Logistic Regression using Python (Sklearn, NumPy, MNIST, Handwriting Recognition, Matplotlib) on the MNIST Dataset for the youtube video: https://www.youtube.com/watch?v=71iXeuKFcQM

MNIST_LogisticRegression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\"> Logistic Regression (MNIST) </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.\n",
    "<br>\n",
    "It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MNIST database of handwritten digits is available on the following website: [MNIST Dataset](http://yann.lecun.com/exdb/mnist/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Four Files are available on this site:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[train-images-idx3-ubyte.gz:  training set images (9912422 bytes)](http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz) \n",
    "<br>\n",
    "[train-labels-idx1-ubyte.gz:  training set labels (28881 bytes)](http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz)\n",
    "<br>\n",
    "[t10k-images-idx3-ubyte.gz:   test set images (1648877 bytes)](http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz) \n",
    "<br>\n",
    "[t10k-labels-idx1-ubyte.gz:   test set labels (4542 bytes)](http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Used for Confusion Matrix\n",
    "from sklearn import metrics\n",
    "import seaborn as sns\n",
    "\n",
    "# Used for Loading MNIST\n",
    "from struct import unpack\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can download the data via command line (you can see this on the youtube video) or you can get them from the website or my github. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading MNIST Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2017-06-29 22:43:27--  http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Resolving yann.lecun.com (yann.lecun.com)... 216.165.22.6\n",
      "Connecting to yann.lecun.com (yann.lecun.com)|216.165.22.6|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 9912422 (9.5M) [application/x-gzip]\n",
      "Saving to: ‘data/train-images-idx3-ubyte.gz’\n",
      "\n",
      "data/train-images-i 100%[=====================>]   9.45M  2.68MB/s   in 4.2s   \n",
      "\n",
      "2017-06-29 22:43:32 (2.23 MB/s) - ‘data/train-images-idx3-ubyte.gz’ saved [9912422/9912422]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# !wget -O data/train-images-idx3-ubyte.gz http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2017-06-18 01:55:28--  http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Resolving yann.lecun.com (yann.lecun.com)... 216.165.22.6\n",
      "Connecting to yann.lecun.com (yann.lecun.com)|216.165.22.6|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 28881 (28K) [application/x-gzip]\n",
      "Saving to: ‘data/train-labels-idx1-ubyte.gz’\n",
      "\n",
      "data/train-labels-i 100%[=====================>]  28.20K  --.-KB/s   in 0.09s  \n",
      "\n",
      "2017-06-18 01:55:28 (307 KB/s) - ‘data/train-labels-idx1-ubyte.gz’ saved [28881/28881]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# !wget -O data/train-labels-idx1-ubyte.gz http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2017-06-18 01:55:29--  http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Resolving yann.lecun.com (yann.lecun.com)... 216.165.22.6\n",
      "Connecting to yann.lecun.com (yann.lecun.com)|216.165.22.6|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1648877 (1.6M) [application/x-gzip]\n",
      "Saving to: ‘data/t10k-images-idx3-ubyte.gz’\n",
      "\n",
      "data/t10k-images-id 100%[=====================>]   1.57M   953KB/s   in 1.7s   \n",
      "\n",
      "2017-06-18 01:55:30 (953 KB/s) - ‘data/t10k-images-idx3-ubyte.gz’ saved [1648877/1648877]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# !wget -O data/t10k-images-idx3-ubyte.gz http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2017-06-18 01:55:31--  http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Resolving yann.lecun.com (yann.lecun.com)... 216.165.22.6\n",
      "Connecting to yann.lecun.com (yann.lecun.com)|216.165.22.6|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 4542 (4.4K) [application/x-gzip]\n",
      "Saving to: ‘data/t10k-labels-idx1-ubyte.gz’\n",
      "\n",
      "data/t10k-labels-id 100%[=====================>]   4.44K  --.-KB/s   in 0s     \n",
      "\n",
      "2017-06-18 01:55:31 (15.4 MB/s) - ‘data/t10k-labels-idx1-ubyte.gz’ saved [4542/4542]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# !wget -O data/t10k-labels-idx1-ubyte.gz http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>If you cant unzip the file, you can try gzip or download it from [my github](https://github.com/mGalarnyk/Python_Tutorials/tree/master/Sklearn/Logistic_Regression/data)</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gzip: can't stat: data/*.gz (data/*.gz.gz): No such file or directory\r\n"
     ]
    }
   ],
   "source": [
    "# decompress gzipped file\n",
    "# !info gzip\n",
    "# !gzip -d data/*.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading MNIST Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def loadmnist(imagefile, labelfile):\n",
    "\n",
    "    # Open the images with gzip in read binary mode\n",
    "    images = open(imagefile, 'rb')\n",
    "    labels = open(labelfile, 'rb')\n",
    "\n",
    "    # Get metadata for images\n",
