PCA SWAT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.0\n",
      "0.10.0\n"
     ]
    }
   ],
   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Utility Functions\n",
    "## Plots\n",
    "def tsne_plot(x1, y1, name=\"graph.png\"):\n",
    "    tsne = TSNE(n_components=2, random_state=0)\n",
    "    X_t = tsne.fit_transform(x1)\n",
    "    plt.scatter(X_t[np.where(y1 == 0), 0], X_t[np.where(y1 == 0), 1], marker='o', color='g', linewidth='1', alpha=0.8, label='Normal', s=2)\n",
    "    plt.scatter(X_t[np.where(y1 == 1), 0], X_t[np.where(y1 == 1), 1], marker='o', color='r', linewidth='1', alpha=0.8, label='Attack', s=2)\n",
    "    plt.legend(loc='best');\n",
    "    plt.savefig(name);\n",
    "    plt.show();\n",
    "    \n",
    "# Plot Keras training history\n",
    "def plot_loss(hist):\n",
    "    plt.plot(hist.history['loss'])\n",
    "    plt.plot(hist.history['val_loss'])\n",
    "    plt.title('model loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'test'], loc='upper left')\n",
    "    plt.yscale('log',basey=10)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"C:/Users/Ruben/Datasets/SWAT/SWaT_Dataset_Attack_v0.xlsx\")\n",
    "\n",
    "df.columns = [\n",
    "\t'TIMESTAMP','FIT101','LIT101','MV101','P101','P102','AIT201','AIT202','AIT203','FIT201','MV201','P201','P202','P203',\n",
    "    'P204','P205','P206','DPIT301','FIT301','LIT301','MV301','MV302','MV303','MV304','P301','P302','AIT401','AIT402','FIT401',\n",
    "    'LIT401','P401','P402','P403','P404','UV401','AIT501','AIT502','AIT503','AIT504','FIT501','FIT502','FIT503','FIT504',\n",
    "    'P501','P502','PIT501','PIT502','PIT503','FIT601','P601','P602','P603','OUTCOME'\n",
    "]\n",
    "\n",
    "# The first row only contains labels\n",
    "# 'TIMESTAMP' is irrelevant for the thesis\n",
    "# The other dropped columns contain either only 0s, only 1s or only 2s and are therefore irrelevant\n",
    "\n",
    "df = df.iloc[1:]\n",
    "df = df.drop(['TIMESTAMP', 'P202', 'P301', 'P401', 'P404', 'P502', 'P601', 'P603'], axis = 1)\n",
    "\n",
    "# The dataset labels attacks by 'A ttack' and 'Attack', and labels normal data as 'Normal'\n",
    "# To keep the same structure in all datasets, the 'A ttack' and 'Attack' values are changed to '-1' and normal values to '1'\n",
    "\n",
    "df['OUTCOME'].replace(to_replace = ['A ttack', 'Attack'], value = 1, inplace = True)\n",
    "df['OUTCOME'].replace(to_replace = ['Normal'], value = 0, inplace = True)\n",
    "\n",
    "# data types need to be numeric to be encoded to z-scores --> convert column object data types to numerics\n",
    "\n",
    "cols = df.columns[df.columns != 'OUTCOME']\n",
    "df[cols] = df[cols].apply(pd.to_numeric, errors='coerce')\n",
    "\n",
    "# Encoding the feature vectors to z-scores\n",
    "\n",
    "cols = list(df.columns[df.columns != 'OUTCOME'])\n",
    "for col in cols:\n",
    "    df[col] = ((df[col] - df[col].mean())/df[col].std(ddof=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating dataframes with and without outcomes\n",
    "x = df.drop([\"OUTCOME\"], axis=1)\n",
    "Y = df[\"OUTCOME\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create PCA with 15 components\n",
    "from sklearn.decomposition import PCA\n",
    "pca_swat = PCA(n_components=15)\n",
    "principalComponents_swat = pca_swat.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the dataframe to add the data to\n",
    "pca_df = pd.DataFrame(data = principalComponents_swat, \n",
    "                                 columns = ['principal component 1', 'principal component 2', 'principal component 3',\n",
    "                                            'principal component 4', 'principal component 5', 'principal component 6',\n",
    "                                            'principal component 7', 'principal component 8', 'principal component 9',\n",
    "                                            'principal component 10', 'principal component 11', 'principal component 12',\n",
    "                                            'principal component 13', 'principal component 14', 'principal component 15'])\n",
    "\n",
    "# principal_wadi_df = pd.DataFrame(data = principalComponents_wadi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the dataset to the newly created dataframe\n",
    "X = pca_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained variation per principal component: [0.40783483 0.10965664 0.07522333 0.06101676 0.0521929  0.04526328\n",
