2019-12-23 01:20:51 +08:00
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{
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"cells": [
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{
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"cell_type": "code",
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2020-01-07 17:29:25 +08:00
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"execution_count": 31,
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2019-12-23 01:20:51 +08:00
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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 json\n",
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"import pandas as pd\n",
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"import numpy as np\n"
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]
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},
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{
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"cell_type": "code",
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2020-01-07 17:29:25 +08:00
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"execution_count": 32,
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2019-12-23 01:20:51 +08:00
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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'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'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'C027':41,\n",
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"\t'C023':42,\n",
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"\t'C013':43,\n",
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"\t'C009':44,\n",
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"\t'00A4':45,\n",
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"\t'00A2':46,\n",
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"\t'00A0':47,\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'000D':88,\n",
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"\t'C017':89,\n",
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"\t'001B':90,\n",
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"\t'C00D':91,\n",
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"\t'C003':92,\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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2020-01-07 17:29:25 +08:00
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"execution_count": 33,
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2019-12-23 01:20:51 +08:00
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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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2020-01-07 17:29:25 +08:00
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"execution_count": 36,
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2019-12-23 01:20:51 +08:00
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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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"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 = {tuple(example_label_df.iloc[i,0:4].values):example_label_df.iloc[i,4] for i in example_label_df.index}\n",
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"example_json_file = root_dir + 'DataSet/result/' + date + '/stream_stat.txt'\n",
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"example_json_f = open(example_json_file, 'r')\n",
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"result_data = list()\n",
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"result_label = 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 = json.loads(line)\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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" except Exception:\n",
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" continue\n",
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" \n",
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" #统计特征\n",
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" packets = example_json['packets']\n",
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" c2s_packets_bytes = list()\n",
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" s2c_packets_bytes = list()\n",
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" c2s_packets_intervals = list()\n",
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" s2c_packets_intervals = list()\n",
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" for packet in packets:\n",
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" if packet['dir'] == 1:\n",
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" c2s_packets_bytes.append(packet['bytes'])\n",
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" c2s_packets_intervals.append(packet['interval'])\n",
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" elif packet['dir'] == 2:\n",
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" s2c_packets_bytes.append(packet['bytes'])\n",
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" s2c_packets_intervals.append(packet['interval'])\n",
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" c2s_bytes = example_json['c2s_bytes']\n",
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" s2c_bytes = example_json['s2c_bytes']\n",
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" c2s_pkts = example_json['c2s_pkts']\n",
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" s2c_pkts = example_json['s2c_pkts']\n",
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" duration = example_json['duration']\n",
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" c2s_packets_bytes_mean = 0\n",
