diff --git a/partALogistic.ipynb b/partALogistic.ipynb
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+++ b/partALogistic.ipynb
@@ -0,0 +1,2745 @@
+{
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<h1>1. Loading Datasets</h1>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "[ TrainingDataBinary.csv info ]\n",
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "RangeIndex: 6000 entries, 0 to 5999\n",
+ "Columns: 129 entries, R1-PA1:VH to marker\n",
+ "dtypes: float64(112), int64(17)\n",
+ "memory usage: 5.9 MB\n",
+ "\n",
+ "[ TestingDataBinary.csv info ]\n",
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "RangeIndex: 100 entries, 0 to 99\n",
+ "Columns: 128 entries, R1-PA1:VH to snort_log4\n",
+ "dtypes: float64(112), int64(16)\n",
+ "memory usage: 100.1 KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "readTrainingBin = pd.read_csv(\"TrainingDataBinary.csv\")\n",
+ "readtest = pd.read_csv(\"TestingDataBinary.csv\")\n",
+ "\n",
+ "\n",
+ "print(\"\\n[ TrainingDataBinary.csv info ]\")\n",
+ "readTrainingBin.info()\n",
+ "\n",
+ "print(\"\\n[ TestingDataBinary.csv info ]\")\n",
+ "readtest.info()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<h1>1.1: Analysing the Data</h1>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
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+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>R1-PA1:VH</th>\n",
+ " <th>R1-PM1:V</th>\n",
+ " <th>R1-PA2:VH</th>\n",
+ " <th>R1-PM2:V</th>\n",
+ " <th>R1-PA3:VH</th>\n",
+ " <th>R1-PM3:V</th>\n",
+ " <th>R1-PA4:IH</th>\n",
+ " <th>R1-PM4:I</th>\n",
+ " <th>R1-PA5:IH</th>\n",
+ " <th>R1-PM5:I</th>\n",
+ " <th>...</th>\n",
+ " <th>control_panel_log4</th>\n",
+ " <th>relay1_log</th>\n",
+ " <th>relay2_log</th>\n",
+ " <th>relay3_log</th>\n",
+ " <th>relay4_log</th>\n",
+ " <th>snort_log1</th>\n",
+ " <th>snort_log2</th>\n",
+ " <th>snort_log3</th>\n",
+ " <th>snort_log4</th>\n",
+ " <th>marker</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>6000.000000</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>6000.0</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>6000.0</td>\n",
+ " <td>6000.0</td>\n",
+ " <td>6000.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>-12.119629</td>\n",
+ " <td>131232.981183</td>\n",
+ " <td>-7.472883</td>\n",
+ " <td>131432.647990</td>\n",
+ " <td>-4.996787</td>\n",
+ " <td>131679.080624</td>\n",
+ " <td>-11.428280</td>\n",
+ " <td>386.786641</td>\n",
+ " <td>-7.515268</td>\n",
+ " <td>383.905649</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.001333</td>\n",
+ " <td>0.001167</td>\n",
+ " <td>0.000500</td>\n",
+ " <td>0.000333</td>\n",
+ " <td>0.000167</td>\n",
+ " <td>0.000167</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.500000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>100.501215</td>\n",
+ " <td>5919.297191</td>\n",
+ " <td>107.427713</td>\n",
+ " <td>1516.363119</td>\n",
+ " <td>102.750200</td>\n",
+ " <td>1560.823960</td>\n",
+ " <td>100.559491</td>\n",
+ " <td>131.042601</td>\n",
+ " <td>107.372367</td>\n",
+ " <td>86.024159</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.036494</td>\n",
+ " <td>0.034139</td>\n",
+ " <td>0.022357</td>\n",
+ " <td>0.018256</td>\n",
+ " <td>0.012910</td>\n",
+ " <td>0.012910</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.500042</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>-179.983232</td>\n",
+ " <td>28809.187230</td>\n",
+ " <td>-179.994691</td>\n",
+ " <td>109269.310700</td>\n",
+ " <td>-179.857182</td>\n",
+ " <td>106034.858800</td>\n",
+ " <td>-179.943125</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-179.954584</td>\n",
+ " <td>3.112870</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>-102.585229</td>\n",
+ " <td>131108.128800</td>\n",
+ " <td>-110.522126</td>\n",
+ " <td>130932.615900</td>\n",
+ " <td>-99.508445</td>\n",
+ " <td>131183.348600</td>\n",
+ " <td>-100.481041</td>\n",
+ " <td>306.800805</td>\n",
+ " <td>-111.566342</td>\n",
+ " <td>312.751880</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>-21.889852</td>\n",
