RegressionModels.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 617,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from time import time\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the train and test data:\n",
    "train = pd.read_csv('data/train_cleaned.csv')\n",
    "ytrain = pd.read_csv('data/y_train.csv')\n",
    "test = pd.read_csv('data/test_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data: (1460, 260)\n",
      "Test Data: (1459, 260)\n"
     ]
    }
   ],
   "source": [
    "assert(train.shape[0] == ytrain.shape[0])\n",
    "print('Train Data:', train.shape)\n",
    "print('Test Data:', test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 620,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_val, y_train, y_val = train_test_split(train, ytrain, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data: (1168, 260)\n",
      "Validation Data: (292, 260)\n"
     ]
    }
   ],
   "source": [
    "assert(x_train.shape[0] == y_train.shape[0])\n",
    "assert(x_val.shape[0] == y_val.shape[0])\n",
    "print('Train Data:', x_train.shape)\n",
    "print('Validation Data:', x_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 622,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>6.233116</td>\n",
       "      <td>23.615501</td>\n",
       "      <td>0.730463</td>\n",
       "      <td>0.730463</td>\n",
       "      <td>1.540963</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.440268</td>\n",
       "      <td>14.087847</td>\n",
       "      <td>14.103852</td>\n",
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       "      <td>6.034741</td>\n",
       "      <td>18.518588</td>\n",
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       "      <td>0.730463</td>\n",
       "      <td>1.540963</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.440268</td>\n",
       "      <td>14.184404</td>\n",
       "      <td>14.184404</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>4.137711</td>\n",
       "      <td>14.920534</td>\n",
       "      <td>0.730463</td>\n",
       "      <td>0.730463</td>\n",
       "      <td>1.540963</td>\n",
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       "      <td>14.189087</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 260 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  LotFrontage    LotArea    Street     Alley  LotShape  LandSlope  \\\n",
       "784    785     4.745132  18.097297  0.730463  0.000000  1.540963        0.0   \n",
       "939    940     6.233116  23.615501  0.730463  0.730463  1.540963        0.0   \n",
       "641    642     6.034741  18.518588  0.730463  0.730463  1.540963        0.0   \n",
       "145    146     4.137711  14.920534  0.730463  0.730463  1.540963        0.0   \n",
       "1182  1183     7.620056  21.711058  0.730463  0.730463  0.000000        0.0   \n",
       "\n",
       "      OverallQual  YearBuilt  YearRemodAdd          ...            \\\n",
       "784      2.259674  14.045907     14.184404          ...             \n",
       "939      2.440268  14.087847     14.103852          ...             \n",
       "641      2.440268  14.184404     14.184404          ...             \n",
       "145      2.259674  14.189087     14.192207          ...             \n",
       "1182     2.885846  14.176584     14.176584          ...             \n",
       "\n",
       "      SaleType_ConLw  SaleType_New  SaleType_Oth  SaleType_WD  \\\n",
       "784                0             0             0            1   \n",
       "939                0             0             0            0   \n",
       "641                0             0             0            1   \n",
       "145                0             0             0            1   \n",
       "1182               0             0             0            1   \n",
       "\n",
       "      SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  \\\n",
       "784                       0                      0                     0   \n",
       "939                       0                      0                     0   \n",
       "641                       0                      0                     0   \n",
       "145                       0                      0                     0   \n",
       "1182                      1                      0                     0   \n",
       "\n",
       "      SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial  \n",
       "784                      0                     1                      0  \n",
       "939                      0                     1                      0  \n",
