ElasticNet and ElasticNetCV on Housing Dataset.ipynb

{
 "cells": [
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elastic Net\n",
    "Elastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.\n",
    "The coefficients of the model are found via an optimization process that seeks to minimize the sum squared error between the predictions (yhat) and the expected target values (y).\n",
    "A problem with linear regression is that estimated coefficients of the model can become large, making the model sensitive to inputs and possibly unstable. This is particularly true for problems with few observations (samples) or more samples (n) than input predictors (p) or variables (so-called p >> n problems).\n",
    "\n",
    "One approach to addressing the stability of regression models is to change the loss function to include additional costs for a model that has large coefficients. Linear regression models that use these modified loss functions during training are referred to collectively as penalized linear regression.\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Importing necessary Packages/Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Importing csv file\n",
    "df=pd.read_csv(\"S:/ML/datasets/Housing.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>RM</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.575</td>\n",
       "      <td>4.98</td>\n",
       "      <td>15.3</td>\n",
       "      <td>504000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.421</td>\n",
       "      <td>9.14</td>\n",
       "      <td>17.8</td>\n",
       "      <td>453600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.185</td>\n",
       "      <td>4.03</td>\n",
       "      <td>17.8</td>\n",
       "      <td>728700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.998</td>\n",
       "      <td>2.94</td>\n",
       "      <td>18.7</td>\n",
       "      <td>701400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.147</td>\n",
       "      <td>5.33</td>\n",
       "      <td>18.7</td>\n",
       "      <td>760200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RM  LSTAT  PTRATIO      MEDV\n",
       "0  6.575   4.98     15.3  504000.0\n",
       "1  6.421   9.14     17.8  453600.0\n",
       "2  7.185   4.03     17.8  728700.0\n",
       "3  6.998   2.94     18.7  701400.0\n",
       "4  7.147   5.33     18.7  760200.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop(columns=\"MEDV\")\n",
    "y=df[\"MEDV\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## data splitting into train and test\n",
    "xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## setting alpha value list\n",
    "alpha=[0.0001,0.001,0.01,0.1,0.3,0.5,1,10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " For Alpha = 0.0001 | R-square :0.6574562214329336 MSE :9727671263.19448 RMSE :98628.95752868161 MAE:72451.41947724935 \n",
      " For Alpha = 0.001 | R-square :0.6574022488689464 MSE :9729203993.877533 RMSE :98636.72740859528 MAE:72455.18851975753 \n",
      " For Alpha = 0.01 | R-square :0.6568566473595847 MSE :9744698165.588886 RMSE :98715.23775785016 MAE:72500.77013104095 \n",
      " For Alpha = 0.1 | R-square :0.6511189787737206 MSE :9907638371.520887 RMSE :99537.12057077444 MAE:73037.98847541994 \n",
      " For Alpha = 0.3 | R-square :0.6391911672238 MSE :10246368300.093344 RMSE :101224.3463801735 MAE:74366.49442538632 \n",
      " For Alpha = 0.5 | R-square :0.6297111757411558 MSE :10515584226.614664 RMSE :102545.52270389315 MAE:75256.58490648311 \n",
      " For Alpha = 1 | R-square :0.6138840942812183 MSE :10965046908.850851 RMSE :104714.11991155181 MAE:76480.16848105645 \n",
      " For Alpha = 10 | R-square :0.5599326915713965 MSE :12497176646.967262 RMSE :111790.77174332085 MAE:80162.42010373622 \n"
     ]
    }
   ],
   "source": [
    "## function to get result on each alpha value\n",
    "scores={}\n",
    "for i in alpha:\n",
    "    model=ElasticNet(alpha=i)\n",
    "    model.fit(xtrain,ytrain)\n",
    "    ypred=model.predict(xtest)\n",
    "    mse=mean_squared_error(ytest,ypred)\n",
    "    rmse=np.sqrt(mse)\n",
    "    mae=mean_absolute_error(ytest,ypred)\n",
    "    scores[i]=model.score(xtest,ytest)\n",
    "    print(\" For Alpha = {} | R-square :{} MSE :{} RMSE :{} MAE:{} \".format(i,model.score(xtest,ytest),mse,rmse,mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.0001: 0.6574562214329336,\n",
       " 0.001: 0.6574022488689464,\n",
       " 0.01: 0.6568566473595847,\n",
       " 0.1: 0.6511189787737206,\n",
       " 0.3: 0.6391911672238,\n",
       " 0.5: 0.6297111757411558,\n",
       " 1: 0.6138840942812183,\n",
       " 10: 0.5599326915713965}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##gives alpha with r2score\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0001"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the max score and its key\n",
