mini_batch_gradient_descent.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()\n",
    "X = boston.data\n",
    "Y = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 13)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
       "        4.9800e+00],\n",
       "       [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
       "        9.1400e+00],\n",
       "       [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
       "        4.0300e+00],\n",
       "       ...,\n",
       "       [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
       "        5.6400e+00],\n",
       "       [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
       "        6.4800e+00],\n",
       "       [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
       "        7.8800e+00]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.41978194,  0.28482986, -1.2879095 , ..., -1.45900038,\n",
       "         0.44105193, -1.0755623 ],\n",
       "       [-0.41733926, -0.48772236, -0.59338101, ..., -0.30309415,\n",
       "         0.44105193, -0.49243937],\n",
       "       [-0.41734159, -0.48772236, -0.59338101, ..., -0.30309415,\n",
       "         0.39642699, -1.2087274 ],\n",
       "       ...,\n",
       "       [-0.41344658, -0.48772236,  0.11573841, ...,  1.17646583,\n",
       "         0.44105193, -0.98304761],\n",
       "       [-0.40776407, -0.48772236,  0.11573841, ...,  1.17646583,\n",
       "         0.4032249 , -0.86530163],\n",
       "       [-0.41500016, -0.48772236,  0.11573841, ...,  1.17646583,\n",
       "         0.44105193, -0.66905833]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "X_transform=sc.fit_transform(X)\n",
    "X_transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.4152492 , -0.48772236,  0.11573841, ...,  1.17646583,\n",
       "         0.44105193, -0.50084979],\n",
       "       [-0.41834124,  3.16044092, -1.0194363 , ..., -0.2568579 ,\n",
       "         0.39204125, -0.88212248],\n",
       "       [-0.41877066,  2.94584308, -1.3316823 , ..., -0.67298414,\n",
       "         0.37570436, -0.93398678],\n",
       "       ...,\n",
       "       [-0.41652931, -0.48772236, -0.86769058, ...,  0.80657583,\n",
       "         0.44105193, -0.39992467],\n",
       "       [ 0.26283202, -0.48772236,  1.01599907, ...,  0.80657583,\n",
       "         0.44105193,  0.94153844],\n",
       "       [-0.41299272,  2.5166474 , -1.29812315, ..., -1.69018163,\n",
       "         0.12681342, -1.07696404]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index=np.random.choice(506, 101, replace=False)\n",
    "X_transform[index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## mini_batch_GD for linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predicted_y(weight,x,intercept):\n",
    "    y_lst=[]\n",
    "    for i in range(len(x)):\n",
    "        y_lst.append(weight@x[i]+intercept)\n",
    "    return np.array(y_lst)\n",
    "    \n",
    "\n",
    "# linear loss\n",
    "def loss(y,y_predicted):\n",
    "    n=len(y)\n",
    "    s=0\n",
    "    for i in range(n):\n",
    "        s+=(y[i]-y_predicted[i])**2\n",
    "    return (1/X.shape[0])*s\n",
    "\n",
    "#derivative of loss w.r.t weight\n",
    "def dldw(x,y,y_predicted):\n",
    "    s=0\n",
    "    n=len(y)\n",
    "    for i in range(n):\n",
    "        s+=-x[i]*(y[i]-y_predicted[i])\n",
    "    return (2/X.shape[0])*s\n",
    "    \n",
    "\n",
    "# derivative of loss w.r.t bias\n",
    "def dldb(y,y_predicted):\n",
    "    n=len(y)\n",
    "    s=0\n",
    "    for i in range(len(y)):\n",
    "        s+=-(y[i]-y_predicted[i])\n",
    "    return (2/X.shape[0]) * s\n",
    "\n",
    "   \n",
    "        \n",
    "        \n",
    "# gradient function\n",
    "def mini_batch_gradient_descent(x,y):\n",
    "    weight_vector=np.random.randn(x.shape[1])\n",
    "    intercept=0\n",
    "    epoch = 500\n",
    "    n = len(x)\n",
    "    linear_loss=[]\n",
    "    learning_rate = 0.01\n",
    "    n_iter=[]\n",
    "    count=1\n",
    "    batch_size=101\n",
    "    for i in tqdm(range(epoch)):\n",
    "        \n",
    "        for j in range(int(n/batch_size)):\n",
    "            random_index=np.random.choice(x.shape[0],batch_size,replace=False) #batch size of 101\n",
    "            x_sample = x[random_index]\n",
    "            y_sample = y[random_index]\n",
