2020-2-capstone-design2 / 2017110275

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이한솔
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2진수로 변경

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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import csv\n",
"import numpy as np\n",
"import os\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 10진수 변환 (한번만)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def transferData(dir_, outputdir_) :\n",
" outputfile = open(outputdir_, 'w', newline='\\n')\n",
" fopen = open(dir_, 'r')\n",
" csvreader = csv.reader(fopen)\n",
" \n",
" fw = csv.writer(outputfile)\n",
" \n",
" for line in csvreader :\n",
" newdata = []\n",
" can_id = int(line[1], 16)\n",
" newdata.append(can_id)\n",
" newdata.append(line[2])\n",
" \n",
" ll = len(line)\n",
" num = int(line[2])\n",
" \n",
" data = []\n",
" if num == 8 :\n",
" data = line[3:11]\n",
" else :\n",
" data = line[3:3+num]\n",
" for _ in range(8-num):\n",
" data.append('0')\n",
" #print(line)\n",
" newdata.extend(list(map(lambda x:int(x, 16), data)))\n",
" \n",
" if line[-1] == 'T' :\n",
" newdata.append(0)\n",
" else :\n",
" newdata.append(1)\n",
" \n",
" fw.writerow(newdata)\n",
"\n",
"def Preprocess(dir_) :\n",
" f1 = open(dir_, 'r')\n",
" fr = csv.reader(f1)\n",
" d = [] \n",
" for line in fr :\n",
" l = []\n",
" l.append(line[0])\n",
" l.append(line[1])\n",
" l.extend(line[2:11])\n",
" \n",
" d.append(l)\n",
" return d"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#transferData('DoS_dataset.csv', 'DoS.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2진수 변환 후 저장 (한번만)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"f = open('DoS.csv', 'r')\n",
"ff = open('DoSres.csv', 'w', newline='\\n')\n",
"cr = csv.reader(f)\n",
"fw = csv.writer(ff)\n",
"\n",
"a = []\n",
"for line in cr:\n",
" #a = []\n",
" CANID = []\n",
" b = []\n",
" canid = bin(int(line[0]))\n",
" #remove 'b0'\n",
" CANID.extend(canid[2:])\n",
" #padding Max len : 11\n",
" n = len(canid[2:])\n",
" if n < 11 :\n",
" padding = ['0' for _ in range(11-n)]\n",
" b.extend(padding)\n",
" b.extend(CANID)\n",
" \n",
" # Data 0~7\n",
" DATA = []\n",
" for i in range(2, 10):\n",
" data = bin(int(line[i]))\n",
" #remove 'b0'\n",
" DATA.extend(data[2:])\n",
" #padding Max len : 8\n",
" n = len(data[2:])\n",
" if n < 8 :\n",
" padding = ['0' for _ in range(8-n)]\n",
" b.extend(padding)\n",
" b.extend(DATA)\n",
" \n",
" # Class \n",
" b.extend(line[-1])\n",
" a.append(b)\n",
" \n",
" #fw.writerow(a)\n",
"\n",
"ff.close()\n",
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"76 ['0', '1', '1', '0', '0', '0', '1', '0', '1', '1', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0', '1', '1', '0', '0', '0', '0', '1', '1', '1', '0', '1', '0', '0', '0', '1', '0', '0', '1', '1', '0', '0', '0', '0', '1', '1', '0', '0', '0', '0', '1', '0', '1', '1', '0', '1', '1', '1', '1', '1']\n",
"76 ['0', '0', '1', '1', '0', '0', '0', '1', '1', '1', '1', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', '1', '1', '0', '0', '0', '1', '1', '1', '1', '0', '0', '0', '0', '1']\n",
"3665771\n"
]
}
],
"source": [
"print(len(a[0]),a[0])\n",
"print(len(a[1]), a[1])\n",
"print(len(a))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 76*76 배열 구성"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"def create2D(data) :\n",
" arr = []\n",
" for i in range(0, len(data)- 75, 76): \n",
" d = []\n",
" for x in range(0, 76) :\n",
" ll = list(map(float, data[i+x]))\n",
" d.append(ll)\n",
" arr.append(d)\n",
" \n",
" return arr"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"resData = create2D(a)\n",
"del a"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"48233\n"
]
}
],
"source": [
"#print(len(resData[0]), resData[0])\n",
"print(len(resData))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
"76\n",
"76\n"
]
}
],
"source": [
"print(resData[0][0])\n",
"print(len(resData[0][0]))\n",
"print(len(resData[0]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 비정상 메시지 포함 유무 체크"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def check(data):\n",
" check = []\n",
" \n",
" for e in data :\n",
" flag = False\n",
" for i in e :\n",
" if i[-1] == 0 :\n",
" check.append(0)\n",
" flag = True\n",
" break\n",
" if flag == False :\n",
" check.append(1)\n",
" \n",
" return check\n",
"\n",
"checkblock = check(resData)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]\n",
"48233\n"
]
}
],
"source": [
"print(checkblock[10010:10030])\n",
"print(len(checkblock))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 데이터 개수 계산"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"33760\n",
"14473\n"
]
}
],
"source": [
"cnt = 0\n",
"for e in checkblock :\n",
" if e == 1 :\n",
" cnt += 1\n",
" \n",
"print(cnt)\n",
"print(len(checkblock)-cnt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 테스트, 트레인 데이터 나누기"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"df = pd.DataFrame(resData)\n",
"n = len(df)\n",
"\n",
"X_train = df.iloc[0:n, 0:76].values.tolist()\n",
"Y_train = checkblock\n",
"\n",
"X_train = np.asarray(X_train)\n",
"Y_train = np.asarray(Y_train)\n",
"#X_train = tf.constant(X_train)\n",
"#print(X_train[-1])\n",
"#Y_train = tf.convert_to_tensor(Y_train)\n",
"\n",
"#Y_train = np.squeeze(Y_train, axis=1)\n",
"x_train, x_test, y_train, y_test = train_test_split(X_train, Y_train, test_size=0.3, random_state=321)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(48233, 76, 76)\n",
"(48233,)\n"
]
}
],
"source": [
"print(X_train.shape)\n",
"print(Y_train.shape)"
]
},
{
"cell_type": "code",
"execution_count": 241,
"metadata": {},
"outputs": [],
"source": [
"for i in x_train:\n",
" for j in i:\n",
" if len(j) != 11:\n",
" print(j)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"scrolled": true
},
"outputs": [
{
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" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
