Ridge Regression - Coefficient Affected.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "036b3fe5",
   "metadata": {},
   "source": [
    "### 1. How are coefficients affected?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3cd25f49",
   "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": 2,
   "id": "dfcffc3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_diabetes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eb5dedbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data.data,columns=data.feature_names)\n",
    "df['TARGET'] = data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f879c84",
   "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>0.019907</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>-0.068332</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>-0.005670</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002861</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>0.022688</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>-0.031988</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 -0.005670 -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  0.019907 -0.017646   151.0  \n",
       "1 -0.039493 -0.068332 -0.092204    75.0  \n",
       "2 -0.002592  0.002861 -0.025930   141.0  \n",
       "3  0.034309  0.022688 -0.009362   206.0  \n",
       "4 -0.002592 -0.031988 -0.046641   135.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1aacdf02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(442, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1e748d4c",
   "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": 7,
   "id": "b6b4e59e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "20fcc1e6",
   "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c60ff78",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "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": "b591b45b",
   "metadata": {},
   "source": [
    "### 2. Higher Coefficients are affected more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eca2885e",
   "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": 11,
   "id": "4934ce26",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_array = np.array(coefs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4dc8a60f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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",
       "    </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>-9.158653</td>\n",
       "      <td>-205.454322</td>\n",
       "      <td>516.693745</td>\n",
       "      <td>340.619999</td>\n",
       "      <td>-895.552002</td>\n",
       "      <td>561.220679</td>\n",
       "      <td>153.893110</td>\n",
       "      <td>126.731397</td>\n",
       "      <td>861.127002</td>\n",
       "      <td>52.421122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0001</th>\n",
       "      <td>-9.116119</td>\n",
       "      <td>-205.329219</td>\n",
       "      <td>516.889664</td>\n",
       "      <td>340.549436</td>\n",
       "      <td>-883.423872</td>\n",
       "      <td>551.559582</td>\n",
       "      <td>148.587063</td>\n",
       "      <td>125.353015</td>\n",
       "      <td>856.485935</td>\n",
       "      <td>52.468916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0010</th>\n",
       "      <td>-8.761487</td>\n",
       "      <td>-204.313404</td>\n",
       "      <td>518.380608</td>\n",
       "      <td>339.967926</td>\n",
       "      <td>-787.700510</td>\n",
       "      <td>475.282181</td>\n",
       "      <td>106.795253</td>\n",
       "      <td>114.629265</td>\n",
       "      <td>819.745723</td>\n",
       "      <td>52.873349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0100</th>\n",
       "      <td>-6.399507</td>\n",
       "      <td>-198.662945</td>\n",
       "      <td>522.056516</td>\n",
       "      <td>336.340595</td>\n",
       "      <td>-383.717571</td>\n",
       "      <td>152.670882</td>\n",
       "      <td>-66.053243</td>\n",
       "      <td>75.608209</td>\n",
       "      <td>659.875219</td>\n",
       "      <td>55.829221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1000</th>\n",
       "      <td>6.643733</td>\n",
       "      <td>-172.237474</td>\n",
       "      <td>485.529585</td>\n",
       "      <td>314.675846</td>\n",
       "      <td>-72.940209</td>\n",
       "      <td>-80.588688</td>\n",
       "      <td>-174.463527</td>\n",
       "      <td>83.614710</td>\n",
       "      <td>484.365316</td>\n",
       "      <td>73.585091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0000</th>\n",
       "      <td>42.242528</td>\n",
       "      <td>-57.304483</td>\n",
       "      <td>282.172125</td>\n",
       "      <td>198.059395</td>\n",
       "      <td>14.363734</td>\n",
       "      <td>-22.551079</td>\n",
       "      <td>-136.929684</td>\n",
       "      <td>102.023069</td>\n",
       "      <td>260.104294</td>\n",
