data_provider/data_tests.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c6103edd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dbb7e553",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the .npy file\n",
    "data = np.load('../dataset/anomaly_detection/WADI/WADI_train.npy')\n",
    "\n",
    "# Convert the numpy array to a pandas DataFrame\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0dd0404b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>113</th>\n",
       "      <th>114</th>\n",
       "      <th>115</th>\n",
       "      <th>116</th>\n",
       "      <th>117</th>\n",
       "      <th>118</th>\n",
       "      <th>119</th>\n",
       "      <th>120</th>\n",
       "      <th>121</th>\n",
       "      <th>122</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.798629</td>\n",
       "      <td>0.300814</td>\n",
       "      <td>0.950348</td>\n",
       "      <td>0.958437</td>\n",
       "      <td>0.515464</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.477736</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.221481</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.798629</td>\n",
       "      <td>0.300814</td>\n",
       "      <td>0.950348</td>\n",
       "      <td>0.958437</td>\n",
       "      <td>0.515464</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.477736</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.221481</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.798629</td>\n",
       "      <td>0.300814</td>\n",
       "      <td>0.950348</td>\n",
       "      <td>0.958437</td>\n",
       "      <td>0.515464</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.477736</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.221481</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.798629</td>\n",
       "      <td>0.294989</td>\n",
       "      <td>0.950069</td>\n",
       "      <td>0.958490</td>\n",
       "      <td>0.516021</td>\n",
       "      <td>0.000201</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.476330</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.213922</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.798629</td>\n",
       "      <td>0.294989</td>\n",
       "      <td>0.950069</td>\n",
       "      <td>0.958490</td>\n",
       "      <td>0.516021</td>\n",
       "      <td>0.000201</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.476330</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.213922</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300885</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 123 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0         1         2         3         4         5    6    7    \\\n",
       "0  0.798629  0.300814  0.950348  0.958437  0.515464  0.000177  0.0  0.0   \n",
       "1  0.798629  0.300814  0.950348  0.958437  0.515464  0.000177  0.0  0.0   \n",
       "2  0.798629  0.300814  0.950348  0.958437  0.515464  0.000177  0.0  0.0   \n",
       "3  0.798629  0.294989  0.950069  0.958490  0.516021  0.000201  0.0  0.0   \n",
       "4  0.798629  0.294989  0.950069  0.958490  0.516021  0.000201  0.0  0.0   \n",
       "\n",
       "        8    9    ...  113  114  115  116  117  118  119       120  121  \\\n",
       "0  0.477736  0.5  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.221481  0.0   \n",
       "1  0.477736  0.5  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.221481  0.0   \n",
       "2  0.477736  0.5  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.221481  0.0   \n",
       "3  0.476330  0.5  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.213922  0.0   \n",
       "4  0.476330  0.5  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.213922  0.0   \n",
       "\n",
       "        122  \n",
       "0  0.300885  \n",
       "1  0.300885  \n",
       "2  0.300885  \n",
       "3  0.300885  \n",
       "4  0.300885  \n",
       "\n",
       "[5 rows x 123 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5949f47e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "metrics = [[\"Accuracy\", \"Precision\"],[234,123]]\n",
    "\n",
    "with open(\"results\"+\"_\"+\".csv\", 'a',newline='') as f:\n",
    "    writer = csv.writer(f)\n",
    "    for row in metrics:\n",
    "        writer.writerow(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "30616256",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the .npy file\n",
    "data = np.load('../dataset/anomaly_detection/WADI/WADI2019_no_scaler/WADI_test_label.npy')\n",
    "\n",
    "# Convert the numpy array to a pandas DataFrame\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d9176de9",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df[df[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3ef4c676",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [0]\n",
       "Index: []"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}