Benchmarkcomponent_dia.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cea766ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import machine learning libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "# To enable plotting graphs in Jupyter notebook\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import statsmodels.api as sm\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import linear_model\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tabulate import tabulate\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.ensemble import VotingRegressor\n",
    "\n",
    "from beautifultable import BeautifulTable\n",
    "table = BeautifulTable()\n",
    "table.column_headers = [\"Model Name\", \"r2\", \"RMSE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6845d9b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>T</th>\n",
       "      <th>L</th>\n",
       "      <th>N</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.0</td>\n",
       "      <td>235</td>\n",
       "      <td>100</td>\n",
       "      <td>25</td>\n",
       "      <td>19.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.0</td>\n",
       "      <td>235</td>\n",
       "      <td>150</td>\n",
       "      <td>50</td>\n",
       "      <td>19.823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.0</td>\n",
       "      <td>235</td>\n",
       "      <td>200</td>\n",
       "      <td>75</td>\n",
       "      <td>19.841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.0</td>\n",
       "      <td>240</td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "      <td>19.826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.0</td>\n",
       "      <td>240</td>\n",
       "      <td>150</td>\n",
       "      <td>75</td>\n",
       "      <td>19.772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20.0</td>\n",
       "      <td>240</td>\n",
       "      <td>200</td>\n",
       "      <td>25</td>\n",
       "      <td>19.775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20.0</td>\n",
       "      <td>245</td>\n",
       "      <td>100</td>\n",
       "      <td>75</td>\n",
       "      <td>19.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20.0</td>\n",
       "      <td>245</td>\n",
       "      <td>150</td>\n",
       "      <td>25</td>\n",
       "      <td>19.803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20.0</td>\n",
       "      <td>245</td>\n",
       "      <td>200</td>\n",
       "      <td>50</td>\n",
       "      <td>19.829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.0</td>\n",
       "      <td>235</td>\n",
       "      <td>100</td>\n",
       "      <td>25</td>\n",
       "      <td>13.800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      X    T    L   N       Y\n",
       "0  20.0  235  100  25  19.795\n",
       "1  20.0  235  150  50  19.823\n",
       "2  20.0  235  200  75  19.841\n",
       "3  20.0  240  100  50  19.826\n",
       "4  20.0  240  150  75  19.772\n",
       "5  20.0  240  200  25  19.775\n",
       "6  20.0  245  100  75  19.785\n",
       "7  20.0  245  150  25  19.803\n",
       "8  20.0  245  200  50  19.829\n",
       "9  14.0  235  100  25  13.800"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dia = pd.read_csv(\"dia.csv\")\n",
    "dia.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6de47403",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(198, 5)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dia.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9c4d730c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 198 entries, 0 to 197\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   X       198 non-null    float64\n",
      " 1   T       198 non-null    int64  \n",
      " 2   L       198 non-null    int64  \n",
      " 3   N       198 non-null    int64  \n",
      " 4   Y       198 non-null    float64\n",
      "dtypes: float64(2), int64(3)\n",
      "memory usage: 7.9 KB\n"
     ]
    }
   ],
   "source": [
    "dia.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f8f11428",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>198.0</td>\n",
       "      <td>7.431818</td>\n",
       "      <td>4.853608</td>\n",
       "      <td>2.000</td>\n",
       "      <td>3.500</td>\n",
       "      <td>5.500</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>20.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>198.0</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>4.092831</td>\n",
       "      <td>235.000</td>\n",
       "      <td>235.000</td>\n",
       "      <td>240.000</td>\n",
       "      <td>245.00000</td>\n",
       "      <td>245.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>198.0</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>40.928314</td>\n",
       "      <td>100.000</td>\n",
       "      <td>100.000</td>\n",
       "      <td>150.000</td>\n",
       "      <td>200.00000</td>\n",
       "      <td>200.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>198.0</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>20.464157</td>\n",
       "      <td>25.000</td>\n",
       "      <td>25.000</td>\n",
       "      <td>50.000</td>\n",
       "      <td>75.00000</td>\n",
       "      <td>75.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>198.0</td>\n",
       "      <td>7.324934</td>\n",
       "      <td>4.840865</td>\n",
       "      <td>1.897</td>\n",
       "      <td>3.481</td>\n",
