Resampling and Regularization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resampling and Regularization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "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>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  radio  newspaper  sales\n",
       "1  230.1   37.8       69.2   22.1\n",
       "2   44.5   39.3       45.1   10.4\n",
       "3   17.2   45.9       69.3    9.3\n",
       "4  151.5   41.3       58.5   18.5\n",
       "5  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATAPATH = 'data/Advertising.csv'\n",
    "data = pd.read_csv(DATAPATH, index_col=0)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scatter_plot(feature):\n",
    "    plt.figure(figsize=(10,5))\n",
    "    plt.scatter(data[feature], data['sales'], c='black')\n",
    "    plt.xlabel(f'Money spent on {feature} ads ($)')\n",
    "    plt.ylabel('Sales (k$)')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "scatter_plot('TV')\n",
    "scatter_plot('radio')\n",
    "scatter_plot('newspaper')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(['sales'], axis=1)\n",
    "y = data['sales'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.072946597100209\n"
     ]
    }
   ],
   "source": [
    "lin_reg = LinearRegression()\n",
    "\n",
    "MSEs = cross_val_score(lin_reg, X, y, scoring='neg_mean_squared_error', cv=5)\n",
    "\n",
    "mean_MSE = np.mean(MSEs)\n",
    "\n",
    "print(-mean_MSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ridge regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True,\n",
       "                             max_iter=None, normalize=False, random_state=None,\n",
       "                             solver='auto', tol=0.001),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'alpha': [1e-15, 1e-10, 1e-08, 0.0001, 0.001, 0.01, 1,\n",
       "                                   5, 10, 20]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge = Ridge()\n",
    "\n",
    "parameters = {'alpha': [1e-15, 1e-10, 1e-8, 1e-4, 1e-3, 1e-2, 1, 5, 10, 20]}\n",
    "\n",
    "ridge_regressor = GridSearchCV(ridge, parameters, scoring='neg_mean_squared_error', cv=5)\n",
    "\n",
    "ridge_regressor.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 20}\n",
      "3.0726713383411433\n"
     ]
    }
   ],
   "source": [
    "print(ridge_regressor.best_params_)\n",
    "print(-ridge_regressor.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lasso "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 1}\n",
      "3.035998320911192\n"
     ]
    }
   ],
   "source": [
    "lasso = Lasso(tol=0.05)\n",
    "\n",
    "parameters = {'alpha': [1e-15, 1e-10, 1e-8, 1e-4, 1e-3, 1e-2, 1, 5, 10, 20]}\n",
    "\n",
    "lasso_regressor = GridSearchCV(lasso, parameters, scoring='neg_mean_squared_error', cv=5)\n",
    "\n",
    "lasso_regressor.fit(X, y)\n",
    "\n",
    "print(lasso_regressor.best_params_)\n",
    "print(-lasso_regressor.best_score_)"
   ]
  },
  {
   "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",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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