+ " <td>131684.814000</td>\n",
+ " <td>-9.665798</td>\n",
+ " <td>131534.374400</td>\n",
+ " <td>-5.483206</td>\n",
+ " <td>131760.033900</td>\n",
+ " <td>-18.601075</td>\n",
+ " <td>380.777245</td>\n",
+ " <td>-8.648798</td>\n",
+ " <td>383.798560</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.500000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>72.960446</td>\n",
+ " <td>132186.279400</td>\n",
+ " <td>96.401581</td>\n",
+ " <td>131985.693300</td>\n",
+ " <td>84.406710</td>\n",
+ " <td>132236.426000</td>\n",
+ " <td>71.088306</td>\n",
+ " <td>452.693697</td>\n",
+ " <td>94.748598</td>\n",
+ " <td>456.493230</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>179.994691</td>\n",
+ " <td>138429.523700</td>\n",
+ " <td>179.971773</td>\n",
+ " <td>138379.377100</td>\n",
+ " <td>179.960314</td>\n",
+ " <td>138479.670200</td>\n",
+ " <td>179.920207</td>\n",
+ " <td>1769.941260</td>\n",
+ " <td>179.988962</td>\n",
+ " <td>912.620240</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>8 rows × 129 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " R1-PA1:VH R1-PM1:V R1-PA2:VH R1-PM2:V R1-PA3:VH \n",
+ "count 6000.000000 6000.000000 6000.000000 6000.000000 6000.000000 \\\n",
+ "mean -12.119629 131232.981183 -7.472883 131432.647990 -4.996787 \n",
+ "std 100.501215 5919.297191 107.427713 1516.363119 102.750200 \n",
+ "min -179.983232 28809.187230 -179.994691 109269.310700 -179.857182 \n",
+ "25% -102.585229 131108.128800 -110.522126 130932.615900 -99.508445 \n",
+ "50% -21.889852 131684.814000 -9.665798 131534.374400 -5.483206 \n",
+ "75% 72.960446 132186.279400 96.401581 131985.693300 84.406710 \n",
+ "max 179.994691 138429.523700 179.971773 138379.377100 179.960314 \n",
+ "\n",
+ " R1-PM3:V R1-PA4:IH R1-PM4:I R1-PA5:IH R1-PM5:I ... \n",
+ "count 6000.000000 6000.000000 6000.000000 6000.000000 6000.000000 ... \\\n",
+ "mean 131679.080624 -11.428280 386.786641 -7.515268 383.905649 ... \n",
+ "std 1560.823960 100.559491 131.042601 107.372367 86.024159 ... \n",
+ "min 106034.858800 -179.943125 0.000000 -179.954584 3.112870 ... \n",
+ "25% 131183.348600 -100.481041 306.800805 -111.566342 312.751880 ... \n",
+ "50% 131760.033900 -18.601075 380.777245 -8.648798 383.798560 ... \n",
+ "75% 132236.426000 71.088306 452.693697 94.748598 456.493230 ... \n",
+ "max 138479.670200 179.920207 1769.941260 179.988962 912.620240 ... \n",
+ "\n",
+ " control_panel_log4 relay1_log relay2_log relay3_log relay4_log \n",
+ "count 6000.0 6000.000000 6000.000000 6000.000000 6000.000000 \\\n",
+ "mean 0.0 0.001333 0.001167 0.000500 0.000333 \n",
+ "std 0.0 0.036494 0.034139 0.022357 0.018256 \n",
+ "min 0.0 0.000000 0.000000 0.000000 0.000000 \n",
+ "25% 0.0 0.000000 0.000000 0.000000 0.000000 \n",
+ "50% 0.0 0.000000 0.000000 0.000000 0.000000 \n",
+ "75% 0.0 0.000000 0.000000 0.000000 0.000000 \n",
+ "max 0.0 1.000000 1.000000 1.000000 1.000000 \n",
+ "\n",
+ " snort_log1 snort_log2 snort_log3 snort_log4 marker \n",
+ "count 6000.000000 6000.000000 6000.0 6000.0 6000.000000 \n",
+ "mean 0.000167 0.000167 0.0 0.0 0.500000 \n",
+ "std 0.012910 0.012910 0.0 0.0 0.500042 \n",
+ "min 0.000000 0.000000 0.0 0.0 0.000000 \n",
+ "25% 0.000000 0.000000 0.0 0.0 0.000000 \n",
+ "50% 0.000000 0.000000 0.0 0.0 0.500000 \n",
+ "75% 0.000000 0.000000 0.0 0.0 1.000000 \n",
+ "max 1.000000 1.000000 0.0 0.0 1.000000 \n",
+ "\n",
+ "[8 rows x 129 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "readTrainingBin.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "R1-PA1:VH float64\n",
+ "R1-PM1:V float64\n",
+ "R1-PA2:VH float64\n",
+ "R1-PM2:V float64\n",
+ "R1-PA3:VH float64\n",
+ " ... \n",
+ "snort_log1 int64\n",
+ "snort_log2 int64\n",
+ "snort_log3 int64\n",
+ "snort_log4 int64\n",
+ "marker int64\n",
+ "Length: 129, dtype: object"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "readTrainingBin.dtypes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 False\n",
+ "1 False\n",
+ "2 False\n",
+ "3 False\n",
+ "4 False\n",
+ " ... \n",
+ "5995 False\n",
+ "5996 False\n",
+ "5997 False\n",