       "      <td>5.416</td>\n",
       "      <td>9.93525</td>\n",
       "      <td>19.841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count        mean        std      min      25%      50%        75%      max\n",
       "X  198.0    7.431818   4.853608    2.000    3.500    5.500   10.00000   20.000\n",
       "T  198.0  240.000000   4.092831  235.000  235.000  240.000  245.00000  245.000\n",
       "L  198.0  150.000000  40.928314  100.000  100.000  150.000  200.00000  200.000\n",
       "N  198.0   50.000000  20.464157   25.000   25.000   50.000   75.00000   75.000\n",
       "Y  198.0    7.324934   4.840865    1.897    3.481    5.416    9.93525   19.841"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dia.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "41508975",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=dia, palette='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "25322081",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dia.drop(\"Y\", axis=1)\n",
    "y = dia[\"Y\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c8769e6",
   "metadata": {},
   "source": [
    "# Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2279958c",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler() \n",
    "x_scaled = scaler.fit_transform(X)\n",
    "X = pd.DataFrame(x_scaled)\n",
    "test_size = 0.3 \n",
    "seed = 7  # Random number seeding for reapeatability of the code\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07db6d1c",
   "metadata": {},
   "source": [
    "# Model Building - Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b1fafb8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression Score:  0.9981\n"
     ]
    }
   ],
   "source": [
    "regression_model = LinearRegression()\n",
    "regression_model.fit(X,y)\n",
    "lin_score = \"%.4f\" % regression_model.score(X_test, y_test)\n",
    "print(\"Linear Regression Score: \",lin_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0ff22e85",
   "metadata": {},
   "outputs": [],
   "source": [
    "#regression_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7f895ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#regression_model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0504d190",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18511944393376567"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression_model.predict(X_test)\n",
    "y_pred = regression_model.predict(X_test)\n",
    "np.sqrt(metrics.mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "da1b6847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9985805951675871\n",
      "0.998122358571347\n"
     ]
    }
   ],
   "source": [
    "print(regression_model.score(X_train, y_train))\n",
    "print(regression_model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f3316a42",
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "511699c7",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.601</td>\n",
       "      <td>2.398754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.260</td>\n",
       "      <td>2.889265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.870</td>\n",
       "      <td>5.924973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.017</td>\n",
       "      <td>4.890247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.815</td>\n",
       "      <td>5.848716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0   2.601   2.398754\n",
       "1   2.260   2.889265\n",
       "2   5.870   5.924973\n",
       "3   5.017   4.890247\n",
       "4   5.815   5.848716"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test = np.array(list(y_test))\n",
    "y_pred = np.array(y_pred)\n",
    "df4= pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten()})\n",
    "df4.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c4170333",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.scatter(y_test, y_pred, c='crimson')\n",
    "\n",
    "plt.title('Actual data VS Predicted data')\n",
    "\n",
    "p1 = max(max(y_pred), max(y_test))\n",
    "p2 = min(min(y_pred), min(y_test))\n",
    "plt.plot([p1, p2], [p1, p2], 'b-')\n",
    "plt.xlabel('Actual', fontsize=15)\n",
    "plt.ylabel('Predictions', fontsize=15)\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c4f0cde3",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CompareModel:\n",
    "    def __init__(self):\n",
    "        import pandas as pd\n",
    "        self._models = pd.DataFrame(\n",
    "            data=['r', 'R^2', 'RMSE'],\n",
    "            columns=['Model']\n",
    "        ).set_index(keys='Model')\n",
    "        \n",
    "    def add(self, model_name, y_test, y_pred):\n",
    "        import numpy as np\n",
    "        self._models[model_name] = np.array(\n",
    "            object=[\n",
    "                np.corrcoef(y_test, y_pred)[0, 1], # r\n",
    "                r2_score(y_true=y_test, y_pred=y_pred), # R^2\n",
    "                np.sqrt(mean_squared_error(y_true=y_test, y_pred=y_pred)) # RMSE\n",
    "                #np.sqrt(np.mean(((y_test-y_pred)/y_test)**2)), # RMSRE\n",
    "                #np.mean(np.abs((y_test-y_pred) / y_test)) * 100 # MAPE\n",
    "            ]\n",
    "        )\n",
    "        \n",
    "    def R2AndRMSE(y_test, y_pred):\n",
    "        from sklearn.metrics import r2_score, mean_squared_error\n",
    "        return r2_score(y_true=y_test, y_pred=y_pred), np.sqrt(mean_squared_error(y_true=y_test, y_pred=y_pred))\n",
