05_Variance and Mean.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3868707b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyreadr\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f36cebe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = './data/'\n",
    "\n",
    "train_normal_path = 'TEP_FaultFree_Training.RData'\n",
    "train_faulty_path = 'TEP_Faulty_Training.RData'\n",
    "\n",
    "# test_normal_path = 'TEP_FaultFree_Testing.RData'\n",
    "# test_faulty_path = 'TEP_Faulty_Testing.RData'\n",
    "\n",
    "train_normal_complete = pyreadr.read_r(path + train_normal_path)['fault_free_training']\n",
    "train_faulty_complete = pyreadr.read_r(path + train_faulty_path)['faulty_training']\n",
    "\n",
    "# test_normal_complete = pyreadr.read_r(path + test_normal_path)['fault_free_testing']\n",
    "# test_faulty_complete = pyreadr.read_r(path + test_faulty_path)['faulty_testing']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "52f48c22",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>faultNumber</th>\n",
       "      <th>simulationRun</th>\n",
       "      <th>sample</th>\n",
       "      <th>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
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       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>...</th>\n",
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       "      <th>xmv_8</th>\n",
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       "      <td>26.721</td>\n",
       "      <td>42.576</td>\n",
       "      <td>2705.0</td>\n",
       "      <td>...</td>\n",
       "      <td>53.414</td>\n",
       "      <td>24.588</td>\n",
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       "      <td>40.176</td>\n",
       "      <td>38.554</td>\n",
       "      <td>43.692</td>\n",
       "      <td>47.427</td>\n",
       "      <td>41.359</td>\n",
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       "      <td>3</td>\n",
       "      <td>0.25038</td>\n",
       "      <td>3660.3</td>\n",
       "      <td>4477.8</td>\n",
       "      <td>9.4426</td>\n",
       "      <td>26.875</td>\n",
       "      <td>42.070</td>\n",
       "      <td>2706.2</td>\n",
       "      <td>...</td>\n",
       "      <td>54.357</td>\n",
       "      <td>24.666</td>\n",
       "      <td>61.275</td>\n",
       "      <td>22.380</td>\n",
       "      <td>40.244</td>\n",
       "      <td>38.990</td>\n",
       "      <td>46.699</td>\n",
       "      <td>47.468</td>\n",
       "      <td>41.199</td>\n",
       "      <td>20.530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.24977</td>\n",
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       "      <td>9.4776</td>\n",
       "      <td>26.758</td>\n",
       "      <td>42.063</td>\n",
       "      <td>2707.2</td>\n",
       "      <td>...</td>\n",
       "      <td>53.946</td>\n",
       "      <td>24.725</td>\n",
       "      <td>59.856</td>\n",
       "      <td>22.277</td>\n",
       "      <td>40.257</td>\n",
       "      <td>38.072</td>\n",
       "      <td>47.541</td>\n",
       "      <td>47.658</td>\n",
       "      <td>41.643</td>\n",
       "      <td>18.089</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.29405</td>\n",
       "      <td>3679.0</td>\n",
       "      <td>4497.0</td>\n",
       "      <td>9.3381</td>\n",
       "      <td>26.889</td>\n",
       "      <td>42.650</td>\n",
       "      <td>2705.1</td>\n",
       "      <td>...</td>\n",
       "      <td>53.658</td>\n",
       "      <td>28.797</td>\n",
       "      <td>60.717</td>\n",
       "      <td>21.947</td>\n",
       "      <td>39.144</td>\n",
       "      <td>41.955</td>\n",
       "      <td>47.645</td>\n",
       "      <td>47.346</td>\n",
       "      <td>41.507</td>\n",
       "      <td>18.461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
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      ],
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       "   faultNumber  simulationRun  sample  xmeas_1  xmeas_2  xmeas_3  xmeas_4  \\\n",
       "0          0.0            1.0       1  0.25038   3674.0   4529.0   9.2320   \n",
       "1          0.0            1.0       2  0.25109   3659.4   4556.6   9.4264   \n",
       "2          0.0            1.0       3  0.25038   3660.3   4477.8   9.4426   \n",
       "3          0.0            1.0       4  0.24977   3661.3   4512.1   9.4776   \n",
       "4          0.0            1.0       5  0.29405   3679.0   4497.0   9.3381   \n",
       "\n",
       "   xmeas_5  xmeas_6  xmeas_7  ...   xmv_2   xmv_3   xmv_4   xmv_5   xmv_6  \\\n",
       "0   26.889   42.402   2704.3  ...  53.744  24.657  62.544  22.137  39.935   \n",
       "1   26.721   42.576   2705.0  ...  53.414  24.588  59.259  22.084  40.176   \n",
       "2   26.875   42.070   2706.2  ...  54.357  24.666  61.275  22.380  40.244   \n",
       "3   26.758   42.063   2707.2  ...  53.946  24.725  59.856  22.277  40.257   \n",
       "4   26.889   42.650   2705.1  ...  53.658  28.797  60.717  21.947  39.144   \n",
       "\n",
       "    xmv_7   xmv_8   xmv_9  xmv_10  xmv_11  \n",
       "0  42.323  47.757  47.510  41.258  18.447  \n",
       "1  38.554  43.692  47.427  41.359  17.194  \n",
       "2  38.990  46.699  47.468  41.199  20.530  \n",
       "3  38.072  47.541  47.658  41.643  18.089  \n",
       "4  41.955  47.645  47.346  41.507  18.461  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_normal_complete.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "617d7ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>faultNumber</th>\n",
       "      <th>simulationRun</th>\n",
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       "      <th>xmeas_7</th>\n",
       "      <th>...</th>\n",
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       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
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       "      <td>0.25109</td>\n",
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       "      <td>4556.6</td>\n",
       "      <td>9.4264</td>\n",
       "      <td>26.721</td>\n",
       "      <td>42.576</td>\n",
       "      <td>2705.0</td>\n",
       "      <td>...</td>\n",
       "      <td>53.414</td>\n",
       "      <td>24.588</td>\n",
       "      <td>59.259</td>\n",
       "      <td>22.084</td>\n",
       "      <td>40.176</td>\n",
       "      <td>38.554</td>\n",
       "      <td>43.692</td>\n",
       "      <td>47.427</td>\n",
       "      <td>41.359</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.25038</td>\n",
       "      <td>3660.3</td>\n",
       "      <td>4477.8</td>\n",
       "      <td>9.4426</td>\n",
       "      <td>26.875</td>\n",
       "      <td>42.070</td>\n",
       "      <td>2706.2</td>\n",
       "      <td>...</td>\n",
       "      <td>54.357</td>\n",
       "      <td>24.666</td>\n",
       "      <td>61.275</td>\n",
       "      <td>22.380</td>\n",
       "      <td>40.244</td>\n",
       "      <td>38.990</td>\n",
       "      <td>46.699</td>\n",
       "      <td>47.468</td>\n",
       "      <td>41.199</td>\n",
       "      <td>20.530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.24977</td>\n",
       "      <td>3661.3</td>\n",
       "      <td>4512.1</td>\n",
       "      <td>9.4776</td>\n",
       "      <td>26.758</td>\n",
       "      <td>42.063</td>\n",
       "      <td>2707.2</td>\n",
       "      <td>...</td>\n",
       "      <td>53.946</td>\n",
       "      <td>24.725</td>\n",
       "      <td>59.856</td>\n",
       "      <td>22.277</td>\n",
       "      <td>40.257</td>\n",
       "      <td>38.072</td>\n",
       "      <td>47.541</td>\n",
       "      <td>47.658</td>\n",
       "      <td>41.643</td>\n",
       "      <td>18.089</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.29405</td>\n",
       "      <td>3679.0</td>\n",
       "      <td>4497.0</td>\n",
       "      <td>9.3381</td>\n",
       "      <td>26.889</td>\n",
       "      <td>42.650</td>\n",
       "      <td>2705.1</td>\n",
       "      <td>...</td>\n",