+ "5998 False\n",
+ "5999 False\n",
+ "Length: 6000, dtype: bool"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "readTrainingBin.duplicated()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "marker\n",
+ "0 3000\n",
+ "1 3000\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "readTrainingBin['marker'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['R1-PA1:VH', 'R1-PM1:V', 'R1-PA2:VH', 'R1-PM2:V', 'R1-PA3:VH',\n",
+ " 'R1-PM3:V', 'R1-PA4:IH', 'R1-PM4:I', 'R1-PA5:IH', 'R1-PM5:I',\n",
+ " 'R1-PA6:IH', 'R1-PM6:I', 'R1-PA7:VH', 'R1-PM7:V', 'R1-PA8:VH',\n",
+ " 'R1-PM8:V', 'R1-PA9:VH', 'R1-PM9:V', 'R1-PA10:IH', 'R1-PM10:I',\n",
+ " 'R1-PA11:IH', 'R1-PM11:I', 'R1-PA12:IH', 'R1-PM12:I', 'R1:F',\n",
+ " 'R1:DF', 'R1-PA:Z', 'R1-PA:ZH', 'R1:S', 'R2-PA1:VH', 'R2-PM1:V',\n",
+ " 'R2-PA2:VH', 'R2-PM2:V', 'R2-PA3:VH', 'R2-PM3:V', 'R2-PA4:IH',\n",
+ " 'R2-PM4:I', 'R2-PA5:IH', 'R2-PM5:I', 'R2-PA6:IH', 'R2-PM6:I',\n",
+ " 'R2-PA7:VH', 'R2-PM7:V', 'R2-PA8:VH', 'R2-PM8:V', 'R2-PA9:VH',\n",
+ " 'R2-PM9:V', 'R2-PA10:IH', 'R2-PM10:I', 'R2-PA11:IH', 'R2-PM11:I',\n",
+ " 'R2-PA12:IH', 'R2-PM12:I', 'R2:F', 'R2:DF', 'R2-PA:Z', 'R2-PA:ZH',\n",
+ " 'R2:S', 'R3-PA1:VH', 'R3-PM1:V', 'R3-PA2:VH', 'R3-PM2:V',\n",
+ " 'R3-PA3:VH', 'R3-PM3:V', 'R3-PA4:IH', 'R3-PM4:I', 'R3-PA5:IH',\n",
+ " 'R3-PM5:I', 'R3-PA6:IH', 'R3-PM6:I', 'R3-PA7:VH', 'R3-PM7:V',\n",
+ " 'R3-PA8:VH', 'R3-PM8:V', 'R3-PA9:VH', 'R3-PM9:V', 'R3-PA10:IH',\n",
+ " 'R3-PM10:I', 'R3-PA11:IH', 'R3-PM11:I', 'R3-PA12:IH', 'R3-PM12:I',\n",
+ " 'R3:F', 'R3:DF', 'R3-PA:Z', 'R3-PA:ZH', 'R3:S', 'R4-PA1:VH',\n",
+ " 'R4-PM1:V', 'R4-PA2:VH', 'R4-PM2:V', 'R4-PA3:VH', 'R4-PM3:V',\n",
+ " 'R4-PA4:IH', 'R4-PM4:I', 'R4-PA5:IH', 'R4-PM5:I', 'R4-PA6:IH',\n",
+ " 'R4-PM6:I', 'R4-PA7:VH', 'R4-PM7:V', 'R4-PA8:VH', 'R4-PM8:V',\n",
+ " 'R4-PA9:VH', 'R4-PM9:V', 'R4-PA10:IH', 'R4-PM10:I', 'R4-PA11:IH',\n",
+ " 'R4-PM11:I', 'R4-PA12:IH', 'R4-PM12:I', 'R4:F', 'R4:DF', 'R4-PA:Z',\n",
+ " 'R4-PA:ZH', 'R4:S', 'control_panel_log1', 'control_panel_log2',\n",
+ " 'control_panel_log3', 'control_panel_log4', 'relay1_log',\n",
+ " 'relay2_log', 'relay3_log', 'relay4_log', 'snort_log1',\n",
+ " 'snort_log2', 'snort_log3', 'snort_log4', 'marker'], dtype=object)"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "headers = readTrainingBin.columns.values\n",
+ "headers"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<h1>2: Data Preparation</h1>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.linear_model import LogisticRegression\n",
+ "from scipy.special import expit\n",
+ "from sklearn.model_selection import train_test_split"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>R1-PA1:VH</th>\n",
+ " <th>R1-PM1:V</th>\n",
+ " <th>R1-PA2:VH</th>\n",
+ " <th>R1-PM2:V</th>\n",
+ " <th>R1-PA3:VH</th>\n",
+ " <th>R1-PM3:V</th>\n",
+ " <th>R1-PA4:IH</th>\n",
+ " <th>R1-PM4:I</th>\n",
+ " <th>R1-PA5:IH</th>\n",
+ " <th>R1-PM5:I</th>\n",
+ " <th>...</th>\n",
+ " <th>control_panel_log4</th>\n",
+ " <th>relay1_log</th>\n",
+ " <th>relay2_log</th>\n",
+ " <th>relay3_log</th>\n",
+ " <th>relay4_log</th>\n",
+ " <th>snort_log1</th>\n",
+ " <th>snort_log2</th>\n",
+ " <th>snort_log3</th>\n",
+ " <th>snort_log4</th>\n",
+ " <th>marker</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>70.399324</td>\n",
+ " <td>127673.0908</td>\n",
+ " <td>-49.572308</td>\n",
+ " <td>127648.0176</td>\n",
+ " <td>-169.578319</td>\n",
+ " <td>127723.2374</td>\n",
+ " <td>65.689611</td>\n",
+ " <td>605.91099</td>\n",
+ " <td>-57.003571</td>\n",
+ " <td>626.78553</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>73.688102</td>\n",
+ " <td>130280.7109</td>\n",
+ " <td>-46.300719</td>\n",
+ " <td>130255.6377</td>\n",
+ " <td>-166.278082</td>\n",
+ " <td>130355.9307</td>\n",
+ " <td>71.831719</td>\n",
+ " <td>483.59351</td>\n",
+ " <td>-50.947407</td>\n",
+ " <td>500.98896</td>\n",
+ " <td>...</td>\n",
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>73.733939</td>\n",
+ " <td>130305.7842</td>\n",
+ " <td>-46.254883</td>\n",
+ " <td>130280.7109</td>\n",
+ " <td>-166.232245</td>\n",
