Logistic Regression Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1ca2b20f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "0167ff8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def standardization(x):\n",
    "#     for i in range(x.shape[1]):\n",
    "#         x.loc[:,i] = (x.loc[:,i] - np.mean(x.loc[:,i]))/np.std(x.loc[:,i])\n",
    "#     return x\n",
    "\n",
    "class LogisticRegression:\n",
    "    def __init__(self,fields):\n",
    "        self.fields = fields\n",
    "        self.weights = np.random.rand(1,self.fields)\n",
    "        self.bias = np.random.rand()\n",
    "    \n",
    "    def weighted_sum(self,X):\n",
    "        #shape of X - rows x fields\n",
    "        #shape of weights - 1 x fields\n",
    "        #shape of ans - 1 x rows\n",
    "        print(\"shape of X.T \",X.T.shape)\n",
    "        print(\"shape of weights \",self.weights.shape)\n",
    "        ans= np.add(np.matmul(self.weights,X.T),self.bias)\n",
    "        print(\"shape of ws ans: \", ans.shape )\n",
    "        return ans\n",
    "    \n",
    "    def sigmoid(self,in_put):\n",
    "        ans = 1/(1+np.exp(in_put.astype(float)))\n",
    "                \n",
    "        return ans\n",
    "    \n",
    "    def gradient(self,y,y_hat,x):\n",
    "        dw = np.matmul(-(y-y_hat),x)\n",
    "        db = -(y-y_hat)\n",
    "        return dw,db\n",
    "    \n",
    "    def error(y,y_hat):\n",
    "        return (1/len(y))*np.sum(y*np.log(y_hat))\n",
    "    \n",
    "    def fit(self,X,y,epoch,learn_rate):\n",
    "        for i in range(epoch):\n",
    "            weighted_sum = self.weighted_sum(X)\n",
    "            print(\"shape of weighted sum: \",weighted_sum.shape)\n",
    "            y_hat = self.sigmoid(weighted_sum)\n",
    "            print(\"shape of y_hat: \",y_hat.shape)\n",
    "            dw,db = self.gradient(y,y_hat,X)\n",
    "            self.weights = self.weights - dw\n",
    "            self.bias = self.bias - db\n",
    "    \n",
    "    def predict(self,X):\n",
    "        fin = []\n",
    "        res = self.sigmoid(self.weighted_sum(X))\n",
    "        for i in range(len(res)):\n",
    "            if res[i]>=0.5:\n",
    "                fin.append(1)\n",
    "            else:\n",
    "                fin.append(0)\n",
    "            \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "d249dd42",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/PCOS_infertility.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "42e0eee4",
   "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>Sl. No</th>\n",
       "      <th>Patient File No.</th>\n",
       "      <th>PCOS (Y/N)</th>\n",
       "      <th>I   beta-HCG(mIU/mL)</th>\n",
       "      <th>II    beta-HCG(mIU/mL)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>271.000000</td>\n",
       "      <td>10271.000000</td>\n",
       "      <td>0.327172</td>\n",
       "      <td>664.549235</td>\n",
       "      <td>238.229518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>156.317519</td>\n",
       "      <td>156.317519</td>\n",
       "      <td>0.469615</td>\n",
       "      <td>3348.920576</td>\n",
       "      <td>1603.826221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>10001.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.110417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>136.000000</td>\n",
       "      <td>10136.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.990000</td>\n",
       "      <td>1.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>271.000000</td>\n",
       "      <td>10271.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>406.000000</td>\n",
       "      <td>10406.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>297.210000</td>\n",
       "      <td>97.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>541.000000</td>\n",
       "      <td>10541.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32460.970000</td>\n",
       "      <td>25000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Sl. No  Patient File No.  PCOS (Y/N)    I   beta-HCG(mIU/mL)  \\\n",
       "count  541.000000        541.000000  541.000000              541.000000   \n",
       "mean   271.000000      10271.000000    0.327172              664.549235   \n",
       "std    156.317519        156.317519    0.469615             3348.920576   \n",
       "min      1.000000      10001.000000    0.000000                1.300000   \n",
       "25%    136.000000      10136.000000    0.000000                1.990000   \n",
       "50%    271.000000      10271.000000    0.000000               20.000000   \n",
       "75%    406.000000      10406.000000    1.000000              297.210000   \n",
       "max    541.000000      10541.000000    1.000000            32460.970000   \n",
       "\n",
       "       II    beta-HCG(mIU/mL)  \n",
       "count              541.000000  \n",
       "mean               238.229518  \n",
       "std               1603.826221  \n",
       "min                  0.110417  \n",
       "25%                  1.990000  \n",
       "50%                  1.990000  \n",
       "75%                 97.630000  \n",
       "max              25000.000000  "
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66e7caf4",
   "metadata": {},
   "source": [
    "# Data splitting and cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "33427401",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "4.9\n",