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" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
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" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
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" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
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" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
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" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
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" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48228</th>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48229</th>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48230</th>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48231</th>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48232</th>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>48233 rows × 76 columns</p>\n",
"</div>"
],
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"2 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
"3 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"4 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"... ... \n",
"48228 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48229 [1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48230 [0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48231 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"48232 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"\n",
" 3 \\\n",
"0 [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"1 [0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ... \n",
"2 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"3 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"4 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"... ... \n",
"48228 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"48229 [0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48230 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"48231 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"48232 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"\n",
" 4 \\\n",
"0 [0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ... \n",
"1 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
"2 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"3 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"4 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"... ... \n",
"48228 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48229 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"48230 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"48231 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48232 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"\n",
" 5 \\\n",
"0 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
"1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"2 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"3 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"4 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"... ... \n",
"48228 [1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48229 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"48230 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"48231 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48232 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"\n",
" 6 \\\n",
"0 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"1 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"2 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"3 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"4 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
"... ... \n",
"48228 [0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48229 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"48230 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48231 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"48232 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
"\n",
" 7 \\\n",
"0 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"1 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"2 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"3 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
"4 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"... ... \n",
"48228 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"48229 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48230 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48231 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
"48232 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ... \n",
"\n",
" 8 \\\n",
"0 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"1 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"2 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"3 [0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"4 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"... ... \n",
"48228 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"48229 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48230 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"48231 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ... \n",
"48232 [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"\n",
" 9 ... \\\n",
"0 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... ... \n",
"1 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... ... \n",
"2 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... ... \n",
"3 [0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... ... \n",
"4 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... ... \n",
"... ... ... \n",
"48228 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... ... \n",
"48229 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... ... \n",
"48230 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... ... \n",