       "641                      0                     1                      0  \n",
       "145                      0                     1                      0  \n",
       "1182                     0                     0                      0  \n",
       "\n",
       "[5 rows x 260 columns]"
      ]
     },
     "execution_count": 622,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling Features:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear models are very much affected by the scale of the features and hence, scaling the features might help the model and hence scaling the features using RobustScaler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True,\n",
       "       with_scaling=True)"
      ]
     },
     "execution_count": 606,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs = RobustScaler()\n",
    "rs.fit(x_train.drop('Id', axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py:3697: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "#Storing the Train and Validation Ids for future use:\n",
    "trainID = x_train['Id'].copy(deep = True)\n",
    "valID = x_val['Id'].copy(deep = True)\n",
    "trainID = train['Id'].copy(deep = True)\n",
    "testID = test['Id'].copy(deep = True)\n",
    "\n",
    "# Dropping them from the dataset to use them for scaling features:\n",
    "x_train.drop('Id', axis = 1, inplace = True)\n",
    "x_val.drop('Id', axis = 1, inplace = True)\n",
    "train.drop('Id', axis = 1, inplace = True)\n",
    "test.drop('Id', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = rs.transform(x_train)\n",
    "x_val = rs.transform(x_val)\n",
    "test = rs.transform(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluation method chosen for this problem, as per the Kaggle Competition, is log RMSE. The log RMSE is RMSE between the logarithm of the predicted value and the logarithm of the observed sales price. (Taking logs means that errors in predicting expensive houses and cheap houses will affect the result equally)\n",
    "\n",
    "Since, we've already applied the log transform on the target variable while feature engineering, RMSE of the predictions is calculated to compute log RMSE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_rmse(y_actual, y_pred):\n",
    "    #y_actual = np.expm1(y_actual)\n",
    "    #y_pred = np.expm1(y_pred)\n",
    "    return np.sqrt(mean_squared_error(y_actual, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicting average sale price of the train data as baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_pred = np.mean(y_train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_preds = [baseline_pred for i in range(len(x_val))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The validation Log RMSE is 0.4166\n"
     ]
    }
   ],
   "source": [
    "print('The validation Log RMSE is {:.4f}'.format(log_rmse(baseline_preds, y_val['SalePrice'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train, y_train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train Log RMSE is 0.0933\n",
      "The validation Log RMSE is 0.1312\n"
     ]
    }
   ],
   "source": [
    "print('The train Log RMSE is {:.4f}'.format(log_rmse(lr.predict(x_train), y_train['SalePrice'])))\n",
    "print('The validation Log RMSE is {:.4f}'.format(log_rmse(lr.predict(x_val), y_val['SalePrice'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an improvement from the baseline model, but we can clearly see that there is a lot of scope for improvement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression with L2 Regularization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_ridge = Ridge(alpha= 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_ridge.fit(x_train, y_train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train Log RMSE is 0.0936\n",
      "The validation Log RMSE is 0.1278\n"
     ]
    }
   ],
   "source": [
    "print('The train Log RMSE is {:.4f}'.format(log_rmse(lr_ridge.predict(x_train), y_train['SalePrice'])))\n",
    "print('The validation Log RMSE is {:.4f}'.format(log_rmse(lr_ridge.predict(x_val), y_val['SalePrice'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid Search for tuning alpha value of Ridge regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import RidgeCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performing coarse grid-search for alpha value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_ridgeCV = RidgeCV(alphas= np.logspace(np.log10(0.0005), np.log10(100), base = 10, num = 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RidgeCV(alphas=array([5.00000e-04, 9.50543e-04, 1.80706e-03, 3.43538e-03, 6.53095e-03,\n",
