day55-regularized-linear-models/ridge-regression-key-understandings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8db106fa",
   "metadata": {},
   "source": [
    "## 1. How are coefficients affected?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "19e28c6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_diabetes\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "e63e060b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_diabetes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "b0dbcf9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data.data,columns=data.feature_names)\n",
    "df['TARGET'] = data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "a2541d45",
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.019908</td>\n",
       "      <td>-0.017646</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.051474</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.068330</td>\n",
       "      <td>-0.092204</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005671</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>-0.025930</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022692</td>\n",
       "      <td>-0.009362</td>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031991</td>\n",
       "      <td>-0.046641</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age       sex       bmi        bp        s1        s2        s3  \\\n",
       "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2  0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
       "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "\n",
       "         s4        s5        s6  TARGET  \n",
       "0 -0.002592  0.019908 -0.017646   151.0  \n",
       "1 -0.039493 -0.068330 -0.092204    75.0  \n",
       "2 -0.002592  0.002864 -0.025930   141.0  \n",
       "3  0.034309  0.022692 -0.009362   206.0  \n",
       "4 -0.002592 -0.031991 -0.046641   135.0  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "70034124",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(442, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "9b154bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(data.data,data.target,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "d890576e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "e636419a",
   "metadata": {},
   "outputs": [],
   "source": [
    "coefs = []\n",
    "r2_scores = []\n",
    "\n",
    "for i in [0,10,100,1000]:\n",
    "    reg = Ridge(alpha=i)\n",
    "    reg.fit(X_train,y_train)\n",
    "    \n",
    "    coefs.append(reg.coef_.tolist())\n",
    "    y_pred = reg.predict(X_test)\n",
    "    r2_scores.append(r2_score(y_test,y_pred))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "9930209e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,9))\n",
    "plt.subplot(221)\n",
    "plt.bar(data.feature_names,coefs[0])\n",
    "plt.title('Alpha = 0 ,r2_score = {}'.format(round(r2_scores[0],2)))\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.bar(data.feature_names,coefs[1])\n",
    "plt.title('Alpha = 10 ,r2_score = {}'.format(round(r2_scores[1],2)))\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.bar(data.feature_names,coefs[2])\n",
    "plt.title('Alpha = 100 ,r2_score = {}'.format(round(r2_scores[2],2)))\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.bar(data.feature_names,coefs[3])\n",
    "plt.title('Alpha = 1000 ,r2_score = {}'.format(round(r2_scores[3],2)))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "742cb2cd",
   "metadata": {},
   "source": [
    "## 2. Higher Coefficients are affected more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "305f471c",
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = [0,0.0001,0.001,0.01,0.1,1,10,100,1000,10000]\n",
    "\n",
    "coefs = []\n",
    "\n",
    "for i in alphas:\n",
    "    reg = Ridge(alpha=i)\n",
    "    reg.fit(X_train,y_train)\n",
    "    \n",
    "    coefs.append(reg.coef_.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e1764f78",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_array = np.array(coefs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "b5b0d9f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0000</th>\n",
       "      <td>-9.160885</td>\n",
       "      <td>-205.462260</td>\n",
       "      <td>516.684624</td>\n",
       "      <td>340.627341</td>\n",
       "      <td>-895.543609</td>\n",
       "      <td>561.214533</td>\n",
       "      <td>153.884786</td>\n",
       "      <td>126.734316</td>\n",
       "      <td>861.121400</td>\n",
       "      <td>52.419828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0001</th>\n",
       "      <td>-9.118336</td>\n",
       "      <td>-205.337133</td>\n",
       "      <td>516.880570</td>\n",
       "      <td>340.556792</td>\n",
       "      <td>-883.415291</td>\n",
       "      <td>551.553259</td>\n",
       "      <td>148.578680</td>\n",
       "      <td>125.355917</td>\n",
       "      <td>856.480254</td>\n",
       "      <td>52.467627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0010</th>\n",
       "      <td>-8.763583</td>\n",
