notebooks/ce12.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computational Exercise 12: Beyond Supervised Learning\n",
    "\n",
    "---\n",
    "\n",
    "**This exercise is optional.** Please note that no code modifications are required in this exercise. Please read through the written portions and outputs, then consider the four written questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the `digits` dataset\n",
    "\n",
    "In this exercise, we'll use a dataset of handwritten digits to illustrate differences between supervised learning, unsupervised learning, semi-supervised learning, and active learning. The dataset is lower resolution than MNIST, which will make it easier to work with for this exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "digits = datasets.load_digits()\n",
    "fig, axs = plt.subplots(ncols=8, nrows=2, figsize=(16, 4))\n",
    "for ax in axs:\n",
    "    for a in ax:\n",
    "        idx = np.random.randint(0, high=len(digits['images']))\n",
    "        a.imshow(digits['images'][idx])\n",
    "        a.set_title('Label: %i' % digits['target'][idx])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised Learning\n",
    "\n",
    "In supervised learning, our goal is to learn a function that maps inputs to a desired output based on a training set of many known input-output pairs. In digit classification, our inputs are images of handwritten digits, and our desired outputs are the digits they represent. Almost everything we've discussed so far in the course, from logistic regression to CNNs to text classification, has been supervised learning. Although we have focused on classification, supervised learning also includes regression (e.g. linear regression). The code below demonstrates supervised learning with the `digits` dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x576 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "mlp = MLPClassifier(hidden_layer_sizes=(256, ))\n",
    "mlp.fit(digits['data'][:1500], digits['target'][:1500])\n",
    "predicted = mlp.predict(digits['data'])\n",
    "fig, axs = plt.subplots(ncols=8, nrows=4, figsize=(16, 8))\n",
    "for ax in axs:\n",
    "    for a in ax:\n",
    "        idx = np.random.randint(1500, high=len(digits['images']))\n",
    "        a.imshow(digits['images'][idx])\n",
    "        a.set_title('Pred: %i; True: %i' % (predicted[idx], digits['target'][idx]))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ONXQi0SjL94kBAJgMlVEAgKlYwglMKqMAAAwjGQUAYBhtegCAKaiYwAQAAIukMgoAMAmWdgIAgIWSjAIAMIw2PQDAVFhnFAAAFkdlFABgKkxgAgCAxZGMAgAwjDY9AMBUmMAEAACLozIKADAFZQcmAABYKMkoAADDDE9Gq+pRVfW+qrq0qp46Oh4AgGGqxj0GGZqMVtXeJC9I8ugk90zyuKq658iYAABYnNGV0fslubS7L+vu65K8Iskpg2MCAGBBRs+mPzbJFSue709y/0GxAAAMVUu4zujoZHSt3/G+0UVVpyU5LUly5FGHNiIAABZmdDK6P8nxK54fl+TK1Rd195lJzkySPbc77kbJKgDATldZzsro6HtGz0tyYlWdUFVHJDk1ydmDYwIAYEGGVka7+/qqOj3JuUn2Jjmruy8eGRMAAIszuk2f7j4nyTmj4wAAGKqy9myaXW50mx4AgCU2vDIKAECSlAlMAACwSJJRAACG0aYHAJgIbXoAAFgglVEAgIlQGQUAgAWSjAIAMIw2PQDARGjTAwDAAqmMAgBMgb3pAQBgsSSjAAAMo00PADABlTKBCQAAFkkyCgDAMNr0AAAToU0PAAALpDIKADARy1gZ3XHJ6L1PvHPecu7TRoex7Y6+7+mjQ9h215z3/NEhsElHHKZJspMcefje0SEAbBv/BwIAYJgdVxkFANitlrFNrzIKAMAwKqMAAFNQ88eSURkFAGAYySgAAMNo0wMATIQJTAAAsEAqowAAE1AplVEAAFgkySgAAMNo0wMATIQ2PQAALJDKKADAVCxfYVRlFACAcSSjAAAMIxkFAJiCmk1gGvXYVIhVj6qq91XVpVX11DXO376q/rKq3llVF1fVEw82pmQUAICDqqq9SV6Q5NFJ7pnkcVV1z1WXPTnJe7v7XkkeluT/raojNhrXBCYAgImY+NJO90tyaXdfliRV9YokpyR574prOslta/ZBbpPkk0mu32hQlVEAAJLkmKo6f8XjtFXnj01yxYrn++fHVnp+knskuTLJu5Oc0d03bPSmKqMAACTJ1d29b4Pza5Vte9Xzb09yUZJHJLlbktdW1Zu6+1PrDaoyCgAwEROfwLQ/yfErnh+XWQV0pScmeVXPXJrkg0nuvtGgklEAADbjvCQnVtUJ80lJpyY5e9U1H05ycpJU1Z2SfF2SyzYaVJseAICD6u7rq+r0JOcm2ZvkrO6+uKqeND//oiS/luQlVfXuzNr6v9jdV280rmQUAGACKptf73OU7j4nyTmrjr1oxa+vTPJtWxlTmx4AgGFURgEApmLahdFDQmUUAIBhJKMAAAyjTQ8AMAU1+e1ADwmVUQAAhlEZBQCYCJVRAABYIMkoAADDaNMDAEyENj0AACyQyigAwFQsX2FUZRQAgHEkowAADKNNDwAwESYwAQDAAqmMAgBMQFWpjAIAwCJJRgEAGEabHgBgIrTpAQBggVRGAQAmQmUUAAAWSDIKAMAw2vQAAFOxfF16lVEAAMaRjAIAMIw2PQDARCzjbHrJ6ERcc97zR4ew7Y6+7+mjQzgkduN3BQCjSEYBAKaglrMy6p5RAACGkYwCADCMNj0AwARUkiXs0quMAgAwjsooAMAklAlMAACwSJJRAACG0aYHAJiIJezSq4wCADCOyigAwESYwAQAAAskGQUAYBhtegCAKSgTmAAAYKFURgEAJqCS7NmzfKVRlVEAAIaRjAIAMIw2PQDARJjABAAAC6QyCgAwEXZgAgCABZKMAgAwzNBktKqOr6rXV9UlVXVxVZ0xMh4AgGHmOzCNeowy+p7R65P8XHdfWFW3TXJBVb22u987OC4AABZgaGW0uz/a3RfOf/3pJJckOXZkTAAALM7oyugXVdVdk9w7ydsHhwIAsHAVs+mHqarbJPnTJD/T3Z9a4/xpVXV+VZ1/1dVXLT5AAAAOieGV0ao6PLNE9I+7+1VrXdPdZyY5M0nuc599vcDwAAAWpFRGF61mv+O/n+SS7v7tkbEAALB4o9v0D0ryQ0keUVUXzR+PGRwTAAALMrRN391vzux+XQCApbeEXfrhlVEAAJbY8AlMAADMmMAEAAALJBkFAGAYbXoAgCkoE5gAAGChVEYBACbA3vQAALBgklEAAIbRpgcAmIgl7NKrjAIAMI7KKADARJjABAAACyQZBQBgGG16AICJWMIuvcooAADjSEYBABhGmx4AYArKbHoAAFgolVEAgAmomMAEAAALpTLKIXPNec8fHcIh8bVPOXt0CNvu/c957OgQDolPfebzo0M4JG53y8NHh8AmfeDj/zo6hG13tzvdZnQI7DKSUQCASSgTmAAAYJFURgEAJmIJC6MqowAAjCMZBQBgGG16AICJMIEJAAAWSGUUAGAKygQmAABYKMkoAADDaNMDAExAxQQmAABYKJVRAICJUBkFAIAFkowCADCMNj0AwEQsYZdeZRQAgHFURgEAJsIEJgAAWCDJKAAAw2jTAwBMQZnABAAACyUZBQBgGG16AIAJqJTZ9AAAsEgqowAAE7GEhVGVUQAAxpGMAgAwjDY9AMBE7FnCPr3KKAAAw6iMAgBMxBIWRlVGAQAYRzIKAMAw2vQAABNQFTswAQDAIqmMAgBMxJ7lK4yqjAIAMI5kFACAYSSjAAATUVXDHpuM71FV9b6qurSqnrrONQ+rqouq6uKq+ruDjemeUQAADqqq9iZ5QZJvTbI/yXlVdXZ3v3fFNUcl+b0kj+ruD1fVVx5sXMkoAMBETHxlp/slubS7L0uSqnpFklOSvHfFNY9P8qru/nCSdPcnDjaoNj0AAElyTFWdv+Jx2qrzxya5YsXz/fNjK31tkqOr6g1VdUFV/fDB3lRlFACAJLm6u/dtcH6tum2ven5YkvskOTnJLZP8fVW9rbvfv96gklEAgAmoJLVmvjcZ+5Mcv+L5cUmuXOOaq7v735L8W1W9Mcm9kqybjGrTAwCwGeclObGqTqiqI5KcmuTsVdf8RZIHV9VhVXWrJPdPcslGg6qMAgBMxJR3YOru66vq9CTnJtmb5KzuvriqnjQ//6LuvqSqXpPkXUluSPLi7n7PRuNKRgEA2JTuPifJOauOvWjV899M8pubHVObHgCAYVRGAQCmYAs7Ie0mKqMAAAwjGQUAYBhtegCAiVjCLr1kFLbq/c957OgQtt3R3/mc0SEcEtf81VNGh8CSu9udbjM6hG33z5/+3OgQtt31N6zeRIhFkowCAExAJdmzhKVR94wCADCMZBQAgGG06QEAJmIJu/QqowAAjLNuZbSqfuUmjtnd/Ws38bUAAEtrGXdg2qhN/4ybOGYnkYwCAHBQGyWjD19YFAAALKV1k9Hu/rtFBgIAsMyqTGACAICF2vLSTlX1jUken+QeSW7d3Y+cH79rkvsleW13X7OdQQIALINl3IFpS8loVT0zyS/lSxXVlZu57kny8iQ/k+R3tyM4AAB2t0236avq1CS/nOS1Sb4pybNWnu/uy5Kcn+Sx2xgfAAC72FbuGf3pJJcmOaW735XkujWuuSTJidsRGADAsqmBj1G2kox+Q5Jzu3utJPSAK5Pc6eaFBADAstjKPaOV5IaDXHOnJJ+96eEAACyvZdyBaSuV0X9K8sD1TlbV3iTfkuTimxsUAADLYSvJ6J8kOamqfm6d809L8jVJXnazowIAYClspU3/3CTfl+Q3qur7M1/Wqap+K8mDk+xL8rYkZ25zjAAAu14l2bN8XfrNJ6Pd/ZmqeniS5yX5gSR756d+NrN7SV+a5PTuvn7bowQAYFfa0qL33X1tkidU1c8muW+SOyS5Nsk/dPdVhyA+AAB2sS1vB5ok3f3JJOducywAAMurailn09+kZLSqjk9y7yS3z6wy+o7uvmI7AwMAYPfb6t70Jyb5vSSPWOPc65I8ubvfv02xAQAslSUsjG4+Ga2qr0ny1szuE/1Akjcn+ViSO2e2vujJSd5cVQ/s7ku3EsR8jdLzk3yku79zK68FAGDn2kpl9FmZJaJnJHlBd39xN6aq2pPkp5I8J8l/T/L9W4zjjMz2tb/dFl8HAMAOtpVF709Ock53/+7KRDRJuvuG7n5eklcneeRWAqiq45J8R5IXb+V1AAC7Tc0nMY14jLKVZPSIJBcd5JqLkhy+xRiem+QXssG+91V1WlWdX1XnX3W1FaQAAHaLrSSj78xsu8+NfE2Sd212wKr6ziSf6O4LNrquu8/s7n3dve+Ox9xxs8MDAOwYB3ZgGvUYZSvJ6H9P8p+r6tFrnayq70jyPUl+fQtjPijJY6vq8iSvSPKIqnrpFl4PAMAOtu4Epqr64TUOvzrJX1XV3yZ5Y5KPJ7lTkodmttzTXyY5ZrNv3t1PS/K0+fs9LMl/7e4f3OzrAQDY2TaaTf+SJL3q2IEi7iOz9kSlxyb5riR/dLMjAwBYMnZg+nJPXFgUSbr7DUnesMj3BABgrHWT0e7+w0UGAgCw7JavLrq1CUwAALCtJKMAAAyzle1AU1W3TvJfknx7kmOTHLnGZd3dd9uG2AAAlkZVsscEpvVV1VFJ3pzknkk+ldk+8tdmtjPTLeeXXZnk89sbIgAAu9VW2vS/nFki+mNJjp4fe06S2yR5YJILk3wgyT22M0AAgGVRNe4xylaS0ccmeWN3/0F3f3H90Z55W5LHJLl7kv9rm2MEAGCX2koyenxm1c8DbsiKe0a7+xOZ7dB06vaEBgDAbreVCUz/nuQLK55fm+TOq675eGYTmwAA2KJl3IFpK5XRKzKrjh7w3iQPqaq9K459S5KPbUdgAADsfltJRv8uyUPrSyn7/0xytyT/u6qeXFX/K8kDkpyzzTECACyFZZzAtJU2/R9mtozTcZlVSV+U5BFJvjvJt82veUtms+4BAOCgNp2MdveFSX5yxfPrk/znqrpPkq9JcnmS87r7hu0OEgCA3WlLOzCtpbsvSHJBklTVN1XVXbr77JsdGQDAEqnUUu7AtN1705+R5M+2eUwAAHap7U5GAQBg0252mx4AgG0weFb7KCqjAAAMozIKADARdmACAIAF2rAyWlUP2eJ4q/eqZ5M+9/kvjA5h2x15+N6DX8QkXPNXTxkdwiFx9IOfOjqEQ+KaNz17dAgssTvc9sjRIWy7w/YsXzVySg7Wpn9Dkt7CeLXF6wEAmFvGlvXBktE3RnIJAMAhsmEy2t0PW1AcAABLrWICEwAALJRkFACAYawzCgAwEcs4sV9lFACAYVRGAQAmQmUUAAAWSDIKAMAw2vQAABNQtZzrjG45Ga2qb0zy+CT3SHLr7n7k/Phdk9wvyWu7+5rtDBIAgN1pS8loVT0zyS/lS+39lVuF7kny8iQ/k+R3tyM4AIBlYgLTBqrq1CS/nOS1Sb4pybNWnu/uy5Kcn+Sx2xgfAAC72FYmMP10kkuTnNLd70py3RrXXJLkxO0IDACA3W8rbfpvSPKS7l4rCT3gyiR3unkhAQAspyWcv7SlymglueEg19wpyWdvejgAACyTrVRG/ynJA9c7WVV7k3xLkotvblAAAMumkuxZwtLoViqjf5LkpKr6uXXOPy3J1yR52c2OCgCApbCVyuhzk3xfkt+oqu/PfFmnqvqtJA9Osi/J25Kcuc0xAgCwS206Ge3uz1TVw5M8L8kPJNk7P/Wzmd1L+tIkp3f39dseJQDAEljGfdq3tOh9d1+b5AlV9bNJ7pvkDkmuTfIP3X3VIYgPAIBd7CbtTd/dn0xy7jbHAgDAkrlJySgAANtvCSfTbz4ZraqzNnlpd/eP3cR4AABYIlupjD7hIOc7syWyOolkFABgC6pqKdcZ3UoyesI6x4/KbDLTf0vy1iRPvZkxAQCwJLaytNOH1jn1oSTvrKpzk7wryd8k+f1tiA0AgF1u25az6u4rkvxlkjO2a0wAgGVSNe4xynavrfrxJCdu85gAAOxS27a0U1XtTfKIzBbBBwBgi/Ys3/ylLS3t9JANxjg+yROTfFOSF9/8sAAAWAZbqYy+IbNlm9ZTSd6Y5OdvTkAAACyPrSSjz8zayegNSa7JbH/6f9iWqAAAlkwl1hndSHc/4xDGAQDAEtrqdqDv7u7nHMJ4AACW1hIWRre0tNPjk3zloQoEAIDls5Vk9PJIRgEA2EZbmcD0siRPqqqju/uaQxUQAMBSquVcZ3QrldFnJTk/yeur6jur6k6HKCYAAJbEhpXRqvrhJBd197uSfPbA4SR/MT+/1su6u7dtZycAgGVRWb7S6MGSxpckeXqSdyV5UzZe9B4AALZkMxXMSpLuftihDQUAgGWjnQ4AMAGzHZhGR7F4W5nABAAA22ozldGjquouWxm0uz98E+MBAGCJbCYZPWP+2Kze5LgAAKywjG36zSSNn0ryL4c4DgAAltBmktHndPczD3kkAABLbp013Hc1E5gAABhGMgoAwDAmGgEATMCyrjMqGQV2rWve9OzRIRwSRz/4qaND2Ha79bsCDm7DZLS7tfEBABahkiWcv+SeUQAAxpGMAgAwjHtGAQAmYs8S9ulVRgEAGEZlFABgApZ1aSeVUQAAhpGMAgAwjDY9AMBELOH8JZVRAAA2p6oeVVXvq6pLq2rd7eCq6r5V9YWq+t6DjakyCgAwCZU9mW5ptKr2JnlBkm9Nsj/JeVV1dne/d43r/p8k525mXJVRAAA2435JLu3uy7r7uiSvSHLKGtf9VJI/TfKJzQwqGQUAIEmOqarzVzxOW3X+2CRXrHi+f37si6rq2CTfk+RFm31TbXoAgAmoDJ/AdHV379vg/FrR9arnz03yi939hdrkh5GMAgCwGfuTHL/i+XFJrlx1zb4kr5gnosckeUxVXd/df77eoJJRAIApqMnvwHRekhOr6oQkH0lyapLHr7ygu0848OuqekmSv9ooEU0kowAAbEJ3X19Vp2c2S35vkrO6++KqetL8/KbvE11JMgoAwKZ09zlJzll1bM0ktLufsJkxJaMAABOxZwm3YLK0EwAAw0hGAQAYRpseAGACJrDO6BAqowAADKMyCgAwESYwDVBVR1XVK6vqH6vqkqr65tExAQCwGFOojD4vyWu6+3ur6ogktxodEAAAizE0Ga2q2yV5SJInJEl3X5fkupExAQCMsoRd+uFt+v+Y5Kokf1BV76iqF1fVrVdfVFWnVdX5VXX+VVdftfgoAQA4JEYno4clOSnJC7v73kn+LclTV1/U3Wd2977u3nfHY+646BgBAA65yiwxG/UYZXQyuj/J/u5++/z5KzNLTgEAWAJDk9Hu/liSK6rq6+aHTk7y3oEhAQCwQFOYTf9TSf54PpP+siRPHBwPAMDiVVJLOINpeDLa3Rcl2Tc6DgAAFm94MgoAwMzy1UXHT2ACAGCJSUYBABhGmx4AYAIqyZ4lnMCkMgoAwDAqowAAE7F8dVGVUQAABpKMAgAwjDY9AMBELOH8JZVRAADGURkFAJiEWsq96VVGAQAYRjIKAMAw2vQAABNQWc4q4TJ+ZgAAJkIyCgDAMNr0AAATYTY9AAAskMooAMBELF9dVGUUAICBVEYn4sjD944OAdghrnnTs0eHsO2OfvBTR4dwSHz0db8+OoRtdwv/v2KbSUYBAKagTGACAICFUhkFAJgAOzABAMCCSUYBABhGmx4AYCJMYAIAgAVSGQUAmIjlq4uqjAIAMJBkFACAYbTpAQAmYgnnL6mMAgAwjsooAMAEzHZgWr7SqMooAADDSEYBABhGmx4AYCJMYAIAgAVSGQUAmIRKmcAEAACLIxkFAGAYbXoAgIkwgQkAABZIMgoAwDDa9AAAE2A7UAAAWDCVUQCAKSgTmAAAYKEkowAADKNNDwAwEdr0AACwQCqjAAATUZZ2AgCAxZGMAgAwjDY9AMAEVJI9y9elVxkFAGAclVEAgIkwgQkAABZIMgoAwDDa9AAAE2EHJgAAWCCVUQCAiTCBCQAAFkgyCgDAMNr0AAATYAcmAABYMMkoAADDaNMDAExCmU0PAACLpDIKADAFZQcmAABYKMkoAADDaNMDAEzEEnbpVUYBABhHZXQiPv2Zz48OYdvd9paHjw4B2CGuedOzR4dwSBz97c8aHcK2u+bcp40OYdea7cC0fLVRlVEAAIaRjAIAMIw2PQDARCxfk15lFACAgVRGAQCmYglLoyqjAAAMIxkFAGAYbXoAgImoJezTq4wCADCMyigAwEQs4QZMKqMAAIwjGQUAYBhtegCAiVjCLr3KKAAA46iMAgBMxRKWRlVGAQAYRjIKAMAw2vQAABNQsQMTAAAslGQUAIBhtOkBAKagbAcKAAALpTIKADARS1gYHV8ZraqnVNXFVfWeqnp5Vd1idEwAACzG0GS0qo5N8tNJ9nX31yfZm+TUkTEBALA4U2jTH5bkllX1+SS3SnLl4HgAAMZYwj790Mpod38kyW8l+XCSjya5trv/evV1VXVaVZ1fVedfdfVViw4TAIBDZHSb/ugkpyQ5IclXJ7l1Vf3g6uu6+8zu3tfd++54zB0XHSYAwALU0H9GGT2B6ZFJPtjdV3X355O8KskDB8cEAMCCjE5GP5zkAVV1q6qqJCcnuWRwTAAALMjQCUzd/faqemWSC5Ncn+QdSc4cGRMAwCh2YBqgu5/e3Xfv7q/v7h/q7s+NjgkAgBurqkdV1fuq6tKqeuoa53+gqt41f7y1qu51sDGnsLQTAMDSq0x7Zaeq2pvkBUm+Ncn+JOdV1dnd/d4Vl30wyUO7+5qqenRmHe/7bzTu8MooAAA7wv2SXNrdl3X3dUlekdmqSF/U3W/t7mvmT9+W5LiDDSoZBQAgSY45sK77/HHaqvPHJrlixfP982Pr+bEkrz7Ym2rTAwBMxdg+/dXdvW+D82tF12teWPXwzJLRbznYm0pGAQDYjP1Jjl/x/LissY17VX1jkhcneXR3//PBBpWMAgBMxMidkDbhvCQnVtUJST6S5NQkj195QVXdJbNNjH6ou9+/mUElowAAHFR3X19Vpyc5N8neJGd198VV9aT5+Rcl+ZUkd0jye7P9jHL9QVr/klEAADanu89Jcs6qYy9a8esfT/LjWxlTMgoAMBF2YAIAgAVSGQUAmIglLIyqjAIAMI5kFACAYbTpAQCmoLKUfXqVUQAAhpGMAgAwjDY9AMBETHw70ENCZRQAgGFURgEAJqBiByYAAFgoldGJOOIwfy+A7fa5z39hdAiHxJGH7x0dwrbbrd/V5X/2X0eHsO3u/CMvHR3CtvvXyz85OoSlJhkFAJiIJezSa9MDADCOyigAwFQsYWlUZRQAgGEkowAADKNNDwAwEXZgAgCABVIZBQCYCDswAQDAAklGAQAYRpseAGAilrBLrzIKAMA4KqMAAFOxhKVRlVEAAIaRjAIAMIw2PQDABFTswAQAAAslGQUAYBhtegCAKSjbgQIAwEKpjAIATMQSFkZVRgEAGEcyCgDAMNr0AABTsYR9epVRAACGURkFAJiEsgMTAAAskmQUAIBhtOkBACbCDkwAALBAKqMAABNQWcqVnVRGAQAYRzIKAMAw2vQAAFOxhH16lVEAAIZRGQUAmAg7MAEAwAJJRgEAGEabHgBgIuzABAAAC6QyCgAwEUtYGFUZBQBgHMkoAADDaNMDAExBmcAEAAALJRkFAGAYbXoAgMlYvj69yigAAMOojAIATEDFBCYAAFgoySgAAMNo0wMATMQSdulVRgEAGGfHVUYvvPCCq295eH1oAW91TJKrF/A+3Hy+q53F97Vz+K52Dt/VzfMfRgdwwDJOYNpxyWh333ER71NV53f3vkW8FzeP72pn8X3tHL6rncN3xU6mTQ8AwDA7rjIKALBb1RJOYVIZXd+ZowNg03xXO4vva+fwXe0cvit2rOru0TEAACy9e937Pn3u371t2Pt/1e2PuGDEvccqowAADCMZBQBgGBOYAAAmYvmmL6mMAgAwkMooAMAEVC3nDkwqowAADCMZBQBgGG16AICJsAMTAAAskMooAMBULF9hVGUUAIBxJKMAAAyjTQ8AMBFL2KVXGQUAYBzJKAAAw2jTAwBMhO1AAQBggVRGAQAmoezABAAAiyQZBQBgGG16AIAJqJjABAAACyUZBQBgGMkoAADDSEYBABjGBCYAgIkwgQkAABZIZRQAYCLswAQAAAskGQUAYBhtegCAKSgTmAAAYKFURgEAJqDmj2WjMgoAwDCSUQAAhtGmBwCYiiXs06uMAgAwjGQUAIBhtOkBACbCdqAAALBAKqMAABNhByYAAFggySgAAMNo0wMATMQSdulVRgEAGEdlFABgKpawNKoyCgDAMJJRAACG0aYHAJgIOzABAMACSUYBACagMtuBadRjUzFWPaqq3ldVl1bVU9c4X1X1O/Pz76qqkw42pmQUAICDqqq9SV6Q5NFJ7pnkcVV1z1WXPTrJifPHaUleeLBxJaMAAGzG/ZJc2t2Xdfd1SV6R5JRV15yS5I965m1Jjqqqr9poUBOYAAAm4MILLzj3lofXMQNDuEVVnb/i+ZndfeaK58cmuWLF8/1J7r9qjLWuOTbJR9d7U8koAMAEdPejRsdwEGvdWdo34Zovo00PAMBm7E9y/IrnxyW58iZc82UkowAAbMZ5SU6sqhOq6ogkpyY5e9U1Zyf54fms+gckuba7123RJ9r0AABsQndfX1WnJzk3yd4kZ3X3xVX1pPn5FyU5J8ljklya5N+TPPFg41b3hm18AAA4ZLTpAQAYRjIKAMAwklEAAIaRjAIAMIxkFACAYSSjAAAMIxkFAGCY/x8Xy1dIwRMSOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 92.59\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "def plot_confusion_matrix(predicted_labels, true_labels, normalize=True, title='Predicted Label', ylab='True Label'):\n",
    "    cm = confusion_matrix(true_labels, predicted_labels)\n",
    "    if normalize == True:\n",
    "        cm = cm / np.sum(cm, axis=1)\n",
    "    fig, ax = plt.subplots(figsize=(12, 12))\n",
    "    im = ax.matshow(cm, cmap='Blues')\n",
    "    fig.colorbar(im, ax=ax)\n",
    "    ax.set_title(title, fontsize=20)\n",
    "    ax.set_ylabel(ylab, fontsize=20)\n",
    "    plt.show()\n",
    "plot_confusion_matrix(predicted[1500:], digits['target'][1500:])\n",
    "print(\"Accuracy = %.2f\" % (100 * sum(digits['target'][1500:] == predicted[1500:]) / len(predicted[1500:])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 12.1: Supervised Learning Application\n",
    "\n",
    "Describe a medical application of supervised learning that was not discussed in class or in our readings. Be sure to identify both the inputs and the desired outputs.\n",
    "\n",
    "--- \n",
    "\n",
    "**YOUR RESPONSE HERE**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unsupervised Learning\n",
    "\n",
    "In unsupervised learning, our goal is to **discover structure in unlabeled data**. In other words, we still have many inputs, but there aren't any corresponding outputs that we'd like to replicate. Unsupervised learning includes:\n",
    "\n",
    "- clustering, in which we divide our dataset into distinct groups with similar characteristics\n",
    "- dimensionality reduction techniques such as PCA, factor analysis, and autoencoders\n",
    "\n",
    "In the code below, we use the k-means clustering algorithm to divide our digits into 10 visually distinct groups. Since our algorithm doesn't know which digits these images represent, the index for a given group won't match the digits contained in it. In the block below, we'll take a look at the images assigned to group 0. Do most of these images represent the same digit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "km = KMeans(n_clusters=10)\n",
    "predicted = km.fit_predict(digits['data'])\n",
    "fig, axs = plt.subplots(ncols=8, nrows=2, figsize=(16, 4))\n",
