1 - Cleaning and Generating Correlation Matrix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5379512b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score as r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "785f5efa",
   "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>Density</th>\n",
       "      <th>YP</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>ROP</th>\n",
       "      <th>Pipe rotation</th>\n",
       "      <th>Flow rate</th>\n",
       "      <th>Inclination</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>Concentration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.314</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.314</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>90</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.314</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>30</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.314</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>30</td>\n",
       "      <td>80</td>\n",
       "      <td>200</td>\n",
       "      <td>90</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.314</td>\n",
       "      <td>20</td>\n",
       "      <td>180</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Density  YP  Temperature  ROP  Pipe rotation  Flow rate  Inclination  \\\n",
       "0    8.314  20           80   30              0        100           90   \n",
       "1    8.314  20           80   30              0        200           90   \n",
       "2    8.314  20           80   30             80        100           90   \n",
       "3    8.314  20           80   30             80        200           90   \n",
       "4    8.314  20          180   30              0        100           90   \n",
       "\n",
       "   Eccentricity  Concentration  \n",
       "0         0.881           0.35  \n",
       "1         0.881           0.14  \n",
       "2         0.881           0.06  \n",
       "3         0.881           0.02  \n",
       "4         0.881           0.11  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('files/dataframe.csv', header = 0)\n",
    "\n",
    "dataset.rename(columns = {'Density, ppg' : 'Density'}, inplace = True)\n",
    "dataset.drop(columns = ['Test No.', 'PV'], inplace = True)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e031df1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Density</th>\n",
       "      <th>YP</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>ROP</th>\n",
       "      <th>Pipe rotation</th>\n",
       "      <th>Flow rate</th>\n",
       "      <th>Inclination</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>Concentration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>116.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>116.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.333310</td>\n",
       "      <td>22.931034</td>\n",
       "      <td>136.034483</td>\n",
       "      <td>31.293103</td>\n",
       "      <td>33.103448</td>\n",
       "      <td>143.103448</td>\n",
       "      <td>83.258621</td>\n",
       "      <td>0.769621</td>\n",
       "      <td>0.113621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.653067</td>\n",
       "      <td>12.718849</td>\n",
       "      <td>44.308784</td>\n",
       "      <td>6.597783</td>\n",
       "      <td>39.571922</td>\n",
       "      <td>45.989863</td>\n",
       "      <td>10.514686</td>\n",
       "      <td>0.160266</td>\n",
       "      <td>0.108719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.314000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>0.541000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.314000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>0.541000</td>\n",
       "      <td>0.017500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.314000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>0.881000</td>\n",
       "      <td>0.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>0.881000</td>\n",
       "      <td>0.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>0.881000</td>\n",
       "      <td>0.410000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Density          YP  Temperature         ROP  Pipe rotation  \\\n",
       "count  116.000000  116.000000   116.000000  116.000000     116.000000   \n",
       "mean     9.333310   22.931034   136.034483   31.293103      33.103448   \n",
       "std      1.653067   12.718849    44.308784    6.597783      39.571922   \n",
       "min      8.314000    0.000000    80.000000   15.000000       0.000000   \n",
       "25%      8.314000   20.000000    80.000000   30.000000       0.000000   \n",
       "50%      8.314000   20.000000   120.000000   30.000000       0.000000   \n",
       "75%     12.000000   40.000000   180.000000   40.000000      80.000000   \n",
       "max     12.000000   40.000000   180.000000   40.000000      80.000000   \n",
       "\n",
       "        Flow rate  Inclination  Eccentricity  Concentration  \n",
       "count  116.000000   116.000000    116.000000     116.000000  \n",
       "mean   143.103448    83.258621      0.769621       0.113621  \n",
       "std     45.989863    10.514686      0.160266       0.108719  \n",
       "min     75.000000    67.000000      0.541000       0.000000  \n",
       "25%    100.000000    67.000000      0.541000       0.017500  \n",
       "50%    150.000000    90.000000      0.881000       0.075000  \n",
       "75%    200.000000    90.000000      0.881000       0.190000  \n",
       "max    250.000000    90.000000      0.881000       0.410000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "90e1531f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.to_csv('files/cleaned_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eff6b64",
   "metadata": {},
   "source": [
    "## Plotting the Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9d58f993",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('files/dataframe.csv', header = 0)\n",
    "df.rename(columns = {'Density, ppg' : 'Density'}, inplace = True)\n",
    "df.drop(columns = ['Test No.'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eeb9f631",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr().round(3)\n",
    "\n",
    "plt.figure(figsize = (10, 8), dpi = 100)\n",
    "mask = np.triu(np.ones_like(corr, dtype = bool))\n",
    "sns.set_theme(style = 'darkgrid')\n",
    "sns.heatmap(corr, annot= True, center = 0, mask = mask, linewidths  = .5, cmap = 'RdYlGn', vmin = -1, vmax = 1)\n",
    "plt.xticks(rotation = 30)\n",
    "plt.title('Correlation Matrix', fontdict = {'fontsize': 14, 'style':'oblique', 'weight': 'bold'})\n",
    "plt.savefig(fname = 'files/correlation_matrix.png', format = 'png');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a06c94",
   "metadata": {},
   "source": [
    "## Feature Ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "26c8f4c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Flow rate        0.485\n",
       "Pipe rotation    0.474\n",
       "Density          0.314\n",
       "Temperature      0.123\n",
       "Inclination      0.110\n",
       "YP               0.097\n",
       "PV               0.096\n",
       "Eccentricity     0.033\n",
       "ROP              0.001\n",
       "Name: Concentration, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_coef = np.abs(corr.iloc[-1, :-1]).sort_values(ascending=False)\n",
    "corr_coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "266f80ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Flow rate        0.343814\n",
       "Pipe rotation    0.269586\n",
       "Density          0.151482\n",
       "Inclination      0.079463\n",
       "Temperature      0.059857\n",
       "ROP              0.036731\n",
       "YP               0.024211\n",
       "PV               0.018759\n",
       "Eccentricity     0.016097\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_clf = RandomForestRegressor(criterion = 'squared_error')\n",
    "forest_clf.fit(df.drop(columns = 'Concentration'), df['Concentration'])\n",
    "gini_importance = pd.Series(index=df.columns[:-1], data = forest_clf.feature_importances_).sort_values(ascending = False)\n",
    "gini_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "12132a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pearson</th>\n",
       "      <th>Gini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Flow rate</td>\n",
       "      <td>Flow rate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pipe rotation</td>\n",
       "      <td>Pipe rotation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Density</td>\n",
       "      <td>Density</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Temperature</td>\n",
       "      <td>Inclination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Inclination</td>\n",
       "      <td>Temperature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>YP</td>\n",
       "      <td>ROP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PV</td>\n",
       "      <td>YP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Eccentricity</td>\n",
       "      <td>PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ROP</td>\n",
       "      <td>Eccentricity</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Pearson           Gini\n",
       "1      Flow rate      Flow rate\n",
       "2  Pipe rotation  Pipe rotation\n",
       "3        Density        Density\n",
       "4    Temperature    Inclination\n",
       "5    Inclination    Temperature\n",
       "6             YP            ROP\n",
       "7             PV             YP\n",
       "8   Eccentricity             PV\n",
       "9            ROP   Eccentricity"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance = pd.DataFrame({'Pearson':corr_coef.index, 'Gini': gini_importance.index})\n",
    "feature_importance.index = np.arange(1,10)\n",
    "feature_importance"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.6 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "vscode": {
   "interpreter": {
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 },
 "nbformat": 4,
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}