    "max(scores, key= lambda x: scores[x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function for getting best alpha "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_best_alpha(alpha):\n",
    "    for i in alpha:\n",
    "        model=ElasticNet(alpha=i)\n",
    "        model.fit(xtrain,ytrain)\n",
    "        ypred=model.predict(xtest)\n",
    "        mse=mean_squared_error(ytest,ypred)\n",
    "        rmse=np.sqrt(mse)\n",
    "        mae=mean_absolute_error(ytest,ypred)\n",
    "        scores[i]=model.score(xtest,ytest)\n",
    "        print(\" For Alpha = {} | R-square :{} MSE :{} RMSE :{} MAE:{} \".format(i,model.score(xtest,ytest),mse,rmse,mae))\n",
    "    return max(scores, key= lambda x: scores[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " For Alpha = 0.0001 | R-square :0.6574562214329336 MSE :9727671263.19448 RMSE :98628.95752868161 MAE:72451.41947724935 \n",
      " For Alpha = 0.001 | R-square :0.6574022488689464 MSE :9729203993.877533 RMSE :98636.72740859528 MAE:72455.18851975753 \n",
      " For Alpha = 0.01 | R-square :0.6568566473595847 MSE :9744698165.588886 RMSE :98715.23775785016 MAE:72500.77013104095 \n",
      " For Alpha = 0.1 | R-square :0.6511189787737206 MSE :9907638371.520887 RMSE :99537.12057077444 MAE:73037.98847541994 \n",
      " For Alpha = 0.3 | R-square :0.6391911672238 MSE :10246368300.093344 RMSE :101224.3463801735 MAE:74366.49442538632 \n",
      " For Alpha = 0.5 | R-square :0.6297111757411558 MSE :10515584226.614664 RMSE :102545.52270389315 MAE:75256.58490648311 \n",
      " For Alpha = 1 | R-square :0.6138840942812183 MSE :10965046908.850851 RMSE :104714.11991155181 MAE:76480.16848105645 \n",
      " For Alpha = 10 | R-square :0.5599326915713965 MSE :12497176646.967262 RMSE :111790.77174332085 MAE:80162.42010373622 \n",
      "Best Alpha is : 0.0001\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Alpha is :\",get_best_alpha(alpha))"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": "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"
    },
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elastic Net Cross Validation\n",
    "\n",
    "#### Cross Validation\n",
    "Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would have a perfect score but would fail to predict anything useful on yet-unseen data. This situation is called overfitting. To avoid it, it is common practice when performing a (supervised) machine learning experiment to hold out part of the available data as a test set X_test, y_test. Note that the word “experiment” is not intended to denote academic use only, because even in commercial settings machine learning usually starts out experimentally. Here is a flowchart of typical cross validation workflow in model training. \n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "#### K-fold CV\n",
    " In the basic approach, called k-fold CV, the training set is split into k smaller sets (other approaches are described below, but generally follow the same principles). The following procedure is followed for each of the k “folds”:\n",
    "\n",
    "        A model is trained using k-1 of the folds as training data;\n",
    "        the resulting model is validated on the remaining part of the data (i.e., it is used as a test set to compute a performance measure such as accuracy).\n",
    "\n",
    "The performance measure reported by k-fold cross-validation is then the average of the values computed in the loop. This approach can be computationally expensive, but does not waste too much data (as is the case when fixing an arbitrary validation set), which is a major advantage in problems such as inverse inference where the number of samples is very small.\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import ElasticNetCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElasticNetCV(alphas=[0.0001, 0.001, 0.01, 0.1, 0.3, 0.5, 1, 10], cv=5)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## model train on ElasticnetCV\n",
    "## have to pass the alphas as alpha list\n",
    "model= ElasticNetCV(alphas=alpha,cv=5)\n",
    "model.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## gives best alpha\n",
    "model.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "461015.37977304717"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred=model.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6568566473595847"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(xtest,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9744698165.588886"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(ytest,ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72500.77013104095"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(ytest,ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6568566473595847"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(ytest,ypred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information - https://en.wikipedia.org/wiki/Elastic_net_regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}