    "for idx in range(16):\n",
    "    digits['images'][predicted == 0][idx]\n",
    "    axs[idx // 8, idx % 8].imshow(digits['images'][predicted == 0][idx])\n",
    "    axs[idx // 8, idx % 8].set_title('Label: %i' % digits['target'][predicted == 0][idx])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot a matrix showing how many images of each digit were assigned to the different groups. For the most part, do the groups represent distinct digits?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BSeeU3zSq6rQkj05yVXd/75Ltz0jy9MxWIPur7n72fPspSZ6S5MYkP9/db1/tHJsuMQUAYBKvSvLSJH+yc0NVPSTJCUm+r7u/VlW3m28/JsmJSb4nyR2TvKOq7trdN+7uBBJTAIAh1DCDkJbT3edW1VG7bP65JC/q7q/N97lqvv2EJK+fb/9UVV2a5D5J/s/uzjHuuwcAYF86tKrOX3Jby2wPd03yQ1V1XlW9u6ruPd9+eJLLl+x3xXzbbqmYAgCMYtrBT9d093ovuN0/ySFJ7pvk3kneWFXfmeXHya160bWKKQAAe+qKJG/umfcn2ZHk0Pn2I5fsd0SSz652MIkpAAB76i1JHpokVXXXJAcmuSbJmUlOrKqDquouSY5O8v7VDqaVDwAwgsrQg5+q6nVJjsvsWtQrkjw/yWlJTquqDye5PsnJ3d1JLq6qNyb5SGbTSD1ttRH5icQUAIA16O6TVnjq8Svs/8IkL1zPOSSmAABDGHu6qH1hsd89AADDkJgCADAErXwAgFFMO4/p5FRMAQAYgoopAMAoDH4CAIDpSUwBABiCVj4AwCgMfgIAgOmpmAIAjKCs/LTY7x4AgGFITAEAGMLkiWlVPaqqPlZVl1bVc6aOBwBgMlXT3QYwaWJaVfsleVmSH05yTJKTquqYKWMCAGAaU1dM75Pk0u7+ZHdfn+T1SU6YOCYAACYw9aj8w5NcvuTxFUl+cKJYAAAmVYO01KcydcV0uU+/v2WnqqdW1flVdf7V11y9D8ICAGBfm7piekWSI5c8PiLJZ3fdqbtPTXJqktzrXtu/JXEFANjsKiqmU1dMP5Dk6Kq6S1UdmOTEJGdOHBMAABOYtGLa3TdU1dOTvD3JfklO6+6Lp4wJAIBpTN3KT3f/dZK/njoOAIBJVZYffbNApm7lAwBAkgEqpgAAJEkZ/DR1AAAAkEhMAQAYhFY+AMAgtPIBAGAAKqYAAINQMQUAgAFITAEAGIJWPgDAILTyAQBgACqmAAAjqPltgamYAgAwBIkpAABD0MoHABhApQx+mjoAAABIJKYAAAxCKx8AYBBa+QAAMAAVUwCAQSx6xVRiOogdO3rqEDbclV+6buoQ9oo7HnKzqUPYcCe/5sKpQ9grTt5++NQh7BVffP8fTB0Ca7Rt29ZLMrq33v+vtt472ry08gEAGIKKKQDAIBa9la9iCgDAEFRMAQBGUPPbAlMxBQBgCBJTAACGoJUPADAIg58AAGAAKqYAAAOolIrp1AEAAEAiMQUAYBBa+QAAg9DKBwCAAaiYAgCMYrELpiqmAACMQWIKAMAQtPIBAEZQBj+pmAIAMAQVUwCAQaiYAgDAACSmAAAMQSsfAGAQWvkAALCKqjqtqq6qqg8v89x/raquqkOXbDulqi6tqo9V1SPXcg6JKQAAa/GqJI/adWNVHZnk4UkuW7LtmCQnJvme+Wv+sKr2W+0EElMAgAFUKlXT3VbT3ecm+cIyT704ybOT9JJtJyR5fXd/rbs/leTSJPdZ7RwSUwAAkuTQqjp/ye2pq72gqh6T5DPd/aFdnjo8yeVLHl8x37ZbBj8BAIxi2rFP13T39rXuXFUHJ3lekkcs9/Qy23qZbd9EYgoAwJ74riR3SfKh+aUARyS5oKruk1mF9Mgl+x6R5LOrHVArHwCAdevuf+ju23X3Ud19VGbJ6D27+3NJzkxyYlUdVFV3SXJ0kvevdkwVUwCAEdTY85hW1euSHJfZtahXJHl+d79yuX27++KqemOSjyS5IcnTuvvG1c4hMQUAYFXdfdIqzx+1y+MXJnnhes4hMQUAGMTIFdN9wTWmAAAMQWIKAMAQtPIBAAahlQ8AAANQMQUAGMViF0xVTAEAGIPEFACAIWjlAwAMwuAnAAAYgIopAMAAqkrFdOoAAAAgkZgCADAIrXwAgEFo5QMAwABUTAEABqFiCgAAA5CYAgAwBK18AIBRLHYnX8UUAIAxSEwBABiCVj4AwCAWfVT+pktMO8mOHT11GBtu27at9wfxXs9609Qh7BUfe/lPTR3Chvujx33/1CHsFQftvzWbQt1b73fgjVvw93qS7L/f1vszuBUTp633jjavTZeYAgBsSbU1E//12Hr/lAMAYFOSmAIAMAStfACAAVSSBe/kq5gCADAGFVMAgCGUwU9TBwAAAInEFACAQWjlAwAMYsE7+SqmAACMQcUUAGAQBj8BAMAAJKYAAAxBKx8AYARl8JOKKQAAQ1AxBQAYQCXZtm2xS6YqpgAADEFiCgDAELTyAQAGYfATAAAMQMUUAGAQVn4CAIABSEwBABjCpIlpVR1ZVe+qqkuq6uKqeuaU8QAATGa+8tNUtxFMfY3pDUl+qbsvqKpvS/LBqjqruz8ycVwAAOxjk1ZMu/vK7r5g/vNXklyS5PApYwIAYBpTV0y/oaqOSnJskvMmDgUAYJ+rGJU/xOCnqrplkjcl+YXu/vIyzz+1qs6vqvOvuebqfR8gAAB73eQV06o6ILOk9DXd/ebl9unuU5OcmiT3vNf23ofhAQDsI6ViOuXJa/bpvzLJJd39u1PGAgDAtKZu5T8gyROSPLSqLprffmTimAAAmMCkrfzufk9m1/oCACy8Be/kT14xBQCAJAMMfgIAYMbgJwAAGIDEFACAIWjlAwCMoAx+UjEFAGAIKqYAAAOoGPykYgoAwBAkpgAADEFiCgAwiKrpbqvHVqdV1VVV9eEl2367qj5aVX9fVX9eVbdZ8twpVXVpVX2sqh65lvcvMQUAYC1eleRRu2w7K8n3dvf3Jfl4klOSpKqOSXJiku+Zv+YPq2q/1U4gMQUAGERVTXZbTXefm+QLu2z72+6+Yf7wfUmOmP98QpLXd/fXuvtTSS5Ncp/VziExBQBgIzw5yd/Mfz48yeVLnrtivm23TBcFAECSHFpV5y95fGp3n7qWF1bV85LckOQ1Ozcts1uvdhyJKQDAICaexvSa7t6+3hdV1clJHp3k+O7emXxekeTIJbsdkeSzqx1LKx8AgD1SVY9K8stJHtPd/7rkqTOTnFhVB1XVXZIcneT9qx1PxRQAgFVV1euSHJdZy/+KJM/PbBT+QUnOmg+gel93/2x3X1xVb0zykcxa/E/r7htXO4fEFABgBDX2kqTdfdIym1+5m/1fmOSF6zmHVj4AAENQMQUAGEBl8sFPk1MxBQBgCJuuYnrjjs5Xrrth9R03mVsffMDUIWy4z53++KlD2CsO+akVL6fZtK780ydOHcJesW3bgpceNpEbbtwxdQh7xf6rLsC4+ezYsepUlJvO1ntHm9emS0wBALamtS0NupVp5QMAMAQVUwCAQSx4wVTFFACAMUhMAQAYglY+AMAgDH4CAIABqJgCAIygDH5SMQUAYAgSUwAAhqCVDwAwgIrBTyqmAAAMQcUUAGAQKqYAADAAiSkAAEPQygcAGMSCd/JVTAEAGIOKKQDAIAx+AgCAAUhMAQAYglY+AMAIyuAnFVMAAIYgMQUAYAha+QAAA6iUUflTBwAAAImKKQDAMBa8YKpiCgDAGCSmAAAMQSsfAGAQ2xa8l69iCgDAEFRMAQAGseAFUxVTAADGIDEFAGAIWvkAAAOoipWfpg4AAAASFVMAgGFsW+yCqYopAABjkJgCADAErXwAgEEY/AQAAANQMQUAGMSCF0xVTAEAGIPEFACAIWjlAwAMoJJUFruXr2IKAMAQVEwBAAZh5ScAABiAxBQAgCGsu5VfVXdPcqckhyb5apKrkvxDd395g2MDAFgcVQu/8tOaEtOqemiSpyR5WGYJ6a52VNWFSc5Iclp3X7NxIQIAsAh2m5hW1Y8neWGSu2Y2i8Fnkrw1yeeSfCHJzZN8e5K7J/mBJNuT/FpV/UmSX+3uz++1yAEA2FJWTEyr6twkD0xySZJTkry+uy/bzf4HJnlIkpOTPD7JiVX1hO4+c2NDBgDYmha8k7/bium3JXnsWhPL7r4+yduTvL2qbpfkuUnudtND/Gb7b6vc+uADNvqwsGZffMNTpg5hw+3Y0VOHsFfccOOOqUPYK/bfb+uNW71xi/4ZPORhvzF1CBvui+/4b1OHsOEWPBccyoqJaXcfu6cH7e6rkvzCnr4eAGDRVJJtC14y3Xr/7AYAYFOSmAIAMITdDX761T08Znf31ruoBgBgL1vwTv5uBz+9YA+P2UkkpgAArMvuEtOH7LMoAACw8tNKT3T3u/dlIAAALDaDnwAAGMK6E9Oq+r6qelFVvbWq3rFk+1FV9biqOmRjQwQA2Pqqpr2tHl+dVlVXVdWHl2y7bVWdVVWfmN8fsuS5U6rq0qr6WFU9ci2fwboS06r69SQXJHl2kh/LN1+Hui3J6zJbjhQAgK3lVUketcu25yQ5u7uPTnL2/HGq6pgkJyb5nvlr/rCq9lvtBGtOTKvqxCS/kuSsJD+Q5LeWPt/dn0xyfpLHrPWYAAD8m21Vk91W093nJvnCLptPSHL6/OfTkzx2yfbXd/fXuvtTSS5Ncp9V3/8aP6ck+fn5QU/o7r9Pcv0y+1yS5Oh1HBMAgDEcWlXnL7k9dQ2vuX13X5kk8/vbzbcfnuTyJftdMd+2W7ubLmpX/y7Jq7p7uYR0p88muf06jgkAwBiu6e7tG3Ss5UqwvdqL1pOYVpIdq+xz+yTXreOYAADMbcJZTD9fVYd195VVdViSq+bbr0hy5JL9jsisgLlb62nlfyLJ/Vd6cn5B6wOTXLyOYwIAsHmdmeTk+c8nJ3nrku0nVtVBVXWXzC71fP9qB1tPYvrGJPesql9a4flTknx3kteu45gAAMxV1WS3NcT2uiT/J8ndquqKqnpKkhcleXhVfSLJw+eP090XZ5Y7fiTJ25I8rbtvXO0c62nl/16Sn0zyP6rqcZlfJ1BVv5Pkh5JsT/K+JKeu45gAAGwC3X3SCk8dv8L+L0zywvWcY82JaXd/taoekuQlSX4myc65qH4xs2tPX53k6d19w3oCAACAZH0V03T3tUmeWFW/mOTeSb49ybVJ3t/dV++F+AAAFkIl2bYJRz9tpHUlpjt19xeSvH2DYwEAYIHtUWJaVUcmOTbJrTOrmF7Y3Zfv/lUAALCydSWmVXV0kj9M8tBlnntnZiOuPr5BsQEALI41jo7fytacmFbVdyf535ldV/qPSd6T5HNJ7pDZ/KXHJ3lPVd2/uy/dC7ECALCFradi+luZJaXPTPKy7v7GKlBVtS3JM5K8OMlvJnncRgYJALAIFrxguq4J9o9P8tfd/QdLk9Ik6e4d3f2SJH+T5GHrDaKq9quqC6vqL9f7WgAAtob1JKYHJrlolX0uSnLAHsTxzCSX7MHrAADYItaTmH4osyVHd+e7k/z9egKoqiOS/GiSV6zndQAAW83IS5LuC+tJTH8zyY9X1Q8v92RV/WiSf591Lj2V2VKnz85s9ahlVdVTq+r8qjr/6mvM4w8AsBWtOPipqv7jMpv/JslfVtXZSc5N8vkkt0/y4MymkPqLJIeu9eRV9egkV3X3B6vquJX26+5Tk5yaJPe61/Ze6/EBADYLKz/tflT+q5LsmgTu/LgeluUHOT0myY8l+ZM1nv8BSR5TVT+S5GZJblVVr+7ux6/x9QAAbBG7S0yftLdP3t2nJDklSeYV0/8qKQUAWEwrJqbdffq+DAQAYNGNMghpKutaknRv6u5zkpwzcRgAAExkmMQUAGDRLXa9dJ2JaVXdIsl/SfLIJIcnOWiZ3bq7v2sDYgMAYIGsOTGtqtskeU+SY5J8Ocmtklyb2YpQN5/v9tkkX9/YEAEAWATrmWD/VzJLSp+S5JD5thcnuWWS+ye5IMk/JrnHRgYIALAIqpJtVZPdRrCexPQxSc7t7j/u7m/Mb9oz70vyI0nunuR5GxwjAAALYD2J6ZGZVUV32pEl15h291WZrQx14saEBgCwWKqmu41gPYnpvya5ccnja5PcYZd9Pp/ZoCgAAFiX9SSml2dWNd3pI0keVFX7Ldn2wCSf24jAAABYLOuZLurdSR5XVTW/xvQNSX4/yV9V1V8kOS7JfZO8fMOjBABYAFZ+WrvTM5sa6ojMqqf/K8lDkzw2ySPm+7w3s9H7AACwLmtOTLv7giQ/t+TxDUl+vKruleS7k3w6yQe6e8dGBwkAsAgWvGB605ck7e4PJvlgklTVD1TVnbr7zJscGQAAC2U9g5/W4plJ/nyDjwkAwAK4yRVTAABuuso4KzBNZaMrpgAAsEckpgAADEErHwBgBAMtDToVFVMAAIaw24ppVT1once7w02IBQBgoVn5affOSdLrOF6tc38AAEiyemJ6bgZLNP/1+hvz95ddO3UYG+7fHXmrqUPYcFv1X307dgz1V2JDbNu2Nb+rbdma72srusXNtuaQh3/+2623Svddn7X11tD53OVfmjoE5nb7m6C7j9tHcQAALLxFH/yz6O8fAIBBbM3eCQDAJlPZupfBrdWKFdOq+ombcuCqOqyq7ndTjgEAwOLYXSv/z6rqg1X1U1V10FoPWFV3q6oXJ7k0ycNucoQAACyE3bXyj0/y4iSvS3JtVb01yXuTnJ/kyiRfTHKzJN+e5O5J7pvkkUm2J7k+ye8n+b29FTgAwFazRSdJWbMVE9PufldVHZvkpCRPS/IfkzxhN8eqJF9K8pIkL+nuf9rAOAEA2OJWmy6qk7w2yWur6m6ZteYfmOROmVVKv5rkqiR/n9lk/O/s7q/uzYABALYqFdM16u6PJflYkpftvXAAAFhU5jEFAGAI5jEFABhAlXlMVUwBABiCiikAwCAWffCTiikAAEOQmAIAMAStfACAQSz42Ke1V0yr6ler6kGr7PNDVfWrNz0sAAAWzXpa+S9Ictwq+zwoyfP3NBgAgEVVSbZVTXYbwUZfY7p/kh0bfEwAABbARiem90pyzQYfEwCABbDbwU9V9c5dNj2xqo5bZtf9khyZ5M5JXrchkQEALJhFny5ptVH5xy35uZMcNb/takeSf07yhiTP2oC4AABYMLtNTLv7G4l7Ve1I8oLu/vW9HhUAAAtnPfOYPinJhXsrEACARTfI4PjJrDkx7e7T92YgAAAsthUT0yWT6b+/u69bbXL9pbr73JscGQDAAqmB5hOdyu4qpudkNuDpHkk+vuTxWux3k6ICAGDh7C4x/fXMEtFrdnkMAAAbbsXEtLtfsLvHAABsrNE7+VX1rCT/KbNi5T9kNjj+4MymDD0qyaeTPK67v7gnx1/0eVwBAFiDqjo8yc8n2d7d35vZpZsnJnlOkrO7++gkZ88f7xGJKQDAILbVdLc12j/Jzatq/8wqpZ9NckKSnbM3nZ7ksXv6/tc8XdQyy5MuZ0eSLye5JMmfd/f5exoYAAD71KFVtTR3O7W7T935oLs/U1W/k+SyJF9N8rfd/bdVdfvuvnK+z5VVdbs9DWA9E+wftzOuJMvl1Uu3PzbJc6rqf3X30/Y0OAAA9plrunv7Sk9W1SGZVUfvkuRLSf6sqh6/kQGsp5V/syRvyWzqqMdndoHrzef3T5hvf0uSI5I8MslFSX62qp68QbECAGxZlWTbfC7TKW5r8LAkn+ruq7v760nenOT+ST5fVYclyfz+qj39DNaTmP63JNuT/GB3v7a7L+vur83vX5PkvknuneRnu/usJI9I8sUkT9nT4AAAGMZlSe5bVQdXVSU5PrPLN89McvJ8n5OTvHVPT7CexPRnkry5u7+83JPdfW2SN2VWTU13/3OSv0nyPXsaHADAIqma7raa7j4vyRlJLshsqqhtSU5N8qIkD6+qTyR5+PzxHlnPNaZ3TPL1Vfb5epLDljy+IrNLAAAA2OS6+/lJnr/L5q9lVj29ydZTMf1Mkh+bTw/wLarqgCSPyWzagJ2+I7OLYwEAYLfWk5j+aZK7Jjmrqh5QVduSpKq2VdUDk5yV5Lvn++10/yQXb1SwAABb1oRzmK5jHtO9aj2t/N/MbPDTjyQ5N8mOqvpCkttmluBWkrfN99s5KutDmV13CgAAu7XmxLS7r0/y6Kp6QmYjrn4gs6T0y0kuTPIn3f0nS/a/MslJGxotAMAWVstOFb841lMxTZJ095/mm9v1AABwk63nGlMAANhr1l0xBQBg481Wfpo6immtmJhW1Y4kO5Ic090fnz/uNRyzu1vCCwDAuuwugTw3s0T0X3d5DAAAG27FxLS7j9vdYwAANtait/INfgIAYAjrvha0qu6c2VKjneTq7r5sw6MCAFhAVYtdMl1TxbSqDq2q362qK5N8Msl5Sd6f5FNV9dmq+u2quu3eDBQAgK1t1cS0qo5Ocn6SZya5fZIbk1yV5Or5z3dI8otJzq+q79x7oQIAsJXtNjGtqm1JXpPkTkneneRhSW7Z3Yd19x2SfFuSR2Q2Yv+oJK/eq9ECAGxRO+cxneo2gtWuMX1Eku1J3pjkpO7+pumiuvtrSd5RVWcneUOSn6iqh3f3WXsl2iQHH7hfvu9Ot95bh5/MIfd++tQhbLgvfuClU4ewV2wb5W/vBrrhxh1Th7BX7L/f1hzfucuv4i1hq15XtxV/X3z8xY+ZOoQN94D//etTh8Dcar+1fyLJ15I8Y9ekdKn5c09P8vUk/2HjwgMAWBCV1IS3EayWmN4zyXu7++rVDtTdVyV5z/w1AACwLqslpkcmuXgdx7s4yZ33PBwAABbVateY3irJl9ZxvC9lNiAKAIB12jZKT30iq1VMD8xsSqi12jF/DQAArMtaVn7aesM/AQAGs3O6qEW2lsT0BVX1gr0dCAAAi20tiel6c3cVVgAA1m23iWl3b83ZqQEABrTgY59WHfwEAAD7xFpa+QAA7HWVbeu+gnJrUTEFAGAIElMAAIaglQ8AMICKwU8qpgAADEHFFABgBGXlJxVTAACGIDEFAGAIWvkAAIPYtuCjn1RMAQAYgsQUAIAhaOUDAAzAPKYqpgAADELFFABgEAY/TayqblNVZ1TVR6vqkqq639QxAQCw741QMX1Jkrd193+oqgOTHDx1QAAA7HuTJqZVdaskD0ryxCTp7uuTXD9lTAAAU1nwTv7krfzvTHJ1kj+uqgur6hVVdYtdd6qqp1bV+VV1/tXXXL3vowQAYK+bOjHdP8k9k7y8u49N8i9JnrPrTt19andv7+7t33Hod+zrGAEA9rrKLDGb6jaCqeO4IskV3X3e/PEZmSWqAAAsmEkT0+7+XJLLq+pu803HJ/nIhCEBADCREUblPyPJa+Yj8j+Z5EkTxwMAsO9VUgs++mnyxLS7L0qyfeo4AACY1uSJKQAAM4tdL51+8BMAACSRmAIAMAitfACAAVSSbQs++EnFFACAIaiYAgAMYrHrpSqmAAAMQmIKAMAQtPIBAAax4GOfVEwBABiDiikAwBAqteAlUxVTAACGIDEFAGAIWvkAAAOoqBgu+vsHAGAQElMAANakqm5TVWdU1Uer6pKqul9V3baqzqqqT8zvD9nT40tMAQAGUVWT3dboJUne1t13T/L9SS5J8pwkZ3f30UnOnj/eIxJTAABWVVW3SvKgJK9Mku6+vru/lOSEJKfPdzs9yWP39BwSUwCAQdSEtzX4ziRXJ/njqrqwql5RVbdIcvvuvjJJ5ve329P3LzEFACBJDq2q85fcnrrL8/snuWeSl3f3sUn+JTehbb8c00UN4osfeOnUIbDAtm3RlUZ27OipQ9grtm3bet/XIQ9+3tQh7BVffPcLpw5hw11/w46pQ9hwW+8d7bFrunv7bp6/IskV3X3e/PEZmSWmn6+qw7r7yqo6LMlVexqAiikAwAhq7MFP3f25JJdX1d3mm45P8pEkZyY5eb7t5CRv3dOPQMUUAIC1ekaS11TVgUk+meRJmRU631hVT0