      " 0.03841825 0.03095436 0.02697387 0.02228184 0.02176221 0.01487002\n",
      " 0.01453647 0.01052565 0.01008005]\n"
     ]
    }
   ],
   "source": [
    "print('Explained variation per principal component: {}'.format(pca_swat.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "      <th>principal component 3</th>\n",
       "      <th>principal component 4</th>\n",
       "      <th>principal component 5</th>\n",
       "      <th>principal component 6</th>\n",
       "      <th>principal component 7</th>\n",
       "      <th>principal component 8</th>\n",
       "      <th>principal component 9</th>\n",
       "      <th>principal component 10</th>\n",
       "      <th>principal component 11</th>\n",
       "      <th>principal component 12</th>\n",
       "      <th>principal component 13</th>\n",
       "      <th>principal component 14</th>\n",
       "      <th>principal component 15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.321657</td>\n",
       "      <td>-1.235763</td>\n",
       "      <td>0.791324</td>\n",
       "      <td>2.155093</td>\n",
       "      <td>-0.402755</td>\n",
       "      <td>-0.097186</td>\n",
       "      <td>-0.029608</td>\n",
       "      <td>-0.273782</td>\n",
       "      <td>0.635633</td>\n",
       "      <td>-0.028125</td>\n",
       "      <td>0.346342</td>\n",
       "      <td>0.098701</td>\n",
       "      <td>-0.004071</td>\n",
       "      <td>-0.117564</td>\n",
       "      <td>-0.249133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.337370</td>\n",
       "      <td>-1.228689</td>\n",
       "      <td>0.793358</td>\n",
       "      <td>2.156559</td>\n",
       "      <td>-0.408887</td>\n",
       "      <td>-0.097646</td>\n",
       "      <td>-0.028220</td>\n",
       "      <td>-0.273434</td>\n",
       "      <td>0.636586</td>\n",
       "      <td>-0.028299</td>\n",
       "      <td>0.347994</td>\n",
       "      <td>0.097148</td>\n",
       "      <td>-0.003425</td>\n",
       "      <td>-0.117889</td>\n",
       "      <td>-0.248649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.339636</td>\n",
       "      <td>-1.227296</td>\n",
       "      <td>0.804090</td>\n",
       "      <td>2.166705</td>\n",
       "      <td>-0.425919</td>\n",
       "      <td>-0.098879</td>\n",
       "      <td>-0.025565</td>\n",
       "      <td>-0.276390</td>\n",
       "      <td>0.645186</td>\n",
       "      <td>-0.025445</td>\n",
       "      <td>0.350539</td>\n",
       "      <td>0.096087</td>\n",
       "      <td>-0.003979</td>\n",
       "      <td>-0.114569</td>\n",
       "      <td>-0.248625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.334245</td>\n",
       "      <td>-1.229301</td>\n",
       "      <td>0.811626</td>\n",
       "      <td>2.168113</td>\n",
       "      <td>-0.445501</td>\n",
       "      <td>-0.099656</td>\n",
       "      <td>-0.018046</td>\n",
       "      <td>-0.281371</td>\n",
       "      <td>0.653437</td>\n",
       "      <td>-0.022431</td>\n",
       "      <td>0.354239</td>\n",
       "      <td>0.094637</td>\n",
       "      <td>-0.004321</td>\n",
       "      <td>-0.108799</td>\n",
       "      <td>-0.251002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.337679</td>\n",
       "      <td>-1.226728</td>\n",
       "      <td>0.816648</td>\n",
       "      <td>2.169527</td>\n",
       "      <td>-0.459347</td>\n",
       "      <td>-0.100294</td>\n",
       "      <td>-0.013336</td>\n",
       "      <td>-0.284073</td>\n",
       "      <td>0.658294</td>\n",
       "      <td>-0.020875</td>\n",
       "      <td>0.356936</td>\n",
       "      <td>0.093566</td>\n",
       "      <td>-0.004346</td>\n",
       "      <td>-0.105883</td>\n",
       "      <td>-0.252034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   principal component 1  principal component 2  principal component 3  \\\n",
       "0              -2.321657              -1.235763               0.791324   \n",
       "1              -2.337370              -1.228689               0.793358   \n",
       "2              -2.339636              -1.227296               0.804090   \n",
       "3              -2.334245              -1.229301               0.811626   \n",
       "4              -2.337679              -1.226728               0.816648   \n",
       "\n",
       "   principal component 4  principal component 5  principal component 6  \\\n",
       "0               2.155093              -0.402755              -0.097186   \n",
       "1               2.156559              -0.408887              -0.097646   \n",