"48231 [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ... ... \n",
"48232 [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... ... \n",
"\n",
" 66 \\\n",
"0 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"1 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"2 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"3 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"4 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
"... ... \n",
"48228 [1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48229 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
"48230 [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"48231 [0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ... \n",
"48232 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"\n",
" 67 \\\n",
"0 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"1 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"2 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
"3 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
"4 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ... \n",
"... ... \n",
"48228 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
"48229 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ... \n",
"48230 [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"48231 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
"48232 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"\n",
" 68 \\\n",
"0 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"1 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"2 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"3 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"4 [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"... ... \n",
"48228 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ... \n",
"48229 [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"48230 [0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ... \n",
"48231 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
"48232 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
"\n",
" 69 \\\n",
"0 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"1 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"2 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
"3 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
"4 [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"... ... \n",
"48228 [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"48229 [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"48230 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
"48231 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"48232 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"\n",
" 70 \\\n",
"0 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"1 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"2 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ... \n",
"3 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ... \n",
"4 [0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ... \n",
"... ... \n",
"48228 [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"48229 [0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ... \n",
"48230 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"48231 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48232 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"\n",
" 71 \\\n",
"0 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"1 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
"2 [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"3 [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"4 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
"... ... \n",
"48228 [0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ... \n",
"48229 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
"48230 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48231 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"48232 [1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"\n",
" 72 \\\n",
"0 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"1 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"2 [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"3 [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"4 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"... ... \n",
"48228 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
"48229 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
"48230 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
"48231 [1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48232 [0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"\n",
" 73 \\\n",
"0 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
"1 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
"2 [0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ... \n",
"3 [0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ... \n",
"4 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"... ... \n",
"48228 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
"48229 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"48230 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"48231 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48232 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"\n",
" 74 \\\n",
"0 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"1 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ... \n",
"2 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
"3 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
"4 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"... ... \n",
"48228 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"48229 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48230 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48231 [1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48232 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"\n",
