SGD_LR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "REYPoTJ1Af7u"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from sklearn.datasets import load_boston\n",
    "from random import seed\n",
    "from random import randrange\n",
    "from csv import reader\n",
    "from math import sqrt\n",
    "from sklearn import preprocessing\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from prettytable import PrettyTable\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 916,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NTQ2iH-kAwbo"
   },
   "outputs": [],
   "source": [
    "bos = load_boston().data\n",
    "tar = load_boston().target\n",
    "X = pd.DataFrame(bos)\n",
    "Y = pd.DataFrame(tar)\n",
    "Z=X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 886,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 13)"
      ]
     },
     "execution_count": 886,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 887,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0     1     2    3      4      5     6       7    8      9     10  \\\n",
       "0  0.00632  18.0  2.31  0.0  0.538  6.575  65.2  4.0900  1.0  296.0  15.3   \n",
       "1  0.02731   0.0  7.07  0.0  0.469  6.421  78.9  4.9671  2.0  242.0  17.8   \n",
       "2  0.02729   0.0  7.07  0.0  0.469  7.185  61.1  4.9671  2.0  242.0  17.8   \n",
       "3  0.03237   0.0  2.18  0.0  0.458  6.998  45.8  6.0622  3.0  222.0  18.7   \n",
       "4  0.06905   0.0  2.18  0.0  0.458  7.147  54.2  6.0622  3.0  222.0  18.7   \n",
       "\n",
       "       11    12  \n",
       "0  396.90  4.98  \n",
       "1  396.90  9.14  \n",
       "2  392.83  4.03  \n",
       "3  394.63  2.94  \n",
       "4  396.90  5.33  "
      ]
     },
     "execution_count": 887,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 888,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0\n",
       "0  24.0\n",
       "1  21.6\n",
       "2  34.7\n",
       "3  33.4\n",
       "4  36.2"
      ]
     },
     "execution_count": 888,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Implementing sklearns's SGDRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 889,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FgUW39s6Be0U"
   },
   "outputs": [],
   "source": [
    "scaler = preprocessing.StandardScaler().fit(X)\n",
    "X_tr = scaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 890,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 879,
     "status": "ok",
     "timestamp": 1545718892607,
     "user": {
      "displayName": "Applied AI Course",
      "photoUrl": "https://lh5.googleusercontent.com/-9_3A9F-kDXs/AAAAAAAAAAI/AAAAAAAAACw/IprjYvUuoXQ/s64/photo.jpg",
      "userId": "00484516897554883881"
     },
     "user_tz": -330
    },
    "id": "HNu1sXtEBBS2",
    "outputId": "a0c5d3d0-345c-4a34-8ccf-0d9f3d7d23fd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22.837369335966102\n"
     ]
    }
   ],
   "source": [
    "clf = SGDRegressor()\n",
    "clf.fit(X_tr, Y)\n",
    "print(mean_squared_error(Y, clf.predict(X_tr)))\n",
    "p=clf.predict(X_tr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Implementing own SGD for Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 929,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SGD_LR:\n",
    "    def __init__(self, w, b, scaler):\n",
    "        self.w = w\n",
    "        self.b = b\n",
    "        self.scaler = scaler\n",
    "        \n",
    "    def predict_LR(self, x):\n",
    "        x = self.scaler.transform(x)\n",
    "        y = np.dot(x,self.w)+self.b\n",
    "        return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 930,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LinearRegression_SGD(X,Y):\n",
    "    scaler = preprocessing.StandardScaler().fit(X)\n",
    "    X_train = scaler.transform(X)\n",
    "    X_train=pd.DataFrame(X_train)\n",
    "    w_now = np.ones((13,))\n",
    "    b_now = 1\n",
    "    learning_rate=0.1;\n",
    "    N = float(len(Y))\n",
    "    error=[]\n",
    "    b_prev =0\n",
    "    SGD_k=40 \n",
    "    starting_x=0\n",
    "    while int(b_prev*1000)!=int(b_now*1000):\n",
    "        x=X_train[starting_x:starting_x+SGD_k]\n",
    "        y=Y[starting_x:starting_x+SGD_k]\n",
    "        w_next = -2*np.sum(np.transpose(np.transpose(x)*(y[0]-np.dot(x,w_now)-b_now)),axis=0)\n",