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" c2s_packets_bytes_median = 0\n",
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" c2s_packets_bytes_std = 0\n",
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" c2s_packets_bytes_max = 0\n",
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" c2s_packets_bytes_min = 0\n",
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"\n",
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" c2s_packets_intervals_mean = 0\n",
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" c2s_packets_intervals_median = 0\n",
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" c2s_packets_intervals_std = 0\n",
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" c2s_packets_intervals_max = 0\n",
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" c2s_packets_intervals_min = 0\n",
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"\n",
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" s2c_packets_bytes_mean = 0\n",
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" s2c_packets_bytes_median = 0\n",
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" s2c_packets_bytes_std = 0\n",
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" s2c_packets_bytes_max = 0\n",
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" s2c_packets_bytes_min = 0\n",
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"\n",
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" s2c_packets_intervals_mean = 0\n",
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" s2c_packets_intervals_median = 0\n",
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" s2c_packets_intervals_std = 0\n",
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" s2c_packets_intervals_max = 0\n",
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" s2c_packets_intervals_min = 0\n",
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" \n",
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" if c2s_bytes > 0:\n",
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" c2s_packets_bytes_mean = np.mean(c2s_packets_bytes)\n",
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" c2s_packets_bytes_median = np.median(c2s_packets_bytes)\n",
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" c2s_packets_bytes_std = np.std(c2s_packets_bytes)\n",
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" c2s_packets_bytes_max = np.max(c2s_packets_bytes)\n",
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" c2s_packets_bytes_min = np.min(c2s_packets_bytes)\n",
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"\n",
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" c2s_packets_intervals_mean = np.mean(c2s_packets_intervals)\n",
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" c2s_packets_intervals_median = np.median(c2s_packets_intervals)\n",
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" c2s_packets_intervals_std = np.std(c2s_packets_intervals)\n",
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" c2s_packets_intervals_max = np.max(c2s_packets_intervals)\n",
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" c2s_packets_intervals_min = np.min(c2s_packets_intervals)\n",
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" \n",
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" if s2c_bytes > 0:\n",
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" s2c_packets_bytes_mean = np.mean(s2c_packets_bytes)\n",
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" s2c_packets_bytes_median = np.median(s2c_packets_bytes)\n",
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" s2c_packets_bytes_std = np.std(s2c_packets_bytes)\n",
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" s2c_packets_bytes_max = np.max(s2c_packets_bytes)\n",
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" s2c_packets_bytes_min = np.min(s2c_packets_bytes)\n",
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"\n",
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" s2c_packets_intervals_mean = np.mean(s2c_packets_intervals)\n",
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" s2c_packets_intervals_median = np.median(s2c_packets_intervals)\n",
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" s2c_packets_intervals_std = np.std(s2c_packets_intervals)\n",
|
|
|
|
|
" s2c_packets_intervals_max = np.max(s2c_packets_intervals)\n",
|
|
|
|
|
" s2c_packets_intervals_min = np.min(s2c_packets_intervals)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
" #tls\n",
|
|
|
|
|
" tls = example_json['tls']\n",
|
|
|
|
|
" extensions_list = tls['extensions_list']\n",
|
|
|
|
|
" #print(extensions_list)\n",
|
|
|
|
|
" ciphers = tls['cipher_suites']\n",
|
|
|
|
|
" #print(ciphers)\n",
|
|
|
|
|
" extensions_arr = np.zeros(34, dtype=np.uint8)\n",
|
|
|
|
|
" cipher_suits_arr = np.zeros(123, dtype=np.uint8)\n",
|
|
|
|
|
" for extension in extensions_list:\n",
|
|
|
|
|
" try:\n",
|
|
|
|
|
" extensions_arr[extensions[extension]]=1\n",
|
|
|
|
|
" except Exception:\n",
|
|
|
|
|
" pass\n",
|
|
|
|
|
" for cipher in ciphers:\n",
|
|
|
|
|
" try:\n",
|
|
|
|
|
" cipher = cipher.upper()\n",
|
|
|
|
|
" cipher_suits_arr[ciper_suits[cipher]]=1\n",
|
|
|
|
|
" except Exception:\n",
|
|
|
|
|
" pass\n",
|
|
|
|
|
" result = [c2s_bytes, c2s_pkts, s2c_bytes, s2c_pkts, duration, c2s_packets_bytes_mean, c2s_packets_bytes_median, c2s_packets_bytes_std,\\\n",
|
|
|
|
|
" c2s_packets_bytes_max, c2s_packets_bytes_min, c2s_packets_intervals_mean, c2s_packets_intervals_median, c2s_packets_intervals_std,\\\n",
|
|
|
|
|
" c2s_packets_intervals_max, c2s_packets_intervals_min, s2c_packets_bytes_mean, s2c_packets_bytes_median, s2c_packets_bytes_std,\\\n",