       "2               2.166705              -0.425919              -0.098879   \n",
       "3               2.168113              -0.445501              -0.099656   \n",
       "4               2.169527              -0.459347              -0.100294   \n",
       "\n",
       "   principal component 7  principal component 8  principal component 9  \\\n",
       "0              -0.029608              -0.273782               0.635633   \n",
       "1              -0.028220              -0.273434               0.636586   \n",
       "2              -0.025565              -0.276390               0.645186   \n",
       "3              -0.018046              -0.281371               0.653437   \n",
       "4              -0.013336              -0.284073               0.658294   \n",
       "\n",
       "   principal component 10  principal component 11  principal component 12  \\\n",
       "0               -0.028125                0.346342                0.098701   \n",
       "1               -0.028299                0.347994                0.097148   \n",
       "2               -0.025445                0.350539                0.096087   \n",
       "3               -0.022431                0.354239                0.094637   \n",
       "4               -0.020875                0.356936                0.093566   \n",
       "\n",
       "   principal component 13  principal component 14  principal component 15  \n",
       "0               -0.004071               -0.117564               -0.249133  \n",
       "1               -0.003425               -0.117889               -0.248649  \n",
       "2               -0.003979               -0.114569               -0.248625  \n",
       "3               -0.004321               -0.108799               -0.251002  \n",
       "4               -0.004346               -0.105883               -0.252034  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y.reset_index(drop=True, inplace=True)\n",
    "merge = pd.concat([X,Y], ignore_index=False, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "      <th>principal component 3</th>\n",
       "      <th>principal component 4</th>\n",
       "      <th>principal component 5</th>\n",
       "      <th>principal component 6</th>\n",
       "      <th>principal component 7</th>\n",
       "      <th>principal component 8</th>\n",
       "      <th>principal component 9</th>\n",
       "      <th>principal component 10</th>\n",
       "      <th>principal component 11</th>\n",
       "      <th>principal component 12</th>\n",
       "      <th>principal component 13</th>\n",
       "      <th>principal component 14</th>\n",
       "      <th>principal component 15</th>\n",
       "      <th>OUTCOME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.321657</td>\n",
       "      <td>-1.235763</td>\n",
       "      <td>0.791324</td>\n",
       "      <td>2.155093</td>\n",
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       "      <td>-0.097186</td>\n",
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       "      <td>0.635633</td>\n",
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       "      <td>0.346342</td>\n",
       "      <td>0.098701</td>\n",
       "      <td>-0.004071</td>\n",
       "      <td>-0.117564</td>\n",
       "      <td>-0.249133</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.337370</td>\n",
       "      <td>-1.228689</td>\n",
       "      <td>0.793358</td>\n",
       "      <td>2.156559</td>\n",
       "      <td>-0.408887</td>\n",
       "      <td>-0.097646</td>\n",
       "      <td>-0.028220</td>\n",
       "      <td>-0.273434</td>\n",
       "      <td>0.636586</td>\n",
       "      <td>-0.028299</td>\n",
       "      <td>0.347994</td>\n",
       "      <td>0.097148</td>\n",
       "      <td>-0.003425</td>\n",
       "      <td>-0.117889</td>\n",
       "      <td>-0.248649</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.339636</td>\n",
       "      <td>-1.227296</td>\n",
       "      <td>0.804090</td>\n",
       "      <td>2.166705</td>\n",
       "      <td>-0.425919</td>\n",
       "      <td>-0.098879</td>\n",
       "      <td>-0.025565</td>\n",
       "      <td>-0.276390</td>\n",
       "      <td>0.645186</td>\n",
       "      <td>-0.025445</td>\n",
       "      <td>0.350539</td>\n",
       "      <td>0.096087</td>\n",
       "      <td>-0.003979</td>\n",
       "      <td>-0.114569</td>\n",
       "      <td>-0.248625</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.334245</td>\n",