    "        b_next = -2*np.sum((y[0]-np.dot(x,w_now)-b_now),axis=0)\n",
    "        w_now = w_now  - (learning_rate*w_next/SGD_k)\n",
    "        b_prev = b_now\n",
    "        b_now = b_now  - (learning_rate*b_next/SGD_k)\n",
    "        learning_rate = 100*learning_rate/101\n",
    "        starting_x=(starting_x+SGD_k)%int(N)\n",
    "    model = SGD_LR(w_now,b,scaler)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 931,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LinearRegression_SGD(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 933,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=model.predict_LR(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 934,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22.049639812752698\n"
     ]
    }
   ],
   "source": [
    "print(mean_squared_error(Y, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 Predicted Cost(implemented own SGD) vs Actual Cost graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 779,
   "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": [
    "import matplotlib.pyplot as plot\n",
    "plot.scatter(y, Y, color = 'red')\n",
    "plot.title('Predicted Cost(implemented SGD) vs Actual Cost')\n",
    "plot.xlabel('Predicted Cost')\n",
    "plot.ylabel('Actual Cost')\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.5 Predicted Cost(sklearn's SGDRegresser) vs Actual Cost graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 780,
   "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": [
    "import matplotlib.pyplot as plot\n",
    "plot.scatter(p, Y, color = 'red')\n",
    "plot.title('Predicted Cost(sklearn''s SGDRegresser) vs Actual Cost')\n",
    "plot.xlabel('Predicted Cost')\n",
    "plot.ylabel('Actual Cost')\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.6 Conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 781,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------------+-------------------+--------------------+\n",
      "|          SGD           |    b-intercept    |        MSE         |\n",
      "+------------------------+-------------------+--------------------+\n",
      "| sklearn's SGDRegresser |   [22.40467957]   | 22.886462687501666 |\n",
      "|    Implemented SGD     | 22.38593580184268 | 22.049639812752698 |\n",
      "+------------------------+-------------------+--------------------+\n"
     ]
    }
   ],
   "source": [
    "from prettytable import PrettyTable    \n",
    "x = PrettyTable()\n",
    "x.field_names = [\"SGD\", \"b-intercept\", \"MSE\"]\n",
    "x.add_row([\"sklearn's SGDRegresser\", clf.intercept_, mean_squared_error(Y, p)])\n",
    "x.add_row([\"Implemented SGD\", b, mean_squared_error(Y, y)])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 782,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------------+--------------------------------------------------------------------------+\n",
      "|          SGD           |                                Weight(w)                                 |\n",
      "+------------------------+--------------------------------------------------------------------------+\n",
      "| sklearn's SGDRegresser | [-0.55909646  0.50573967 -0.17425154  0.78482746 -0.97485016  3.06303848 |\n",
      "|                        |  -0.00807852 -1.9831831   0.86247802 -0.45484589 -1.87056832  0.86374818 |\n",
      "|                        |                               -3.59176261]                               |\n",
      "|    Implemented SGD     | [-0.86999025  1.03315906 -0.09334903  0.73508172 -1.66690393  2.52984497 |\n",
      "|                        |   0.05787059 -2.95216413  1.97917718 -1.40372078 -1.95795741  0.90854843 |\n",
      "|                        |                               -3.98537903]                               |\n",
      "+------------------------+--------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "from prettytable import PrettyTable    \n",
    "x = PrettyTable()\n",
    "x.field_names = [\"SGD\", \"Weight(w)\"]\n",
    "x.add_row([\"sklearn's SGDRegresser\",clf.coef_])\n",
    "x.add_row([\"Implemented SGD\",np.array(w)])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Untitled3.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}