lyWZKf3NODS0wBAAawGVZ+6u6LkizX7j9+I44/+vsHAGBBSEwBABiCVj4AwCDWsQLTlqRiCgDAEFRMAQAGsdj1UhVTAAAGITEFAGAIWvkAAINY8LFPKqYAAIxBxRQAYACzlZ8Wu2SqYgoAwBAkpgAADEErHwBgEAY/AQDAAFRMAQCGUCmDnwAAYHoSUwAAhqCVDwAwCIOfAABgABJTAACGoJUPADAAS5KqmAIAMAgVUwCAEZTBTyqmAAAMQWIKAMAQtPIBAAahlQ8AAANQMQUAGESZLgoAAKYnMQUAYAha+QAAA6gk2xa7k69iCgDAGFRMAQAGYfATAAAMQGIKAMAQtPIBAAZh5ScAABiAiikAwCAMfgIAgAFITAEAGIJWPgDAAKz8pGIKAMAgJKYAAAxBKx8AYAhlVP7UAQAAQKJiCgAwhrLyk4opAABDkJgCADAErXwAgEEseCdfxRQAgDFsuorpV667Ied87Oqpw9hwDzr60KlD2HA7uqcOYa/YtgWvTH/y6y6aOoS94o8e931Th7BXHLRtv6lD2HBXv/M3pg6BNdqxYwv+bh/kLc1Wftp6/49ZDxVTAACGIDEFAGAIm66VDwCwVS12I1/FFACAQaiYAgCMYsFLpiqmAAAMQWIKAMAQtPIBAAZRC97LVzEFAGAIKqYAAINY8IWfVEwBABiDxBQAgCFo5QMADGLBO/kqpgAAjEFiCgAwiprwttYQq/arqgur6i/nj29bVWdV1Sfm94fs6duXmAIAsB7PTHLJksfPSXJ2dx+d5Oz54z0iMQUAYE2q6ogkP5rkFUs2n5Dk9PnPpyd57J4e3+AnAIABzDrqkw5/OrSqzl/y+NTuPnWXfX4vybOTfNuSbbfv7iuTpLuvrKrb7WkAElMAAJLkmu7evtKTVfXoJFd19wer6ri9EYDEFACAtXhAksdU1Y8kuVmSW1XVq5N8vqoOm1dLD0ty1Z6ewDWmAAAjqNmSpFPdVtPdp3T3Ed19VJITk7yzux+f5MwkJ893OznJW/f0I5CYAgBwU7woycOr6hNJHj5/vEe08gEABrFZVn7q7nOSnDP/+Z+THL8Rx528YlpVz6qqi6vqw1X1uqq62dQxAQCw702amFbV4Ul+Psn27v7eJPtlds0CAAALZoRW/v5Jbl5VX09ycJLPThwPAMA0Nksvfy+ZtGLa3Z9J8jtJLktyZZJru/tvd92vqp5aVedX1fnXfvGf93WYAADsA1O38g/JbBmruyS5Y5JbVNXjd92vu0/t7u3dvf3Wh3z7vg4TAGAfqEn/G8HUg58eluRT3X11d389yZuT3H/imAAAmMDUiellSe5bVQdXVWU21cAlE8cEAMAEJh381N3nVdUZSS5IckOSC5OcOmVMAABTWcsKTFvZ5KPyu/v5SZ4/dRwAAExr8sQUAIDZTFELXjCd/BpTAABIIjEFAGAQWvkAAKNY8F6+iikAAENQMQUAGMQoKzBNRcUUAIAhSEwBABiCVj4AwCAWfeUnFVMAAIagYgoAMIgFL5iqmAIAMAaJKQAAQ9DKBwAYQWXhe/kqpgAADEFiCgDAELTyAQAGYUlSAAAYgIopAMAAKlZ+UjEFAGAIm65i+umr/2+e8rL3TB3GhvvH3//3U4ew4bZt0etkrrv+xqlD2HCv+pljpw5hr7jq2uumDmGvuN2t95s6hA339Rt76hD2iv233lcFe9WmS0wBALaqrVnSWTutfAAAhqBiCgAwigUvmaqYAgAwBIkpAABD0MoHABiElZ8AAGAAKqYAAIOw8hMAAAxAYgoAwBC08gEABrHgnXwVUwAAxqBiCgAwigUvmaqYAgAwBIkpAABD0MoHABhAxcpPKqYAAAxBYgoAwBC08gEARlCWJFUxBQBgCCqmAACDWPCCqYopAABjkJgCADAErXwAgFEseC9fxRQAgCGomAIADKGs/DR1AAAAkEhMAQAYhFY+AMAgrPwEAAADUDEFABhAZeFni1IxBQBgDBJTAACGoJUPADCKBe/lq5gCADAEiSkAwCBqwv9Wja3qyKp6V1VdUlUXV9Uz59tvW1VnVdUn5veH7On7l5gCALAWNyT5pe6+R5L7JnlaVR2T5DlJzu7uo5OcPX+8RySmAACsqruv7O4L5j9/JcklSQ5PckKS0+e7nZ7ksXt6DoOfAAAGMfHKT4dW1flLHp/a3acut2NVHZXk2CTnJbl9d1+ZzJLXqrrdngYgMQUAIEmu6e7tq+1UVbdM8qYkv9DdX64NzKYlpgAAgxh9tqiqOiCzpPQ13f3m+ebPV9Vh82rpYUmu2tPju8YUAIBV1aw0+sokl3T37y556swkJ89/PjnJW/f0HCqmAACsxQOSPCHJP1TVRfNtz03yoiRvrKqnJLksyU/u6QkkpgAAI6jJBz/tVne/JytfbXD8RpxDKx8AgCFITAEAGIJWPgDAMAbu5e8DKqYAAAxBxRQAYACVsQc/7QsqpgAADEFiCgDAELTyAQAGseCdfBVTAADGsOkqpl+/+h+v+ezLf/yf9sGpDk1yzT44T5Lk5i/fV2fakvbpd8VN5vvaPHxXm4fv6qa589QB7LTog582XWLa3d+xL85TVed39/Z9cS5uGt/V5uL72jx8V5uH74qtQisfAIAhbLqKKQDAVlULPvxJxXRlp04dAGvmu9pcfF+bh+9q8/BdsSVUd08dAwDAwvv+Y+/Vb3/3+yY7/2G3PvCDU1+rrGIKAMAQJKYAAAzB4CcAgEEs9tAnFVMAAAahYgoAMIAqKz+pmAIAMASJKQAAQ9DKBwAYhJWfAABgACqmAACjWOyCqYopAABjkJgCADAErXwAgEEseCdfxRQAgDFITAEAGIJWPgDAICxJCgAAA1AxBQAYQln5aeoAAAAgkZgCADAIrXwAgAFUDH5SMQUAYAgSUwAAhiAxBQBgCBJTAACGYPATAMAgDH4CAIABqJgCAAzCyk8AADAAiSkAAEPQygcAGEEZ/KRiCgDAEFRMAQAGUPPbIlMxBQBgCBJTAACGoJUPADCKBe/lq5gCADAEiSkAAEPQygcAGIQlSQEAYAAqpgAAg7DyEwAADEBiCgDAELTyAQAGseCdfBVTAADGoGIKADCKBS+ZqpgCADAEiSkAAEPQygcAGISVnwAAYAASUwCAAVRmKz9NdVtTjFWPqqqPVdWlVfWcjf4MJKYAAKyqqvZL8rIkP5zkmCQnVdUxG3kOiSkAAGtxnySXdvcnu/v6JK9PcsJGnsDgJwCAAVxwwQfffvMD6tAJQ7hZVZ2/5PGp3X3qkseHJ7l8yeMrkvzgRgYgMQUAGEB3P2rqGFax3JWovZEn0MoHAGAtrkhy5JLHRyT57EaeQGIKAMBafCDJ0VV1l6o6MMmJSc7cyBNo5QMAsKruvqGqnp7k7Un2S3Jad1+8keeo7g29NAAAAPaIVj4AAEOQmAIAMASJKQAAQ5CYAgAwBIkpAABDkJgCADAEiSkAAEP4/wFySaquTDiZ9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(predicted, digits['target'], normalize=False, title='Predicted Group', ylab='Digit (Label)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 12.2: Unsupervised Learning Application\n",
    "\n",
    "Describe a medical application of unsupervised learning that was not discussed in class or in our readings. Be sure to describe the goal and explain why supervised learning can't or shouldn't be used instead.\n",
    "\n",
    "--- \n",
    "\n",
    "**YOUR RESPONSE HERE**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Semi-Supervised Learning\n",
    "\n",
    "In semi-supervised learning, our goal is the same as in supervised learning: we'd to learn a function that maps inputs to a desired output based on known input-output pairs. Unlike in supervised learning, however, **the output (i.e. label) is known only for *some* of our training examples, and unknown for others**. In this situation, we could use supervised learning by training only on the examples with known labels. However, in semi-supervised learning we also use the unlabeled examples to help us understand the structure of the data, leading to better performance. Intuitively, semi-supervised learning works by identifying distinct clusters of examples such that all examples with the same label belong to a single cluster.\n",
    "\n",
    "We begin by specifying that 40 of our 1577 handwritten digits will be labeled. We'll set all other labels to -1, which tells our algorithm that the label is unknown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 1797 handwritten digits, 1757 of which are not labeled\n"
     ]
    }