       "      <td>-204.321125</td>\n",
       "      <td>518.371729</td>\n",
       "      <td>339.975385</td>\n",
       "      <td>-787.690766</td>\n",
       "      <td>475.274718</td>\n",
       "      <td>106.786540</td>\n",
       "      <td>114.632063</td>\n",
       "      <td>819.739542</td>\n",
       "      <td>52.872100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0100</th>\n",
       "      <td>-6.401088</td>\n",
       "      <td>-198.669767</td>\n",
       "      <td>522.048548</td>\n",
       "      <td>336.348363</td>\n",
       "      <td>-383.709187</td>\n",
       "      <td>152.663678</td>\n",
       "      <td>-66.060583</td>\n",
       "      <td>75.611090</td>\n",
       "      <td>659.869402</td>\n",
       "      <td>55.828128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1000</th>\n",
       "      <td>6.642753</td>\n",
       "      <td>-172.242166</td>\n",
       "      <td>485.523872</td>\n",
       "      <td>314.682122</td>\n",
       "      <td>-72.939323</td>\n",
       "      <td>-80.590053</td>\n",
       "      <td>-174.466515</td>\n",
       "      <td>83.616653</td>\n",
       "      <td>484.363285</td>\n",
       "      <td>73.584154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0000</th>\n",
       "      <td>42.242217</td>\n",
       "      <td>-57.305508</td>\n",
       "      <td>282.170831</td>\n",
       "      <td>198.061386</td>\n",
       "      <td>14.363544</td>\n",
       "      <td>-22.551274</td>\n",
       "      <td>-136.930053</td>\n",
       "      <td>102.023193</td>\n",
       "      <td>260.104308</td>\n",
       "      <td>98.552274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0000</th>\n",
       "      <td>21.174004</td>\n",
       "      <td>1.659796</td>\n",
       "      <td>63.659772</td>\n",
       "      <td>48.493240</td>\n",
       "      <td>18.421492</td>\n",
       "      <td>12.875448</td>\n",
       "      <td>-38.915435</td>\n",
       "      <td>38.842464</td>\n",
       "      <td>61.612405</td>\n",
       "      <td>35.505355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.0000</th>\n",
       "      <td>2.858979</td>\n",
       "      <td>0.629452</td>\n",
       "      <td>7.540604</td>\n",
       "      <td>5.849997</td>\n",
       "      <td>2.710879</td>\n",
       "      <td>2.142134</td>\n",
       "      <td>-4.834047</td>\n",
       "      <td>5.108223</td>\n",
       "      <td>7.448466</td>\n",
       "      <td>4.576129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000.0000</th>\n",
       "      <td>0.295726</td>\n",
       "      <td>0.069290</td>\n",
       "      <td>0.769004</td>\n",
       "      <td>0.597829</td>\n",
       "      <td>0.282900</td>\n",
       "      <td>0.225936</td>\n",
       "      <td>-0.495607</td>\n",
       "      <td>0.527031</td>\n",
       "      <td>0.761497</td>\n",
       "      <td>0.471029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000.0000</th>\n",
       "      <td>0.029674</td>\n",
       "      <td>0.006995</td>\n",
       "      <td>0.077054</td>\n",
       "      <td>0.059915</td>\n",
       "      <td>0.028412</td>\n",
       "      <td>0.022715</td>\n",
       "      <td>-0.049686</td>\n",
       "      <td>0.052870</td>\n",
       "      <td>0.076321</td>\n",
       "      <td>0.047241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  age         sex         bmi          bp          s1  \\\n",
       "alpha                                                                   \n",
       "0.0000      -9.160885 -205.462260  516.684624  340.627341 -895.543609   \n",
       "0.0001      -9.118336 -205.337133  516.880570  340.556792 -883.415291   \n",
       "0.0010      -8.763583 -204.321125  518.371729  339.975385 -787.690766   \n",
       "0.0100      -6.401088 -198.669767  522.048548  336.348363 -383.709187   \n",
       "0.1000       6.642753 -172.242166  485.523872  314.682122  -72.939323   \n",
       "1.0000      42.242217  -57.305508  282.170831  198.061386   14.363544   \n",
       "10.0000     21.174004    1.659796   63.659772   48.493240   18.421492   \n",
       "100.0000     2.858979    0.629452    7.540604    5.849997    2.710879   \n",
       "1000.0000    0.295726    0.069290    0.769004    0.597829    0.282900   \n",
       "10000.0000   0.029674    0.006995    0.077054    0.059915    0.028412   \n",
       "\n",
       "                    s2          s3          s4          s5         s6  \n",
       "alpha                                                                  \n",
       "0.0000      561.214533  153.884786  126.734316  861.121400  52.419828  \n",
       "0.0001      551.553259  148.578680  125.355917  856.480254  52.467627  \n",
       "0.0010      475.274718  106.786540  114.632063  819.739542  52.872100  \n",
       "0.0100      152.663678  -66.060583   75.611090  659.869402  55.828128  \n",
       "0.1000      -80.590053 -174.466515   83.616653  484.363285  73.584154  \n",
       "1.0000      -22.551274 -136.930053  102.023193  260.104308  98.552274  \n",