       "       1.24159e-02, 2.36037e-02, 4.48726e-02, 8.53066e-02, 1.62175e-01,\n",
       "       3.08309e-01, 5.86121e-01, 1.11427e+00, 2.11831e+00, 4.02710e+00,\n",
       "       7.65585e+00, 1.45544e+01, 2.76692e+01, 5.26015e+01, 1.00000e+02]),\n",
       "    cv=None, fit_intercept=True, gcv_mode=None, normalize=False,\n",
       "    scoring=None, store_cv_values=False)"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_ridgeCV.fit(x_train, y_train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha : 14.554432082969205\n"
     ]
    }
   ],
   "source": [
    "print(\"Best alpha :\", lr_ridgeCV.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train Log RMSE is 0.1112\n",
      "The validation Log RMSE is 0.1138\n"
     ]
    }
   ],
   "source": [
    "print('The train Log RMSE is {:.4f}'.format(log_rmse(lr_ridgeCV.predict(x_train), y_train['SalePrice'])))\n",
    "print('The validation Log RMSE is {:.4f}'.format(log_rmse(lr_ridgeCV.predict(x_val), y_val['SalePrice'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, performing fine grid search to further tune the alpha value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_ridgeCV = RidgeCV(alphas= np.linspace(4, 10, num = 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RidgeCV(alphas=array([ 4.     ,  4.31579,  4.63158,  4.94737,  5.26316,  5.57895,\n",
       "        5.89474,  6.21053,  6.52632,  6.84211,  7.15789,  7.47368,\n",
       "        7.78947,  8.10526,  8.42105,  8.73684,  9.05263,  9.36842,\n",
       "        9.68421, 10.     ]),\n",
       "    cv=None, fit_intercept=True, gcv_mode=None, normalize=False,\n",
       "    scoring=None, store_cv_values=False)"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_ridgeCV.fit(x_train, y_train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha : 10.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Best alpha :\", lr_ridgeCV.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train Log RMSE is 0.1089\n",
      "The validation Log RMSE is 0.1130\n"
     ]
    }
   ],
   "source": [
    "print('The train Log RMSE is {:.4f}'.format(log_rmse(lr_ridgeCV.predict(x_train), y_train['SalePrice'])))\n",
    "print('The validation Log RMSE is {:.4f}'.format(log_rmse(lr_ridgeCV.predict(x_val), y_val['SalePrice'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression with L1 Regularization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_lasso = Lasso()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_lasso.fit(x_train, y_train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train Log RMSE is 0.3732\n",
      "The validation Log RMSE is 0.3958\n"
     ]
    }
   ],
   "source": [
    "print('The train Log RMSE is {:.4f}'.format(log_rmse(lr_lasso.predict(x_train), y_train['SalePrice'])))\n",
    "print('The validation Log RMSE is {:.4f}'.format(log_rmse(lr_lasso.predict(x_val), y_val['SalePrice'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This must be because of the strong regularization affect of the default alpha value, which is 1.0. We can perform grid search on alpha values to tune the alpha value to obtain a better performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid Search for tuning alpha value of Lasso regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LassoCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performing coarse grid-search for alpha value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_lassoCV = LassoCV(alphas= np.logspace(np.log10(0.0005), np.log10(100), base = 10, num = 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LassoCV(alphas=array([5.00000e-04, 9.50543e-04, 1.80706e-03, 3.43538e-03, 6.53095e-03,\n",
       "       1.24159e-02, 2.36037e-02, 4.48726e-02, 8.53066e-02, 1.62175e-01,\n",
       "       3.08309e-01, 5.86121e-01, 1.11427e+00, 2.11831e+00, 4.02710e+00,\n",
       "       7.65585e+00, 1.45544e+01, 2.76692e+01, 5.26015e+01, 1.00000e+02]),\n",
       "    copy_X=True, cv='warn', eps=0.001, fit_intercept=True, max_iter=1000,\n",
       "    n_alphas=100, n_jobs=None, normalize=False, positive=False,\n",
       "    precompute='auto', random_state=None, selection='cyclic', tol=0.0001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_lassoCV.fit(x_train, y_train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha : 0.0004999999999999999\n"
     ]
    }
   ],
   "source": [
    "print(\"Best alpha :\", lr_lassoCV.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train Log RMSE is 0.1048\n",
      "The validation Log RMSE is 0.1110\n"
     ]
    }
   ],
   "source": [
    "print('The train Log RMSE is {:.4f}'.format(log_rmse(lr_lassoCV.predict(x_train), y_train['SalePrice'])))\n",