       "      <td>98.552692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0000</th>\n",
       "      <td>21.174024</td>\n",
       "      <td>1.659837</td>\n",
       "      <td>63.659826</td>\n",
       "      <td>48.492858</td>\n",
       "      <td>18.421510</td>\n",
       "      <td>12.875466</td>\n",
       "      <td>-38.915435</td>\n",
       "      <td>38.842475</td>\n",
       "      <td>61.612302</td>\n",
       "      <td>35.505383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.0000</th>\n",
       "      <td>2.858980</td>\n",
       "      <td>0.629453</td>\n",
       "      <td>7.540605</td>\n",
       "      <td>5.849949</td>\n",
       "      <td>2.710879</td>\n",
       "      <td>2.142135</td>\n",
       "      <td>-4.834047</td>\n",
       "      <td>5.108223</td>\n",
       "      <td>7.448451</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>0.597824</td>\n",
       "      <td>0.282900</td>\n",
       "      <td>0.225936</td>\n",
       "      <td>-0.495607</td>\n",
       "      <td>0.527031</td>\n",
       "      <td>0.761496</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>0.059914</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      -9.158653 -205.454322  516.693745  340.619999 -895.552002   \n",
       "0.0001      -9.116119 -205.329219  516.889664  340.549436 -883.423872   \n",
       "0.0010      -8.761487 -204.313404  518.380608  339.967926 -787.700510   \n",
       "0.0100      -6.399507 -198.662945  522.056516  336.340595 -383.717571   \n",
       "0.1000       6.643733 -172.237474  485.529585  314.675846  -72.940209   \n",
       "1.0000      42.242528  -57.304483  282.172125  198.059395   14.363734   \n",
       "10.0000     21.174024    1.659837   63.659826   48.492858   18.421510   \n",
       "100.0000     2.858980    0.629453    7.540605    5.849949    2.710879   \n",
       "1000.0000    0.295726    0.069290    0.769004    0.597824    0.282900   \n",
       "10000.0000   0.029674    0.006995    0.077054    0.059914    0.028412   \n",
       "\n",
       "                    s2          s3          s4          s5         s6  \n",
       "alpha                                                                  \n",
       "0.0000      561.220679  153.893110  126.731397  861.127002  52.421122  \n",
       "0.0001      551.559582  148.587063  125.353015  856.485935  52.468916  \n",
       "0.0010      475.282181  106.795253  114.629265  819.745723  52.873349  \n",
       "0.0100      152.670882  -66.053243   75.608209  659.875219  55.829221  \n",
       "0.1000      -80.588688 -174.463527   83.614710  484.365316  73.585091  \n",
       "1.0000      -22.551079 -136.929684  102.023069  260.104294  98.552692  \n",
       "10.0000      12.875466  -38.915435   38.842475   61.612302  35.505383  \n",
       "100.0000      2.142135   -4.834047    5.108223    7.448451   4.576129  \n",
       "1000.0000     0.225936   -0.495607    0.527031    0.761496   0.471029  \n",
       "10000.0000    0.022715   -0.049686    0.052870    0.076321   0.047241  "
      ]
     },
     "execution_count": 12,
     "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": 13,
   "id": "564bcc01",
   "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": 14,
   "id": "2c684ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_array = np.array(coefs).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d01d6871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c6e0757d90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "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": "a28b85f0",
   "metadata": {},
   "source": [
    "## 3. Impact on Bias and Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "08781902",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "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": 17,
   "id": "b1e0cb75",
   "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": 18,
   "id": "60840c64",
   "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": 19,
   "id": "cec5a028",
   "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": 20,
   "id": "bad5aa82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c6d9f12340>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "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": "58075e65",
   "metadata": {},
   "source": [
    "## 4. Effect of Regularization on Loss Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eadef2ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[27.82809103]\n",
      "-2.29474455867698\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "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": 22,
   "id": "fdaea5f4",
   "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": 23,
   "id": "3fa263ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(m):\n",
    "    return m*X - 2.29"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "af12f889",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}