day52-types-of-gradient-descent/batch-gradient-descent.ipynb

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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7d78b116",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_diabetes\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6b46625d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = load_diabetes(return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c2ad6cd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(442, 10)\n",
      "(442,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a70d2eb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a8ec5613",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = LinearRegression()\n",
    "reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d3e3934e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  -9.16088483 -205.46225988  516.68462383  340.62734108 -895.54360867\n",
      "  561.21453306  153.88478595  126.73431596  861.12139955   52.41982836]\n",
      "151.88334520854633\n"
     ]
    }
   ],
   "source": [
    "print(reg.coef_)\n",
    "print(reg.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d8b0bb30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4399387660024645"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = reg.predict(X_test)\n",
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b5f8dc27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(353, 10)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "777721d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "class GDRegressor:\n",
    "    \n",
    "    def __init__(self,learning_rate=0.01,epochs=100):\n",
    "        \n",
    "        self.coef_ = None\n",
    "        self.intercept_ = None\n",
    "        self.lr = learning_rate\n",
    "        self.epochs = epochs\n",
    "        \n",
    "    def fit(self,X_train,y_train):\n",
    "        # init your coefs\n",
    "        self.intercept_ = 0\n",
    "        self.coef_ = np.ones(X_train.shape[1])\n",
    "        \n",
    "        for i in range(self.epochs):\n",
    "            # update all the coef and the intercept\n",
    "            y_hat = np.dot(X_train,self.coef_) + self.intercept_\n",
    "            #print(\"Shape of y_hat\",y_hat.shape)\n",
    "            intercept_der = -2 * np.mean(y_train - y_hat)\n",
    "            self.intercept_ = self.intercept_ - (self.lr * intercept_der)\n",
    "            \n",
    "            coef_der = -2 * np.dot((y_train - y_hat),X_train)/X_train.shape[0]\n",
    "            self.coef_ = self.coef_ - (self.lr * coef_der)\n",
    "        \n",
    "        print(self.intercept_,self.coef_)\n",
    "    \n",
    "    def predict(self,X_test):\n",
    "        return np.dot(X_test,self.coef_) + self.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4ecc1e6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdr = GDRegressor(epochs=1000,learning_rate=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "e7e6aec9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "152.0135263267291 [  14.38915082 -173.72674118  491.54504015  323.91983579  -39.32680194\n",
      " -116.01099114 -194.04229501  103.38216641  451.63385893   97.57119174]\n"
     ]
    }
   ],
   "source": [
    "gdr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "8e5e4410",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = gdr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "84f66db7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4534524671450598"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8df72af",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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