|
|
|
|
|
" s2c_packets_bytes_max, s2c_packets_bytes_min, s2c_packets_intervals_mean, s2c_packets_intervals_median, s2c_packets_intervals_std,\\\n",
|
|
|
|
|
" s2c_packets_intervals_max, s2c_packets_intervals_min]\n",
|
|
|
|
|
" result += list(cipher_suits_arr)\n",
|
|
|
|
|
" result += list(extensions_arr)\n",
|
|
|
|
|
" result_data.append(result)\n",
|
|
|
|
|
" i += 1\n",
|
|
|
|
|
"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 = ['c2s_bytes', 'c2s_pkts', 's2c_bytes', 's2c_pkts', 'duration', 'c2s_packets_bytes_mean', 'c2s_packets_bytes_median', 'c2s_packets_bytes_std',\\\n",
|
|
|
|
|
" 'c2s_packets_bytes_max', 'c2s_packets_bytes_min', 'c2s_packets_intervals_mean', 'c2s_packets_intervals_median', 'c2s_packets_intervals_std',\\\n",
|
|
|
|
|
" 'c2s_packets_intervals_max', 'c2s_packets_intervals_min', 's2c_packets_bytes_mean', 's2c_packets_bytes_median', 's2c_packets_bytes_std',\\\n",
|
|
|
|
|
" 's2c_packets_bytes_max', 's2c_packets_bytes_min', 's2c_packets_intervals_mean', 's2c_packets_intervals_median', 's2c_packets_intervals_std',\\\n",
|
|
|
|
|
" 's2c_packets_intervals_max', 's2c_packets_intervals_min']\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",
|
|
|
|
|
"example_csv_file = root_dir + 'Experiment/StatFeature/CsvFile/' + date + '/examples.csv'\n",
|
|
|
|
|
"result_df.to_csv(example_csv_file, index=False)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2020-01-07 17:29:25 +08:00
|
|
|
"execution_count": 37,
|
2019-12-23 01:20:51 +08:00
|
|
|
"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",
|
2020-01-07 17:29:25 +08:00
|
|
|
"execution_count": 38,
|
2019-12-23 01:20:51 +08:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"hupu: 489846\n",
|
|
|
|
|
"weibo: 897897\n",
|
|
|
|
|
"douyin: 158497\n",
|
|
|
|
|
"toutiao: 213989\n",
|
|
|
|
|
"zhihu: 968036\n"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"# 统计每个app的包数\n",
|
|
|
|
|
"date = '2019-12-20_21'\n",
|
|
|
|
|
"root_dir = '/Users/Leo/Documents/github/GradProj/'\n",
|
|
|
|
|
"exmaples_file = root_dir + 'Experiment/StatFeature/CsvFile/' + date + '/examples.csv'\n",
|
|
|
|
|
"app2pktsDict = dict()\n",
|
|
|
|
|
"with open(exmaples_file) as f:\n",
|
|
|
|
|
" lines = f.readlines()\n",
|
|
|
|
|
" i = 0\n",
|
|
|
|
|
" for line in lines:\n",
|
|
|
|
|
" if i == 0:\n",
|
|
|
|
|
" i += 1\n",
|
|
|
|
|
" continue;\n",
|
|
|
|
|
" line = line.split(',')\n",
|
|
|
|
|
" pkts = int(line[1]) + int(line[3])\n",
|
|
|
|
|
" appName = line[-1]\n",
|
|
|
|
|
" if appName not in app2pktsDict.keys():\n",
|
|
|
|
|
" app2pktsDict[appName] = 0\n",
|
|
|
|
|
" app2pktsDict[appName] += pkts \n",
|
|
|
|
|
"for appName, pkts in app2pktsDict.items():\n",
|
|
|
|
|
" appName = appName[:-1]\n",
|
|
|
|
|
" print(appName + ': ', pkts)\n",
|
|
|
|
|
" "
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2020-01-07 17:29:25 +08:00
|
|
|
"execution_count": 39,
|
2019-12-23 01:20:51 +08:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"zhihu 6403\n",
|
|
|
|
|
"weibo 5487\n",
|
|
|
|
|
"douyin 3964\n",
|
|
|
|
|
"hupu 2304\n",
|
|
|
|
|
"toutiao 520\n",
|
|
|
|
|
"Name: label, dtype: int64"
|
|
|
|
|
]
|
|
|
|
|
},
|
2020-01-07 17:29:25 +08:00
|
|
|
"execution_count": 39,
|
2019-12-23 01:20:51 +08:00
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEVCAYAAADpbDJPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkCTN25wBUFVXMfv4PcD
|
|
|
|
|
"text/plain": [
|
2020-01-07 17:29:25 +08:00
|
|
|
"<matplotlib.figure.Figure at 0x113f03390>"
|
2019-12-23 01:20:51 +08:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"examples_df = pd.read_csv(exmaples_file)\n",
|
|
|
|
|
"class_counts = examples_df['label'].value_counts()\n",
|
|
|
|
|
"class_counts.plot.bar()\n",
|
|
|
|
|
"class_counts"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2020-01-07 17:29:25 +08:00
|
|
|
"execution_count": 40,
|
2019-12-23 01:20:51 +08:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"183\n",
|
|
|
|
|
" precision recall f1\n",
|
2020-01-07 17:29:25 +08:00
|
|
|
"LogisticRegression 0.775161 0.775161 0.775161\n",
|
|
|
|
|
"SVM 0.831906 0.831906 0.831906\n",
|
|
|
|
|
"GaussianNB 0.729122 0.729122 0.729122\n",
|
|
|
|
|
"tree 0.984582 0.984582 0.984582\n",
|
|
|
|
|
"RandomForest 0.989507 0.989507 0.989507\n"
|
2019-12-23 01:20:51 +08:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2020-01-07 17:29:25 +08:00
|
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAFcCAYAAAAzq/4LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFqnXrDncLGOFN3jwYJ5
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2019-12-23 01:20:51 +08:00
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"text/plain": [
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2020-01-07 17:29:25 +08:00
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"<matplotlib.figure.Figure at 0x10e68fa58>"
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2019-12-23 01:20:51 +08:00
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.svm import SVC\n",
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"from sklearn.naive_bayes import GaussianNB\n",
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"from sklearn import tree\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.metrics import f1_score,recall_score,precision_score\n",
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"import random\n",
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"examples = examples_df.values.copy()\n",
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"print(len(examples[0]))\n",
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"#只取25个流统计特征\n",