    "print('The validation Log RMSE is {:.4f}'.format(log_rmse(lr_lassoCV.predict(x_val), y_val['SalePrice'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, performing fine grid search to further tune the alpha value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_lassoCV = LassoCV(alphas= np.logspace(np.log10(0.0001), np.log10(0.001), base = 10, num = 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LassoCV(alphas=array([0.0001 , 0.00011, 0.00013, 0.00014, 0.00016, 0.00018, 0.00021,\n",
       "       0.00023, 0.00026, 0.0003 , 0.00034, 0.00038, 0.00043, 0.00048,\n",
       "       0.00055, 0.00062, 0.0007 , 0.00078, 0.00089, 0.001  ]),\n",
       "    copy_X=True, cv='warn', eps=0.001, fit_intercept=True, max_iter=1000,\n",
       "    n_alphas=100, n_jobs=None, normalize=False, positive=False,\n",
       "    precompute='auto', random_state=None, selection='cyclic', tol=0.0001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_lassoCV.fit(x_train, y_train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha : 0.0004832930238571752\n"
     ]
    }
   ],
   "source": [
    "print(\"Best alpha :\", lr_lassoCV.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train Log RMSE is 0.1044\n",
      "The validation Log RMSE is 0.1109\n"
     ]
    }
   ],
   "source": [
    "print('The train Log RMSE is {:.4f}'.format(log_rmse(lr_lassoCV.predict(x_train), y_train['SalePrice'])))\n",
    "print('The validation Log RMSE is {:.4f}'.format(log_rmse(lr_lassoCV.predict(x_val), y_val['SalePrice'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Regressor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=10, min_samples_split=10,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(x_train, y_train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train Log RMSE is 0.1247\n",
      "The validation Log RMSE is 0.1533\n"
     ]
    }
   ],
   "source": [
    "print('The train Log RMSE is {:.4f}'.format(log_rmse(rf.predict(x_train), y_train['SalePrice'])))\n",
    "print('The validation Log RMSE is {:.4f}'.format(log_rmse(rf.predict(x_val), y_val['SalePrice'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the model performance has improved by choosing a more complex and flexible model that can better discern the trends in the data. But again, we have the problem of the overfitting and hence we've to tune the model to reduce the overfitting of the model to the train dataset to improve the validation dataset performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 192 candidates, totalling 576 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:    5.7s\n",
      "[Parallel(n_jobs=6)]: Done 188 tasks      | elapsed:   39.6s\n",
      "[Parallel(n_jobs=6)]: Done 438 tasks      | elapsed:  2.3min\n",
      "[Parallel(n_jobs=6)]: Done 576 out of 576 | elapsed:  3.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV took 208.91 seconds for 192 candidate parameter settings.\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestRegressor()\n",
    "\n",
    "param_grid = {'min_samples_split': [2, 10, 50, 100],\n",
    "              'min_samples_leaf': [1, 10, 50, 100],\n",
    "              'n_estimators': [100, 500, 1000],\n",
    "              'max_depth': [1, 5, 10, None]\n",
    "             }\n",
    "\n",
    "# run grid search\n",
    "grid_search = GridSearchCV(clf, param_grid=param_grid, cv=3, scoring= 'neg_mean_squared_error', n_jobs = 6, verbose=True)\n",
    "start = time()\n",
    "grid_search.fit(x_train, y_train['SalePrice'])\n",
    "\n",
    "print(\"GridSearchCV took %.2f seconds for %d candidate parameter settings.\"\n",
    "      % (time() - start, len(grid_search.cv_results_['params'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.02250919145728302,\n",
       " {'max_depth': None,\n",
       "  'min_samples_leaf': 1,\n",
       "  'min_samples_split': 2,\n",
       "  'n_estimators': 1000})"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_, grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor(max_depth = 10, min_samples_split= 2, min_samples_leaf= 1)#, min_samples_leaf= 20, min_samples_split= 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=10,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(x_train, y_train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train Log RMSE is 0.0715\n",
      "The validation Log RMSE is 0.1536\n"
     ]
    }
   ],
   "source": [
    "print('The train Log RMSE is {:.4f}'.format(log_rmse(rf.predict(x_train), y_train['SalePrice'])))\n",