       "      <td>53.658</td>\n",
       "      <td>28.797</td>\n",
       "      <td>60.717</td>\n",
       "      <td>21.947</td>\n",
       "      <td>39.144</td>\n",
       "      <td>41.955</td>\n",
       "      <td>47.645</td>\n",
       "      <td>47.346</td>\n",
       "      <td>41.507</td>\n",
       "      <td>18.461</td>\n",
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      "text/plain": [
       "   faultNumber  simulationRun  sample  xmeas_1  xmeas_2  xmeas_3  xmeas_4  \\\n",
       "0            1            1.0       1  0.25038   3674.0   4529.0   9.2320   \n",
       "1            1            1.0       2  0.25109   3659.4   4556.6   9.4264   \n",
       "2            1            1.0       3  0.25038   3660.3   4477.8   9.4426   \n",
       "3            1            1.0       4  0.24977   3661.3   4512.1   9.4776   \n",
       "4            1            1.0       5  0.29405   3679.0   4497.0   9.3381   \n",
       "\n",
       "   xmeas_5  xmeas_6  xmeas_7  ...   xmv_2   xmv_3   xmv_4   xmv_5   xmv_6  \\\n",
       "0   26.889   42.402   2704.3  ...  53.744  24.657  62.544  22.137  39.935   \n",
       "1   26.721   42.576   2705.0  ...  53.414  24.588  59.259  22.084  40.176   \n",
       "2   26.875   42.070   2706.2  ...  54.357  24.666  61.275  22.380  40.244   \n",
       "3   26.758   42.063   2707.2  ...  53.946  24.725  59.856  22.277  40.257   \n",
       "4   26.889   42.650   2705.1  ...  53.658  28.797  60.717  21.947  39.144   \n",
       "\n",
       "    xmv_7   xmv_8   xmv_9  xmv_10  xmv_11  \n",
       "0  42.323  47.757  47.510  41.258  18.447  \n",
       "1  38.554  43.692  47.427  41.359  17.194  \n",
       "2  38.990  46.699  47.468  41.199  20.530  \n",
       "3  38.072  47.541  47.658  41.643  18.089  \n",
       "4  41.955  47.645  47.346  41.507  18.461  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_faulty_complete.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cb72ba9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = train_normal_complete.columns[3:55]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9190af2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b4fb88ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_prob_features = len(cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5f92790b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "438782fb",
   "metadata": {},
   "source": [
    "### Train normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b2bc090b",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b7965327",
   "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>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.475455</td>\n",
       "      <td>0.523545</td>\n",
       "      <td>0.484252</td>\n",
       "      <td>0.496565</td>\n",
       "      <td>0.509471</td>\n",
       "      <td>0.506657</td>\n",
       "      <td>0.490172</td>\n",
       "      <td>0.483362</td>\n",
       "      <td>0.529642</td>\n",
       "      <td>0.473183</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466924</td>\n",
       "      <td>0.480118</td>\n",
       "      <td>0.509257</td>\n",
       "      <td>0.516536</td>\n",
       "      <td>0.472463</td>\n",
       "      <td>0.483312</td>\n",
       "      <td>0.511119</td>\n",
       "      <td>0.481638</td>\n",
       "      <td>0.471319</td>\n",
       "      <td>0.478722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.114972</td>\n",
       "      <td>0.117146</td>\n",
       "      <td>0.117945</td>\n",
       "      <td>0.114773</td>\n",
       "      <td>0.113308</td>\n",
       "      <td>0.117368</td>\n",
       "      <td>0.113015</td>\n",
       "      <td>0.111506</td>\n",
       "      <td>0.112460</td>\n",
       "      <td>0.114341</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116710</td>\n",
       "      <td>0.115498</td>\n",
       "      <td>0.118562</td>\n",
       "      <td>0.111041</td>\n",
       "      <td>0.108233</td>\n",
       "      <td>0.125519</td>\n",
       "      <td>0.125329</td>\n",
       "      <td>0.122042</td>\n",
       "      <td>0.101071</td>\n",
       "      <td>0.114822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.475455  0.523545  0.484252  0.496565  0.509471  0.506657   \n",
       "variance  0.114972  0.117146  0.117945  0.114773  0.113308  0.117368   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3  \\\n",
       "mean      0.490172  0.483362  0.529642  0.473183  ...  0.466924  0.480118   \n",
       "variance  0.113015  0.111506  0.112460  0.114341  ...  0.116710  0.115498   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.509257  0.516536  0.472463  0.483312  0.511119  0.481638   \n",
       "variance  0.118562  0.111041  0.108233  0.125519  0.125329  0.122042   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.471319  0.478722  \n",
       "variance  0.101071  0.114822  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(scaler.fit_transform(X = train_normal_complete[(train_normal_complete.faultNumber == 0) & (train_normal_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d073ce2d",
   "metadata": {},
   "source": [
    "### Train Faulty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4b2a0947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([20, 19, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "            1],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_faulty_complete.faultNumber.value_counts().index\n",
    "# from the result we can see that there is 20 different\n",
    "# faulty type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2f31a1d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.680747</td>\n",
       "      <td>0.500849</td>\n",
       "      <td>0.501459</td>\n",
       "      <td>0.548355</td>\n",
       "      <td>0.508912</td>\n",
       "      <td>0.490065</td>\n",
       "      <td>0.372495</td>\n",
       "      <td>0.504687</td>\n",
       "      <td>0.499941</td>\n",
       "      <td>0.504185</td>\n",
       "      <td>...</td>\n",
       "      <td>0.513960</td>\n",
       "      <td>0.679713</td>\n",
       "      <td>0.508735</td>\n",
       "      <td>0.463179</td>\n",
       "      <td>0.498315</td>\n",
       "      <td>0.481409</td>\n",
       "      <td>0.496769</td>\n",
       "      <td>0.386152</td>\n",
       "      <td>0.492432</td>\n",
       "      <td>0.474835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.174527</td>\n",
       "      <td>0.113365</td>\n",
       "      <td>0.122547</td>\n",
       "      <td>0.153856</td>\n",
       "      <td>0.112852</td>\n",
       "      <td>0.114099</td>\n",
       "      <td>0.161318</td>\n",
       "      <td>0.127491</td>\n",
       "      <td>0.100474</td>\n",
       "      <td>0.119555</td>\n",
       "      <td>...</td>\n",
       "      <td>0.124692</td>\n",
       "      <td>0.174845</td>\n",
       "      <td>0.124893</td>\n",
       "      <td>0.145309</td>\n",
       "      <td>0.122364</td>\n",
       "      <td>0.121821</td>\n",
       "      <td>0.123596</td>\n",
       "      <td>0.241598</td>\n",
       "      <td>0.116913</td>\n",
       "      <td>0.110175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.680747  0.500849  0.501459  0.548355  0.508912  0.490065   \n",
       "variance  0.174527  0.113365  0.122547  0.153856  0.112852  0.114099   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3  \\\n",
       "mean      0.372495  0.504687  0.499941  0.504185  ...  0.513960  0.679713   \n",
       "variance  0.161318  0.127491  0.100474  0.119555  ...  0.124692  0.174845   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.508735  0.463179  0.498315  0.481409  0.496769  0.386152   \n",
       "variance  0.124893  0.145309  0.122364  0.121821  0.123596  0.241598   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.492432  0.474835  \n",
       "variance  0.116913  0.110175  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 1\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 1) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a56fa646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.524938</td>\n",
