Naive Bayes on MNIST Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Dataset Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the sklearn MNIST dataset\n",
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = digits.data\n",
    "Y = digits.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X[0].reshape(8,8))\n",
    "print(Y[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  0.  5. 13.  9.  1.  0.  0.  0.  0. 13. 15. 10. 15.  5.  0.  0.  3.\n",
      " 15.  2.  0. 11.  8.  0.  0.  4. 12.  0.  0.  8.  8.  0.  0.  5.  8.  0.\n",
      "  0.  9.  8.  0.  0.  4. 11.  0.  1. 12.  7.  0.  0.  2. 14.  5. 10. 12.\n",
      "  0.  0.  0.  0.  6. 13. 10.  0.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "print(X[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Training the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnb = MultinomialNB()\n",
    "gnb = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train\n",
    "mnb.fit(X,Y)\n",
    "gnb.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9053978853644964\n",
      "0.8580968280467446\n"
     ]
    }
   ],
   "source": [
    "#Score\n",
    "print(mnb.score(X,Y))\n",
    "print(gnb.score(X,Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8103537583567821"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(gnb,X,Y,scoring=\"accuracy\",cv=10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8819396216300838"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(mnb,X,Y,scoring=\"accuracy\",cv=10).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constructing and Visualising Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Use this method directly - \"\"\"\n",
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[175   0   0   0   3   0   0   0   0   0]\n",
      " [  0 137  14   0   0   1   2   0  13  15]\n",
      " [  0   7 160   0   0   0   0   0   8   2]\n",
      " [  0   0   2 159   0   2   0   5   8   7]\n",
      " [  1   0   0   0 173   0   0   4   3   0]\n",
      " [  0   0   0   0   1 157   1   1   2  20]\n",
      " [  0   2   0   0   1   1 176   0   1   0]\n",
      " [  0   0   0   0   0   0   0 178   1   0]\n",
      " [  0  11   1   0   1   0   1   1 154   5]\n",
      " [  0   1   0   1   1   1   0  11   7 158]]\n"
     ]
    }
   ],
   "source": [
    "Y_mnb = mnb.predict(X)\n",
    "cnf_matrix = confusion_matrix(Y,Y_mnb)\n",
    "print(cnf_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note - Diagonal entries represent number of elements where Predicted class = Actual class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_labels = np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[175   0   0   0   3   0   0   0   0   0]\n",
      " [  0 137  14   0   0   1   2   0  13  15]\n",
      " [  0   7 160   0   0   0   0   0   8   2]\n",
      " [  0   0   2 159   0   2   0   5   8   7]\n",
      " [  1   0   0   0 173   0   0   4   3   0]\n",
      " [  0   0   0   0   1 157   1   1   2  20]\n",
      " [  0   2   0   0   1   1 176   0   1   0]\n",
      " [  0   0   0   0   0   0   0 178   1   0]\n",
      " [  0  11   1   0   1   0   1   1 154   5]\n",
      " [  0   1   0   1   1   1   0  11   7 158]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(cnf_matrix, classes=class_labels, normalize=False, title='Confusion Matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[176   0   0   0   1   0   0   1   0   0]\n",
      " [  0 154   0   0   0   0   3   5  14   6]\n",
      " [  0  13 113   0   0   1   1   0  49   0]\n",
      " [  0   2   2 145   0   6   0   7  20   1]\n",
      " [  1   1   0   0 152   1   2  21   3   0]\n",
      " [  0   0   0   3   0 169   1   6   2   1]\n",
      " [  0   1   0   0   0   1 179   0   0   0]\n",
      " [  0   0   0   0   1   1   0 177   0   0]\n",
      " [  0   8   0   1   0   3   0  12 150   0]\n",
      " [  1   6   0   5   1   3   0  17  20 127]]\n",
      "Confusion matrix, without normalization\n",
      "[[176   0   0   0   1   0   0   1   0   0]\n",
      " [  0 154   0   0   0   0   3   5  14   6]\n",
      " [  0  13 113   0   0   1   1   0  49   0]\n",
      " [  0   2   2 145   0   6   0   7  20   1]\n",
      " [  1   1   0   0 152   1   2  21   3   0]\n",
      " [  0   0   0   3   0 169   1   6   2   1]\n",
      " [  0   1   0   0   0   1 179   0   0   0]\n",
      " [  0   0   0   0   1   1   0 177   0   0]\n",
      " [  0   8   0   1   0   3   0  12 150   0]\n",
      " [  1   6   0   5   1   3   0  17  20 127]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y_gnb = gnb.predict(X)\n",
    "cnf_matrix = confusion_matrix(Y,Y_gnb)\n",
    "print(cnf_matrix)\n",
    "plot_confusion_matrix(cnf_matrix, classes=class_labels, normalize=False, title='Confusion Matrix')"
   ]
  },
  {
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}