+ " <td>130381.0040</td>\n",
+ " <td>71.808800</td>\n",
+ " <td>483.59351</td>\n",
+ " <td>-50.913030</td>\n",
+ " <td>500.98896</td>\n",
+ " <td>...</td>\n",
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>74.083443</td>\n",
+ " <td>130581.5902</td>\n",
+ " <td>-45.899649</td>\n",
+ " <td>130556.5169</td>\n",
+ " <td>-165.882741</td>\n",
+ " <td>130656.8100</td>\n",
+ " <td>72.152575</td>\n",
+ " <td>482.86107</td>\n",
+ " <td>-50.437475</td>\n",
+ " <td>499.15786</td>\n",
+ " <td>...</td>\n",
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>74.553268</td>\n",
+ " <td>131083.0556</td>\n",
+ " <td>-45.424094</td>\n",
+ " <td>131057.9823</td>\n",
+ " <td>-165.424375</td>\n",
+ " <td>131158.2754</td>\n",
+ " <td>72.118198</td>\n",
+ " <td>484.50906</td>\n",
+ " <td>-50.013486</td>\n",
+ " <td>497.69298</td>\n",
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+ " <td>0</td>\n",
+ " </tr>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5995</th>\n",
+ " <td>116.889120</td>\n",
+ " <td>131860.3269</td>\n",
+ " <td>-3.076783</td>\n",
+ " <td>131810.1804</td>\n",
+ " <td>-123.094253</td>\n",
+ " <td>131910.4735</td>\n",
+ " <td>114.780635</td>\n",
+ " <td>376.10794</td>\n",
+ " <td>-5.254023</td>\n",
+ " <td>374.82617</td>\n",
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5996</th>\n",
+ " <td>116.849013</td>\n",
+ " <td>131810.1804</td>\n",
+ " <td>-3.116890</td>\n",
+ " <td>131760.0339</td>\n",
+ " <td>-123.128630</td>\n",
+ " <td>131885.4002</td>\n",
+ " <td>114.769176</td>\n",
+ " <td>376.29105</td>\n",
+ " <td>-5.322778</td>\n",
+ " <td>374.82617</td>\n",
+ " <td>...</td>\n",
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5997</th>\n",
+ " <td>116.384917</td>\n",
+ " <td>131734.9606</td>\n",
+ " <td>-3.586716</td>\n",
+ " <td>131684.8140</td>\n",
+ " <td>-123.586996</td>\n",
+ " <td>131785.1071</td>\n",
+ " <td>114.299351</td>\n",
+ " <td>376.47416</td>\n",
+ " <td>-5.849899</td>\n",
+ " <td>374.82617</td>\n",
+ " <td>...</td>\n",
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5998</th>\n",
+ " <td>111.125164</td>\n",
+ " <td>130506.3704</td>\n",
+ " <td>-8.846468</td>\n",
+ " <td>130456.2238</td>\n",
+ " <td>-128.858208</td>\n",
+ " <td>130556.5169</td>\n",
+ " <td>106.667553</td>\n",
+ " <td>478.83265</td>\n",
+ " <td>-13.464508</td>\n",
+ " <td>477.73399</td>\n",
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+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5999</th>\n",
+ " <td>110.878793</td>\n",
+ " <td>130481.2971</td>\n",
+ " <td>-9.092840</td>\n",
+ " <td>130456.2238</td>\n",
+ " <td>-129.104580</td>\n",
+ " <td>130556.5169</td>\n",
+ " <td>106.392533</td>\n",
+ " <td>478.83265</td>\n",
+ " <td>-13.750987</td>\n",
+ " <td>477.91710</td>\n",
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>6000 rows × 129 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " R1-PA1:VH R1-PM1:V R1-PA2:VH R1-PM2:V R1-PA3:VH \n",
+ "0 70.399324 127673.0908 -49.572308 127648.0176 -169.578319 \\\n",
+ "1 73.688102 130280.7109 -46.300719 130255.6377 -166.278082 \n",
+ "2 73.733939 130305.7842 -46.254883 130280.7109 -166.232245 \n",
+ "3 74.083443 130581.5902 -45.899649 130556.5169 -165.882741 \n",
+ "4 74.553268 131083.0556 -45.424094 131057.9823 -165.424375 \n",
+ "... ... ... ... ... ... \n",
+ "5995 116.889120 131860.3269 -3.076783 131810.1804 -123.094253 \n",
+ "5996 116.849013 131810.1804 -3.116890 131760.0339 -123.128630 \n",
+ "5997 116.384917 131734.9606 -3.586716 131684.8140 -123.586996 \n",
+ "5998 111.125164 130506.3704 -8.846468 130456.2238 -128.858208 \n",
+ "5999 110.878793 130481.2971 -9.092840 130456.2238 -129.104580 \n",
+ "\n",
+ " R1-PM3:V R1-PA4:IH R1-PM4:I R1-PA5:IH R1-PM5:I ... \n",
+ "0 127723.2374 65.689611 605.91099 -57.003571 626.78553 ... \\\n",
+ "1 130355.9307 71.831719 483.59351 -50.947407 500.98896 ... \n",
+ "2 130381.0040 71.808800 483.59351 -50.913030 500.98896 ... \n",
+ "3 130656.8100 72.152575 482.86107 -50.437475 499.15786 ... \n",
+ "4 131158.2754 72.118198 484.50906 -50.013486 497.69298 ... \n",
+ "... ... ... ... ... ... ... \n",