      "16.6\n",
      "17.6\n",
      "4.6\n",
      "5.5\n",
      "2.2\n",
      "18.9\n",
      "10\n",
      "16\n",
      "5.4\n",
      "3.7\n",
      "5.5\n",
      "7.2\n",
      "2.5\n",
      "18.5\n",
      "7.7\n",
      "0.19\n",
      "0.6\n",
      "2.5\n",
      "3.7\n",
      "0.8\n",
      "2.3\n",
      "1\n",
      "0.7\n",
      "6.3\n",
      "19.6\n",
      "18.2\n",
      "7.6\n",
      "1.7\n",
      "5.6\n",
      "3.7\n",
      "5.2\n",
      "20\n"
     ]
    }
   ],
   "source": [
    "for i in range(data.shape[0]):\n",
    "    print(data.iloc[i,5])\n",
    "    if data.iloc[i,5] == 'a':\n",
    "        data.iloc[i,5] = 0.0      \n",
    "    data.iloc[i,5] = float(data.iloc[i,5])\n",
    "\n",
    "#data['AMH(ng/mL)'] = data['AMH(ng/mL)'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "30612162",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split into train,test,validation (80%,20%,20%)\n",
    "train, validate, test = np.split(data.sample(frac=1, random_state=42),[int(.6*len(data)), int(.8*len(data))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "d93e3299",
   "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>Sl. No</th>\n",
       "      <th>Patient File No.</th>\n",
       "      <th>PCOS (Y/N)</th>\n",
       "      <th>I   beta-HCG(mIU/mL)</th>\n",
       "      <th>II    beta-HCG(mIU/mL)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>324.000000</td>\n",
       "      <td>324.000000</td>\n",
       "      <td>324.000000</td>\n",
       "      <td>324.000000</td>\n",
       "      <td>324.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>268.987654</td>\n",
       "      <td>10268.987654</td>\n",
       "      <td>0.311728</td>\n",
       "      <td>510.039160</td>\n",
       "      <td>134.101477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>157.355132</td>\n",
       "      <td>157.355132</td>\n",
       "      <td>0.463916</td>\n",
       "      <td>2927.582867</td>\n",
       "      <td>470.925387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>10001.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.110417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>131.500000</td>\n",
       "      <td>10131.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.990000</td>\n",
       "      <td>1.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>272.500000</td>\n",
       "      <td>10272.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>399.500000</td>\n",
       "      <td>10399.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>273.260000</td>\n",
       "      <td>79.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>541.000000</td>\n",
       "      <td>10541.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32460.970000</td>\n",
       "      <td>4490.180000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Sl. No  Patient File No.  PCOS (Y/N)    I   beta-HCG(mIU/mL)  \\\n",
       "count  324.000000        324.000000  324.000000              324.000000   \n",
       "mean   268.987654      10268.987654    0.311728              510.039160   \n",
       "std    157.355132        157.355132    0.463916             2927.582867   \n",
       "min      1.000000      10001.000000    0.000000                1.300000   \n",
       "25%    131.500000      10131.500000    0.000000                1.990000   \n",
       "50%    272.500000      10272.500000    0.000000               12.000000   \n",
       "75%    399.500000      10399.500000    1.000000              273.260000   \n",
       "max    541.000000      10541.000000    1.000000            32460.970000   \n",
       "\n",
       "       II    beta-HCG(mIU/mL)  \n",
       "count              324.000000  \n",
       "mean               134.101477  \n",
       "std                470.925387  \n",
       "min                  0.110417  \n",
       "25%                  1.990000  \n",
       "50%                  1.990000  \n",
       "75%                 79.300000  \n",
       "max               4490.180000  "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "fef6f7a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sl. No', 'Patient File No.', 'PCOS (Y/N)', '  I   beta-HCG(mIU/mL)',\n",
       "       'II    beta-HCG(mIU/mL)', 'AMH(ng/mL)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "f21ecba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.iloc[1,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "5bb02d74",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train['PCOS (Y/N)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "45b21f9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(324,)"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "94078cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train[['  I   beta-HCG(mIU/mL)','II    beta-HCG(mIU/mL)','AMH(ng/mL)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "48a14b8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val = validate[['  I   beta-HCG(mIU/mL)','II    beta-HCG(mIU/mL)','AMH(ng/mL)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "04e0e2d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_val = validate['PCOS (Y/N)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "ad701899",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = test[['  I   beta-HCG(mIU/mL)','II    beta-HCG(mIU/mL)','AMH(ng/mL)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "c38e3708",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = test['PCOS (Y/N)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "b41547a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "e3cdbb78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of X.T  (3, 324)\n",