    "    images.read(4)  # skip the magic_number\n",
    "    number_of_images = images.read(4)\n",
    "    number_of_images = unpack('>I', number_of_images)[0]\n",
    "    rows = images.read(4)\n",
    "    rows = unpack('>I', rows)[0]\n",
    "    cols = images.read(4)\n",
    "    cols = unpack('>I', cols)[0]\n",
    "\n",
    "    # Get metadata for labels\n",
    "    labels.read(4)\n",
    "    N = labels.read(4)\n",
    "    N = unpack('>I', N)[0]\n",
    "\n",
    "    # Get data\n",
    "    x = np.zeros((N, rows*cols), dtype=np.uint8)  # Initialize numpy array\n",
    "    y = np.zeros(N, dtype=np.uint8)  # Initialize numpy array\n",
    "    for i in range(N):\n",
    "        for j in range(rows*cols):\n",
    "            tmp_pixel = images.read(1)  # Just a single byte\n",
    "            tmp_pixel = unpack('>B', tmp_pixel)[0]\n",
    "            x[i][j] = tmp_pixel\n",
    "        tmp_label = labels.read(1)\n",
    "        y[i] = unpack('>B', tmp_label)[0]\n",
    "\n",
    "    images.close()\n",
    "    labels.close()\n",
    "    return (x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_img, train_lbl = loadmnist('data/train-images-idx3-ubyte'\n",
    "                                 , 'data/train-labels-idx1-ubyte')\n",
    "test_img, test_lbl = loadmnist('data/t10k-images-idx3-ubyte'\n",
    "                               , 'data/t10k-labels-idx1-ubyte')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n"
     ]
    }
   ],
   "source": [
    "print(train_img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000,)\n"
     ]
    }
   ],
   "source": [
    "print(train_lbl.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 784)\n"
     ]
    }
   ],
   "source": [
    "print(test_img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "print(test_lbl.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Showing Training Digits and Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1192dea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,4))\n",
    "for index, (image, label) in enumerate(zip(train_img[0:5], train_lbl[0:5])):\n",
    "    plt.subplot(1, 5, index + 1)\n",
    "    plt.imshow(np.reshape(image, (28,28)), cmap=plt.cm.gray)\n",
    "    plt.title('Training: %i\\n' % label, fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   3  18  18  18 126 136 175  26 166 255\n",
      " 247 127   0   0   0   0   0   0   0   0   0   0   0   0  30  36  94 154\n",
      " 170 253 253 253 253 253 225 172 253 242 195  64   0   0   0   0   0   0\n",
      "   0   0   0   0   0  49 238 253 253 253 253 253 253 253 253 251  93  82\n",
      "  82  56  39   0   0   0   0   0   0   0   0   0   0   0   0  18 219 253\n",
      " 253 253 253 253 198 182 247 241   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0  80 156 107 253 253 205  11   0  43 154\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0  14   1 154 253  90   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0 139 253 190   2   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0  11 190 253  70   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  35 241\n",
      " 225 160 108   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0  81 240 253 253 119  25   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0  45 186 253 253 150  27   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0  16  93 252 253 187\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0 249 253 249  64   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0  46 130 183 253\n",
      " 253 207   2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0  39 148 229 253 253 253 250 182   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0  24 114 221 253 253 253\n",
      " 253 201  78   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0  23  66 213 253 253 253 253 198  81   2   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0  18 171 219 253 253 253 253 195\n",
      "  80   9   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "  55 172 226 253 253 253 253 244 133  11   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0 136 253 253 253 212 135 132  16\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0]\n"
     ]
    }
   ],
   "source": [
    "# This is how the computer sees the number 5\n",
    "print(train_img[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Logistic Regression on Entire Dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Logistic Regression Sklearn Documentation](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) <br>\n",
    "One thing I like to mention is the importance of parameter tuning. While it may not have mattered much for the toy digits dataset, it can make a major difference on larger and more complex datasets you have. <b>Please see the parameter: solver</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 1: </b> Import the model you want to use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In sklearn, all machine learning models are implemented as Python classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 2:</b> Make an instance of the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# all parameters not specified are set to their defaults\n",
    "# default solver is incredibly slow thats why we change it\n",