" 75 \n",
"0 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
"1 [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"2 [1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"3 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"4 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"... ... \n",
"48228 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48229 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"48230 [1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48231 [0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"48232 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"\n",
"[48233 rows x 76 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(14470, 76, 76)\n"
]
}
],
"source": [
"print(x_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# Model\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, LSTM, Conv1D, Dropout, GlobalAveragePooling1D, MaxPooling1D\n",
"from keras.utils import np_utils\n",
"from keras.layers.normalization import BatchNormalization"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential_1\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv1d_1 (Conv1D) (None, 74, 64) 14656 \n",
"_________________________________________________________________\n",
"global_average_pooling1d_1 ( (None, 64) 0 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) (None, 64) 0 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 1) 65 \n",
"=================================================================\n",
"Total params: 14,721\n",
"Trainable params: 14,721\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model = Sequential()\n",
"model.add(Conv1D(64, 3, input_shape=(76, 76), activation='relu'))\n",
"#model.add(Conv1D(64, 3, activation='relu'))\n",
"#model.add(MaxPooling1D(2))\n",
"#model.add(Conv1D(128, 2, activation='relu'))\n",
"#model.add(Conv1D(128, 2, activation='relu'))\n",
"model.add(GlobalAveragePooling1D())\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(1, activation='sigmoid'))\n",
"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
"model.summary()\n",
"model.save('DoSCNN.h5')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"338/338 [==============================] - 3s 8ms/step - loss: 0.1279 - accuracy: 0.9501 - val_loss: 0.0247 - val_accuracy: 0.9948\n",
"Epoch 2/10\n",
"338/338 [==============================] - 2s 7ms/step - loss: 0.0276 - accuracy: 0.9941 - val_loss: 0.0199 - val_accuracy: 0.9950\n",
"Epoch 3/10\n",
"338/338 [==============================] - 2s 7ms/step - loss: 0.0217 - accuracy: 0.9952 - val_loss: 0.0175 - val_accuracy: 0.9953\n",
"Epoch 4/10\n",
"338/338 [==============================] - 2s 7ms/step - loss: 0.0183 - accuracy: 0.9959 - val_loss: 0.0164 - val_accuracy: 0.9957\n",
"Epoch 5/10\n",
"338/338 [==============================] - 2s 7ms/step - loss: 0.0174 - accuracy: 0.9963 - val_loss: 0.0163 - val_accuracy: 0.9960\n",
"Epoch 6/10\n",
"338/338 [==============================] - 2s 7ms/step - loss: 0.0157 - accuracy: 0.9967 - val_loss: 0.0136 - val_accuracy: 0.9966\n",
"Epoch 7/10\n",
"338/338 [==============================] - 2s 7ms/step - loss: 0.0142 - accuracy: 0.9971 - val_loss: 0.0126 - val_accuracy: 0.9970\n",
"Epoch 8/10\n",
"338/338 [==============================] - 2s 7ms/step - loss: 0.0135 - accuracy: 0.9972 - val_loss: 0.0115 - val_accuracy: 0.9973\n",
"Epoch 9/10\n",
"338/338 [==============================] - 2s 7ms/step - loss: 0.0118 - accuracy: 0.9975 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
"Epoch 10/10\n",
"338/338 [==============================] - 2s 7ms/step - loss: 0.0114 - accuracy: 0.9977 - val_loss: 0.0101 - val_accuracy: 0.9976\n"
]
}
],
"source": [
"history = model.fit(x_train, y_train, batch_size=100, epochs=10,validation_data=(x_test, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"453/453 [==============================] - 1s 1ms/step - loss: 0.0101 - accuracy: 0.9976\n",
"\n",
" Test accuracy : 0.9976\n"
]
}
],
"source": [
"print(\"\\n Test accuracy : %.4f\"%(model.evaluate(x_test, y_test)[1]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import csv\n",
"import numpy as np\n",
"import os\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 10진수 변환 (한번만)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def transferData(dir_, outputdir_) :\n",
" outputfile = open(outputdir_, 'w', newline='\\n')\n",
" fopen = open(dir_, 'r')\n",
" csvreader = csv.reader(fopen)\n",
" \n",
" fw = csv.writer(outputfile)\n",
" \n",
" for line in csvreader :\n",
" newdata = []\n",
" can_id = int(line[1], 16)\n",
" newdata.append(can_id)\n",
" newdata.append(line[2])\n",
" \n",
" ll = len(line)\n",
" num = int(line[2])\n",
" \n",
" data = []\n",
" if num == 8 :\n",
" data = line[3:11]\n",
" else :\n",
" data = line[3:3+num]\n",
" for _ in range(8-num):\n",
" data.append('0')\n",
" #print(line)\n",
" newdata.extend(list(map(lambda x:int(x, 16), data)))\n",
" \n",
" if line[-1] == 'T' :\n",
" newdata.append(0)\n",
" else :\n",
" newdata.append(1)\n",
" \n",
" fw.writerow(newdata)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#transferData('Fuzzy_dataset.csv', 'Fuzzy.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2진수 변환 후 저장 (한번만) <br>\n",
"Timestamp, DLC 제외"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"f = open('Fuzzy.csv', 'r')\n",
"#ff = open('DoSres.csv', 'w', newline='\\n')\n",
"cr = csv.reader(f)\n",
"#fw = csv.writer(ff)\n",
"\n",
"a = []\n",
"for line in cr:\n",
" #a = []\n",
" CANID = []\n",
" b = []\n",
" canid = bin(int(line[0]))\n",
" #remove 'b0'\n",
" CANID.extend(canid[2:])\n",
" #padding Max len : 11\n",
" n = len(canid[2:])\n",
" if n < 11 :\n",
" padding = ['0' for _ in range(11-n)]\n",
" b.extend(padding)\n",
" b.extend(CANID)\n",
" \n",
" # Data 0~7\n",
" DATA = []\n",
" for i in range(2, 10):\n",
" data = bin(int(line[i]))\n",
" #remove 'b0'\n",