   ],
   "source": [
    "n_labeled_points = 40\n",
    "n_total_samples = len(digits['target'])\n",
    "unlabeled_indices = np.random.choice(n_total_samples, n_total_samples - n_labeled_points, replace=False)\n",
    "labeled_indices = np.arange(n_total_samples)[~unlabeled_indices]\n",
    "y_train = np.copy(digits['target'])\n",
    "y_train[unlabeled_indices] = -1\n",
    "\n",
    "print('We have', n_total_samples, 'handwritten digits,', len(unlabeled_indices), 'of which are not labeled')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot a row of handwritten digits that *are* labeled, and a second row of handwritten digits that are not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=8, nrows=2, figsize=(16, 4))\n",
    "for i, ax in enumerate(axs[0]):\n",
    "    idx = np.random.choice(labeled_indices)\n",
    "    ax.imshow(digits['images'][idx])\n",
    "    ax.set_title('Label: %i' % digits['target'][idx])\n",
    "for i, ax in enumerate(axs[1]):\n",
    "    idx = np.random.choice(unlabeled_indices)\n",
    "    ax.imshow(digits['images'][idx])\n",
    "    ax.set_title('Label: unknown')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now use a semi-supervised learning algorithm called `label_propagation` to infer the unknown labels based on the known labels. Then, we'll plot the confusion matrix and calculate the accuracy of our predictions for the unknown labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 77.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.semi_supervised import LabelPropagation\n",
    "lp_model = LabelPropagation(gamma=0.25, max_iter=20)\n",
    "lp_model.fit(digits['data'], y_train)\n",
    "predicted_labels = lp_model.transduction_[unlabeled_indices]\n",
    "true_labels = digits['target'][unlabeled_indices]\n",
    "plot_confusion_matrix(predicted_labels, true_labels)\n",
    "print(\"Accuracy = %.2f\" % (100 * sum(true_labels == predicted_labels) / len(true_labels)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 12.3: Semi-Supervised Learning Application\n",
    "\n",
    "Describe a medical application of semi-supervised learning that was not discussed in class or in our readings. Be sure to describe the goal and explain why we might expect labels to be available for some inputs but not others.\n",
    "\n",
    "---\n",
    "\n",
    "**YOUR RESPONSE HERE**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Active Learning\n",
    "\n",
    "In active learning, we begin the same way we did in semi-supervised learning: our output (i.e. label) is known only for *some* of our training examples, and unknown for others. This time, however, **we'll have an opportunity to *request* the labels for additional examples of our choosing**. In active learning, we assume **we must be judicious in making these requests** because (a) we can only make a limited number of them, or (b) each request is associated with a cost. For example, we might be requesting the labels from a clinician who's on service and can't afford to spend all day labeling our data. \n",
    "\n",
    "Our goal, then, is to carefully select which labels we need most. We should pick labels for which we're most uncertain, or that we expect will improve our ability to identify the best model parameters.\n",
    "\n",
    "In the example below, we begin by training the same semi-supervised learning algorithm as before -- `label_propagation` -- with only 10 labeled points, resulting in low accuracy. We then identify the 10 examples whose labels we're most uncertain about, as measured by the entropy of our predictive distribution over the labels. After obtaining the labels for these 10 points, we retrain our model. We repeat this process 5 times while printing the confusion matrix and accuracy in each iteration. Finally, we plot the images whose labels we requested.\n",
    "\n",
    "Since we have access to all the labels in this example, \"obtaining new labels\" is simply a matter of making them available to the `label_propagation` algorithm. In a real-world problem, however, active learning would involve acquiring new labels from an outside source (e.g. an expert).\n",
    "\n",
    "An import question is whether active learning is more effective than simply requesting labels at random. Is the accuracy of active learning after iteration 3 (when we have 40 labeled examples) better than the accuracy we obtained using semi-supervised learning with 40 randomly-selected labels?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iteration 0 ______________________________________________________________________\n",
      "Label Spreading model: 10 labeled & 1787 unlabeled (1797 total)\n",
      "Confusion matrix\n",
      "[[115   0  11   0   6  23  10  16]\n",
      " [  1 141   8   0   0   2  23   1]\n",
      " [  3   2 151   0   4   1   7  14]\n",
      " [ 21   0   0 110   1  36  10   2]\n",
      " [  1   0  19   3  93   1   4  60]\n",
      " [  2   0   0  21   0 154   0   3]\n",
      " [ 22   4   0   0   1   0 149   0]\n",
      " [  0   0  47   0   2   3  14 113]]\n",
      "Accuracy = 57.41\n",
      "\n",