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"examples = np.c_[examples[:,:25].copy(),examples[:,-1].copy()]\n",
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"#print(examples)\n",
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"score_df = pd.DataFrame(np.zeros((5,3)),index = ['LogisticRegression', 'SVM', 'GaussianNB', 'tree', 'RandomForest'], \\\n",
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" columns = ['precision', 'recall', 'f1'])\n",
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"#def a():\n",
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"\n",
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2020-01-07 17:29:25 +08:00
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"\n",
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2019-12-23 01:20:51 +08:00
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"f1_score_list = list()\n",
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"recall_score_list = list()\n",
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"precision_score_list = list()\n",
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"for i in range(1):\n",
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" np.random.shuffle(examples)\n",
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" examples_train = examples[:int(len(examples)*0.75)]\n",
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" examples_test = examples[int(len(examples)*0.75):]\n",
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" x_train = examples_train[:,0:-1]\n",
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" y_train = examples_train[:,-1]\n",
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" x_test = examples_test[:,0:-1]\n",
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" y_test = examples_test[:,-1]\n",
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" classifer = LogisticRegression()\n",
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" classifer.fit(x_train, y_train)\n",
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" y_pred = classifer.predict(x_test)\n",
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" f1_score_list.append(f1_score(y_test, y_pred, average='micro'))\n",
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" recall_score_list.append(recall_score(y_test, y_pred, average='micro'))\n",
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" precision_score_list.append(precision_score(y_test, y_pred, average='micro'))\n",
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"scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n",
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"score_df.loc['LogisticRegression'] = scores\n",
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"\n",
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"f1_score_list = list()\n",
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"recall_score_list = list()\n",
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"precision_score_list = list()\n",
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"for i in range(1):\n",
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2020-01-07 17:29:25 +08:00
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" np.random.shuffle(examples)\n",
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2019-12-23 01:20:51 +08:00
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|
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" examples_train = examples[:int(len(examples)*0.75)]\n",
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" examples_test = examples[int(len(examples)*0.75):]\n",
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" x_train = examples_train[:,0:-1]\n",
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" y_train = examples_train[:,-1]\n",
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" x_test = examples_test[:,0:-1]\n",
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" y_test = examples_test[:,-1]\n",
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" classifer = SVC()\n",
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" classifer.fit(x_train, y_train)\n",
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|
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" y_pred = classifer.predict(x_test)\n",
|
|
|
|
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" f1_score_list.append(f1_score(y_test, y_pred, average='micro'))\n",
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|
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" recall_score_list.append(recall_score(y_test, y_pred, average='micro'))\n",
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" precision_score_list.append(precision_score(y_test, y_pred, average='micro'))\n",
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"scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n",
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"score_df.loc['SVM'] = scores\n",
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"\n",
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"f1_score_list = list()\n",
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"recall_score_list = list()\n",
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"precision_score_list = list()\n",
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|
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"for i in range(1):\n",
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|
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" #np.random.shuffle(examples)\n",
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|
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" examples_train = examples[:int(len(examples)*0.75)]\n",