" DATA.extend(data[2:])\n",
" #padding Max len : 8\n",
" n = len(data[2:])\n",
" if n < 8 :\n",
" padding = ['0' for _ in range(8-n)]\n",
" b.extend(padding)\n",
" b.extend(DATA)\n",
" \n",
" # Class \n",
" b.extend(line[-1])\n",
" a.append(b)\n",
" \n",
" #fw.writerow(a)\n",
"\n",
"#ff.close()\n",
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"76 ['1', '0', '1', '0', '1', '0', '0', '0', '1', '0', '1', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '1', '0', '1', '1', '0', '0', '0', '0', '0', '1', '0', '0', '0', '1', '0', '1', '0', '0', '0', '0', '0', '1']\n",
"76 ['0', '1', '0', '1', '0', '1', '1', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', '0', '1', '0', '0', '1', '0', '0', '0', '1']\n",
"3838860\n"
]
}
],
"source": [
"print(len(a[0]),a[0])\n",
"print(len(a[1]), a[1])\n",
"print(len(a))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 76*76 배열 구성"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def create2D(data) :\n",
" arr = []\n",
" for i in range(0, len(data)- 75, 76): \n",
" d = []\n",
" for x in range(0, 76) :\n",
" ll = list(map(float, data[i+x]))\n",
" d.append(ll)\n",
" arr.append(d)\n",
" \n",
" return arr"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"resData = create2D(a)\n",
"del a"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"50511\n"
]
},
{
"data": {
"text/plain": [
"3838836"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#print(len(resData[0]), resData[0])\n",
"print(len(resData))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]\n",
"76\n",
"76\n"
]
}
],
"source": [
"print(resData[0][0])\n",
"print(len(resData[0][0]))\n",
"print(len(resData[0]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 비정상 메시지 포함 유무 체크"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def check(data):\n",
" check = []\n",
" \n",
" for e in data :\n",
" flag = False\n",
" for i in e :\n",
" if i[-1] == 0 :\n",
" check.append(0)\n",
" flag = True\n",
" break\n",
" if flag == False :\n",
" check.append(1)\n",
" \n",
" return check\n",
"\n",
"checkblock = check(resData)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]\n",
"50511\n"
]
}
],
"source": [
"print(checkblock[10010:10030])\n",
"print(len(checkblock))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 데이터 개수 계산"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"33352\n",
"17159\n"
]
}
],
"source": [
"cnt = 0\n",
"for e in checkblock :\n",
" if e == 1 :\n",
" cnt += 1\n",
" \n",
"print(cnt)\n",
"print(len(checkblock)-cnt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 테스트, 트레인 데이터 나누기"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"df = pd.DataFrame(resData)\n",
"n = len(df)\n",
"\n",
"X_train = df.iloc[0:n, 0:76].values.tolist()\n",
"Y_train = checkblock\n",
"\n",
"X_train = np.asarray(X_train)\n",
"Y_train = np.asarray(Y_train)\n",
"#X_train = tf.constant(X_train)\n",
"#print(X_train[-1])\n",
"#Y_train = tf.convert_to_tensor(Y_train)\n",
"\n",
"#Y_train = np.squeeze(Y_train, axis=1)\n",
"x_train, x_test, y_train, y_test = train_test_split(X_train, Y_train, test_size=0.3, random_state=321)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(50511, 76, 76)\n",
"(50511,)\n"
]
}
],
"source": [
"print(X_train.shape)\n",
"print(Y_train.shape)"
]
},
{
"cell_type": "code",
"execution_count": 241,
"metadata": {},
"outputs": [],
"source": [
"for i in x_train:\n",
" for j in i:\n",
" if len(j) != 11:\n",
" print(j)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": true
},
"outputs": [
{
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" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>...</td>\n",
" <td>[1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50506</th>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50507</th>\n",
" <td>[1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50508</th>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50509</th>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50510</th>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
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" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
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" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>50511 rows × 76 columns</p>\n",
"</div>"
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"50510 [0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"\n",
" 71 \\\n",
"0 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"1 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"2 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"3 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"4 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"... ... \n",
"50506 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
"50507 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"50508 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"50509 [1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"50510 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"\n",
" 72 \\\n",
"0 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"1 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"2 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"3 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"4 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"... ... \n",
"50506 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
"50507 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"50508 [1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"50509 [0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"50510 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"\n",
" 73 \\\n",
"0 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"1 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"2 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"3 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"4 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"... ... \n",