    "print('The validation Log RMSE is {:.4f}'.format(log_rmse(rf.predict(x_val), y_val['SalePrice'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting Algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbr = GradientBoostingRegressor(n_estimators = 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=1000, n_iter_no_change=None, presort='auto',\n",
       "             random_state=None, subsample=1.0, tol=0.0001,\n",
       "             validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbr.fit(x_train, y_train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train Log RMSE is 0.0155\n",
      "The validation Log RMSE is 0.1256\n"
     ]
    }
   ],
   "source": [
    "print('The train Log RMSE is {:.4f}'.format(log_rmse(gbr.predict(x_train), y_train['SalePrice'])))\n",
    "print('The validation Log RMSE is {:.4f}'.format(log_rmse(gbr.predict(x_val), y_val['SalePrice'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid Search to obtain Best Parameters:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we perform coarse grid search and then we can further narrow down on the hyper-parameters, if we want to, to find the ideal optimal parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 432 candidates, totalling 1296 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:   35.7s\n",
      "[Parallel(n_jobs=6)]: Done 188 tasks      | elapsed:  3.0min\n",
      "[Parallel(n_jobs=6)]: Done 438 tasks      | elapsed:  8.5min\n",
      "[Parallel(n_jobs=6)]: Done 788 tasks      | elapsed: 16.0min\n",
      "[Parallel(n_jobs=6)]: Done 1238 tasks      | elapsed: 26.6min\n",
      "[Parallel(n_jobs=6)]: Done 1296 out of 1296 | elapsed: 27.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV took 1654.25 seconds for 432 candidate parameter settings.\n"
     ]
    }
   ],
   "source": [
    "clf = GradientBoostingRegressor()\n",
    "\n",
    "param_grid = {'learning_rate': [0.1, 0.05, 0.01],\n",
    "              'max_depth': [3, 7, 10],\n",
    "              'min_samples_leaf': [1, 20, 50, 100],\n",
    "              \"min_samples_split\": [2, 10, 25, 50],\n",
    "              \"n_estimators\": [1000],\n",
    "              \"subsample\": [0.6, 0.8, 1.0]\n",
    "             }\n",
    "\n",
    "# run grid search\n",
    "grid_search = GridSearchCV(clf, param_grid=param_grid, cv=3, scoring= 'neg_mean_squared_error', n_jobs = 6, verbose=True)\n",
    "start = time()\n",
    "grid_search.fit(x_train, y_train['SalePrice'])\n",
    "\n",
    "print(\"GridSearchCV took %.2f seconds for %d candidate parameter settings.\"\n",
    "      % (time() - start, len(grid_search.cv_results_['params'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.01675222585912139,\n",
       " {'learning_rate': 0.05,\n",
       "  'max_depth': 3,\n",
       "  'min_samples_leaf': 1,\n",
       "  'min_samples_split': 25,\n",
       "  'n_estimators': 1000,\n",
       "  'subsample': 0.6})"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_, grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbr = GradientBoostingRegressor(n_estimators= 1000, learning_rate= 0.01, max_depth= 7, min_samples_leaf=1, min_samples_split= 25, subsample= 0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.01, loss='ls', max_depth=7, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=25, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=1000, n_iter_no_change=None, presort='auto',\n",
       "             random_state=None, subsample=0.6, tol=0.0001,\n",
       "             validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbr.fit(x_train, y_train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train Log RMSE is 0.0328\n",
      "The validation Log RMSE is 0.1254\n"
     ]
    }
   ],
   "source": [
    "print('The train Log RMSE is {:.4f}'.format(log_rmse(gbr.predict(x_train), y_train['SalePrice'])))\n",
    "print('The validation Log RMSE is {:.4f}'.format(log_rmse(gbr.predict(x_val), y_val['SalePrice'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = xgb.XGBRegressor(n_estimators= 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=1000,\n",
       "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model.fit(x_train, y_train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train Log RMSE is 0.0201\n",
      "The validation Log RMSE is 0.1285\n"
     ]
    }
   ],
   "source": [
    "print('The train Log RMSE is {:.4f}'.format(log_rmse(xgb_model.predict(x_train), y_train['SalePrice'])))\n",