       "      <td>-1.229301</td>\n",
       "      <td>0.811626</td>\n",
       "      <td>2.168113</td>\n",
       "      <td>-0.445501</td>\n",
       "      <td>-0.099656</td>\n",
       "      <td>-0.018046</td>\n",
       "      <td>-0.281371</td>\n",
       "      <td>0.653437</td>\n",
       "      <td>-0.022431</td>\n",
       "      <td>0.354239</td>\n",
       "      <td>0.094637</td>\n",
       "      <td>-0.004321</td>\n",
       "      <td>-0.108799</td>\n",
       "      <td>-0.251002</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.337679</td>\n",
       "      <td>-1.226728</td>\n",
       "      <td>0.816648</td>\n",
       "      <td>2.169527</td>\n",
       "      <td>-0.459347</td>\n",
       "      <td>-0.100294</td>\n",
       "      <td>-0.013336</td>\n",
       "      <td>-0.284073</td>\n",
       "      <td>0.658294</td>\n",
       "      <td>-0.020875</td>\n",
       "      <td>0.356936</td>\n",
       "      <td>0.093566</td>\n",
       "      <td>-0.004346</td>\n",
       "      <td>-0.105883</td>\n",
       "      <td>-0.252034</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   principal component 1  principal component 2  principal component 3  \\\n",
       "0              -2.321657              -1.235763               0.791324   \n",
       "1              -2.337370              -1.228689               0.793358   \n",
       "2              -2.339636              -1.227296               0.804090   \n",
       "3              -2.334245              -1.229301               0.811626   \n",
       "4              -2.337679              -1.226728               0.816648   \n",
       "\n",
       "   principal component 4  principal component 5  principal component 6  \\\n",
       "0               2.155093              -0.402755              -0.097186   \n",
       "1               2.156559              -0.408887              -0.097646   \n",
       "2               2.166705              -0.425919              -0.098879   \n",
       "3               2.168113              -0.445501              -0.099656   \n",
       "4               2.169527              -0.459347              -0.100294   \n",
       "\n",
       "   principal component 7  principal component 8  principal component 9  \\\n",
       "0              -0.029608              -0.273782               0.635633   \n",
       "1              -0.028220              -0.273434               0.636586   \n",
       "2              -0.025565              -0.276390               0.645186   \n",
       "3              -0.018046              -0.281371               0.653437   \n",
       "4              -0.013336              -0.284073               0.658294   \n",
       "\n",
       "   principal component 10  principal component 11  principal component 12  \\\n",
       "0               -0.028125                0.346342                0.098701   \n",
       "1               -0.028299                0.347994                0.097148   \n",
       "2               -0.025445                0.350539                0.096087   \n",
       "3               -0.022431                0.354239                0.094637   \n",
       "4               -0.020875                0.356936                0.093566   \n",
       "\n",
       "   principal component 13  principal component 14  principal component 15  \\\n",
       "0               -0.004071               -0.117564               -0.249133   \n",
       "1               -0.003425               -0.117889               -0.248649   \n",
       "2               -0.003979               -0.114569               -0.248625   \n",
       "3               -0.004321               -0.108799               -0.251002   \n",
       "4               -0.004346               -0.105883               -0.252034   \n",
       "\n",
       "   OUTCOME  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    395298\n",
       "1     54621\n",
       "Name: OUTCOME, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge['OUTCOME'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the TSNE plot with PCA values\n",
    "normal = merge[merge['OUTCOME'] == 0].sample(9900)\n",
    "attack = merge[merge['OUTCOME'] == 1].sample(9900)\n",
    "\n",
    "df1 = normal.append(attack).sample(frac=1).reset_index(drop=True)\n",
    "\n",
    "z = df1.drop(['OUTCOME'], axis = 1).values\n",
    "y = df1[\"OUTCOME\"].values\n",
    "\n",
    "tsne_plot(z, y, \"SWaT_PCA.png\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7 (tensorflow)",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}