"50506 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"50507 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"50508 [0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"50509 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"50510 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"\n",
" 74 \\\n",
"0 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
"1 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"2 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"3 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
"4 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"... ... \n",
"50506 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"50507 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"50508 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"50509 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"50510 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"\n",
" 75 \n",
"0 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ... \n",
"1 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
"2 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"3 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"4 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"... ... \n",
"50506 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"50507 [1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"50508 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"50509 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
"50510 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"\n",
"[50511 rows x 76 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(15154, 76, 76)\n"
]
}
],
"source": [
"print(x_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# Model\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, LSTM, Conv1D, Dropout, GlobalAveragePooling1D, MaxPooling1D\n",
"from keras.utils import np_utils\n",
"from keras.layers.normalization import BatchNormalization"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv1d (Conv1D) (None, 74, 64) 14656 \n",
"_________________________________________________________________\n",
"global_average_pooling1d (Gl (None, 64) 0 \n",
"_________________________________________________________________\n",
"dropout (Dropout) (None, 64) 0 \n",
"_________________________________________________________________\n",
"dense (Dense) (None, 1) 65 \n",
"=================================================================\n",
"Total params: 14,721\n",
"Trainable params: 14,721\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model = Sequential()\n",
"model.add(Conv1D(64, 3, input_shape=(76, 76), activation='relu'))\n",
"#model.add(Conv1D(64, 3, activation='relu'))\n",
"#model.add(MaxPooling1D(2))\n",
"#model.add(Conv1D(128, 2, activation='relu'))\n",
"#model.add(Conv1D(128, 2, activation='relu'))\n",
"model.add(GlobalAveragePooling1D())\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(1, activation='sigmoid'))\n",
"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
"model.summary()\n",
"model.save('FuzzyCNN.h5')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"354/354 [==============================] - 3s 8ms/step - loss: 0.0752 - accuracy: 0.9806 - val_loss: 0.0163 - val_accuracy: 0.9957782 - accuracy: \n",
"Epoch 2/10\n",
"354/354 [==============================] - 2s 7ms/step - loss: 0.0164 - accuracy: 0.9964 - val_loss: 0.0136 - val_accuracy: 0.9965\n",
"Epoch 3/10\n",
"354/354 [==============================] - 2s 7ms/step - loss: 0.0127 - accuracy: 0.9972 - val_loss: 0.0130 - val_accuracy: 0.9968\n",
"Epoch 4/10\n",
"354/354 [==============================] - 2s 7ms/step - loss: 0.0103 - accuracy: 0.9979 - val_loss: 0.0105 - val_accuracy: 0.9976\n",
"Epoch 5/10\n",
"354/354 [==============================] - 3s 7ms/step - loss: 0.0094 - accuracy: 0.9982 - val_loss: 0.0092 - val_accuracy: 0.9978\n",
"Epoch 6/10\n",
"354/354 [==============================] - 2s 7ms/step - loss: 0.0080 - accuracy: 0.9984 - val_loss: 0.0086 - val_accuracy: 0.9979\n",
"Epoch 7/10\n",
"354/354 [==============================] - 2s 7ms/step - loss: 0.0075 - accuracy: 0.9985 - val_loss: 0.0093 - val_accuracy: 0.9979\n",
"Epoch 8/10\n",
"354/354 [==============================] - 2s 7ms/step - loss: 0.0066 - accuracy: 0.9987 - val_loss: 0.0074 - val_accuracy: 0.9983\n",
"Epoch 9/10\n",
"354/354 [==============================] - 2s 7ms/step - loss: 0.0064 - accuracy: 0.9987 - val_loss: 0.0064 - val_accuracy: 0.9985\n",
"Epoch 10/10\n",
"354/354 [==============================] - 2s 7ms/step - loss: 0.0056 - accuracy: 0.9989 - val_loss: 0.0056 - val_accuracy: 0.9988\n"
]
}
],
"source": [
"history = model.fit(x_train, y_train, batch_size=100, epochs=10,validation_data=(x_test, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"474/474 [==============================] - 1s 1ms/step - loss: 0.0056 - accuracy: 0.9988\n",
"\n",
" Test accuracy : 0.9988\n"
]
}
],
"source": [
"print(\"\\n Test accuracy : %.4f\"%(model.evaluate(x_test, y_test)[1]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import csv\n",
"import numpy as np\n",
"import os\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 10진수 변환 (한번만)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def transferData(dir_, outputdir_) :\n",
" outputfile = open(outputdir_, 'w', newline='\\n')\n",
" fopen = open(dir_, 'r')\n",
" csvreader = csv.reader(fopen)\n",
" \n",
" fw = csv.writer(outputfile)\n",
" \n",
" for line in csvreader :\n",
" newdata = []\n",
" can_id = int(line[1], 16)\n",
" newdata.append(can_id)\n",
" newdata.append(line[2])\n",
" \n",
" ll = len(line)\n",
" num = int(line[2])\n",
" \n",
" data = []\n",
" if num == 8 :\n",
" data = line[3:11]\n",
" else :\n",
" data = line[3:3+num]\n",
" for _ in range(8-num):\n",
" data.append('0')\n",
" #print(line)\n",