       "      <td>0.539477</td>\n",
       "      <td>0.586323</td>\n",
       "      <td>0.567615</td>\n",
       "      <td>0.509475</td>\n",
       "      <td>0.522741</td>\n",
       "      <td>0.381073</td>\n",
       "      <td>0.517898</td>\n",
       "      <td>0.530039</td>\n",
       "      <td>0.706364</td>\n",
       "      <td>...</td>\n",
       "      <td>0.584429</td>\n",
       "      <td>0.526394</td>\n",
       "      <td>0.529088</td>\n",
       "      <td>0.446941</td>\n",
       "      <td>0.705816</td>\n",
       "      <td>0.486907</td>\n",
       "      <td>0.513312</td>\n",
       "      <td>0.374403</td>\n",
       "      <td>0.487384</td>\n",
       "      <td>0.478802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.115587</td>\n",
       "      <td>0.109855</td>\n",
       "      <td>0.131329</td>\n",
       "      <td>0.124729</td>\n",
       "      <td>0.113364</td>\n",
       "      <td>0.121129</td>\n",
       "      <td>0.170005</td>\n",
       "      <td>0.113320</td>\n",
       "      <td>0.112980</td>\n",
       "      <td>0.210955</td>\n",
       "      <td>...</td>\n",
       "      <td>0.137222</td>\n",
       "      <td>0.114711</td>\n",
       "      <td>0.117294</td>\n",
       "      <td>0.135720</td>\n",
       "      <td>0.212051</td>\n",
       "      <td>0.125697</td>\n",
       "      <td>0.124662</td>\n",
       "      <td>0.233649</td>\n",
       "      <td>0.101691</td>\n",
       "      <td>0.115102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.524938  0.539477  0.586323  0.567615  0.509475  0.522741   \n",
       "variance  0.115587  0.109855  0.131329  0.124729  0.113364  0.121129   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3  \\\n",
       "mean      0.381073  0.517898  0.530039  0.706364  ...  0.584429  0.526394   \n",
       "variance  0.170005  0.113320  0.112980  0.210955  ...  0.137222  0.114711   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.529088  0.446941  0.705816  0.486907  0.513312  0.374403   \n",
       "variance  0.117294  0.135720  0.212051  0.125697  0.124662  0.233649   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.487384  0.478802  \n",
       "variance  0.101691  0.115102  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 2\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 2) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f2e41ed3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.474994</td>\n",
       "      <td>0.523528</td>\n",
       "      <td>0.485352</td>\n",
       "      <td>0.496197</td>\n",
       "      <td>0.509473</td>\n",
       "      <td>0.505882</td>\n",
       "      <td>0.490862</td>\n",
       "      <td>0.482972</td>\n",
       "      <td>0.529712</td>\n",
       "      <td>0.474299</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466856</td>\n",
       "      <td>0.479941</td>\n",
       "      <td>0.510317</td>\n",
       "      <td>0.517145</td>\n",
       "      <td>0.473565</td>\n",
       "      <td>0.483268</td>\n",
       "      <td>0.511223</td>\n",
       "      <td>0.481923</td>\n",
       "      <td>0.471980</td>\n",
       "      <td>0.478709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.114972</td>\n",
       "      <td>0.117151</td>\n",
       "      <td>0.118235</td>\n",
       "      <td>0.114807</td>\n",
       "      <td>0.113308</td>\n",
       "      <td>0.117061</td>\n",
       "      <td>0.113591</td>\n",
       "      <td>0.112043</td>\n",
       "      <td>0.112436</td>\n",
       "      <td>0.114217</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116717</td>\n",
       "      <td>0.115440</td>\n",
       "      <td>0.118306</td>\n",
       "      <td>0.111538</td>\n",
       "      <td>0.108128</td>\n",
       "      <td>0.125530</td>\n",
       "      <td>0.125276</td>\n",
       "      <td>0.122002</td>\n",
       "      <td>0.101164</td>\n",
       "      <td>0.114822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.474994  0.523528  0.485352  0.496197  0.509473  0.505882   \n",
       "variance  0.114972  0.117151  0.118235  0.114807  0.113308  0.117061   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3  \\\n",
       "mean      0.490862  0.482972  0.529712  0.474299  ...  0.466856  0.479941   \n",
       "variance  0.113591  0.112043  0.112436  0.114217  ...  0.116717  0.115440   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.510317  0.517145  0.473565  0.483268  0.511223  0.481923   \n",
       "variance  0.118306  0.111538  0.108128  0.125530  0.125276  0.122002   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.471980  0.478709  \n",
       "variance  0.101164  0.114822  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 3\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 3) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7da1131d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.475078</td>\n",
       "      <td>0.523541</td>\n",
       "      <td>0.484112</td>\n",
       "      <td>0.496367</td>\n",
       "      <td>0.509731</td>\n",
       "      <td>0.506917</td>\n",
       "      <td>0.490181</td>\n",
       "      <td>0.484184</td>\n",
       "      <td>0.299999</td>\n",
       "      <td>0.473042</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466930</td>\n",
       "      <td>0.479807</td>\n",
       "      <td>0.509542</td>\n",
       "      <td>0.516769</td>\n",
       "      <td>0.472319</td>\n",
       "      <td>0.483354</td>\n",
       "      <td>0.511092</td>\n",
       "      <td>0.480258</td>\n",
       "      <td>0.593705</td>\n",
       "      <td>0.478764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.114920</td>\n",
       "      <td>0.117143</td>\n",
       "      <td>0.117914</td>\n",
       "      <td>0.114705</td>\n",
       "      <td>0.113247</td>\n",
       "      <td>0.117309</td>\n",
       "      <td>0.113024</td>\n",
       "      <td>0.111510</td>\n",
       "      <td>0.065990</td>\n",
       "      <td>0.114357</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116713</td>\n",
       "      <td>0.115436</td>\n",
       "      <td>0.118541</td>\n",
       "      <td>0.111170</td>\n",
       "      <td>0.108237</td>\n",
       "      <td>0.125508</td>\n",
       "      <td>0.125321</td>\n",
       "      <td>0.122409</td>\n",
       "      <td>0.096018</td>\n",
       "      <td>0.114811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.475078  0.523541  0.484112  0.496367  0.509731  0.506917   \n",
       "variance  0.114920  0.117143  0.117914  0.114705  0.113247  0.117309   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3  \\\n",
       "mean      0.490181  0.484184  0.299999  0.473042  ...  0.466930  0.479807   \n",
       "variance  0.113024  0.111510  0.065990  0.114357  ...  0.116713  0.115436   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.509542  0.516769  0.472319  0.483354  0.511092  0.480258   \n",
       "variance  0.118541  0.111170  0.108237  0.125508  0.125321  0.122409   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.593705  0.478764  \n",
       "variance  0.096018  0.114811  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 4\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 4) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b88a6115",
   "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>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.483474</td>\n",
       "      <td>0.556086</td>\n",
       "      <td>0.522616</td>\n",
       "      <td>0.48488</td>\n",
       "      <td>0.509744</td>\n",
       "      <td>0.534649</td>\n",
       "      <td>0.485080</td>\n",
       "      <td>0.521961</td>\n",
       "      <td>0.500175</td>\n",
       "      <td>0.565101</td>\n",
       "      <td>...</td>\n",
       "      <td>0.547082</td>\n",
       "      <td>0.484846</td>\n",
       "      <td>0.496803</td>\n",
       "      <td>0.455651</td>\n",
       "      <td>0.538180</td>\n",
       "      <td>0.486156</td>\n",
       "      <td>0.521602</td>\n",