    "logisticRegr = LogisticRegression(solver = 'lbfgs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 3:</b> Training the model on the data, storing the information learned from the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model is learning the relationship between x (digits) and y (labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='lbfgs', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logisticRegr.fit(train_img, train_lbl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 4:</b> Predict the labels of new data (new images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uses the information the model learned during the model training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7], dtype=uint8)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns a NumPy Array\n",
    "# Predict for One Observation (image)\n",
    "logisticRegr.predict(test_img[0].reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 2, 1, 0, 4, 1, 4, 9, 6, 9], dtype=uint8)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict for Multiple Observations (images) at Once\n",
    "logisticRegr.predict(test_img[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measuring Model Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "accuracy (fraction of correct predictions): correct predictions / total number of data points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basically, how the model performs on new data (test set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9174\n"
     ]
    }
   ],
   "source": [
    "score = logisticRegr.score(test_img, test_lbl)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A confusion matrix is a table that is often used to describe the performance of a classification model (or \"classifier\") on a set of test data for which the true values are known. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Note: Seaborn needs to be installed for this portion </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# !conda install seaborn -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make predictions on test data\n",
    "predictions = logisticRegr.predict(test_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm = metrics.confusion_matrix(test_lbl, predictions)\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pEAghhBBCKgRCCCGEW/JZgUAqBEIIIYSQCoEQQgjhFvkeAiGEEELkO1IhEEII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u89Zr+S6w2CbsCrAO4+D+gQb5wKfQScF3eISrwA3BH9nQC+jTHLHtz9RaAwOvlzIJveiMvcDzwG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d7c21d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "sns.heatmap(cm_normalized, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "all_sample_title = 'Accuracy Score: {:.3f}'.format(score) \n",
    "plt.title(all_sample_title, size = 15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display Misclassified images with Predicted Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index = 0\n",
    "misclassifiedIndexes = []\n",
    "for label, predict in zip(test_lbl, predictions):\n",
    "    if label != predict: \n",
    "        misclassifiedIndexes.append(index)\n",
    "    index +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12982a210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,4))\n",
    "for plotIndex, badIndex in enumerate(misclassifiedIndexes[0:5]):\n",
    "    plt.subplot(1, 5, plotIndex + 1)\n",
    "    plt.imshow(np.reshape(test_img[badIndex], (28,28)), cmap=plt.cm.gray)\n",
    "    plt.title('Predicted: {}, Actual: {}'.format(predictions[badIndex], test_lbl[badIndex]), fontsize = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking Performance Based on Training Set Size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A confusion matrix is a table that is often used to describe the performance of a classification model (or \"classifier\") on a set of test data for which the true values are known. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regr = LogisticRegression(solver = 'lbfgs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C0/k/hjIrVfVvsVlxN0a/papjYsddq5a2MDKA3BjX3qq6NKaHsu3/c2m3cp2Z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13ada0450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (24,8));\n",
    "plt.tight_layout()\n",
    "\n",
    "for plotIndex, sample_size in enumerate([100, 1000, 60000]):\n",
    "    X_train = train_img[:sample_size].reshape(sample_size, 784)\n",
    "    y_train = train_lbl[:sample_size]\n",
    "    regr.fit(X_train, y_train)\n",
    "    predicted = regr.predict(test_img)\n",
    "    cm = metrics.confusion_matrix(test_lbl, predicted)\n",
    "    cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "    sns.heatmap(cm_normalized, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r', ax = axes[plotIndex], cbar = False);\n",
    "    accuracyString = '{:g} Training Samples Score: {:.3f}'.format(sample_size, regr.score(test_img, test_lbl)) \n",
    "    axes[plotIndex].set_title(accuracyString, size = 25);\n",
    "\n",
    "axes[0].set_ylabel('Actual label', fontsize = 30);\n",
    "axes[1].set_xlabel('Predicted label', fontsize = 30);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<b>if this tutorial doesn't cover what you are looking for, please leave a comment on the youtube video and I will try to cover what you are interested in. </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[youtube video](https://www.youtube.com/watch?v=71iXeuKFcQM)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}