    "    \n",
    "    @property\n",
    "    def models(self):\n",
    "        return self._models\n",
    "    \n",
    "    @models.setter\n",
    "    def models(self, _):\n",
    "        print('Cannot perform such task.')\n",
    "    \n",
    "    def show(self, **kwargs):\n",
    "        import matplotlib.pyplot as plt\n",
    "        kwargs['marker'] = kwargs.get('marker', 'X')\n",
    "        self._models.plot(**kwargs)\n",
    "        plt.xticks(range(len(self._models)), self._models.index)\n",
    "        plt.xlabel('')\n",
    "        plt.axis('auto')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "64286df9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.998122358571347\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.18511944393376567"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Metrics\n",
    "lr= CompareModel.R2AndRMSE(y_test=y_test, y_pred=y_pred)\n",
    "lr_r2 = r2_score(y_true=y_test, y_pred=y_pred)\n",
    "print(lr_r2)\n",
    "\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "lr_rmse = np.sqrt(mean_squared_error(y_true=y_test, y_pred=y_pred))\n",
    "lr_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "349aee24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+-------+-------+\n",
      "|    Model Name     |  r2   | RMSE  |\n",
      "+-------------------+-------+-------+\n",
      "| Linear Regression | 0.998 | 0.185 |\n",
      "+-------------------+-------+-------+\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from beautifultable import BeautifulTable\n",
    "table = BeautifulTable()\n",
    "table.column_headers = [\"Model Name\", \"r2\", \"RMSE\"]\n",
    "\n",
    "table1 = table.append_row([\"Linear Regression\", lr_r2,lr_rmse])\n",
    "print(table)\n",
    "\n",
    "plot = CompareModel()\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "plot.add(model_name='Linear Regression', y_test=y_test, y_pred=y_pred)\n",
    "plot.show(figsize=(10, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b95d4df",
   "metadata": {},
   "source": [
    "# Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4eed7ba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9923575327984944, 0.3734758284767354)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting training set to lasso regression model\n",
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "\n",
    "lasso = Lasso(alpha=0.1)\n",
    "lasso.fit(X_train,y_train)\n",
    "\n",
    "\n",
    "# Predicting the yield\n",
    "y_pred = lasso.predict(X_test)\n",
    "\n",
    "CompareModel.R2AndRMSE(y_test=y_test, y_pred=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "357e8a84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+-------+-------+\n",
      "|    Model Name     |  r2   | RMSE  |\n",
      "+-------------------+-------+-------+\n",
      "| Linear Regression | 0.998 | 0.185 |\n",
      "+-------------------+-------+-------+\n",
      "|       Lasso       | 0.992 | 0.373 |\n",
      "+-------------------+-------+-------+\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lasso_r2 = r2_score(y_true=y_test, y_pred=y_pred)\n",
    "lasso_rmse = np.sqrt(mean_squared_error(y_true=y_test, y_pred=y_pred))\n",
    "\n",
    "table.append_row([\"Lasso\", lasso_r2,lasso_rmse])\n",
    "print(table)\n",
    "\n",
    "plot.add('Lasso', y_test, y_pred)\n",
    "plot.show(figsize=(5, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e4832d8",
   "metadata": {},
   "source": [
    "# Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c0eebb37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9978756342040652, 0.1969066382828026)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge = Ridge(alpha=.1)\n",
    "ridge.fit(X_train,y_train)\n",
    "y_pred = ridge.predict(X_test)\n",
    "\n",
    "\n",
    "CompareModel.R2AndRMSE(y_test=y_test, y_pred=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bf4689cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+-------+-------+\n",
      "|    Model Name     |  r2   | RMSE  |\n",
      "+-------------------+-------+-------+\n",
      "| Linear Regression | 0.998 | 0.185 |\n",
      "+-------------------+-------+-------+\n",
      "|       Lasso       | 0.992 | 0.373 |\n",
      "+-------------------+-------+-------+\n",
      "|       Ridge       | 0.998 | 0.197 |\n",
      "+-------------------+-------+-------+\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ridge_r2 = r2_score(y_true=y_test, y_pred=y_pred)\n",
    "ridge_rmse = np.sqrt(mean_squared_error(y_true=y_test, y_pred=y_pred))\n",
    "\n",
    "table.append_row([\"Ridge\", ridge_r2,ridge_rmse])\n",
    "print(table)\n",
    "\n",
    "plot.add('Ridge', y_test, y_pred)\n",
    "plot.show(figsize=(5, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d93d0514",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fad1d87f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9981465905826786, 0.18392102991889336)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Random Forest model to the dataset\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rfr = RandomForestRegressor(n_estimators=10, random_state=7, n_jobs=-1)\n",
    "rfr.fit(X_train, y_train)\n",
    "\n",
    "# Predicting the yield\n",
    "y_pred = rfr.predict(X_test)\n",
    "\n",
    "CompareModel.R2AndRMSE(y_test=y_test, y_pred=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "aee1b740",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+-------+-------+\n",