       "      <td>0.495636</td>\n",
       "      <td>0.551851</td>\n",
       "      <td>0.536809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.125491</td>\n",
       "      <td>0.107313</td>\n",
       "      <td>0.110070</td>\n",
       "      <td>0.12861</td>\n",
       "      <td>0.113484</td>\n",
       "      <td>0.119035</td>\n",
       "      <td>0.126947</td>\n",
       "      <td>0.115798</td>\n",
       "      <td>0.109893</td>\n",
       "      <td>0.116545</td>\n",
       "      <td>...</td>\n",
       "      <td>0.115519</td>\n",
       "      <td>0.124587</td>\n",
       "      <td>0.112126</td>\n",
       "      <td>0.135306</td>\n",
       "      <td>0.115208</td>\n",
       "      <td>0.124527</td>\n",
       "      <td>0.123651</td>\n",
       "      <td>0.125187</td>\n",
       "      <td>0.106983</td>\n",
       "      <td>0.108789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3  xmeas_4   xmeas_5   xmeas_6   xmeas_7  \\\n",
       "mean      0.483474  0.556086  0.522616  0.48488  0.509744  0.534649  0.485080   \n",
       "variance  0.125491  0.107313  0.110070  0.12861  0.113484  0.119035  0.126947   \n",
       "\n",
       "           xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3     xmv_4  \\\n",
       "mean      0.521961  0.500175  0.565101  ...  0.547082  0.484846  0.496803   \n",
       "variance  0.115798  0.109893  0.116545  ...  0.115519  0.124587  0.112126   \n",
       "\n",
       "             xmv_5     xmv_6     xmv_7     xmv_8     xmv_9    xmv_10    xmv_11  \n",
       "mean      0.455651  0.538180  0.486156  0.521602  0.495636  0.551851  0.536809  \n",
       "variance  0.135306  0.115208  0.124527  0.123651  0.125187  0.106983  0.108789  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 5\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 5) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6d9faad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.041868</td>\n",
       "      <td>0.465242</td>\n",
       "      <td>0.390239</td>\n",
       "      <td>0.605146</td>\n",
       "      <td>0.473240</td>\n",
       "      <td>0.542422</td>\n",
       "      <td>0.840696</td>\n",
       "      <td>0.350478</td>\n",
       "      <td>0.510865</td>\n",
       "      <td>0.261451</td>\n",
       "      <td>...</td>\n",
       "      <td>0.701873</td>\n",
       "      <td>0.945790</td>\n",
       "      <td>0.519305</td>\n",
       "      <td>0.292913</td>\n",
       "      <td>0.286354</td>\n",
       "      <td>0.519311</td>\n",
       "      <td>0.496519</td>\n",
       "      <td>0.822876</td>\n",
       "      <td>0.501693</td>\n",
       "      <td>0.273441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.141666</td>\n",
       "      <td>0.108823</td>\n",
       "      <td>0.171020</td>\n",
       "      <td>0.118547</td>\n",
       "      <td>0.073104</td>\n",
       "      <td>0.076118</td>\n",
       "      <td>0.292878</td>\n",
       "      <td>0.107779</td>\n",
       "      <td>0.110072</td>\n",
       "      <td>0.194177</td>\n",
       "      <td>...</td>\n",
       "      <td>0.175775</td>\n",
       "      <td>0.188925</td>\n",
       "      <td>0.327927</td>\n",
       "      <td>0.378244</td>\n",
       "      <td>0.302643</td>\n",
       "      <td>0.069808</td>\n",
       "      <td>0.069523</td>\n",
       "      <td>0.337569</td>\n",
       "      <td>0.368380</td>\n",
       "      <td>0.175030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.041868  0.465242  0.390239  0.605146  0.473240  0.542422   \n",
       "variance  0.141666  0.108823  0.171020  0.118547  0.073104  0.076118   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3  \\\n",
       "mean      0.840696  0.350478  0.510865  0.261451  ...  0.701873  0.945790   \n",
       "variance  0.292878  0.107779  0.110072  0.194177  ...  0.175775  0.188925   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.519305  0.292913  0.286354  0.519311  0.496519  0.822876   \n",
       "variance  0.327927  0.378244  0.302643  0.069808  0.069523  0.337569   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.501693  0.273441  \n",
       "variance  0.368380  0.175030  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 6\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 6) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c65ac303",
   "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>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.399962</td>\n",
       "      <td>0.484565</td>\n",
       "      <td>0.459826</td>\n",
       "      <td>0.517095</td>\n",
       "      <td>0.509434</td>\n",
       "      <td>0.491596</td>\n",
       "      <td>0.441428</td>\n",
       "      <td>0.531561</td>\n",
       "      <td>0.480179</td>\n",
       "      <td>0.559316</td>\n",
       "      <td>...</td>\n",
       "      <td>0.464247</td>\n",
       "      <td>0.400655</td>\n",
       "      <td>0.610230</td>\n",
       "      <td>0.451671</td>\n",
       "      <td>0.535024</td>\n",
       "      <td>0.508313</td>\n",
       "      <td>0.522015</td>\n",
       "      <td>0.465443</td>\n",
       "      <td>0.532583</td>\n",
       "      <td>0.476150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.167653</td>\n",
       "      <td>0.109226</td>\n",
       "      <td>0.140119</td>\n",
       "      <td>0.138332</td>\n",
       "      <td>0.113471</td>\n",
       "      <td>0.105607</td>\n",
       "      <td>0.143792</td>\n",
       "      <td>0.135246</td>\n",
       "      <td>0.090718</td>\n",
       "      <td>0.126328</td>\n",
       "      <td>...</td>\n",
       "      <td>0.136889</td>\n",
       "      <td>0.168398</td>\n",
       "      <td>0.132166</td>\n",
       "      <td>0.162367</td>\n",
       "      <td>0.126966</td>\n",
       "      <td>0.122309</td>\n",
       "      <td>0.124041</td>\n",
       "      <td>0.175184</td>\n",
       "      <td>0.126377</td>\n",
       "      <td>0.115945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.399962  0.484565  0.459826  0.517095  0.509434  0.491596   \n",
       "variance  0.167653  0.109226  0.140119  0.138332  0.113471  0.105607   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3  \\\n",
       "mean      0.441428  0.531561  0.480179  0.559316  ...  0.464247  0.400655   \n",
       "variance  0.143792  0.135246  0.090718  0.126328  ...  0.136889  0.168398   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.610230  0.451671  0.535024  0.508313  0.522015  0.465443   \n",
       "variance  0.132166  0.162367  0.126966  0.122309  0.124041  0.175184   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.532583  0.476150  \n",
       "variance  0.126377  0.115945  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 7\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 7) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3b01b447",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.266158</td>\n",
       "      <td>0.481645</td>\n",
       "      <td>0.515940</td>\n",
       "      <td>0.508022</td>\n",
       "      <td>0.509737</td>\n",
       "      <td>0.494795</td>\n",
       "      <td>0.437249</td>\n",
       "      <td>0.486450</td>\n",
       "      <td>0.476386</td>\n",
       "      <td>0.497211</td>\n",
       "      <td>...</td>\n",
       "      <td>0.490947</td>\n",
       "      <td>0.266485</td>\n",
       "      <td>0.517046</td>\n",
       "      <td>0.405039</td>\n",
       "      <td>0.484318</td>\n",
       "      <td>0.487998</td>\n",
       "      <td>0.498325</td>\n",
       "      <td>0.510561</td>\n",
       "      <td>0.549560</td>\n",
       "      <td>0.507250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.186247</td>\n",
       "      <td>0.109509</td>\n",
       "      <td>0.098319</td>\n",
       "      <td>0.119062</td>\n",
       "      <td>0.113082</td>\n",
       "      <td>0.102113</td>\n",
       "      <td>0.114780</td>\n",
       "      <td>0.108112</td>\n",
       "      <td>0.109409</td>\n",
       "      <td>0.118211</td>\n",
       "      <td>...</td>\n",