    "print('The validation Log RMSE is {:.4f}'.format(log_rmse(xgb_model.predict(x_val), y_val['SalePrice'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyper-Parameter Tuning for XGBoost:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid Search to obtain Best Parameters:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we perform coarse grid search and then we can further narrow down on the hyper-parameters, if we want to, to find the ideal optimal parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "def xValXGBoost(dataset, target, k, cs):\n",
    "\n",
    "    kf = KFold(n_splits = k)\n",
    "    \n",
    "    train_errors = {c : [] for c in cs}\n",
    "    val_errors = {c : [] for c in cs}\n",
    "    \n",
    "    for train, validate in kf.split(dataset):\n",
    "        \n",
    "        cv_train, cv_validate = dataset.iloc[train], dataset.iloc[validate]\n",
    "        cv_train_Y, cv_validate_Y = target.iloc[train], target.iloc[validate]\n",
    "        \n",
    "        for c in cs:\n",
    "            xgb_model = xgb.XGBRegressor(booster='gbtree', max_depth = 7, n_estimators= 1000, learning_rate= 0.01, min_child_weight = 8, gamma = 0, reg_lambda = 0.00001, reg_alpha= 0.1)\n",
    "            xgb_model.fit(cv_train, cv_train_Y)\n",
    "            \n",
    "            train_error_c = log_rmse(xgb_model.predict(cv_train), cv_train_Y)\n",
    "            val_error_c = log_rmse(xgb_model.predict(cv_validate), cv_validate_Y)\n",
    "            train_errors[c] += [train_error_c]\n",
    "            val_errors[c] += [val_error_c]\n",
    "\n",
    "    return train_errors, val_errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Max_depth Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m_depth = [i for i in range(3, 11)]\n",
    "train_errors_m_depth, val_errors_m_depth = xValXGBoost(pd.DataFrame(x_train), y_train['SalePrice'], 'max_depth' 3, m_depth)\n",
    "\n",
    "#Calculating the Mean and the standard errors corresponding to the C values and the 1 standard error Mean(AUC) value:\n",
    "train_mean_m_depth = {i: sum(train_errors_m_depth[i])/len(train_errors_m_depth[i]) for i in train_errors_m_depth.keys()}\n",
    "val_mean_m_depth = {i: sum(val_errors_m_depth[i])/len(val_errors_m_depth[i]) for i in val_errors_m_depth.keys()}\n",
    "\n",
    "#Plotting the graph as per the description above:\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.plot(m_depth, train_mean_m_depth.values(), label = 'Train Mean Values')\n",
    "plt.plot(m_depth, val_mean_m_depth.values(), label = 'Val Mean Values')\n",
    "#plt.xticks(np.log10(lr), np.round(lr, 2))\n",
    "plt.xlabel('Max Depth')\n",
    "plt.ylabel('Mean(errors)')\n",
    "plt.title('Cross Validation errors + Confidence Limits \\n as a Function of Max Depth Values')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Estimators Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "est = np.delete(np.linspace(0, 5000, 11, dtype = 'int'), 0)\n",
    "train_errors_est, val_errors_est = xValXGBoost(pd.DataFrame(x_train), y_train['SalePrice'], 3, est)\n",
    "\n",
    "#Calculating the Mean and the standard errors corresponding to the C values and the 1 standard error Mean(AUC) value:\n",
    "train_mean_est = {i: sum(train_errors_est[i])/len(train_errors_est[i]) for i in train_errors_est.keys()}\n",
    "val_mean_est = {i: sum(val_errors_est[i])/len(val_errors_est[i]) for i in val_errors_est.keys()}\n",
    "\n",
    "#Plotting the graph as per the description above:\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.plot(est, train_mean_est.values(), label = 'Train Mean Values')\n",
    "plt.plot(est, val_mean_est.values(), label = 'Val Mean Values')\n",
    "plt.xlabel('#Estimators')\n",
    "plt.ylabel('Mean(errors)')\n",
    "plt.title('Cross Validation errors + Confidence Limits \\n as a Function of #Estimators')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Learning Rate Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = np.logspace(np.log10(0.005), np.log10(1), base = 10, num = 10)\n",
    "train_errors_lr, val_errors_lr = xValXGBoost(pd.DataFrame(x_train), y_train['SalePrice'], 3, lr)\n",
    "\n",
    "#Calculating the Mean and the standard errors corresponding to the C values and the 1 standard error Mean(AUC) value:\n",
    "train_mean_lr = {i: sum(train_errors_lr[i])/len(train_errors_lr[i]) for i in train_errors_lr.keys()}\n",
    "val_mean_lr = {i: sum(val_errors_lr[i])/len(val_errors_lr[i]) for i in val_errors_lr.keys()}\n",
    "\n",
    "#Plotting the graph as per the description above:\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.plot(np.log10(lr), train_mean_lr.values(), label = 'Train Mean Values')\n",
    "plt.plot(np.log10(lr), val_mean_lr.values(), label = 'Val Mean Values')\n",
    "plt.xticks(np.log10(lr), np.round(lr, 2))\n",
    "plt.xlabel('Learning Rate')\n",
    "plt.ylabel('Mean(errors)')\n",