      "shape of weights  (1, 3)\n",
      "shape of ws ans:  (1, 324)\n",
      "shape of weighted sum:  (1, 324)\n",
      "shape of y_hat:  (1, 324)\n",
      "shape of X.T  (3, 324)\n",
      "shape of weights  (1, 3)\n",
      "shape of ws ans:  (1, 324)\n",
      "shape of weighted sum:  (1, 324)\n",
      "shape of y_hat:  (1, 324)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vashist/anaconda3/lib/python3.9/site-packages/pandas/core/internals/blocks.py:351: RuntimeWarning: overflow encountered in exp\n",
      "  result = func(self.values, **kwargs)\n",
      "/tmp/ipykernel_2388980/2850534753.py:28: FutureWarning: Calling a ufunc on non-aligned DataFrames (or DataFrame/Series combination). Currently, the indices are ignored and the result takes the index/columns of the first DataFrame. In the future , the DataFrames/Series will be aligned before applying the ufunc.\n",
      "Convert one of the arguments to a NumPy array (eg 'ufunc(df1, np.asarray(df2)') to keep the current behaviour, or align manually (eg 'df1, df2 = df1.align(df2)') before passing to the ufunc to obtain the future behaviour and silence this warning.\n",
      "  dw = np.matmul(-(y-y_hat),x)\n",
      "/tmp/ipykernel_2388980/2850534753.py:18: FutureWarning: Calling a ufunc on non-aligned DataFrames (or DataFrame/Series combination). Currently, the indices are ignored and the result takes the index/columns of the first DataFrame. In the future , the DataFrames/Series will be aligned before applying the ufunc.\n",
      "Convert one of the arguments to a NumPy array (eg 'ufunc(df1, np.asarray(df2)') to keep the current behaviour, or align manually (eg 'df1, df2 = df1.align(df2)') before passing to the ufunc to obtain the future behaviour and silence this warning.\n",
      "  ans= np.add(np.matmul(self.weights,X.T),self.bias)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Dot product shape mismatch, (1, 453) vs (324, 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[290], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[274], line 41\u001b[0m, in \u001b[0;36mLogisticRegression.fit\u001b[0;34m(self, X, y, epoch, learn_rate)\u001b[0m\n\u001b[1;32m     39\u001b[0m y_hat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msigmoid(weighted_sum)\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshape of y_hat: \u001b[39m\u001b[38;5;124m\"\u001b[39m,y_hat\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m---> 41\u001b[0m dw,db \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgradient\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43my_hat\u001b[49m\u001b[43m,\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     42\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweights \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweights \u001b[38;5;241m-\u001b[39m dw\n\u001b[1;32m     43\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbias \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbias \u001b[38;5;241m-\u001b[39m db\n",
      "Cell \u001b[0;32mIn[274], line 28\u001b[0m, in \u001b[0;36mLogisticRegression.gradient\u001b[0;34m(self, y, y_hat, x)\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgradient\u001b[39m(\u001b[38;5;28mself\u001b[39m,y,y_hat,x):\n\u001b[0;32m---> 28\u001b[0m     dw \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatmul\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43my_hat\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     29\u001b[0m     db \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m(y\u001b[38;5;241m-\u001b[39my_hat)\n\u001b[1;32m     30\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m dw,db\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py:2113\u001b[0m, in \u001b[0;36mNDFrame.__array_ufunc__\u001b[0;34m(self, ufunc, method, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m   2109\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m   2110\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array_ufunc__\u001b[39m(\n\u001b[1;32m   2111\u001b[0m     \u001b[38;5;28mself\u001b[39m, ufunc: np\u001b[38;5;241m.\u001b[39mufunc, method: \u001b[38;5;28mstr\u001b[39m, \u001b[38;5;241m*\u001b[39minputs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any\n\u001b[1;32m   2112\u001b[0m ):\n\u001b[0;32m-> 2113\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marraylike\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray_ufunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mufunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/arraylike.py:260\u001b[0m, in \u001b[0;36marray_ufunc\u001b[0;34m(self, ufunc, method, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m    257\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m _standardize_out_kwarg(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    259\u001b[0m \u001b[38;5;66;03m# for backwards compatibility check and potentially fallback for non-aligned frames\u001b[39;00m\n\u001b[0;32m--> 260\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43m_maybe_fallback\u001b[49m\u001b[43m(\u001b[49m\u001b[43mufunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    261\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n\u001b[1;32m    262\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/arraylike.py:238\u001b[0m, in \u001b[0;36m_maybe_fallback\u001b[0;34m(ufunc, method, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m    235\u001b[0m                 new_inputs\u001b[38;5;241m.