       "      <td>0.102127</td>\n",
       "      <td>0.186087</td>\n",
       "      <td>0.114017</td>\n",
       "      <td>0.092894</td>\n",
       "      <td>0.114313</td>\n",
       "      <td>0.126131</td>\n",
       "      <td>0.125479</td>\n",
       "      <td>0.184865</td>\n",
       "      <td>0.095266</td>\n",
       "      <td>0.115996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.266158  0.481645  0.515940  0.508022  0.509737  0.494795   \n",
       "variance  0.186247  0.109509  0.098319  0.119062  0.113082  0.102113   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3  \\\n",
       "mean      0.437249  0.486450  0.476386  0.497211  ...  0.490947  0.266485   \n",
       "variance  0.114780  0.108112  0.109409  0.118211  ...  0.102127  0.186087   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.517046  0.405039  0.484318  0.487998  0.498325  0.510561   \n",
       "variance  0.114017  0.092894  0.114313  0.126131  0.125479  0.184865   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.549560  0.507250  \n",
       "variance  0.095266  0.115996  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 8\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 8) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ddbdd34c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.476663</td>\n",
       "      <td>0.523743</td>\n",
       "      <td>0.494294</td>\n",
       "      <td>0.496139</td>\n",
       "      <td>0.509208</td>\n",
       "      <td>0.514798</td>\n",
       "      <td>0.489006</td>\n",
       "      <td>0.501343</td>\n",
       "      <td>0.500226</td>\n",
       "      <td>0.475938</td>\n",
       "      <td>...</td>\n",
       "      <td>0.475510</td>\n",
       "      <td>0.485898</td>\n",
       "      <td>0.508979</td>\n",
       "      <td>0.530963</td>\n",
       "      <td>0.461542</td>\n",
       "      <td>0.483690</td>\n",
       "      <td>0.510791</td>\n",
       "      <td>0.474896</td>\n",
       "      <td>0.454628</td>\n",
       "      <td>0.479020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.114845</td>\n",
       "      <td>0.116535</td>\n",
       "      <td>0.115375</td>\n",
       "      <td>0.114712</td>\n",
       "      <td>0.113368</td>\n",
       "      <td>0.116247</td>\n",
       "      <td>0.111016</td>\n",
       "      <td>0.110828</td>\n",
       "      <td>0.106969</td>\n",
       "      <td>0.112961</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116243</td>\n",
       "      <td>0.115277</td>\n",
       "      <td>0.121120</td>\n",
       "      <td>0.110760</td>\n",
       "      <td>0.109767</td>\n",
       "      <td>0.125564</td>\n",
       "      <td>0.125210</td>\n",
       "      <td>0.120684</td>\n",
       "      <td>0.103877</td>\n",
       "      <td>0.114727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.476663  0.523743  0.494294  0.496139  0.509208  0.514798   \n",
       "variance  0.114845  0.116535  0.115375  0.114712  0.113368  0.116247   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3  \\\n",
       "mean      0.489006  0.501343  0.500226  0.475938  ...  0.475510  0.485898   \n",
       "variance  0.111016  0.110828  0.106969  0.112961  ...  0.116243  0.115277   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.508979  0.530963  0.461542  0.483690  0.510791  0.474896   \n",
       "variance  0.121120  0.110760  0.109767  0.125564  0.125210  0.120684   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.454628  0.479020  \n",
       "variance  0.103877  0.114727  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 9\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 9) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d09f2325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.491848</td>\n",
       "      <td>0.515479</td>\n",
       "      <td>0.460434</td>\n",
       "      <td>0.495495</td>\n",
       "      <td>0.509208</td>\n",
       "      <td>0.515447</td>\n",
       "      <td>0.470704</td>\n",
       "      <td>0.462723</td>\n",
       "      <td>0.529611</td>\n",
       "      <td>0.493578</td>\n",
       "      <td>...</td>\n",
       "      <td>0.498905</td>\n",
       "      <td>0.494675</td>\n",
       "      <td>0.479235</td>\n",
       "      <td>0.501401</td>\n",
       "      <td>0.472186</td>\n",
       "      <td>0.483656</td>\n",
       "      <td>0.512294</td>\n",
       "      <td>0.445317</td>\n",
       "      <td>0.51799</td>\n",
       "      <td>0.477003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.116749</td>\n",
       "      <td>0.119820</td>\n",
       "      <td>0.111489</td>\n",
       "      <td>0.107459</td>\n",
       "      <td>0.113376</td>\n",
       "      <td>0.118390</td>\n",
       "      <td>0.126864</td>\n",
       "      <td>0.108434</td>\n",
       "      <td>0.113302</td>\n",
       "      <td>0.115977</td>\n",
       "      <td>...</td>\n",
       "      <td>0.121136</td>\n",
       "      <td>0.116123</td>\n",
       "      <td>0.108611</td>\n",
       "      <td>0.115505</td>\n",
       "      <td>0.116417</td>\n",
       "      <td>0.125515</td>\n",
       "      <td>0.125101</td>\n",
       "      <td>0.119655</td>\n",
       "      <td>0.10612</td>\n",
       "      <td>0.114370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.491848  0.515479  0.460434  0.495495  0.509208  0.515447   \n",
       "variance  0.116749  0.119820  0.111489  0.107459  0.113376  0.118390   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3  \\\n",
       "mean      0.470704  0.462723  0.529611  0.493578  ...  0.498905  0.494675   \n",
       "variance  0.126864  0.108434  0.113302  0.115977  ...  0.121136  0.116123   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9   xmv_10  \\\n",
       "mean      0.479235  0.501401  0.472186  0.483656  0.512294  0.445317  0.51799   \n",
       "variance  0.108611  0.115505  0.116417  0.125515  0.125101  0.119655  0.10612   \n",
       "\n",
       "            xmv_11  \n",
       "mean      0.477003  \n",
       "variance  0.114370  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 10\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 10) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "95f8c294",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.474186</td>\n",
       "      <td>0.515650</td>\n",
       "      <td>0.479476</td>\n",
       "      <td>0.497344</td>\n",
       "      <td>0.509188</td>\n",
       "      <td>0.509143</td>\n",
       "      <td>0.499925</td>\n",
       "      <td>0.482978</td>\n",
       "      <td>0.486871</td>\n",
       "      <td>0.481300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.470501</td>\n",
       "      <td>0.476248</td>\n",
       "      <td>0.510200</td>\n",
       "      <td>0.522921</td>\n",
       "      <td>0.477801</td>\n",
       "      <td>0.481078</td>\n",
       "      <td>0.510095</td>\n",
       "      <td>0.476579</td>\n",
       "      <td>0.511814</td>\n",
       "      <td>0.483237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.113692</td>\n",
       "      <td>0.117925</td>\n",
       "      <td>0.117032</td>\n",
       "      <td>0.114894</td>\n",
       "      <td>0.113129</td>\n",
       "      <td>0.118361</td>\n",
       "      <td>0.111308</td>\n",
       "      <td>0.111390</td>\n",
       "      <td>0.106229</td>\n",
       "      <td>0.115768</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116953</td>\n",
       "      <td>0.114769</td>\n",
       "      <td>0.119139</td>\n",
       "      <td>0.107720</td>\n",
       "      <td>0.110495</td>\n",
       "      <td>0.125074</td>\n",
       "      <td>0.125276</td>\n",
       "      <td>0.123297</td>\n",
       "      <td>0.169959</td>\n",
       "      <td>0.116189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.474186  0.515650  0.479476  0.497344  0.509188  0.509143   \n",
       "variance  0.113692  0.117925  0.117032  0.114894  0.113129  0.118361   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3  \\\n",
       "mean      0.499925  0.482978  0.486871  0.481300  ...  0.470501  0.476248   \n",
