My-Logistic-regression-Mini_batch_SGD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random as rd\n",
    "import numpy as np\n",
    "import seaborn as sn\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu1 = -1\n",
    "mu2 = 3\n",
    "sig1 = 0.5\n",
    "sig2 = 1\n",
    "N = 300\n",
    "np.random.seed(10)\n",
    "x11=np.random.randn(N,1)*sig1 + mu1+1\n",
    "x12=np.random.randn(N,1)*sig1 + mu1+4\n",
    "x21=np.random.randn(N,1)*sig2 + mu2+1\n",
    "x22=np.random.randn(N,1)*sig2 + mu2+4\n",
    "c = np.vstack((np.zeros((N,1)), np.ones((N,1))))\n",
    "x1 = np.hstack((x11,x12))\n",
    "x2 = np.hstack((x21,x22))\n",
    "\n",
    "X = np.hstack( (np.vstack( (x1,x2) ),c) )\n",
    "np.random.shuffle(X)\n",
    "dataset = pd.DataFrame(data=X, columns=['x','y','c'])\n",
    "\n",
    "\n",
    "## Pseudo code used: pseudo code in class \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3.891640</td>\n",
       "      <td>8.824669</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.446282</td>\n",
       "      <td>3.456742</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.128767</td>\n",
       "      <td>3.368589</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.200069</td>\n",
       "      <td>3.217469</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>4</td>\n",
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      ],
      "text/plain": [
       "          x         y    c\n",
       "0  3.891640  8.824669  1.0\n",
       "1  0.446282  3.456742  0.0\n",
       "2 -0.128767  3.368589  0.0\n",
       "3 -0.200069  3.217469  0.0\n",
       "4  2.687609  8.379338  1.0"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9199153410>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dataset[dataset['c']==0]['x'], dataset[dataset['c']==0]['y'], marker='o',s=25, color='g')\n",
    "plt.scatter(dataset[dataset['c']>0]['x'], dataset[dataset['c']>0]['y'], marker='o',s=25, color='r')\n",
    "plt.legend(['Class 0', 'Class 1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip uninstall ipnb "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Jean_logisticRegression(object):\n",
    "    \n",
    "    def __init__(self, features, target, params, lr=0.05, tolerance=1e-4, Max_iter=400, verbose=True):\n",
    "        self.features=features\n",
    "        self.target=target\n",
    "        self.lr=lr\n",
    "        self.tolerance=tolerance\n",
    "        self.Max_iter=Max_iter\n",
    "        self.params=params\n",
    "        self.verbose=verbose\n",
    "        \n",
    "    def add_intercept(self, intercept):\n",
    "        self.intercept=intercept\n",
    "        self.features=np.hstack([self.intercept, self.features])\n",
    "        print(\"Intercept added successfully\")\n",
    "        \n",
    "    def sigmoid(self, x):\n",
    "        return 1./(1+np.exp(-x))\n",
    "\n",
    "    def get_gradient(self, theta, X, y):\n",
    "        #ipdb.set_trace\n",
    "        return np.dot(y-self.sigmoid(np.dot(theta, X)), X)\n",
    "    \n",
    "    def loss(self):\n",
    "        return (-1.0/len(self.target))*sum([self.target[i]*np.log(self.sigmoid(np.dot(self.params, self.features[i,:])))+(1-self.target[i])*np.log(1-self.sigmoid(np.dot(self.params, self.features[i,:]))) for i in range(len(self.target))])\n",
    "    \n",
    "    def fit(self):\n",
    "        Norm=np.linalg.norm\n",
    "        Iter=0\n",
    "        old_params=self.params+1\n",
    "        while(Norm(self.params-old_params)>self.tolerance and Iter<=self.Max_iter):\n",
    "            old_params=self.params\n",
    "            indices=list(range(self.features.shape[0]))\n",
    "            np.random.shuffle(indices)\n",
    "            # We just change the way we compute the gradient\n",
    "            Mini_batch=indices[:50]\n",
    "            #print(Mini_batch)\n",
    "            gradient=sum([self.get_gradient(self.params, self.features[i,:], self.target[i]) for i in Mini_batch])\n",
    "            self.params=self.params+self.lr*gradient\n",
    "            if self.verbose:\n",
    "                print(\"Loss: %.6f\"%self.loss())\n",
    "            Iter += 1\n",
    "        print('Number of iterations:', Iter-1)\n",
    "        return self.params\n",
    "    \n",
    "    def predict(self, Features):\n",
    "        Predictions=[]\n",
    "        Intercept=np.ones(Features.shape[0]).reshape(-1,1)\n",
    "        Features=np.hstack([Intercept, Features])\n",
    "        for i in range(Features.shape[0]):\n",
    "            if self.sigmoid(np.dot(Features[i,:], self.params))>0.5:\n",