    "            y_predicted = predicted_y(weight_vector,x_sample,intercept)\n",
    "            # updation of weight and bias for every batch size\n",
    "            weight_vector = weight_vector - learning_rate *dldw(x_sample,y_sample,y_predicted) \n",
    "            intercept = intercept - learning_rate * dldb(y_sample,y_predicted)\n",
    "            \n",
    "            \n",
    "            \n",
    "            linear_loss.append(loss(y_sample,y_predicted)) \n",
    "            n_iter.append(count)\n",
    "            count+=1\n",
    "        \n",
    "        \n",
    "    plt.plot(n_iter,linear_loss)\n",
    "    plt.xlabel(\"number of iteration\")\n",
    "    plt.ylabel(\"loss\")\n",
    "    \n",
    "    return weight_vector,intercept\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████| 500/500 [00:04<00:00, 112.93it/s]\n"
     ]
    },
    {
     "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": [
    "w,b=mini_batch_gradient_descent(X_transform,Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight: [-0.73428813  0.8650556  -0.31333454  0.75795315 -1.3795654   2.81488447\n",
      " -0.08932872 -2.69209692  1.48867477 -0.98746929 -1.88996003  0.85715014\n",
      " -3.7846456 ]\n",
      "bias: 22.56098731136974\n"
     ]
    }
   ],
   "source": [
    "print(\"weight:\",w)\n",
    "print(\"bias:\",b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# predict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(inp):\n",
    "    y_lst=[]\n",
    "    for i in range(len(inp)):\n",
    "        y_lst.append(w@inp[i]+b)\n",
    "    return np.array(y_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30.71044348, 24.9577656 , 30.7507872 , 29.09939098, 28.42051988,\n",
       "       25.57176027, 22.98726968, 19.47849763, 11.30705569, 19.00569254,\n",
       "       19.06159435, 21.63499528, 20.9334761 , 20.16038525, 19.77173082,\n",
       "       19.86014632, 21.23537498, 17.32781782, 16.58065429, 18.74523323,\n",
       "       12.67574207, 18.01219212, 16.17374445, 14.05989818, 16.05182226,\n",
       "       13.73128609, 15.89051236, 15.12904676, 20.06499028, 21.42436813,\n",
       "       11.72310557, 18.42642473,  9.09115957, 14.48234691, 13.96219781,\n",
       "       23.56767889, 22.095432  , 23.0665979 , 22.91667898, 30.96372615,\n",
       "       33.94396836, 28.19285187, 25.23597211, 24.64269084, 22.80293301,\n",
       "       21.78102012, 20.10802493, 17.65748705,  8.53035988, 16.92525843,\n",
       "       20.97058935, 23.64676807, 27.59619647, 23.86720884, 16.49168393,\n",
       "       30.89108121, 25.49874392, 32.54121725, 21.83249782, 20.8416689 ,\n",
       "       17.55427821, 18.04458914, 23.84241759, 22.69704729, 24.02143742,\n",
       "       30.14890516, 25.13389169, 21.54814033, 17.71292411, 21.17379317,\n",
       "       25.16224568, 21.55270414, 24.45062519, 23.96793079, 25.67062647,\n",
       "       24.02038823, 22.77707508, 23.28989834, 21.35097962, 22.43150636,\n",
       "       28.42210058, 26.83566338, 25.96363544, 24.89835012, 24.79758595,\n",
       "       27.77107397, 22.03018032, 25.6230859 , 30.95529221, 31.21325087,\n",
       "       27.27545136, 27.53993491, 27.85173438, 28.05185933, 25.79616   ,\n",
       "       28.77656751, 24.71698949, 36.16987138, 35.60670629, 32.54811935,\n",
       "       25.10022245, 26.1829209 , 20.17883412, 20.65035527, 21.71655195,\n",
       "       18.64707064, 17.31905905, 20.97355547, 22.96670112, 20.07000399,\n",
       "       21.14009835, 27.00486322, 20.95000424, 20.9538941 , 25.46488149,\n",
       "       20.64904216, 23.78342762, 24.040414  , 20.60536738, 21.13387463,\n",
       "       20.3902488 , 20.90398111, 18.90546177, 14.60750268, 18.86788764,\n",
       "       20.85412537, 12.77177005, 15.41945001, 19.33341781, 14.33558067,\n",
       "       20.51116347, 19.91543546, 20.57869652, 16.17412462, 13.61419551,\n",
       "       17.69501127, 16.21554642, 19.77479507, 14.03247   , 16.75005203,\n",
       "       13.84352111,  3.94297929, 15.71531919, 13.0193252 ,  9.4348953 ,\n",
       "       12.97439722, 16.73659935,  9.24579248, 10.52527503, 15.73683347,\n",
       "       21.90465828, 19.20599949, 21.29816114, 18.25752877, 23.74878384,\n",
       "       21.5705197 , 14.5344992 , 33.02895253, 28.58128959, 26.76249311,\n",
       "       32.63578713, 36.71987328, 40.85955408, 42.28913667, 24.45309886,\n",