       "10.0000      12.875448  -38.915435   38.842464   61.612405  35.505355  \n",
       "100.0000      2.142134   -4.834047    5.108223    7.448466   4.576129  \n",
       "1000.0000     0.225936   -0.495607    0.527031    0.761497   0.471029  \n",
       "10000.0000    0.022715   -0.049686    0.052870    0.076321   0.047241  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_df = pd.DataFrame(input_array,columns=data.feature_names)\n",
    "coef_df['alpha'] = alphas\n",
    "coef_df.set_index('alpha')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "02e56359",
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = [0,0.0001,0.0005,0.001,0.005,0.1,0.5,1,5,10]\n",
    "\n",
    "coefs = []\n",
    "\n",
    "for i in alphas:\n",
    "    reg = Ridge(alpha=i)\n",
    "    reg.fit(X_train,y_train)\n",
    "    \n",
    "    coefs.append(reg.coef_.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "8b3f3c6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_array = np.array(coefs).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "238fb787",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1659a8e32e0>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.plot(alphas,np.zeros(len(alphas)),color='black',linewidth=5)\n",
    "for i in range(input_array.shape[0]):\n",
    "    plt.plot(alphas,input_array[i],label=data.feature_names[i])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "101a157b",
   "metadata": {},
   "source": [
    "## 3. Impact on Bias and Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "1cd23224",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "m = 100\n",
    "X = 5 * np.random.rand(m, 1) - 2\n",
    "y = 0.7 * X ** 2 - 2 * X + 3 + np.random.randn(m, 1)\n",
    "\n",
    "plt.scatter(X, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "2137ce5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X.reshape(100,1),y.reshape(100),test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "ccf70e08",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly = PolynomialFeatures(degree=15)\n",
    "\n",
    "X_train = poly.fit_transform(X_train)\n",
    "X_test = poly.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "75628a13",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.evaluate import bias_variance_decomp\n",
    "\n",
    "alphas = np.linspace(0,30,100)\n",
    "\n",
    "loss = []\n",
    "bias = []\n",
    "variance = []\n",
    "\n",
    "for i in alphas:\n",
    "    reg = Ridge(alpha=i)\n",
    "    avg_expected_loss, avg_bias, avg_var = bias_variance_decomp(\n",
    "        reg, X_train, y_train, X_test, y_test, \n",
    "        loss='mse',\n",
    "        random_seed=123)\n",
    "    loss.append(avg_expected_loss)\n",
    "    bias.append(avg_bias)\n",
    "    variance.append(avg_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "d7ab73dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1659a707e50>"
      ]
     },
     "execution_count": 118,
     "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": [
    "plt.plot(alphas,loss,label='loss')\n",
    "plt.plot(alphas,bias,label='Bias')\n",
    "plt.plot(alphas,variance,label='Variance')\n",
    "plt.ylim(0,5)\n",
    "plt.xlabel('Alpha')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd5cb150",
   "metadata": {},
   "source": [
    "## 4. Effect of Regularization on Loss Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "11074cf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[27.82809103]\n",
      "-2.29474455867698\n"
     ]
    },
    {
     "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": [
    "from sklearn.datasets import make_regression\n",
    "\n",
    "X,y = make_regression(n_samples=100, n_features=1, n_informative=1, n_targets=1,noise=20,random_state=13)\n",
    "\n",
    "plt.scatter(X,y)\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "reg = LinearRegression()\n",
    "reg.fit(X,y)\n",
    "print(reg.coef_)\n",
    "print(reg.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "20ca4a66",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cal_loss(m,alpha):\n",
    "    return np.sum((y - m*X.ravel() + 2.29)**2) + alpha*m*m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "ac86ef50",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(m):\n",
    "    return m*X - 2.29"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "bdb2e4fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = np.linspace(-45,100,100)\n",
    "plt.figure(figsize=(4,6))\n",
    "for j in [0,10,20,30,40,50,100]:\n",
    "    loss = []\n",
    "    for i in range(m.shape[0]):\n",
    "        loss_i = cal_loss(m[i],j)\n",
    "        loss.append(loss_i)\n",
    "    plt.plot(m,loss,label='alpha = {}'.format(j))\n",
    "plt.legend()\n",
    "plt.xlabel('Alpha')\n",
    "plt.ylabel('Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "752a28f0",
   "metadata": {},
   "source": [
    "### for 2 coefficients\n",
    "<img src='https://explained.ai/regularization/images/lagrange-animation.gif'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5527872c",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}