Ridge Regularization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7a5e68e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "45806aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "93a67ea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=load_diabetes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "01f4222a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[ 0.03807591,  0.05068012,  0.06169621, ..., -0.00259226,\n",
       "          0.01990842, -0.01764613],\n",
       "        [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n",
       "         -0.06832974, -0.09220405],\n",
       "        [ 0.08529891,  0.05068012,  0.04445121, ..., -0.00259226,\n",
       "          0.00286377, -0.02593034],\n",
       "        ...,\n",
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       "         -0.04687948,  0.01549073],\n",
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       "        [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n",
       "         -0.00421986,  0.00306441]]),\n",
       " 'target': array([151.,  75., 141., 206., 135.,  97., 138.,  63., 110., 310., 101.,\n",
       "         69., 179., 185., 118., 171., 166., 144.,  97., 168.,  68.,  49.,\n",
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       "         94., 183.,  66., 173.,  72.,  49.,  64.,  48., 178., 104., 132.,\n",
       "        220.,  57.]),\n",
       " 'frame': None,\n",
       " 'DESCR': '.. _diabetes_dataset:\\n\\nDiabetes dataset\\n----------------\\n\\nTen baseline variables, age, sex, body mass index, average blood\\npressure, and six blood serum measurements were obtained for each of n =\\n442 diabetes patients, as well as the response of interest, a\\nquantitative measure of disease progression one year after baseline.\\n\\n**Data Set Characteristics:**\\n\\n  :Number of Instances: 442\\n\\n  :Number of Attributes: First 10 columns are numeric predictive values\\n\\n  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\\n\\n  :Attribute Information:\\n      - age     age in years\\n      - sex\\n      - bmi     body mass index\\n      - bp      average blood pressure\\n      - s1      tc, total serum cholesterol\\n      - s2      ldl, low-density lipoproteins\\n      - s3      hdl, high-density lipoproteins\\n      - s4      tch, total cholesterol / HDL\\n      - s5      ltg, possibly log of serum triglycerides level\\n      - s6      glu, blood sugar level\\n\\nNote: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\\n\\nSource URL:\\nhttps://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\\n\\nFor more information see:\\nBradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\\n(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)',\n",
       " 'feature_names': ['age',\n",
       "  'sex',\n",
       "  'bmi',\n",
       "  'bp',\n",
       "  's1',\n",
       "  's2',\n",
       "  's3',\n",
       "  's4',\n",
       "  's5',\n",
       "  's6'],\n",
       " 'data_filename': 'C:\\\\Users\\\\Abdur rahim nishad\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\diabetes_data.csv.gz',\n",
       " 'target_filename': 'C:\\\\Users\\\\Abdur rahim nishad\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\diabetes_target.csv.gz'}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "408e2e3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b210fcd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "342a84fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "L=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1ace5d06",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6d8d5f47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8ea6c881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  23.45388514 -247.43107084  492.10188174  329.36498638 -970.79784704\n",
      "  573.54460121  182.41386124  255.9162021   794.21654496   89.32074078]\n",
      "152.13619339070766\n"
     ]
    }
   ],
   "source": [
    "print(L.coef_)\n",
    "print(L.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1b3155cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=L.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9d9d53e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5188118914964637"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "26f58ef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5159868419842792"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "400fdecc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.5188118914964637\n",
      "RMSE 48.72710829141399\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score,mean_squared_error\n",
    "\n",
    "print(\"R2 score\",r2_score(y_test,y_pred))\n",
    "print(\"RMSE\",np.sqrt(mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "396aa1b7",
   "metadata": {},
   "source": [
    "# Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "664212d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1a5072e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "R=Ridge(alpha=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "141d86c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1000)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c80cb668",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.25917884  0.05634256  0.77491396  0.60827155  0.23265473  0.18351618\n",
      " -0.51255842  0.52541202  0.73278302  0.52702311]\n",
      "151.83466638044493\n"
     ]
    }
   ],
   "source": [
    "print(R.coef_)\n",
    "print(R.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b50bea7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1=R.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a5b66313",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.0022195533951115953\n",
      "RMSE 70.16664159944268\n"
     ]
    }
   ],
   "source": [
    "print(\"R2 score\",r2_score(y_test,y_pred1))\n",
    "print(\"RMSE\",np.sqrt(mean_squared_error(y_test,y_pred1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1bef28a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4923.35759334464"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test,y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "edb0fce3",
   "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",
    "x1 = 5 * np.random.rand(m, 1) - 2\n",
    "x2 = 0.7 * x1 ** 2 - 2 * x1 + 3 + np.random.randn(m, 1)\n",
    "\n",
    "plt.scatter(x1, x2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "39e9f4d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x244f8080b20>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "def get_preds_ridge(x1, x2, alpha):\n",
    "    model = Pipeline([\n",
    "        ('poly_feats', PolynomialFeatures(degree=16)),\n",
    "        ('ridge', Ridge(alpha=alpha))\n",
    "    ])\n",
    "    model.fit(x1, x2)\n",
    "    return model.predict(x1)\n",
    "\n",
    "alphas = [0, 20, 200]\n",
    "cs = ['r', 'g', 'b']\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(x1, x2, 'b+', label='Datapoints')\n",
    "\n",
    "for alpha, c in zip(alphas, cs):\n",
    "    preds = get_preds_ridge(x1, x2, alpha)\n",
    "    # Plot\n",
    "    plt.plot(sorted(x1[:, 0]), preds[np.argsort(x1[:, 0])], c, label='Alpha: {}'.format(alpha))\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7af587fa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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