Logistic Regression/Logistic Regression with Regularization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regularization in Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from Logistic_Regression_Regularization import *\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Andrew Ng Coursera data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('ex2data2.txt',header = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.051267</td>\n",
       "      <td>0.69956</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.092742</td>\n",
       "      <td>0.68494</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.213710</td>\n",
       "      <td>0.69225</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.375000</td>\n",
       "      <td>0.50219</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.513250</td>\n",
       "      <td>0.46564</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0        1  2\n",
       "0  0.051267  0.69956  1\n",
       "1 -0.092742  0.68494  1\n",
       "2 -0.213710  0.69225  1\n",
       "3 -0.375000  0.50219  1\n",
       "4 -0.513250  0.46564  1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "plt.plot(data[0][data[2] == 1],data[1][data[2] == 1],'k+',label = 'Admit')\n",
    "plt.plot(data[0][data[2] == 0],data[1][data[2] == 0],'yo',label = 'Not Admit')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Polynomial Feature with 6 degree "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg = logistic_regression_reg()\n",
    "X = data[[0,1]]\n",
    "y = data[2].values\n",
    "X1 = log_reg.mapfeature(X,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(118, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_theta = np.zeros((X1.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"Reg_CostFunction.PNG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cost = log_reg.costfunction(initial_theta,X1,y,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6931471805599454"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"Reg_GradientDescent.PNG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "grad = log_reg.gradient(initial_theta,X1,y,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ones    0.008475\n",
       "x10     0.018788\n",
       "x11     0.000078\n",
       "x20     0.050345\n",
       "x21     0.011501\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grad[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "grad = log_reg.gradient(initial_theta,X1,y,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ones    0.008475\n",
       "x10     0.018788\n",
       "x11     0.000078\n",
       "x20     0.050345\n",
       "x21     0.011501\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grad[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Minimum Cost Optimization using Scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "mincost=log_reg.minimumcost(log_reg.costfunction,initial_theta,X1,y,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.27268729,  0.62557014,  1.18096656, -2.01919804, -0.91761481,\n",
       "       -1.4319422 ,  0.1237592 , -0.36513061, -0.35703374, -0.17485819,\n",
       "       -1.45843764, -0.05129633, -0.61603955, -0.27464134, -1.19282524,\n",
       "       -0.24270352, -0.20570014, -0.04499764, -0.27782693, -0.29525851,\n",
       "       -0.45613306, -1.04377807,  0.02762819, -0.29265632,  0.01543414,\n",
       "       -0.32759322, -0.14389196, -0.92460091])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mincost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred = log_reg.predict(X1,mincost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8305084745762712"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(y,ypred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Verifying using Sklearn Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8305084745762712"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "linrig = LogisticRegression(C = 1).fit(X1,y)\n",
    "linrig.score(X1,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotdata(lambda_coff):\n",
    "    initita_theta = np.ones(X1.shape[1])\n",
    "    mincost=log_reg.minimumcost(log_reg.costfunction,initial_theta,X1,y,lambda_coff)\n",
    "    xs = np.linspace(-1,1.5,50)\n",
    "    ys = np.linspace(-1,1.5,50)\n",
    "    xx,yy = np.meshgrid(xs,ys)\n",
    "    xsdata = pd.DataFrame({0:xx.ravel(),1:yy.ravel()})\n",
    "    Xdata = log_reg.mapfeature(xsdata,6)\n",
    "    initita_theta = np.zeros(X1.shape[1])\n",
    "    htheta = log_reg.hypothesis(Xdata,mincost)\n",
    "    htheta = htheta.reshape(50,50)\n",
    "    plt.plot(data[0][data[2] == 1],data[1][data[2] == 1],'k+',label = 'Admit')\n",
    "    plt.plot(data[0][data[2] == 0],data[1][data[2] == 0],'yo',label = 'Not Admit')\n",
    "    plt.contour(xx,yy,htheta,[0.5])\n",
    "    plt.show()\n",
    "    return mincost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overfitting (No regularization (Lambda = 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.8728813559322034\n"
     ]
    }
   ],
   "source": [
    "mincost = plotdata(0)\n",
    "ypred = log_reg.predict(X1,mincost)\n",
    "print('Accuracy',log_reg.score(y,ypred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.8305084745762712\n"
     ]
    }
   ],
   "source": [
    "mincost = plotdata(1)\n",
    "ypred = log_reg.predict(X1,mincost)\n",
    "print('Accuracy',log_reg.score(y,ypred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Underfitting ( lambda = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.6101694915254238\n"
     ]
    }
   ],
   "source": [
    "mincost = plotdata(100)\n",
    "ypred = log_reg.predict(X1,mincost)\n",
    "print('Accuracy',log_reg.score(y,ypred))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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