    "plt.title('Cross Validation errors + Confidence Limits \\n as a Function of Learning Rate Values')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above graph, we can see that the sweet spot for learning rate is around 0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Min Child Weight Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m_child = list(range(1,11,1))\n",
    "train_errors_m_child, val_errors_m_child = xValXGBoost(pd.DataFrame(x_train), y_train['SalePrice'], 3, m_child)\n",
    "\n",
    "#Calculating the Mean and the standard errors corresponding to the C values and the 1 standard error Mean(AUC) value:\n",
    "train_mean_m_child = {i: sum(train_errors_m_child[i])/len(train_errors_m_child[i]) for i in train_errors_m_child.keys()}\n",
    "val_mean_m_child = {i: sum(val_errors_m_child[i])/len(val_errors_m_child[i]) for i in val_errors_m_child.keys()}\n",
    "\n",
    "#Plotting the graph as per the description above:\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.plot(m_child, train_mean_m_child.values(), label = 'Train Mean Values')\n",
    "plt.plot(m_child, val_mean_m_child.values(), label = 'Val Mean Values')\n",
    "plt.xlabel('Min Child Weight')\n",
    "plt.ylabel('Mean(errors)')\n",
    "plt.title('Cross Validation errors + Confidence Limits \\n as a Function of Min Child Weight Values')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gamma Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gm = np.linspace(0, 5, 11)\n",
    "train_errors_gm, val_errors_gm = xValXGBoost(pd.DataFrame(x_train), y_train['SalePrice'], 3, gm)\n",
    "\n",
    "#Calculating the Mean and the standard errors corresponding to the C values and the 1 standard error Mean(AUC) value:\n",
    "train_mean_gm = {i: sum(train_errors_gm[i])/len(train_errors_gm[i]) for i in train_errors_gm.keys()}\n",
    "val_mean_gm = {i: sum(val_errors_gm[i])/len(val_errors_gm[i]) for i in val_errors_gm.keys()}\n",
    "\n",
    "#Plotting the graph as per the description above:\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.plot(gm, train_mean_gm.values(), label = 'Train Mean Values')\n",
    "plt.plot(gm, val_mean_gm.values(), label = 'Val Mean Values')\n",
    "plt.xlabel('Gamma')\n",
    "plt.ylabel('Mean(errors)')\n",
    "plt.title('Cross Validation errors + Confidence Limits \\n as a Function of Gamma Values')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### L2 Regularization - Lambda Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lmbda = [1e-5, 1e-2, 0.1, 1, 10, 100]\n",
    "train_errors_lmbda, val_errors_lmbda = xValXGBoost(pd.DataFrame(x_train), y_train['SalePrice'], 3, lmbda)\n",
    "\n",
    "#Calculating the Mean and the standard errors corresponding to the C values and the 1 standard error Mean(AUC) value:\n",
    "train_mean_lmbda = {i: sum(train_errors_lmbda[i])/len(train_errors_lmbda[i]) for i in train_errors_lmbda.keys()}\n",
    "val_mean_lmbda = {i: sum(val_errors_lmbda[i])/len(val_errors_lmbda[i]) for i in val_errors_lmbda.keys()}\n",
    "\n",
    "#Plotting the graph as per the description above:\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.plot(np.log10(lmbda), train_mean_lmbda.values(), label = 'Train Mean Values')\n",
    "plt.plot(np.log10(lmbda), val_mean_lmbda.values(), label = 'Val Mean Values')\n",
    "plt.xlabel('L2 Regularization - Lambda')\n",
    "plt.ylabel('Mean(errors)')\n",
    "plt.title('Cross Validation errors + Confidence Limits \\n as a Function of L2 Lambda Values')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### L1 Regularization - Lambda Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alph = [1e-5, 1e-2, 0.1, 1, 10, 100]\n",
    "train_errors_alph, val_errors_alph = xValXGBoost(pd.DataFrame(x_train), y_train['SalePrice'], 3, alph)\n",
    "\n",
    "#Calculating the Mean and the standard errors corresponding to the C values and the 1 standard error Mean(AUC) value:\n",
    "train_mean_alph = {i: sum(train_errors_alph[i])/len(train_errors_alph[i]) for i in train_errors_alph.keys()}\n",
    "val_mean_alph = {i: sum(val_errors_alph[i])/len(val_errors_alph[i]) for i in val_errors_alph.keys()}\n",
    "\n",
    "#Plotting the graph as per the description above:\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.plot(np.log10(alph), train_mean_alph.values(), label = 'Train Mean Values')\n",
    "plt.plot(np.log10(alph), val_mean_alph.values(), label = 'Val Mean Values')\n",
    "plt.xlabel('L1 Regularization - Alpha')\n",
    "plt.ylabel('Mean(errors)')\n",
    "plt.title('Cross Validation errors + Confidence Limits \\n as a Function of L1 Alpha Values')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [],
