E01_PCA hyperparameter n_components.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "79589e60",
   "metadata": {},
   "source": [
    "this experiment were intend for proffing the n component_n that equal with 0.9 had a meaning that the we ask the model to capture or hold 90% of the variance and the number of components require to capture 90%\n",
    "\n",
    "also want to know the result how many Principal Component that can be capture in this kind of setting\n",
    "\n",
    "source [here](https://www.datacamp.com/tutorial/principal-component-analysis-in-python)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "45ad12c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c9fdc370",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./normal_training.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0c5605f6",
   "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>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",
       "      <th>xmeas_4</th>\n",
       "      <th>xmeas_5</th>\n",
       "      <th>xmeas_6</th>\n",
       "      <th>xmeas_7</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",
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       "  </thead>\n",
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       "      <td>42.402</td>\n",
       "      <td>2704.3</td>\n",
       "      <td>...</td>\n",
       "      <td>53.744</td>\n",
       "      <td>24.657</td>\n",
       "      <td>62.544</td>\n",
       "      <td>22.137</td>\n",
       "      <td>39.935</td>\n",
       "      <td>42.323</td>\n",
       "      <td>47.757</td>\n",
       "      <td>47.510</td>\n",
       "      <td>41.258</td>\n",
       "      <td>18.447</td>\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",
       "      <td>17.194</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</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",
       "</div>"
      ],
      "text/plain": [
       "   faultNumber  simulationRun  sample  xmeas_1  xmeas_2  xmeas_3  xmeas_4  \\\n",
       "0            0              1       1  0.25038   3674.0   4529.0   9.2320   \n",
       "1            0              1       2  0.25109   3659.4   4556.6   9.4264   \n",
       "2            0              1       3  0.25038   3660.3   4477.8   9.4426   \n",
       "3            0              1       4  0.24977   3661.3   4512.1   9.4776   \n",
       "4            0              1       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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e84dfcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = data[data.simulationRun==1].iloc[:,3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2fa64268",
   "metadata": {},
   "outputs": [
    {
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       "      <th>xmeas_1</th>\n",
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       "      <th>...</th>\n",
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       "      <td>120.41</td>\n",
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       "      <td>47.757</td>\n",
       "      <td>47.510</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.25109</td>\n",
       "      <td>3659.4</td>\n",
       "      <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>75.000</td>\n",
       "      <td>120.41</td>\n",
       "      <td>0.33620</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",
       "      <td>17.194</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\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>74.771</td>\n",
       "      <td>120.42</td>\n",
       "      <td>0.33563</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>0.24977</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>75.224</td>\n",
       "      <td>120.39</td>\n",
       "      <td>0.33553</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>75.388</td>\n",
       "      <td>120.39</td>\n",
       "      <td>0.32632</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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       "<p>5 rows × 52 columns</p>\n",
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      "text/plain": [
       "   xmeas_1  xmeas_2  xmeas_3  xmeas_4  xmeas_5  xmeas_6  xmeas_7  xmeas_8  \\\n",
       "0  0.25038   3674.0   4529.0   9.2320   26.889   42.402   2704.3   74.863   \n",
       "1  0.25109   3659.4   4556.6   9.4264   26.721   42.576   2705.0   75.000   \n",
       "2  0.25038   3660.3   4477.8   9.4426   26.875   42.070   2706.2   74.771   \n",
       "3  0.24977   3661.3   4512.1   9.4776   26.758   42.063   2707.2   75.224   \n",
       "4  0.29405   3679.0   4497.0   9.3381   26.889   42.650   2705.1   75.388   \n",
       "\n",
       "   xmeas_9  xmeas_10  ...   xmv_2   xmv_3   xmv_4   xmv_5   xmv_6   xmv_7  \\\n",
       "0   120.41   0.33818  ...  53.744  24.657  62.544  22.137  39.935  42.323   \n",
       "1   120.41   0.33620  ...  53.414  24.588  59.259  22.084  40.176  38.554   \n",
       "2   120.42   0.33563  ...  54.357  24.666  61.275  22.380  40.244  38.990   \n",
       "3   120.39   0.33553  ...  53.946  24.725  59.856  22.277  40.257  38.072   \n",
       "4   120.39   0.32632  ...  53.658  28.797  60.717  21.947  39.144  41.955   \n",
       "\n",
       "    xmv_8   xmv_9  xmv_10  xmv_11  \n",
       "0  47.757  47.510  41.258  18.447  \n",
       "1  43.692  47.427  41.359  17.194  \n",
       "2  46.699  47.468  41.199  20.530  \n",
       "3  47.541  47.658  41.643  18.089  \n",
       "4  47.645  47.346  41.507  18.461  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "df7b04d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f90a9334",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scale = scale(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e1d7dafa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 52)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scale.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "85074e94",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e8fa424a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pcat = pca.fit_transform(df_scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a1441e29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 30)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d155fe4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conclusion so the n_components=0.9 will have result \n",
    "# of 52 reduction into 30"
   ]
  }
 ],
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