+ "5995 131910.4735 114.780635 376.10794 -5.254023 374.82617 ... \n",
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+ "5997 131785.1071 114.299351 376.47416 -5.849899 374.82617 ... \n",
+ "5998 130556.5169 106.667553 478.83265 -13.464508 477.73399 ... \n",
+ "5999 130556.5169 106.392533 478.83265 -13.750987 477.91710 ... \n",
+ "\n",
+ " control_panel_log4 relay1_log relay2_log relay3_log relay4_log \n",
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+ "5998 0 0 0 0 0 \n",
+ "5999 0 0 0 0 0 \n",
+ "\n",
+ "[6000 rows x 129 columns]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "readTrainingBin"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "RangeIndex: 6000 entries, 0 to 5999\n",
+ "Columns: 129 entries, R1-PA1:VH to marker\n",
+ "dtypes: float64(112), int64(17)\n",
+ "memory usage: 5.9 MB\n"
+ ]
+ }
+ ],
+ "source": [
+ "readTrainingBin.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " <th>R1-PA2:VH</th>\n",
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+ " <th>1</th>\n",
+ " <td>73.688102</td>\n",
+ " <td>130280.7109</td>\n",
+ " <td>-46.300719</td>\n",
+ " <td>130255.6377</td>\n",
+ " <td>-166.278082</td>\n",
+ " <td>130355.9307</td>\n",
+ " <td>71.831719</td>\n",
+ " <td>483.59351</td>\n",
+ " <td>-50.947407</td>\n",
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+ " <th>2</th>\n",
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+ " <td>130305.7842</td>\n",
+ " <td>-46.254883</td>\n",
+ " <td>130280.7109</td>\n",
+ " <td>-166.232245</td>\n",
+ " <td>130381.0040</td>\n",
+ " <td>71.808800</td>\n",
+ " <td>483.59351</td>\n",
+ " <td>-50.913030</td>\n",
+ " <td>500.98896</td>\n",
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
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+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>74.083443</td>\n",
+ " <td>130581.5902</td>\n",
+ " <td>-45.899649</td>\n",
+ " <td>130556.5169</td>\n",
+ " <td>-165.882741</td>\n",
+ " <td>130656.8100</td>\n",
+ " <td>72.152575</td>\n",
+ " <td>482.86107</td>\n",
+ " <td>-50.437475</td>\n",
+ " <td>499.15786</td>\n",
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+ " <td>74.553268</td>\n",
+ " <td>131083.0556</td>\n",
+ " <td>-45.424094</td>\n",
+ " <td>131057.9823</td>\n",
+ " <td>-165.424375</td>\n",
+ " <td>131158.2754</td>\n",
+ " <td>72.118198</td>\n",
+ " <td>484.50906</td>\n",
+ " <td>-50.013486</td>\n",
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+ "[6000 rows x 128 columns]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X = readTrainingBin.drop(columns = 'marker')\n",
+ "X"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y = readTrainingBin['marker']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_train, X_test, y_train, y_test = train_test_split(X,y, random_state=1, test_size=0.2, shuffle = True, stratify =y)"
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+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
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+ "X_train"
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+ "execution_count": 15,
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+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "X_train\n",
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "Index: 4800 entries, 4859 to 725\n",
+ "Columns: 128 entries, R1-PA1:VH to snort_log4\n",
+ "dtypes: float64(112), int64(16)\n",
+ "memory usage: 4.7 MB\n",
+ "\n",
+ "\n",
+ "X_test\n",
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "Index: 1200 entries, 3315 to 2379\n",
+ "Columns: 128 entries, R1-PA1:VH to snort_log4\n",
+ "dtypes: float64(112), int64(16)\n",
+ "memory usage: 1.2 MB\n",
+ "\n",
+ "\n",
+ "y_train\n",
+ "<class 'pandas.core.series.Series'>\n",
+ "Index: 4800 entries, 4859 to 725\n",
+ "Series name: marker\n",
+ "Non-Null Count Dtype\n",
+ "-------------- -----\n",
+ "4800 non-null int64\n",
+ "dtypes: int64(1)\n",
+ "memory usage: 75.0 KB\n",
+ "\n",
+ "\n",
+ "y_test\n",
+ "<class 'pandas.core.series.Series'>\n",
+ "Index: 1200 entries, 3315 to 2379\n",