       "variance  0.111308  0.111390  0.106229  0.115768  ...  0.116953  0.114769   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.510200  0.522921  0.477801  0.481078  0.510095  0.476579   \n",
       "variance  0.119139  0.107720  0.110495  0.125074  0.125276  0.123297   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.511814  0.483237  \n",
       "variance  0.169959  0.116189  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 11\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 11) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8953cdee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.298264</td>\n",
       "      <td>0.510777</td>\n",
       "      <td>0.558171</td>\n",
       "      <td>0.487641</td>\n",
       "      <td>0.519468</td>\n",
       "      <td>0.512856</td>\n",
       "      <td>0.438979</td>\n",
       "      <td>0.474588</td>\n",
       "      <td>0.524361</td>\n",
       "      <td>0.543731</td>\n",
       "      <td>...</td>\n",
       "      <td>0.171321</td>\n",
       "      <td>0.29708</td>\n",
       "      <td>0.614566</td>\n",
       "      <td>0.230094</td>\n",
       "      <td>0.663996</td>\n",
       "      <td>0.490477</td>\n",
       "      <td>0.497286</td>\n",
       "      <td>0.530498</td>\n",
       "      <td>0.413892</td>\n",
       "      <td>0.246187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.172235</td>\n",
       "      <td>0.119454</td>\n",
       "      <td>0.134867</td>\n",
       "      <td>0.120506</td>\n",
       "      <td>0.114048</td>\n",
       "      <td>0.109356</td>\n",
       "      <td>0.159228</td>\n",
       "      <td>0.117349</td>\n",
       "      <td>0.104428</td>\n",
       "      <td>0.128534</td>\n",
       "      <td>...</td>\n",
       "      <td>0.110105</td>\n",
       "      <td>0.17163</td>\n",
       "      <td>0.053365</td>\n",
       "      <td>0.085864</td>\n",
       "      <td>0.133682</td>\n",
       "      <td>0.120619</td>\n",
       "      <td>0.118558</td>\n",
       "      <td>0.189442</td>\n",
       "      <td>0.068631</td>\n",
       "      <td>0.044929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.298264  0.510777  0.558171  0.487641  0.519468  0.512856   \n",
       "variance  0.172235  0.119454  0.134867  0.120506  0.114048  0.109356   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2    xmv_3  \\\n",
       "mean      0.438979  0.474588  0.524361  0.543731  ...  0.171321  0.29708   \n",
       "variance  0.159228  0.117349  0.104428  0.128534  ...  0.110105  0.17163   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.614566  0.230094  0.663996  0.490477  0.497286  0.530498   \n",
       "variance  0.053365  0.085864  0.133682  0.120619  0.118558  0.189442   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.413892  0.246187  \n",
       "variance  0.068631  0.044929  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 12\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 12) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ad7a5b29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.356265</td>\n",
       "      <td>0.528878</td>\n",
       "      <td>0.527163</td>\n",
       "      <td>0.514017</td>\n",
       "      <td>0.506237</td>\n",
       "      <td>0.522746</td>\n",
       "      <td>0.460533</td>\n",
       "      <td>0.492498</td>\n",
       "      <td>0.428611</td>\n",
       "      <td>0.476573</td>\n",
       "      <td>...</td>\n",
       "      <td>0.14314</td>\n",
       "      <td>0.354225</td>\n",
       "      <td>0.353883</td>\n",
       "      <td>0.225057</td>\n",
       "      <td>0.576575</td>\n",
       "      <td>0.486561</td>\n",
       "      <td>0.509034</td>\n",
       "      <td>0.576125</td>\n",
       "      <td>0.522714</td>\n",
       "      <td>0.303120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.134288</td>\n",
       "      <td>0.114978</td>\n",
       "      <td>0.111659</td>\n",
       "      <td>0.108742</td>\n",
       "      <td>0.112263</td>\n",
       "      <td>0.101085</td>\n",
       "      <td>0.162668</td>\n",
       "      <td>0.102611</td>\n",
       "      <td>0.078672</td>\n",
       "      <td>0.122668</td>\n",
       "      <td>...</td>\n",
       "      <td>0.04557</td>\n",
       "      <td>0.134352</td>\n",
       "      <td>0.041778</td>\n",
       "      <td>0.045372</td>\n",
       "      <td>0.112653</td>\n",
       "      <td>0.124015</td>\n",
       "      <td>0.125090</td>\n",
       "      <td>0.315034</td>\n",
       "      <td>0.024059</td>\n",
       "      <td>0.036829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.356265  0.528878  0.527163  0.514017  0.506237  0.522746   \n",
       "variance  0.134288  0.114978  0.111659  0.108742  0.112263  0.101085   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...    xmv_2     xmv_3  \\\n",
       "mean      0.460533  0.492498  0.428611  0.476573  ...  0.14314  0.354225   \n",
       "variance  0.162668  0.102611  0.078672  0.122668  ...  0.04557  0.134352   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.353883  0.225057  0.576575  0.486561  0.509034  0.576125   \n",
       "variance  0.041778  0.045372  0.112653  0.124015  0.125090  0.315034   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.522714  0.303120  \n",
       "variance  0.024059  0.036829  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 13\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 13) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "642c432d",
   "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>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.475461</td>\n",
       "      <td>0.523148</td>\n",
       "      <td>0.484215</td>\n",
       "      <td>0.498322</td>\n",
       "      <td>0.516260</td>\n",
       "      <td>0.474153</td>\n",
       "      <td>0.481676</td>\n",
       "      <td>0.491088</td>\n",
       "      <td>0.489133</td>\n",
       "      <td>0.471288</td>\n",
       "      <td>...</td>\n",
       "      <td>0.46673</td>\n",
       "      <td>0.480257</td>\n",
       "      <td>0.514331</td>\n",
       "      <td>0.512223</td>\n",
       "      <td>0.470652</td>\n",
       "      <td>0.482946</td>\n",
       "      <td>0.510946</td>\n",
       "      <td>0.480947</td>\n",
       "      <td>0.494724</td>\n",
       "      <td>0.478586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.114943</td>\n",
       "      <td>0.117025</td>\n",
       "      <td>0.118016</td>\n",
       "      <td>0.116340</td>\n",
       "      <td>0.111135</td>\n",
       "      <td>0.119142</td>\n",
       "      <td>0.111719</td>\n",
       "      <td>0.113128</td>\n",
       "      <td>0.273003</td>\n",
       "      <td>0.114124</td>\n",
       "      <td>...</td>\n",
       "      <td>0.11677</td>\n",
       "      <td>0.115445</td>\n",
       "      <td>0.117707</td>\n",
       "      <td>0.108496</td>\n",
       "      <td>0.108059</td>\n",
       "      <td>0.126124</td>\n",
       "      <td>0.125303</td>\n",
       "      <td>0.121904</td>\n",
       "      <td>0.272880</td>\n",
       "      <td>0.115013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.475461  0.523148  0.484215  0.498322  0.516260  0.474153   \n",
       "variance  0.114943  0.117025  0.118016  0.116340  0.111135  0.119142   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...    xmv_2     xmv_3  \\\n",
       "mean      0.481676  0.491088  0.489133  0.471288  ...  0.46673  0.480257   \n",
       "variance  0.111719  0.113128  0.273003  0.114124  ...  0.11677  0.115445   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.514331  0.512223  0.470652  0.482946  0.510946  0.480947   \n",
       "variance  0.117707  0.108496  0.108059  0.126124  0.125303  0.121904   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.494724  0.478586  \n",
       "variance  0.272880  0.115013  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 14\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 14) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0f1626a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.475691</td>\n",