" newdata.extend(list(map(lambda x:int(x, 16), data)))\n",
" \n",
" if line[-1] == 'T' :\n",
" newdata.append(0)\n",
" else :\n",
" newdata.append(1)\n",
" \n",
" fw.writerow(newdata)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#transferData('Spoofing_dataset.csv', 'Spoofing.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2진수 변환 후 저장 (한번만) <br>\n",
"Timestamp, DLC 제외"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"f = open('Spoofing.csv', 'r')\n",
"#ff = open('.csv', 'w', newline='\\n')\n",
"cr = csv.reader(f)\n",
"#fw = csv.writer(ff)\n",
"\n",
"a = []\n",
"for line in cr:\n",
" #a = []\n",
" CANID = []\n",
" b = []\n",
" canid = bin(int(line[0]))\n",
" #remove 'b0'\n",
" CANID.extend(canid[2:])\n",
" #padding Max len : 11\n",
" n = len(canid[2:])\n",
" if n < 11 :\n",
" padding = ['0' for _ in range(11-n)]\n",
" b.extend(padding)\n",
" b.extend(CANID)\n",
" \n",
" # Data 0~7\n",
" DATA = []\n",
" for i in range(2, 10):\n",
" data = bin(int(line[i]))\n",
" #remove 'b0'\n",
" DATA.extend(data[2:])\n",
" #padding Max len : 8\n",
" n = len(data[2:])\n",
" if n < 8 :\n",
" padding = ['0' for _ in range(8-n)]\n",
" b.extend(padding)\n",
" b.extend(DATA)\n",
" \n",
" # Class \n",
" b.extend(line[-1])\n",
" a.append(b)\n",
" \n",
" #fw.writerow(a)\n",
"\n",
"#ff.close()\n",
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"76 ['0', '0', '1', '0', '1', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0', '0', '0', '0', '1', '0', '1', '0', '0', '1', '1', '0', '1', '0', '1', '0', '1', '0', '0', '1', '0', '0', '1']\n",
"76 ['0', '1', '0', '1', '1', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0', '1', '0', '1', '0', '0', '0', '0', '0', '0', '0', '1']\n",
"4443142\n"
]
}
],
"source": [
"print(len(a[0]),a[0])\n",
"print(len(a[1]), a[1])\n",
"print(len(a))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 76*76 배열 구성"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def create2D(data) :\n",
" arr = []\n",
" for i in range(0, len(data)- 75, 76): \n",
" d = []\n",
" for x in range(0, 76) :\n",
" ll = list(map(float, data[i+x]))\n",
" d.append(ll)\n",
" arr.append(d)\n",
" \n",
" return arr"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"resData = create2D(a)\n",
"del a"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"58462\n"
]
}
],
"source": [
"#print(len(resData[0]), resData[0])\n",
"print(len(resData))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0]\n",
"76\n",
"76\n"
]
}
],
"source": [
"print(resData[0][0])\n",
"print(len(resData[0][0]))\n",
"print(len(resData[0]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 비정상 메시지 포함 유무 체크"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def check(data):\n",
" check = []\n",
" \n",
" for e in data :\n",
" flag = False\n",
" for i in e :\n",
" if i[-1] == 0 :\n",
" check.append(0)\n",
" flag = True\n",
" break\n",
" if flag == False :\n",
" check.append(1)\n",
" \n",
" return check\n",
"\n",
"checkblock = check(resData)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
"58462\n"
]
}
],
"source": [
"print(checkblock[10010:10030])\n",
"print(len(checkblock))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 데이터 개수 계산"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"33370\n",
"25092\n"
]
}
],
"source": [
"cnt = 0\n",
"for e in checkblock :\n",
" if e == 1 :\n",
" cnt += 1\n",
" \n",
"print(cnt)\n",
"print(len(checkblock)-cnt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 테스트, 트레인 데이터 나누기"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"df = pd.DataFrame(resData)\n",
"n = len(df)\n",
"\n",
"X_train = df.iloc[0:n, 0:76].values.tolist()\n",
"Y_train = checkblock\n",
"\n",
"X_train = np.asarray(X_train)\n",
"Y_train = np.asarray(Y_train)\n",
"#X_train = tf.constant(X_train)\n",
"#print(X_train[-1])\n",
"#Y_train = tf.convert_to_tensor(Y_train)\n",
"\n",
"#Y_train = np.squeeze(Y_train, axis=1)\n",
"x_train, x_test, y_train, y_test = train_test_split(X_train, Y_train, test_size=0.3, random_state=321)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(58462, 76, 76)\n",
"(58462,)\n"
]
}
],
"source": [
"print(X_train.shape)\n",
"print(Y_train.shape)"
]
},
{
"cell_type": "code",
"execution_count": 241,
"metadata": {},
"outputs": [],
"source": [
"for i in x_train:\n",
" for j in i:\n",
" if len(j) != 11:\n",
" print(j)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <th>2</th>\n",
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" <th>4</th>\n",
" <th>5</th>\n",
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" <th>74</th>\n",
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" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
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" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
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" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58457</th>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58458</th>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58459</th>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
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" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
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" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
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" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
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" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
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" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
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" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>58460</th>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
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" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58461</th>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