+ "Series name: marker\n",
+ "Non-Null Count Dtype\n",
+ "-------------- -----\n",
+ "1200 non-null int64\n",
+ "dtypes: int64(1)\n",
+ "memory usage: 18.8 KB\n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"X_train\")\n",
+ "X_train.info()\n",
+ "print(\"\\n\")\n",
+ "print(\"X_test\")\n",
+ "X_test.info()\n",
+ "print(\"\\n\")\n",
+ "print(\"y_train\")\n",
+ "y_train.info()\n",
+ "print(\"\\n\")\n",
+ "print(\"y_test\")\n",
+ "y_test.info()\n",
+ "print(\"\\n\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
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+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "source": [
+ "X_train.value_counts()"
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+ },
+ {
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+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
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+ " ..\n",
+ "-60.481425 132236.4260 179.542055 132186.2794 59.536044 132286.5725 -57.685391 311.47011 179.473300 320.99183 62.503966 308.90657 -60.469966 132236.4260 0.0 0.0 0.0 0.0 -58.596394 313.66743 -0.492744 6.95818 130.748969 6.04263 60.000 0.00 12.740412 -0.058563 0 -65.305481 131315.4531 174.762274 131169.1875 54.709170 131403.1094 111.107486 315.542236 -11.670228 326.168060 -128.295596 313.631043 -65.278017 131296.7969 0.0 0.0 0.0 0.0 110.349431 318.340302 164.841616 6.975174 -57.367859 7.038116 60.000999 0.0 12.600163 -3.086686 0 -65.305729 130857.3961 174.734939 130832.3229 54.751847 130882.4694 111.113705 313.30121 -11.653962 323.18915 -128.468597 311.28700 -65.277082 130857.3961 0.0 0.0 0.0 0.0 110.300105 315.86475 165.779608 6.77507 -59.037571 6.40885 60.000 0.0 12.585376 -3.088209 0 -60.424129 132286.5725 179.605080 131634.6675 59.610529 132336.7191 -57.645284 308.723460 179.524866 318.611400 62.572721 307.258580 -60.406940 132085.9864 0.0 0.0 0.0 0.0 -58.544828 311.470110 0.171887 6.59196 128.657673 6.22574 60.000000 0.0 12.886121 -0.060473 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
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+ "-61.833605 132035.8398 178.201333 132010.7666 58.195323 132111.0596 -62.641476 318.61140 177.330438 320.99183 57.754146 319.16073 -61.816417 132060.9131 0.0 0.0 0.0 0.0 -62.521155 319.71006 0.000000 0.00000 0.000000 0.00000 59.999 0.01 11.919940 0.018146 0 -66.612854 131143.8125 173.454894 131006.5938 53.407288 131234.7031 106.482239 323.993683 -13.554382 324.434265 -133.558052 324.182495 -66.590881 131128.6875 0.0 0.0 0.0 0.0 106.454775 324.199677 0.000000 0.000000 0.000000 0.000000 60.000000 0.0 11.511754 -3.021346 0 -66.612073 130681.8832 173.445784 130681.8832 53.434044 130681.8832 106.449829 321.54116 -13.533263 321.54116 -133.590839 321.35805 -66.577696 130681.8832 0.0 0.0 0.0 0.0 106.438370 321.54116 0.000000 0.00000 0.000000 0.00000 60.000 0.0 11.538667 -3.019913 0 -61.759121 132085.9864 178.264359 131459.1546 58.264078 132161.2062 -62.395104 317.146520 177.593998 317.512740 57.628095 316.597190 -61.747662 131910.4735 0.0 0.0 0.0 0.0 -62.389374 317.146520 0.000000 0.00000 0.000000 0.00000 60.000000 0.0 11.846582 0.013325 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
+ "-61.856524 132060.9131 178.184145 132035.8398 58.178135 132111.0596 -62.635746 318.97762 177.255953 320.80872 57.708309 319.52695 -61.833605 132060.9131 0.0 0.0 0.0 0.0 -62.561262 319.71006 0.000000 0.00000 0.000000 0.00000 60.000 0.00 11.894755 0.015408 0 -66.634827 131165.3125 173.432928 131011.1953 53.385315 131251.5469 106.432802 323.930725 -13.576355 324.199677 -133.541564 324.279785 -66.607363 131141.4688 0.0 0.0 0.0 0.0 106.432802 324.136719 0.000000 0.000000 0.000000 0.000000 60.000000 0.0 11.521349 -3.021094 0 -66.634992 130706.9565 173.417136 130656.8100 53.411126 130732.0298 106.352426 321.54116 -13.550452 321.35805 -133.625217 320.99183 -66.600614 130706.9565 0.0 0.0 0.0 0.0 106.386803 321.35805 0.000000 0.00000 0.000000 0.00000 60.000 0.0 11.535422 -3.016762 0 -61.776309 132111.0596 178.247170 131484.2279 58.241160 132161.2062 -62.429481 317.146520 177.565350 317.512740 57.593718 317.146520 -61.770580 131910.4735 0.0 0.0 0.0 0.0 -62.429481 317.329630 0.000000 0.00000 0.000000 0.00000 60.000000 0.0 11.851964 0.012123 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