       "       25.07353741, 37.23120844, 22.7787089 , 26.07659471, 26.3845589 ,\n",
       "       22.16543549, 23.92797533, 22.49775181, 28.82039683, 26.22543972,\n",
       "       30.80944722, 25.59964722, 29.05343374, 31.3746007 , 32.61615744,\n",
       "       34.44755931, 27.20677816, 33.46264679, 30.45954119, 21.99460739,\n",
       "       24.29520968, 35.84338973, 33.39444873, 32.54748222, 34.73421096,\n",
       "       31.37543424, 30.82812979, 33.57428125, 32.0853789 , 31.45990151,\n",
       "       40.38470103, 36.2915433 , 32.75773043, 34.83461444, 30.87461354,\n",
       "       31.46181289, 29.2817727 , 37.46873189, 40.95575437, 42.15943649,\n",
       "       22.43119336, 23.47556673, 17.41950068, 23.46217039, 16.55956722,\n",
       "       22.12786672, 16.62250862, 22.55573222, 25.02627011, 10.63420667,\n",
       "       24.24104244, 26.15594636, 27.72896263, 24.26707286, 29.26361657,\n",
       "       32.93671006, 23.32889279, 32.00536585, 29.25977525, 38.14548926,\n",
       "       39.66456919, 37.35394624, 31.99800665, 35.4464127 , 30.98402067,\n",
       "       23.95395922, 33.04548127, 38.07409101, 37.13132412, 31.66996628,\n",
       "       24.79643661, 30.0911774 , 32.60281998, 28.22439202, 28.15478344,\n",
       "       27.03732302, 23.27796183, 23.89709996, 27.39599846, 16.32267237,\n",
       "       13.38662441, 20.31807512, 20.00565149, 21.54797928, 24.53817937,\n",
       "       24.55445433, 25.40960679, 25.69306074, 30.98111006, 24.40125609,\n",
       "       22.11270213, 36.79791347, 43.4964747 , 36.35588633, 34.79221399,\n",
       "       34.75903033, 37.16565077, 41.19287117, 34.35939095, 35.71630452,\n",
       "       27.91235606, 31.10390662, 40.71490874, 39.19572147, 25.27461576,\n",
       "       21.73963977, 26.85133371, 27.94733624, 35.56709892, 35.77855476,\n",
       "       33.19062885, 35.44762659, 34.69617964, 29.66328274, 34.46776006,\n",
       "       38.15036851, 33.72230804, 40.1202621 , 44.30280091, 31.60026192,\n",
       "       27.59329745, 20.23669374, 26.53730037, 26.6723136 , 26.4973201 ,\n",
       "       32.63406472, 33.65987862, 30.99509904, 25.28559226, 23.77099421,\n",
       "       28.09064598, 26.88433859, 18.90593683, 29.21517709, 32.19257411,\n",
       "       30.86862477, 28.45112133, 28.46136564, 32.50128445, 32.55716951,\n",
       "       29.78721044, 34.65075747, 31.6989876 , 28.74730624, 23.50748642,\n",
       "       18.32888776, 26.86168494, 23.09176981, 25.54018536, 25.57948891,\n",
       "       20.55359638, 17.62301632, 18.42025946, 24.4105035 , 21.50372525,\n",
       "       25.00196558, 24.96897215, 22.9534565 , 19.34879914, 25.3234819 ,\n",
       "       25.11338628, 24.04414012, 19.56154144, 22.32100092, 25.54028214,\n",
       "       22.90241803, 20.3026595 , 23.7652812 , 22.5833072 , 21.98856733,\n",
       "       20.91377542, 20.21859642, 19.36682394, 22.22253234, 21.25278893,\n",
       "       21.38117128, 31.07474662, 24.113872  , 28.14418215, 29.28851897,\n",
       "       17.77632512, 15.96353036, 25.75022179, 27.69206522, 23.78986182,\n",
       "       21.97349235, 22.14361355, 18.39472635, 25.31877214, 15.3591608 ,\n",
       "       17.70827862, 20.34492794, 23.47264626, 23.01620931, 19.65397013,\n",
       "       23.18081246, 19.30674617, 18.38986125, 20.75482514, 38.37077553,\n",
       "       13.74951001, 15.24282582,  9.93121185, 22.97170468, 32.49047574,\n",
       "       34.51251804, 24.39288563, 25.91072501,  5.43067526,  0.06928901,\n",
       "       25.46172891, 17.62879233, 20.02241708, 15.85338538, 16.68931539,\n",
       "       16.04148528, 18.38443997, 13.14588819, 12.73504334,  2.96562692,\n",
       "        7.8342208 ,  5.96347434,  5.51221826,  6.11419412, 13.92223189,\n",
       "       17.03221681, 17.24138957,  9.59989341, 20.13629984, 17.88523157,\n",
       "       20.22839629, 19.1308728 , 16.05715486,  6.81223257, 10.67572588,\n",
       "       11.99028837, 17.78224858, 18.15027399, 13.03300611,  7.81826624,\n",
       "        9.2150816 ,  7.44500663, 19.54065028, 12.75957871, 19.33123258,\n",
       "       15.3401447 , 16.38283939,  0.77046965, 11.29029438, -4.07144128,\n",
       "        9.5463258 , 13.28620183,  6.83699736,  7.3069581 , 14.85501282,\n",
       "       19.73884802, 18.10769316, 17.9900172 , 12.56763771, 13.87413656,\n",