" <td>...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
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" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>58462 rows × 76 columns</p>\n",
"</div>"
],
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"\n",
" 67 \\\n",
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"... ... \n",
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"... ... \n",
"58457 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
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"\n",
" 71 \\\n",
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"4 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"... ... \n",
"58457 [1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"58458 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ... \n",
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"58460 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
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"\n",
" 72 \\\n",
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"... ... \n",
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"... ... \n",
"58457 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ... \n",
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"58460 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"58461 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"\n",
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"4 [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"... ... \n",
"58457 [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"58458 [0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ... \n",
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"58460 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
"58461 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"\n",
" 75 \n",
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"1 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"2 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
"3 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ... \n",
"4 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"... ... \n",
"58457 [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"58458 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
"58459 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"58460 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"58461 [1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
"\n",
"[58462 rows x 76 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(17539, 76, 76)\n"
]
}
],
"source": [
"print(x_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# Model\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, LSTM, Conv1D, Dropout, GlobalAveragePooling1D, MaxPooling1D\n",
"from keras.utils import np_utils\n",
"from keras.layers.normalization import BatchNormalization"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv1d (Conv1D) (None, 74, 64) 14656 \n",
"_________________________________________________________________\n",
"global_average_pooling1d (Gl (None, 64) 0 \n",
"_________________________________________________________________\n",
"dropout (Dropout) (None, 64) 0 \n",
"_________________________________________________________________\n",
"dense (Dense) (None, 1) 65 \n",
"=================================================================\n",
"Total params: 14,721\n",
"Trainable params: 14,721\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model = Sequential()\n",
"model.add(Conv1D(64, 3, input_shape=(76, 76), activation='relu'))\n",
"model.add(GlobalAveragePooling1D())\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(1, activation='sigmoid'))\n",
"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
"model.summary()\n",
"model.save('SpoofingCNN.h5')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"410/410 [==============================] - 3s 8ms/step - loss: 0.0596 - accuracy: 0.9876 - val_loss: 0.0148 - val_accuracy: 0.9960\n",
"Epoch 2/10\n",
"410/410 [==============================] - 3s 7ms/step - loss: 0.0137 - accuracy: 0.9970 - val_loss: 0.0136 - val_accuracy: 0.9965\n",
"Epoch 3/10\n",
"410/410 [==============================] - 3s 7ms/step - loss: 0.0108 - accuracy: 0.9976 - val_loss: 0.0108 - val_accuracy: 0.9971\n",
"Epoch 4/10\n",
"410/410 [==============================] - 3s 7ms/step - loss: 0.0096 - accuracy: 0.9980 - val_loss: 0.0104 - val_accuracy: 0.9975\n",
"Epoch 5/10\n",
"410/410 [==============================] - 3s 7ms/step - loss: 0.0086 - accuracy: 0.9983 - val_loss: 0.0089 - val_accuracy: 0.9978\n",
"Epoch 6/10\n",
"410/410 [==============================] - 3s 7ms/step - loss: 0.0069 - accuracy: 0.9986 - val_loss: 0.0081 - val_accuracy: 0.9979\n",
"Epoch 7/10\n",
"410/410 [==============================] - 3s 7ms/step - loss: 0.0060 - accuracy: 0.9988 - val_loss: 0.0073 - val_accuracy: 0.9981\n",
"Epoch 8/10\n",
"410/410 [==============================] - 3s 7ms/step - loss: 0.0054 - accuracy: 0.9989 - val_loss: 0.0076 - val_accuracy: 0.9981\n",
"Epoch 9/10\n",
"410/410 [==============================] - 3s 7ms/step - loss: 0.0051 - accuracy: 0.9989 - val_loss: 0.0064 - val_accuracy: 0.9983\n",
"Epoch 10/10\n",
"410/410 [==============================] - 3s 7ms/step - loss: 0.0047 - accuracy: 0.9991 - val_loss: 0.0053 - val_accuracy: 0.9987\n"
]
}
],
"source": [
"history = model.fit(x_train, y_train, batch_size=100, epochs=10,validation_data=(x_test, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"549/549 [==============================] - 1s 1ms/step - loss: 0.0053 - accuracy: 0.9987\n",
"\n",
" Test accuracy : 0.9987\n"
]
}
],
"source": [
"print(\"\\n Test accuracy : %.4f\"%(model.evaluate(x_test, y_test)[1]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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