      "|    Model Name     |  r2   | RMSE  |\n",
      "+-------------------+-------+-------+\n",
      "| Linear Regression | 0.998 | 0.185 |\n",
      "+-------------------+-------+-------+\n",
      "|       Lasso       | 0.992 | 0.373 |\n",
      "+-------------------+-------+-------+\n",
      "|       Ridge       | 0.998 | 0.197 |\n",
      "+-------------------+-------+-------+\n",
      "|   Random Forest   | 0.998 | 0.184 |\n",
      "+-------------------+-------+-------+\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_r2 = r2_score(y_true=y_test, y_pred=y_pred)\n",
    "rf_rmse = np.sqrt(mean_squared_error(y_true=y_test, y_pred=y_pred))\n",
    "table.append_row([\"Random Forest\", rf_r2,rf_rmse])\n",
    "print(table)\n",
    "\n",
    "plot.add('Random Forest', y_test, y_pred)\n",
    "plot.show(figsize=(5, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcf97243",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "07112888",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[22:41:01] WARNING: d:\\bld\\xgboost-split_1645118015404\\work\\src\\objective\\regression_obj.cu:188: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[22:41:01] WARNING: D:\\bld\\xgboost-split_1645118015404\\work\\src\\learner.cc:576: \n",
      "Parameters: { \"silent\", \"verbose\" } might not be used.\n",
      "\n",
      "  This could be a false alarm, with some parameters getting used by language bindings but\n",
      "  then being mistakenly passed down to XGBoost core, or some parameter actually being used\n",
      "  but getting flagged wrongly here. Please open an issue if you find any such cases.\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.9978057896461552, 0.20011739346785626)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting XGBoost to the dataset  (reg:squarederror)\n",
    "from xgboost import XGBRegressor\n",
    "xgb = XGBRegressor(n_jobs=4, silent=False, objective='reg:linear',\n",
    "                   max_depth=3, random_state=50, n_estimators=100,\n",
    "                   learning_rate=0.3, verbose=True)\n",
    "xgb.fit(X_train, y_train)\n",
    "\n",
    "# Predicting the yield\n",
    "y_pred = xgb.predict(X_test)\n",
    "\n",
    "CompareModel.R2AndRMSE(y_test=y_test, y_pred=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4e825eae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+-------+-------+\n",
      "|    Model Name     |  r2   | RMSE  |\n",
      "+-------------------+-------+-------+\n",
      "| Linear Regression | 0.998 | 0.185 |\n",
      "+-------------------+-------+-------+\n",
      "|       Lasso       | 0.992 | 0.373 |\n",
      "+-------------------+-------+-------+\n",
      "|       Ridge       | 0.998 | 0.197 |\n",
      "+-------------------+-------+-------+\n",
      "|   Random Forest   | 0.998 | 0.184 |\n",
      "+-------------------+-------+-------+\n",
      "|      XGBoost      | 0.998 |  0.2  |\n",
      "+-------------------+-------+-------+\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "XGBoost_r2 = r2_score(y_true=y_test, y_pred=y_pred)\n",
    "XGBoost_rmse = np.sqrt(mean_squared_error(y_true=y_test, y_pred=y_pred))\n",
    "table.append_row([\"XGBoost\", XGBoost_r2,XGBoost_rmse])\n",
    "print(table)\n",
    "\n",
    "plot.add('XGBoost', y_test, y_pred)\n",
    "plot.show(figsize=(5, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4cf63b1",
   "metadata": {},
   "source": [
    "# Voting Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "de94f38c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting training set to voting regression model\n",
    "from sklearn.ensemble import VotingRegressor\n",
    "vr = VotingRegressor(estimators=[('LinReg', regression_model),\n",
    "                                 ('RanFor', rfr),\n",
    "                                 ('XGBoost', xgb)\n",
    "                                ],\n",
    "                     n_jobs=-1,\n",
    "                     weights=[4, 2, 3])\n",
    "vr.fit(X_train, y_train)\n",
    "\n",
    "# Predicting the yield\n",
    "y_pred = vr.predict(X_test)\n",
    "\n",
    "CompareModel.R2AndRMSE(y_test=y_test, y_pred=y_pred)\n",
    "\n",
    "vr_r2 = r2_score(y_true=y_test, y_pred=y_pred)\n",
    "vr_rmse = np.sqrt(mean_squared_error(y_true=y_test, y_pred=y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2ff6a865",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+-------+-------+\n",
      "|    Model Name     |  r2   | RMSE  |\n",
      "+-------------------+-------+-------+\n",
      "| Linear Regression | 0.998 | 0.185 |\n",
      "+-------------------+-------+-------+\n",
      "|       Lasso       | 0.992 | 0.373 |\n",
      "+-------------------+-------+-------+\n",
      "|       Ridge       | 0.998 | 0.197 |\n",
      "+-------------------+-------+-------+\n",
      "|   Random Forest   | 0.998 | 0.184 |\n",
      "+-------------------+-------+-------+\n",
      "|      XGBoost      | 0.998 |  0.2  |\n",
      "+-------------------+-------+-------+\n",
      "| Voting Regression | 0.998 | 0.183 |\n",
      "+-------------------+-------+-------+\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.append_row([\"Voting Regression\", vr_r2,vr_rmse])\n",
    "print(table)\n",
    "\n",
    "plot.add('Voting Regression', y_test, y_pred)\n",
    "plot.show(figsize=(15, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28d7f9c7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}