       "        9.78588788, 15.76584933, 14.60366766, 14.08382844, 12.87141757,\n",
       "       17.49970536, 18.13155876, 20.94280671, 17.13227881, 16.16433054,\n",
       "       13.68171094, 14.8849934 ,  9.04600521,  5.00656416, 13.11503706,\n",
       "       13.09846799, 17.56248089, 18.87341622, 18.32331656, 11.67248564,\n",
       "       12.21179326, 17.86832444, 18.41359725, 17.61572731, 17.30237021,\n",
       "       16.67963836, 19.53572859, 18.65784079, 22.84578181, 15.51701512,\n",
       "       15.9345682 , 12.70341993, 13.04375536, 17.43204181, 18.69631035,\n",
       "       19.2445085 , 20.29980664, 20.01470688, 22.69797955, 20.34292402,\n",
       "       17.79041123, 14.10774285, 16.19021522, 16.62387541, 18.18965409,\n",
       "       19.58922407, 22.13036948, 21.93314394, 25.35893343, 15.59547628,\n",
       "       15.33509831, 20.03751608, 10.9437617 , 18.73262477, 21.51106898,\n",
       "       22.84146144, 26.53007043, 28.07569279, 20.52057129, 19.10379329,\n",
       "       22.01962735, 19.2389521 , 20.8929439 , 13.27104545,  9.55890964,\n",
       "        4.91453489, 15.26443457, 17.57092965, 21.03228365, 21.09945847,\n",
       "       17.43047798, 14.31399761, 19.55761131, 21.72413537, 18.74335853,\n",
       "       20.84969294, 23.92212666, 22.60632276, 27.97086427, 26.4808418 ,\n",
       "       22.59045975])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=predict(X_transform)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>y_actual</th>\n",
       "      <th>y_predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.0</td>\n",
       "      <td>30.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.6</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.7</td>\n",
       "      <td>30.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.4</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.2</td>\n",
       "      <td>28.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>22.4</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>20.6</td>\n",
       "      <td>22.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>23.9</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>22.0</td>\n",
       "      <td>26.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>11.9</td>\n",
       "      <td>22.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     y_actual  y_predicted\n",
       "0        24.0         30.7\n",
       "1        21.6         25.0\n",
       "2        34.7         30.8\n",
       "3        33.4         29.1\n",
       "4        36.2         28.4\n",
       "..        ...          ...\n",
       "501      22.4         23.9\n",
       "502      20.6         22.6\n",
       "503      23.9         28.0\n",
       "504      22.0         26.5\n",
       "505      11.9         22.6\n",
       "\n",
       "[506 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pred=pd.DataFrame()\n",
    "df_pred[\"y_actual\"]=Y\n",
    "df_pred[\"y_predicted\"]=np.round(y_pred,1)\n",
    "df_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDRegressor(alpha=0, average=False, early_stopping=False, epsilon=0.1,\n",
       "             eta0=0.001, fit_intercept=True, l1_ratio=0.15,\n",
       "             learning_rate='constant', loss='squared_loss', max_iter=1000,\n",
       "             n_iter_no_change=5, penalty='l2', power_t=0.25, random_state=None,\n",
       "             shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0,\n",
       "             warm_start=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDRegressor\n",
    "model=SGDRegressor(alpha=0,learning_rate=\"constant\",eta0=0.001)\n",
    "model.fit(X_transform,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.53049421])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.81589739,  0.91268359, -0.16276166,  0.71300785, -1.81615915,\n",
       "        2.75186925, -0.04546646, -2.97644044,  1.80125191, -1.16941354,\n",
       "       -1.98864102,  0.85320111, -3.6691723 ])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.73428813,  0.8650556 , -0.31333454,  0.75795315, -1.3795654 ,\n",
       "        2.81488447, -0.08932872, -2.69209692,  1.48867477, -0.98746929,\n",
       "       -1.88996003,  0.85715014, -3.7846456 ])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.56098731136974"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}