MNIST Solution using SVM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MNIST Solution using SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this notebook, the goal was to find out what is the number represented by an 28x28 pixel image.\n",
    "\n",
    "The method used was SVM.\n",
    "\n",
    "To tune the hyperparameters, it was used both grid search and random search independently. They results were then compared.\n",
    "\n",
    "This approach obtained a 95.6% accuracy using a sample of 5000 images chosen at random."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Imports\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import Normalize\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from random import choices\n",
    "from sklearn import model_selection\n",
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "from time import time\n",
    "import scipy\n",
    "\n",
    "from sklearn import datasets, svm, metrics\n",
    "from sklearn.datasets import fetch_mldata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# custom functions\n",
    "def show_random_digits(images, targets, sample_size=12, title_text='Digit {}' ):\n",
    "    nsamples=sample_size\n",
    "    rand_idx = np.random.choice(images.shape[0],nsamples)\n",
    "    images_and_labels = list(zip(images[rand_idx], targets[rand_idx]))\n",
    "\n",
    "\n",
    "    img = plt.figure(1, figsize=(15, 12), dpi=160)\n",
    "    for index, (image, label) in enumerate(images_and_labels):\n",
    "        label = int(label)\n",
    "        plt.subplot(np.ceil(nsamples/6.0), 6, index + 1)\n",
    "        plt.axis('off')\n",
    "        #each image is flat, we have to reshape to 2D array 28x28-784\n",
    "        plt.imshow(image.reshape(28,28), cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "        plt.title(title_text.format(label))\n",
    "\n",
    "def plot_confusion_matrix(confusion_matrix, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    plt.figure(1, figsize=(15, 15), dpi=120)\n",
    "    plt.imshow(confusion_matrix, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['DESCR', 'COL_NAMES', 'target', 'data'])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data\n",
    "mnist = fetch_mldata('MNIST original', data_home='./')\n",
    "mnist.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = mnist.data\n",
    "targets = mnist.target\n",
    "\n",
    "# sample\n",
    "items = range(len(images))\n",
    "new_items = choices(items, k = 5000)\n",
    "images = images[new_items]\n",
    "targets = targets[new_items]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 784)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000,)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x1920 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show some digits at random\n",
    "show_random_digits(images, targets, 18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Full dataset classification\n",
    "X_data = images/255.0\n",
    "Y = targets\n",
    "\n",
    "# train test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_data, Y, test_size=0.2, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  27 out of  27 | elapsed:  4.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV took 283.43 seconds for 9 candidates parameter settings.\n",
      "Best parameters: {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "Best score: 0.94375\n"
     ]
    }
   ],
   "source": [
    "# Grid Search for best parameters\n",
    "parameters = {'C': [1, 10, 100], 'gamma': [0.1, 0.01, 0.001], 'kernel': ['rbf']}\n",
    "clf_svm = svm.SVC()\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search = GridSearchCV(estimator=clf_svm, param_grid=parameters, n_jobs=-1, verbose=2)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"GridSearchCV took %.2f seconds for 9 candidates\"\n",
    "      \" parameter settings.\" % ((time() - start)))\n",
    "\n",
    "clf_grid_best = grid_search.best_estimator_\n",
    "best_score_grid_search = grid_search.best_score_\n",
    "\n",
    "print ('Best parameters:', best_params_grid_search)\n",
    "print ('Best score:', best_score_grid_search)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  27 out of  27 | elapsed:  4.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomizedSearchCV took 312.77 seconds for 9 candidates parameter settings.\n",
      "Best parameters: {'C': 597, 'gamma': 0.03370050005850599, 'kernel': 'rbf'}\n",
      "Best score: 0.951\n"
     ]
    }
   ],
   "source": [
    "# Random Search for best parameters\n",
    "parameters = {'C': range(1, 1000), 'gamma': scipy.stats.expon(scale=.1), 'kernel': ['rbf']}\n",
    "clf_svm = svm.SVC()\n",
    "\n",
    "\n",
    "# run randomized search\n",
    "n_iter_search = 9\n",
    "random_search = RandomizedSearchCV(clf_svm, param_distributions=parameters,\n",
    "                                   n_iter=n_iter_search, n_jobs=-1, verbose=2)\n",
    "\n",
    "start = time()\n",
    "random_search.fit(X_train, y_train)\n",
    "print(\"RandomizedSearchCV took %.2f seconds for %d candidates\"\n",
    "      \" parameter settings.\" % ((time() - start), n_iter_search))\n",
    "\n",
    "clf_random_best = random_search.best_estimator_\n",
    "best_score_random_search = random_search.best_score_\n",
    "\n",
    "print ('Best parameters:', best_params_random_search)\n",
    "print ('Best score:', best_score_random_search)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediction\n",
    "y_pred = clf_random_best.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x1920 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Some of the predictions\n",
    "show_random_digits(X_test, y_pred, 18, title_text=\"Predicted: {}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.956\n",
      "Classification report for classifier SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.99      0.97      0.98       109\n",
      "        1.0       0.97      0.98      0.98       110\n",
      "        2.0       0.94      0.98      0.96       105\n",
      "        3.0       0.95      0.95      0.95        84\n",
      "        4.0       0.93      0.92      0.92       111\n",
      "        5.0       0.98      0.94      0.96       105\n",
      "        6.0       0.95      0.99      0.97        94\n",
      "        7.0       1.00      0.91      0.95        99\n",
      "        8.0       0.92      0.98      0.95        88\n",
      "        9.0       0.93      0.94      0.93        95\n",
      "\n",
      "avg / total       0.96      0.96      0.96      1000\n",
      "\n",
      "\n",
      "Confusion matrix:\n",
      "[[106   0   1   0   0   0   1   0   1   0]\n",
      " [  0 108   1   0   0   0   1   0   0   0]\n",
      " [  0   0 103   1   1   0   0   0   0   0]\n",
      " [  0   0   0  80   0   0   0   0   3   1]\n",
      " [  1   1   1   0 102   0   1   0   0   5]\n",
      " [  0   0   0   2   0  99   2   0   2   0]\n",
      " [  0   0   0   0   0   1  93   0   0   0]\n",
      " [  0   1   4   0   3   0   0  90   1   0]\n",
      " [  0   0   0   0   0   1   0   0  86   1]\n",
      " [  0   1   0   1   4   0   0   0   0  89]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Results\n",
    "print(\"Accuracy: {}\".format(metrics.accuracy_score(y_test, y_pred)))\n",
    "\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "      % (clf_svm_best, metrics.classification_report(y_test, y_pred)))\n",
    "      \n",
    "cm = metrics.confusion_matrix(y_test, y_pred)\n",
    "print(\"Confusion matrix:\\n%s\" % cm)\n",
    "plot_confusion_matrix(cm)"
   ]
  }
 ],
 "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
}