.keys()).difference(set(result_key))\n",
|
|||
|
|
"example_keys = example_label_df.iloc[:,0:4].values.copy()\n",
|
|||
|
|
"for i,value in enumerate(list(example_keys)):\n",
|
|||
|
|
" #print(tuple(value))\n",
|
|||
|
|
" if tuple(value) in keys:\n",
|
|||
|
|
" print(i)\n",
|
|||
|
|
"'''"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 30,
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": true
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"%matplotlib inline\n",
|
|||
|
|
"import os\n",
|
|||
|
|
"import numpy as np\n",
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"import matplotlib.pyplot as plt"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 35,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"164\n",
|
|||
|
|
" precision recall f1\n",
|
|||
|
|
"LogisticRegression 0.886296 0.886296 0.886296\n",
|
|||
|
|
"SVM 0.897751 0.897751 0.897751\n",
|
|||
|
|
"GaussianNB 0.694103 0.694103 0.694103\n",
|
|||
|
|
"tree 0.911328 0.911328 0.911328\n",
|
|||
|
|
"RandomForest 0.905388 0.905388 0.905388\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAFcCAYAAAAzq/4LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nztRLjQKujogewFHAdZL
|
|||
|
|
"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
|
|||
|
|
}
|