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" examples_test = examples[int(len(examples)*0.75):]\n",
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" x_train = examples_train[:,0:-1]\n",
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" y_train = examples_train[:,-1]\n",
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" x_test = examples_test[:,0:-1]\n",
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" y_test = examples_test[:,-1]\n",
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|
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" classifer = GaussianNB()\n",
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" classifer.fit(x_train, y_train)\n",
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|
|
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" y_pred = classifer.predict(x_test)\n",
|
|
|
|
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" f1_score_list.append(f1_score(y_test, y_pred, average='micro'))\n",
|
|
|
|
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" recall_score_list.append(recall_score(y_test, y_pred, average='micro'))\n",
|
|
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|
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" precision_score_list.append(precision_score(y_test, y_pred, average='micro'))\n",
|
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|
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"scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n",
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"score_df.loc['GaussianNB'] = scores\n",
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"\n",
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|
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"f1_score_list = list()\n",
|
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|
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"recall_score_list = list()\n",
|
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|
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"precision_score_list = list()\n",
|
|
|
|
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"for i in range(1):\n",
|
|
|
|
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" #np.random.shuffle(examples)\n",
|
|
|
|
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" examples_train = examples[:int(len(examples)*0.75)]\n",
|
|
|
|
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" examples_test = examples[int(len(examples)*0.75):]\n",
|
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|
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" x_train = examples_train[:,0:-1]\n",
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|
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" y_train = examples_train[:,-1]\n",
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" x_test = examples_test[:,0:-1]\n",
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|
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" y_test = examples_test[:,-1]\n",
|
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|
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" classifer = tree.DecisionTreeClassifier()\n",
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|
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" classifer.fit(x_train, y_train)\n",
|
|
|
|
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" y_pred = classifer.predict(x_test)\n",
|
|
|
|
|
" f1_score_list.append(f1_score(y_test, y_pred, average='micro'))\n",
|
|
|
|
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" recall_score_list.append(recall_score(y_test, y_pred, average='micro'))\n",
|
|
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|
|
" precision_score_list.append(precision_score(y_test, y_pred, average='micro'))\n",
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|
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"scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n",
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"score_df.loc['tree'] = scores\n",
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"\n",
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|
|
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"f1_score_list = list()\n",
|
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"recall_score_list = list()\n",
|
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|
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"precision_score_list = list()\n",
|
|
|
|
|
"for i in range(1):\n",
|
2020-01-07 17:29:25 +08:00
|
|
|
" np.random.shuffle(examples)\n",
|
2019-12-23 01:20:51 +08:00
|
|
|
" 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",
|
|
|
|
|
"scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n",
|
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|
|
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"score_df.loc['RandomForest'] = scores\n",
|
|
|
|
|
"print(score_df)\n",
|
|
|
|
|
"ax = score_df.plot.bar(title='statistics_feature')\n",
|
|
|
|
|
"fig = ax.get_figure()\n",
|
|
|
|
|
"#fig.savefig('../figure/base_feature.svg')\n",
|
|
|
|
|
"#print(score_df)"
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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": null,
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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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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.2"
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}
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},
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"nbformat": 4,
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|
|
"nbformat_minor": 2
|
|
|
|
|
}
|