       "      <td>0.518147</td>\n",
       "      <td>0.490152</td>\n",
       "      <td>0.494798</td>\n",
       "      <td>0.509978</td>\n",
       "      <td>0.510510</td>\n",
       "      <td>0.506897</td>\n",
       "      <td>0.479048</td>\n",
       "      <td>0.500165</td>\n",
       "      <td>0.476400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.489481</td>\n",
       "      <td>0.483058</td>\n",
       "      <td>0.509537</td>\n",
       "      <td>0.541695</td>\n",
       "      <td>0.477205</td>\n",
       "      <td>0.482243</td>\n",
       "      <td>0.514638</td>\n",
       "      <td>0.493626</td>\n",
       "      <td>0.477593</td>\n",
       "      <td>0.480547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.114968</td>\n",
       "      <td>0.116915</td>\n",
       "      <td>0.117767</td>\n",
       "      <td>0.114393</td>\n",
       "      <td>0.112958</td>\n",
       "      <td>0.115611</td>\n",
       "      <td>0.107943</td>\n",
       "      <td>0.108861</td>\n",
       "      <td>0.106970</td>\n",
       "      <td>0.114316</td>\n",
       "      <td>...</td>\n",
       "      <td>0.119082</td>\n",
       "      <td>0.114884</td>\n",
       "      <td>0.118360</td>\n",
       "      <td>0.105500</td>\n",
       "      <td>0.108472</td>\n",
       "      <td>0.126385</td>\n",
       "      <td>0.125416</td>\n",
       "      <td>0.118810</td>\n",
       "      <td>0.100021</td>\n",
       "      <td>0.115893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.475691  0.518147  0.490152  0.494798  0.509978  0.510510   \n",
       "variance  0.114968  0.116915  0.117767  0.114393  0.112958  0.115611   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3  \\\n",
       "mean      0.506897  0.479048  0.500165  0.476400  ...  0.489481  0.483058   \n",
       "variance  0.107943  0.108861  0.106970  0.114316  ...  0.119082  0.114884   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.509537  0.541695  0.477205  0.482243  0.514638  0.493626   \n",
       "variance  0.118360  0.105500  0.108472  0.126385  0.125416  0.118810   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.477593  0.480547  \n",
       "variance  0.100021  0.115893  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 15\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 15) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b00583cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.492170</td>\n",
       "      <td>0.513065</td>\n",
       "      <td>0.502294</td>\n",
       "      <td>0.509699</td>\n",
       "      <td>0.509188</td>\n",
       "      <td>0.497966</td>\n",
       "      <td>0.446279</td>\n",
       "      <td>0.450946</td>\n",
       "      <td>0.529582</td>\n",
       "      <td>0.497532</td>\n",
       "      <td>...</td>\n",
       "      <td>0.487368</td>\n",
       "      <td>0.490847</td>\n",
       "      <td>0.503777</td>\n",
       "      <td>0.493861</td>\n",
       "      <td>0.493772</td>\n",
       "      <td>0.483534</td>\n",
       "      <td>0.512296</td>\n",
       "      <td>0.500826</td>\n",
       "      <td>0.463286</td>\n",
       "      <td>0.479957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.117613</td>\n",
       "      <td>0.119177</td>\n",
       "      <td>0.114485</td>\n",
       "      <td>0.115070</td>\n",
       "      <td>0.113128</td>\n",
       "      <td>0.118570</td>\n",
       "      <td>0.109495</td>\n",
       "      <td>0.107034</td>\n",
       "      <td>0.112978</td>\n",
       "      <td>0.116871</td>\n",
       "      <td>...</td>\n",
       "      <td>0.113952</td>\n",
       "      <td>0.115650</td>\n",
       "      <td>0.115196</td>\n",
       "      <td>0.114894</td>\n",
       "      <td>0.118854</td>\n",
       "      <td>0.124968</td>\n",
       "      <td>0.124295</td>\n",
       "      <td>0.117080</td>\n",
       "      <td>0.098161</td>\n",
       "      <td>0.115695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.492170  0.513065  0.502294  0.509699  0.509188  0.497966   \n",
       "variance  0.117613  0.119177  0.114485  0.115070  0.113128  0.118570   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3  \\\n",
       "mean      0.446279  0.450946  0.529582  0.497532  ...  0.487368  0.490847   \n",
       "variance  0.109495  0.107034  0.112978  0.116871  ...  0.113952  0.115650   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.503777  0.493861  0.493772  0.483534  0.512296  0.500826   \n",
       "variance  0.115196  0.114894  0.118854  0.124968  0.124295  0.117080   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.463286  0.479957  \n",
       "variance  0.098161  0.115695  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 16\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 16) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "03ea8ba6",
   "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>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.534627</td>\n",
       "      <td>0.542433</td>\n",
       "      <td>0.490583</td>\n",
       "      <td>0.489919</td>\n",
       "      <td>0.519697</td>\n",
       "      <td>0.491379</td>\n",
       "      <td>0.485191</td>\n",
       "      <td>0.484217</td>\n",
       "      <td>0.567248</td>\n",
       "      <td>0.498787</td>\n",
       "      <td>...</td>\n",
       "      <td>0.483425</td>\n",
       "      <td>0.540314</td>\n",
       "      <td>0.511031</td>\n",
       "      <td>0.488824</td>\n",
       "      <td>0.491896</td>\n",
       "      <td>0.506824</td>\n",
       "      <td>0.502967</td>\n",
       "      <td>0.489331</td>\n",
       "      <td>0.385579</td>\n",
       "      <td>0.530043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.107585</td>\n",
       "      <td>0.097934</td>\n",
       "      <td>0.115076</td>\n",
       "      <td>0.117692</td>\n",
       "      <td>0.118042</td>\n",
       "      <td>0.117860</td>\n",
       "      <td>0.124500</td>\n",
       "      <td>0.117627</td>\n",
       "      <td>0.120549</td>\n",
       "      <td>0.119712</td>\n",
       "      <td>...</td>\n",
       "      <td>0.111984</td>\n",
       "      <td>0.107167</td>\n",
       "      <td>0.115174</td>\n",
       "      <td>0.114728</td>\n",
       "      <td>0.117503</td>\n",
       "      <td>0.122886</td>\n",
       "      <td>0.117357</td>\n",
       "      <td>0.112352</td>\n",
       "      <td>0.157305</td>\n",
       "      <td>0.111158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.534627  0.542433  0.490583  0.489919  0.519697  0.491379   \n",
       "variance  0.107585  0.097934  0.115076  0.117692  0.118042  0.117860   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3  \\\n",
       "mean      0.485191  0.484217  0.567248  0.498787  ...  0.483425  0.540314   \n",
       "variance  0.124500  0.117627  0.120549  0.119712  ...  0.111984  0.107167   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.511031  0.488824  0.491896  0.506824  0.502967  0.489331   \n",
       "variance  0.115174  0.114728  0.117503  0.122886  0.117357  0.112352   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.385579  0.530043  \n",
       "variance  0.157305  0.111158  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 17\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 17) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f937e434",
   "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>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.209658</td>\n",
       "      <td>0.635374</td>\n",
       "      <td>0.635792</td>\n",
       "      <td>0.577433</td>\n",
       "      <td>0.440248</td>\n",
       "      <td>0.604157</td>\n",
       "      <td>0.652591</td>\n",
       "      <td>0.467981</td>\n",
       "      <td>0.479714</td>\n",
       "      <td>0.485897</td>\n",
       "      <td>...</td>\n",
       "      <td>0.386017</td>\n",
       "      <td>0.207882</td>\n",
       "      <td>0.646056</td>\n",
       "      <td>0.318369</td>\n",
       "      <td>0.436365</td>\n",
