ridge-regression-from-scratch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a10b779f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.metrics import r2_score\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "21b7ec3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = load_diabetes(return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "38c0add6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "faa0003e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e6535aaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ac9596c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = Ridge(alpha=0.1,solver='cholesky')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69248782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.1, solver='cholesky')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1512b194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.46930668289209765"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = reg.predict(X_test)\n",
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "50b94ae8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  44.02025512 -241.69666596  452.98163524  332.04993719  -76.34010147\n",
      "  -68.52063199 -164.98817213  149.9687712   431.61985919   58.51762582]\n",
      "150.89050209333024\n"
     ]
    }
   ],
   "source": [
    "print(reg.coef_)\n",
    "print(reg.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "70cbee29",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Meraridge:\n",
    "    def __init__(self,alpha=.1):\n",
    "        self.alpha = alpha\n",
    "        self.coef_=None\n",
    "        self.intercept_=None\n",
    "    def fit(self,x_train,y_train):\n",
    "        x_train=np.insert(x_train,0,1,axis=1)\n",
    "        I=np.identity(x_train.shape[1])\n",
    "        I[0][0]=0\n",
    "        result=np.linalg.inv(np.dot(x_train.T,x_train)+self.alpha*I).dot(x_train.T).dot(y_train)\n",
    "        self.intercept_=result[0]\n",
    "        self.coef_=result[1:]\n",
    "        \n",
    "    def predict(self,x_test):\n",
    "        return np.dot(x_test,self.coef_)+self.intercept_\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6d9cfda7",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg=Meraridge()\n",
    "reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7d8a324a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2aeefc97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.46930668289209776"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ed0b45f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  44.02025512, -241.69666596,  452.98163524,  332.04993719,\n",
       "        -76.34010147,  -68.52063199, -164.98817213,  149.9687712 ,\n",
       "        431.61985919,   58.51762582])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75663302",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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