   "source": [
    "#xgb_model = xgb.XGBRegressor(booster='gbtree', max_depth = 6, n_estimators= 1000, learning_rate= 0.1, reg_lambda = 10, reg_alpha = 0.1)\n",
    "xgb_model = xgb.XGBRegressor(booster='gbtree', max_depth = 3, n_estimators= 1000, learning_rate= 0.1, reg_lambda = 0.01, min_child_weight= 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [],
   "source": [
    "#xgb_model = xgb.XGBRegressor(booster='gbtree', max_depth = 6, n_estimators= 1000, learning_rate= 0.1, reg_lambda = 10, reg_alpha = 0.1)\n",
    "xgb_model = xgb.XGBRegressor(booster='gbtree', max_depth = 7, n_estimators= 1000, learning_rate= 0.01, min_child_weight = 8, gamma = 0, reg_lambda = 0.00001, reg_alpha= 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.01, max_delta_step=0,\n",
       "       max_depth=7, min_child_weight=8, missing=None, n_estimators=1000,\n",
       "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0.1, reg_lambda=1e-05, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 505,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model.fit(x_train, y_train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train Log RMSE is 0.0485\n",
      "The validation Log RMSE is 0.1271\n"
     ]
    }
   ],
   "source": [
    "print('The train Log RMSE is {:.4f}'.format(log_rmse(xgb_model.predict(x_train), y_train['SalePrice'])))\n",
    "print('The validation Log RMSE is {:.4f}'.format(log_rmse(xgb_model.predict(x_val), y_val['SalePrice'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 18 candidates, totalling 54 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=6)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=6)]: Done  38 tasks      | elapsed:   58.0s\n",
      "[Parallel(n_jobs=6)]: Done  54 out of  54 | elapsed:  1.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV took 79.08 seconds for 18 candidate parameter settings.\n"
     ]
    }
   ],
   "source": [
    "clf = xgb.XGBRegressor(booster='gbtree')\n",
    "\n",
    "param_grid = {\"max_depth\": [6, 8],\n",
    "              \"learning_rate\": [0.01],\n",
    "              \"reg_lambda\": [0.00001],\n",
    "              \"reg_alpha\": [0.1],\n",
    "              \"subsample\": [0.5, 0.8, 0.1],\n",
    "              \"colsample_bytree\": [0.5, 0.8, 0.1],\n",
    "              #\"max_features\": [25, 50, None],\n",
    "              #\"min_child_weight\": [1, 4, 10],\n",
    "              \"n_estimators\": [1000]\n",
    "             }\n",
    "\n",
    "# run grid search\n",
    "grid_search = GridSearchCV(clf, param_grid=param_grid, cv=3, scoring= 'neg_mean_squared_error', n_jobs = 6, verbose=True)\n",
    "start = time()\n",
    "grid_search.fit(x_train, y_train['SalePrice'])\n",
    "\n",
    "print(\"GridSearchCV took %.2f seconds for %d candidate parameter settings.\"\n",
    "      % (time() - start, len(grid_search.cv_results_['params'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.01716437803320512,\n",
       " {'colsample_bytree': 0.8,\n",
       "  'learning_rate': 0.01,\n",
       "  'max_depth': 6,\n",
       "  'n_estimators': 1000,\n",
       "  'reg_alpha': 0.1,\n",
       "  'reg_lambda': 1e-05,\n",
       "  'subsample': 0.5})"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_, grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 577,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = xgb.XGBRegressor(booster='gbtree', max_depth = 7, n_estimators= 1000, learning_rate= 0.01, min_child_weight = 8, gamma = 0, reg_lambda = 0.00001, reg_alpha= 0.1, colsample_bytree=0.8, subsample=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=0.8, gamma=0, learning_rate=0.01, max_delta_step=0,\n",
       "       max_depth=7, min_child_weight=8, missing=None, n_estimators=1000,\n",
       "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0.1, reg_lambda=1e-05, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=0.5)"
      ]
     },
     "execution_count": 578,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#xgb_model.fit(x_train_encoded[test_encoded.columns].drop(['Id'], axis = 1), x_train_encoded['SalePrice'])\n",
    "xgb_model.fit(x_train, y_train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The train Log RMSE is 0.0658\n",
      "The validation Log RMSE is 0.1084\n"
     ]
    }
   ],
   "source": [
    "print('The train Log RMSE is {:.4f}'.format(log_rmse(xgb_model.predict(x_train), y_train['SalePrice'])))\n",
    "print('The validation Log RMSE is {:.4f}'.format(log_rmse(xgb_model.predict(x_val), y_val['SalePrice'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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