       "      <td>0.479185</td>\n",
       "      <td>0.438703</td>\n",
       "      <td>0.391061</td>\n",
       "      <td>0.424946</td>\n",
       "      <td>0.207259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.205132</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>0.150964</td>\n",
       "      <td>0.189144</td>\n",
       "      <td>0.092895</td>\n",
       "      <td>0.151386</td>\n",
       "      <td>0.264502</td>\n",
       "      <td>0.146207</td>\n",
       "      <td>0.073117</td>\n",
       "      <td>0.131141</td>\n",
       "      <td>...</td>\n",
       "      <td>0.321063</td>\n",
       "      <td>0.205958</td>\n",
       "      <td>0.193886</td>\n",
       "      <td>0.280485</td>\n",
       "      <td>0.283161</td>\n",
       "      <td>0.098359</td>\n",
       "      <td>0.107718</td>\n",
       "      <td>0.293962</td>\n",
       "      <td>0.205746</td>\n",
       "      <td>0.086008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.209658  0.635374  0.635792  0.577433  0.440248  0.604157   \n",
       "variance  0.205132  0.138584  0.150964  0.189144  0.092895  0.151386   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3  \\\n",
       "mean      0.652591  0.467981  0.479714  0.485897  ...  0.386017  0.207882   \n",
       "variance  0.264502  0.146207  0.073117  0.131141  ...  0.321063  0.205958   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.646056  0.318369  0.436365  0.479185  0.438703  0.391061   \n",
       "variance  0.193886  0.280485  0.283161  0.098359  0.107718  0.293962   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.424946  0.207259  \n",
       "variance  0.205746  0.086008  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 18\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 18) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e6b9ad1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.483523</td>\n",
       "      <td>0.521801</td>\n",
       "      <td>0.481430</td>\n",
       "      <td>0.493803</td>\n",
       "      <td>0.489387</td>\n",
       "      <td>0.495290</td>\n",
       "      <td>0.490443</td>\n",
       "      <td>0.476705</td>\n",
       "      <td>0.545624</td>\n",
       "      <td>0.473850</td>\n",
       "      <td>...</td>\n",
       "      <td>0.462848</td>\n",
       "      <td>0.489040</td>\n",
       "      <td>0.506510</td>\n",
       "      <td>0.498009</td>\n",
       "      <td>0.473573</td>\n",
       "      <td>0.482635</td>\n",
       "      <td>0.507732</td>\n",
       "      <td>0.484071</td>\n",
       "      <td>0.515715</td>\n",
       "      <td>0.483522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.115802</td>\n",
       "      <td>0.118188</td>\n",
       "      <td>0.117498</td>\n",
       "      <td>0.112923</td>\n",
       "      <td>0.166474</td>\n",
       "      <td>0.123752</td>\n",
       "      <td>0.110973</td>\n",
       "      <td>0.112220</td>\n",
       "      <td>0.116500</td>\n",
       "      <td>0.114654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.115836</td>\n",
       "      <td>0.116167</td>\n",
       "      <td>0.117837</td>\n",
       "      <td>0.125385</td>\n",
       "      <td>0.108814</td>\n",
       "      <td>0.125553</td>\n",
       "      <td>0.125688</td>\n",
       "      <td>0.124831</td>\n",
       "      <td>0.111478</td>\n",
       "      <td>0.113804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.483523  0.521801  0.481430  0.493803  0.489387  0.495290   \n",
       "variance  0.115802  0.118188  0.117498  0.112923  0.166474  0.123752   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3  \\\n",
       "mean      0.490443  0.476705  0.545624  0.473850  ...  0.462848  0.489040   \n",
       "variance  0.110973  0.112220  0.116500  0.114654  ...  0.115836  0.116167   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.506510  0.498009  0.473573  0.482635  0.507732  0.484071   \n",
       "variance  0.117837  0.125385  0.108814  0.125553  0.125688  0.124831   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.515715  0.483522  \n",
       "variance  0.111478  0.113804  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 19\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 19) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a75637b8",
   "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>xmeas_1</th>\n",
       "      <th>xmeas_2</th>\n",
       "      <th>xmeas_3</th>\n",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</th>\n",
       "      <th>xmeas_8</th>\n",
       "      <th>xmeas_9</th>\n",
       "      <th>xmeas_10</th>\n",
       "      <th>...</th>\n",
       "      <th>xmv_2</th>\n",
       "      <th>xmv_3</th>\n",
       "      <th>xmv_4</th>\n",
       "      <th>xmv_5</th>\n",
       "      <th>xmv_6</th>\n",
       "      <th>xmv_7</th>\n",
       "      <th>xmv_8</th>\n",
       "      <th>xmv_9</th>\n",
       "      <th>xmv_10</th>\n",
       "      <th>xmv_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.515911</td>\n",
       "      <td>0.534149</td>\n",
       "      <td>0.481272</td>\n",
       "      <td>0.537752</td>\n",
       "      <td>0.487114</td>\n",
       "      <td>0.427001</td>\n",
       "      <td>0.544510</td>\n",
       "      <td>0.486449</td>\n",
       "      <td>0.550094</td>\n",
       "      <td>0.489404</td>\n",
       "      <td>...</td>\n",
       "      <td>0.489895</td>\n",
       "      <td>0.517143</td>\n",
       "      <td>0.492789</td>\n",
       "      <td>0.659156</td>\n",
       "      <td>0.480051</td>\n",
       "      <td>0.506421</td>\n",
       "      <td>0.514978</td>\n",
       "      <td>0.479798</td>\n",
       "      <td>0.566538</td>\n",
       "      <td>0.510767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>0.109910</td>\n",
       "      <td>0.111421</td>\n",
       "      <td>0.110526</td>\n",
       "      <td>0.106625</td>\n",
       "      <td>0.116467</td>\n",
       "      <td>0.106656</td>\n",
       "      <td>0.113516</td>\n",
       "      <td>0.112581</td>\n",
       "      <td>0.103867</td>\n",
       "      <td>0.116481</td>\n",
       "      <td>...</td>\n",
       "      <td>0.115585</td>\n",
       "      <td>0.110598</td>\n",
       "      <td>0.124803</td>\n",
       "      <td>0.186676</td>\n",
       "      <td>0.115707</td>\n",
       "      <td>0.120098</td>\n",
       "      <td>0.124848</td>\n",
       "      <td>0.121039</td>\n",
       "      <td>0.109930</td>\n",
       "      <td>0.114908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           xmeas_1   xmeas_2   xmeas_3   xmeas_4   xmeas_5   xmeas_6  \\\n",
       "mean      0.515911  0.534149  0.481272  0.537752  0.487114  0.427001   \n",
       "variance  0.109910  0.111421  0.110526  0.106625  0.116467  0.106656   \n",
       "\n",
       "           xmeas_7   xmeas_8   xmeas_9  xmeas_10  ...     xmv_2     xmv_3  \\\n",
       "mean      0.544510  0.486449  0.550094  0.489404  ...  0.489895  0.517143   \n",
       "variance  0.113516  0.112581  0.103867  0.116481  ...  0.115585  0.110598   \n",
       "\n",
       "             xmv_4     xmv_5     xmv_6     xmv_7     xmv_8     xmv_9  \\\n",
       "mean      0.492789  0.659156  0.480051  0.506421  0.514978  0.479798   \n",
       "variance  0.124803  0.186676  0.115707  0.120098  0.124848  0.121039   \n",
       "\n",
       "            xmv_10    xmv_11  \n",
       "mean      0.566538  0.510767  \n",
       "variance  0.109930  0.114908  \n",
       "\n",
       "[2 rows x 52 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDV 20\n",
    "data = pd.DataFrame(scaler.fit_transform(X = train_faulty_complete[(train_faulty_complete.faultNumber == 20) & (train_faulty_complete.simulationRun.isin(range(400)))].loc[:,cols]))\n",
    "\n",
    "means = data.mean()\n",
    "variances = data.std()\n",
    "\n",
    "model_prob = pd.DataFrame(index = cols, data = {'mean': means.values, 'variance': variances.values})\n",
    "model_prob.head(n_prob_features).T"
   ]
  }
 ],
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