+ " 179.891559 131484.2279 59.902737 130857.3961 -60.086084 131559.4477 176.539756 422.06855 57.335887 418.77257 -62.670124 418.22324 179.903018 131308.7150 0.0 0.0 0.0 0.0 177.072607 419.68812 0.000000 0.00000 0.000000 0.00000 59.999 0.00 8.822680 0.074010 0 173.445784 129478.3663 53.497069 129453.2930 -66.514670 129478.3663 -11.871686 426.829410 -130.960963 423.533430 108.982302 423.350320 173.474432 129478.3663 0.0 0.0 0.0 0.0 -11.287269 424.632090 0.000000 0.000000 0.000000 0.000000 60.000000 0.0 8.591154 -3.032516 0 173.417136 129428.2197 53.451233 129428.2197 -66.560507 129503.4396 -11.716987 422.61788 -131.058366 418.95568 108.861981 420.05434 173.440054 129453.2930 0.0 0.0 0.0 0.0 -11.304457 420.60367 0.000000 0.00000 0.000000 0.00000 59.999 0.0 8.658456 -3.042107 0 179.761058 131885.5313 59.823305 131730.9219 -60.235295 131954.2813 176.679386 418.327332 57.560121 415.843964 -62.405089 415.609375 179.783024 131856.8281 0.0 0.0 0.0 0.0 177.278147 416.576386 0.000000 0.00000 0.000000 0.00000 59.999001 0.0 8.954109 0.069583 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
+ "Name: count, Length: 1200, dtype: int64"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_test.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "marker\n",
+ "1 2400\n",
+ "0 2400\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_train.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "marker\n",
+ "1 600\n",
+ "0 600\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_test.value_counts()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<h1>3. Choosing a Model: Logistic Regression</h1>"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Creating a LG model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "log_reg = LogisticRegression(max_iter=10000, random_state =1)#.fit(X_train, y_train)\n",
+ "#check if it can predict\n",
+ "#log_reg.predict(X_train)\n"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<h1>3.1. Training the Model</h1>"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "fit() trains the model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=10000, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=10000, random_state=1)</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "LogisticRegression(max_iter=10000, random_state=1)"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "log_reg.fit(X_train, y_train)\n",
+ "#log_reg.score(X_train, y_train)\n",
+ "#log_reg.score(X_test, y_test)\n"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<h1>3.2. Initial Evaluation of the Model</h1>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "firstPred = log_reg.predict(X_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[563, 37],\n",
+ " [128, 472]], dtype=int64)"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from sklearn import metrics\n",
+ "from sklearn.metrics import confusion_matrix\n",
+ "from sklearn.metrics import classification_report\n",
+ "from sklearn.metrics import ConfusionMatrixDisplay\n",
+ "\n",
+ "cnf_matrix = metrics.confusion_matrix(y_test, firstPred)\n",
+ "cnf_matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " normal 0.81 0.94 0.87 600\n",
+ " abnormal 0.93 0.79 0.85 600\n",
+ "\n",
+ " accuracy 0.86 1200\n",
+ " macro avg 0.87 0.86 0.86 1200\n",
+ "weighted avg 0.87 0.86 0.86 1200\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "target_names = ['normal', 'abnormal']\n",
+ "print(classification_report(y_test, firstPred, target_names=target_names))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<h1> 4. Hyperparameter Tuning</h1>"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Grid search adjusts the C parameter and chooses the best C value "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameter (CV score=0.873):\n",
+ "{'C': 92.49147277217335}\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.model_selection import GridSearchCV\n",