    "                Predictions.append(1.0)\n",
    "            else:\n",
    "                Predictions.append(0.0)\n",
    "\n",
    "\n",
    "        return np.array(Predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu1 = -1\n",
    "mu2 = 3\n",
    "sig1 = 0.5\n",
    "sig2 = 1\n",
    "N = 300\n",
    "np.random.seed(10)\n",
    "x11=np.random.randn(N,1)*sig1 + mu1\n",
    "x12=np.random.randn(N,1)*sig1 + mu1+3\n",
    "x21=np.random.randn(N,1)*sig2 + mu2\n",
    "x22=np.random.randn(N,1)*sig2 + mu2+3\n",
    "c = np.vstack((np.zeros((N,1)), np.ones((N,1))))\n",
    "x1 = np.hstack((x11,x12))\n",
    "x2 = np.hstack((x21,x22))\n",
    "\n",
    "X = np.hstack( (np.vstack( (x1,x2) ),c) )\n",
    "np.random.shuffle(X)\n",
    "#dataset = pd.DataFrame(data=X, columns=['x','y','c'])\n",
    "\n",
    "\n",
    "## Pseudo code used: pseudo code in class \n",
    "\n",
    "\n",
    "Features=X[:,:-1]\n",
    "Target=X[:,-1]\n",
    "theta=np.random.randn(1+Features.shape[1])\n",
    "\n",
    "\n",
    "logistic_regression=Jean_logisticRegression(features=Features, target=Target, params=theta, lr=0.005, Max_iter=500, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept added successfully\n"
     ]
    }
   ],
   "source": [
    "Intercept_column=np.ones_like(Target).reshape(-1,1)\n",
    "logistic_regression.add_intercept(Intercept_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 1.150983\n",
      "Loss: 0.979947\n",
      "Loss: 0.846801\n",
      "Loss: 0.706980\n",
      "Loss: 0.595855\n",
      "Loss: 0.520620\n",
      "Loss: 0.457658\n",
      "Loss: 0.398156\n",
      "Loss: 0.354607\n",
      "Loss: 0.321788\n",
      "Loss: 0.288885\n",
      "Loss: 0.264527\n",
      "Loss: 0.243623\n",
      "Loss: 0.226244\n",
      "Loss: 0.213946\n",
      "Loss: 0.201565\n",
      "Loss: 0.191626\n",
      "Loss: 0.180944\n",
      "Loss: 0.172081\n",
      "Loss: 0.164624\n",
      "Loss: 0.157714\n",
      "Loss: 0.151254\n",
      "Loss: 0.144335\n",
      "Loss: 0.138438\n",
      "Loss: 0.134346\n",
      "Loss: 0.130916\n",
      "Loss: 0.127480\n",
      "Loss: 0.123197\n",
      "Loss: 0.119236\n",
      "Loss: 0.115837\n",
      "Loss: 0.113253\n",
      "Loss: 0.109153\n",
      "Loss: 0.106763\n",
      "Loss: 0.103925\n",
      "Loss: 0.101612\n",
      "Loss: 0.099622\n",
      "Loss: 0.097530\n",
      "Loss: 0.096053\n",
      "Loss: 0.093885\n",
      "Loss: 0.091071\n",
      "Loss: 0.089595\n",
      "Loss: 0.087697\n",
      "Loss: 0.086520\n",
      "Loss: 0.084519\n",
      "Loss: 0.082925\n",
      "Loss: 0.081511\n",
      "Loss: 0.080432\n",
      "Loss: 0.079138\n",
      "Loss: 0.077786\n",
      "Loss: 0.076823\n",
      "Loss: 0.075543\n",
      "Loss: 0.074555\n",
      "Loss: 0.073944\n",
      "Loss: 0.073179\n",
      "Loss: 0.071709\n",
      "Loss: 0.070638\n",
      "Loss: 0.069240\n",
      "Loss: 0.068320\n",
      "Loss: 0.067510\n",
      "Loss: 0.066840\n",
      "Loss: 0.065867\n",
      "Loss: 0.065083\n",
      "Loss: 0.064309\n",
      "Loss: 0.063525\n",
      "Loss: 0.062724\n",
      "Loss: 0.062109\n",
      "Loss: 0.061548\n",
      "Loss: 0.061015\n",
      "Loss: 0.060483\n",
      "Loss: 0.059726\n",
      "Loss: 0.058925\n",
      "Loss: 0.058346\n",
      "Loss: 0.057701\n",
      "Loss: 0.057233\n",
      "Loss: 0.056797\n",
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      "Loss: 0.015838\n",
      "Number of iterations: 500\n"
     ]
    }
   ],
   "source": [
    "theta_optimal=logistic_regression.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu1 = -1\n",
    "mu2 = 3\n",
    "sig1 = 0.5\n",
    "sig2 = 1\n",
    "N = 2000\n",
    "np.random.seed(10)\n",
    "x11=np.random.randn(N,1)*sig1 + mu1\n",
    "x12=np.random.randn(N,1)*sig1 + mu1+3\n",
    "x21=np.random.randn(N,1)*sig2 + mu2\n",
    "x22=np.random.randn(N,1)*sig2 + mu2+3\n",
    "c = np.vstack((np.zeros((N,1)), np.ones((N,1))))\n",
    "x1 = np.hstack((x11,x12))\n",
    "x2 = np.hstack((x21,x22))\n",
    "\n",
    "X1 = np.hstack( (np.vstack( (x1,x2) ),c) )\n",
    "np.random.shuffle(X)\n",
    "#dataset = pd.DataFrame(data=X, columns=['x','y','c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "Features=X1[:,:-1]\n",
    "Target=X1[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.825 %\n"
     ]
    }
   ],
   "source": [
    "Accuraccy=(logistic_regression.predict(Features)==Target).sum()/len(Target)\n",
    "print(\"Accuracy: {} %\".format(100*Accuraccy))"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}