\u001b[39mappend(x)\n\u001b[1;32m    237\u001b[0m         \u001b[38;5;66;03m# call the ufunc on those transformed inputs\u001b[39;00m\n\u001b[0;32m--> 238\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mufunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnew_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    240\u001b[0m \u001b[38;5;66;03m# signal that we didn't fallback / execute the ufunc yet\u001b[39;00m\n\u001b[1;32m    241\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py:2113\u001b[0m, in \u001b[0;36mNDFrame.__array_ufunc__\u001b[0;34m(self, ufunc, method, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m   2109\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m   2110\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array_ufunc__\u001b[39m(\n\u001b[1;32m   2111\u001b[0m     \u001b[38;5;28mself\u001b[39m, ufunc: np\u001b[38;5;241m.\u001b[39mufunc, method: \u001b[38;5;28mstr\u001b[39m, \u001b[38;5;241m*\u001b[39minputs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any\n\u001b[1;32m   2112\u001b[0m ):\n\u001b[0;32m-> 2113\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marraylike\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray_ufunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mufunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/arraylike.py:265\u001b[0m, in \u001b[0;36marray_ufunc\u001b[0;34m(self, ufunc, method, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m    262\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[1;32m    264\u001b[0m \u001b[38;5;66;03m# for binary ops, use our custom dunder methods\u001b[39;00m\n\u001b[0;32m--> 265\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mmaybe_dispatch_ufunc_to_dunder_op\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mufunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    266\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n\u001b[1;32m    267\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/_libs/ops_dispatch.pyx:107\u001b[0m, in \u001b[0;36mpandas._libs.ops_dispatch.maybe_dispatch_ufunc_to_dunder_op\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py:1634\u001b[0m, in \u001b[0;36mDataFrame.__matmul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   1630\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__matmul__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other: AnyArrayLike \u001b[38;5;241m|\u001b[39m DataFrame) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[1;32m   1631\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1632\u001b[0m \u001b[38;5;124;03m    Matrix multiplication using binary `@` operator in Python>=3.5.\u001b[39;00m\n\u001b[1;32m   1633\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1634\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py:1603\u001b[0m, in \u001b[0;36mDataFrame.dot\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   1601\u001b[0m     rvals \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(other)\n\u001b[1;32m   1602\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m lvals\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m!=\u001b[39m rvals\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m-> 1603\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1604\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDot product shape mismatch, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlvals\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m vs \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrvals\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1605\u001b[0m         )\n\u001b[1;32m   1607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, DataFrame):\n\u001b[1;32m   1608\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor(\n\u001b[1;32m   1609\u001b[0m         np\u001b[38;5;241m.\u001b[39mdot(lvals, rvals), index\u001b[38;5;241m=\u001b[39mleft\u001b[38;5;241m.\u001b[39mindex, columns\u001b[38;5;241m=\u001b[39mother\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[1;32m   1610\u001b[0m     )\n",
      "\u001b[0;31mValueError\u001b[0m: Dot product shape mismatch, (1, 453) vs (324, 3)"
     ]
    }
   ],
   "source": [
    "model.fit(X_train,y_train,20,0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85bf349f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#checking for the right epoch hyperparameter:\n",
    "err = 100000\n",
    "err_new = -1\n",
    "learning_rate = 0.5\n",
    "epoch = 100\n",
    "while(error_new<=error):\n",
    "    for e in range(epoch):\n",
    "        model = LinearRegression(3)\n",
    "        model.fit(X_train,y_train,e,learning_rate)\n",
    "        prediction = model.predict(x_validate)\n",
    "        err_new = model.error(y_val,prediction)\n",
    "        err = err_new\n",
    "\n",
    "    epoch = e\n",
    "\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3db5ea3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression(3)\n",
    "model.fit(X_train,y_train,epoch,learning_rate)\n",
    "final_predictions = model.predict(X_test)\n",
    "final_error = model.error(y_test,final_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9e581aa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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