+ "from sklearn.model_selection import KFold\n",
+ "from sklearn.model_selection import cross_val_score\n",
+ "\n",
+ "param_grid = {\n",
+ " \"C\": np.logspace(0, 4, 60),\n",
+ "}\n",
+ "\n",
+ "im_lg = GridSearchCV(log_reg, param_grid, n_jobs=2, cv=KFold(n_splits=5, random_state=0, shuffle=True))\n",
+ "im_lg.fit(X_train, y_train)\n",
+ "print(\"Best parameter (CV score=%0.3f):\" % im_lg.best_score_)\n",
+ "print(im_lg.best_params_)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "The K fold cross validation is chosen as it will go through all of the permuations of training and testing the data. The stratified cross validation is not used as the given dataset is not divided into categories/imbalanced. \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#get_score(LogisticRegression(), X_train_scaled, X_test_scaled, y_train, y_test)\n",
+ "results = cross_val_score(im_lg, X, y, cv=KFold(n_splits=5, random_state=0, shuffle=True))\n",
+ "print(\"Accuracy: %.3f%% (%.3f%%)\" % (results.mean()*100.0, results.std()*100.0))\n"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<h1> 5. Metric Evaluation</h1>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([1, 0, 0, ..., 0, 0, 0], dtype=int64)"
+ ]
+ },
+ "execution_count": 127,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#confusion Matrix\n",
+ "\n",
+ "y_pred = im_lg.predict(X_test)\n",
+ "y_pred\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3315 1\n",
+ "5843 0\n",
+ "915 0\n",
+ "1958 1\n",
+ "4461 1\n",
+ " ..\n",
+ "3519 0\n",
+ "5130 0\n",
+ "963 0\n",
+ "1672 0\n",
+ "2379 1\n",
+ "Name: marker, Length: 1200, dtype: int64"
+ ]
+ },
+ "execution_count": 128,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[571, 29],\n",
+ " [124, 476]], dtype=int64)"
+ ]
+ },
+ "execution_count": 129,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "confusion_matrix(y_test, y_pred)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.82 0.95 0.88 600\n",
+ " 1 0.94 0.79 0.86 600\n",
+ "\n",
+ " accuracy 0.87 1200\n",
+ " macro avg 0.88 0.87 0.87 1200\n",
+ "weighted avg 0.88 0.87 0.87 1200\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "print(classification_report(y_test, y_pred))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cm =confusion_matrix(y_test, y_pred)\n",
+ "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels = target_names)\n",
+ "disp.plot()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "86.45833333333334"
+ ]
+ },
+ "execution_count": 132,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "log_reg.score(X_train, y_train)*100"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "87.04166666666666"
+ ]
+ },
+ "execution_count": 133,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "im_lg.score(X_train, y_train)*100"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import cross_val_score\n",
+ "from sklearn.model_selection import StratifiedKFold\n",
+ "\n",
+ "cvScore = cross_val_score(im_lg, X, y, cv=StratifiedKFold(n_splits=5, shuffle=True, random_state=1), scoring='f1_macro')\n",
+ "print (cvScore)\n",
+ "print (\" StratifiedKFold Cross-Validation Accuracy: %0.2f%% | Standard Deviation: %0.2f%%\" % (100*cvScore.mean(), 100*cvScore.std()))\n"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<h1> 6. Testing Data</h1>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\60172\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "y_testpred = im_lg.predict(readtest.values)\n",
+ "y_testpred = pd.DataFrame(y_testpred, columns=['predicted marker'])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "predicted marker\n",
+ "0 50\n",
+ "1 50\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 135,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_testpred.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#readtest['marker'] = np.transpose(y_testpred)\n",
+ "y_testpred.to_csv('testresult.csv')\n"
+ ]
+ }
+ ],
+ "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": "3.11.3"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}