      "Iteration 1 ______________________________________________________________________\n",
      "Label Spreading model: 20 labeled & 1777 unlabeled (1797 total)\n",
      "Confusion matrix\n",
      "[[167   0   0   0   1   0   4   0   0   5]\n",
      " [  0 106  23   1   0   6   9   7   2  27]\n",
      " [  0   1 158   1   0   0   2  12   0   0]\n",
      " [  0   1   2 130   0   4   0   6   4  35]\n",
      " [  3  20   0   0 116   0  33   4   3   0]\n",
      " [  0   0   0   0   2 165   3   0   0   9]\n",
      " [  0   2   0   0   0   1 175   0   0   1]\n",
      " [  0  15   1   0   5   2   0 135  18   0]\n",
      " [  0  33   6   2   0   5  14  12  57  44]\n",
      " [  0   0   0   3  12  12   1   2   2 145]]\n",
      "Accuracy = 76.20\n",
      "\n",
      "Iteration 2 ______________________________________________________________________\n",
      "Label Spreading model: 30 labeled & 1767 unlabeled (1797 total)\n",
      "Confusion matrix\n",
      "[[174   0   0   0   1   0   0   0   0   0]\n",
      " [  0 141  22   0   0   0   4   0  10   1]\n",
      " [  1   1 155   1   0   0   0   4  12   0]\n",
      " [  0   1   1 149   0   4   0   4   6  15]\n",
      " [  3  42   0   0 112   0  18   1   3   0]\n",
      " [  0   0   0   1   2 165   3   0   0   8]\n",
      " [  0   1   0   0   0   1 175   0   1   1]\n",
      " [  0  14   1   0   0   0   0 141  19   0]\n",
      " [  0  11   1  15   0   3   4   5 105  28]\n",
      " [  1  13   0   7   8   4   0   8   2 133]]\n",
      "Accuracy = 82.06\n",
      "\n",
      "Iteration 3 ______________________________________________________________________\n",
      "Label Spreading model: 40 labeled & 1757 unlabeled (1797 total)\n",
      "Confusion matrix\n",
      "[[174   0   0   0   1   0   0   0   0   0]\n",
      " [  0 147  21   1   0   0   4   0   4   0]\n",
      " [  1   5 155   1   0   0   0   3   9   0]\n",
      " [  0   1   0 153   0   3   0   3  11   8]\n",
      " [  3  41   0   0 109   0  14   1   4   7]\n",
      " [  0   0   0   1   0 153   1   0  16   7]\n",
      " [  0   1   0   0   0   3 174   0   1   0]\n",
      " [  0  14   1   0   0   0   0 123  33   4]\n",
      " [  0  10   1  10   0   1   1   4 131   9]\n",
      " [  1   3   0   6   8   1   0   3   5 147]]\n",
      "Accuracy = 83.44\n",
      "\n",
      "Iteration 4 ______________________________________________________________________\n",
      "Label Spreading model: 50 labeled & 1747 unlabeled (1797 total)\n",
      "Confusion matrix\n",
      "[[174   0   0   0   1   0   0   0   0   0]\n",
      " [  0 147  21   1   0   0   4   0   4   0]\n",
      " [  1   5 155   1   0   0   0   3   9   0]\n",
      " [  0   1   0 150   0   2   0   3  15   6]\n",
      " [  3  40   0   0 104   0  13  12   4   3]\n",
      " [  0   0   0   1   0 145   1   0  17  14]\n",
      " [  0   1   0   0   0   3 174   0   1   0]\n",
      " [  0   0   0   0   0   0   0 168   3   1]\n",
      " [  0   8   1   9   0   0   1   1 145   0]\n",
      " [  1   2   0   9   5   0   0   1  11 142]]\n",
      "Accuracy = 86.09\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 50 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "n_labeled_points = 10\n",
    "n_total_samples = len(digits['target'])\n",
    "n_iterations = 5\n",
    "\n",
    "unlabeled_indices = np.random.choice(n_total_samples, n_total_samples - n_labeled_points, replace=False)\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_figheight(8)\n",
    "fig.set_figwidth(16)\n",
    "\n",
    "for i in range(n_iterations):\n",
    "\n",
    "    y_train = np.copy(digits['target'])\n",
    "    y_train[unlabeled_indices] = -1\n",
    "\n",
    "    lp_model = LabelPropagation(gamma=0.25, max_iter=20)\n",
    "    lp_model.fit(digits['data'], y_train)\n",
    "\n",
    "    predicted_labels = lp_model.transduction_[unlabeled_indices]\n",
    "    true_labels = digits['target'][unlabeled_indices]\n",
    "\n",
    "    cm = confusion_matrix(true_labels, predicted_labels, labels=lp_model.classes_)\n",
    "\n",
    "    print(\"\\nIteration %i %s\" % (i, 70 * \"_\"))\n",
    "    print(\"Label Spreading model: %d labeled & %d unlabeled (%d total)\"\n",
    "          % (n_labeled_points, n_total_samples - n_labeled_points,\n",
    "             n_total_samples))\n",
    "    print(\"Confusion matrix\")\n",
    "    print(cm)\n",
    "    print(\"Accuracy = %.2f\" % (100 * sum(true_labels == predicted_labels) / len(true_labels)))\n",
    "\n",
    "    # compute the entropies of transduced label distributions\n",
    "    pred_entropies = stats.distributions.entropy(\n",
    "        lp_model.label_distributions_.T)\n",
    "\n",
    "    # select up to 5 digit examples that the classifier is most uncertain about\n",
    "    uncertainty_index = np.argsort(pred_entropies)[::-1]\n",
    "    uncertainty_index = uncertainty_index[\n",
    "        np.in1d(uncertainty_index, unlabeled_indices)][:10]\n",
    "\n",
    "    # keep track of indices that we get labels for\n",
    "    delete_indices = np.array([], dtype=int)\n",
    "\n",
    "    # for more than 5 iterations, visualize the gain only on the first 5\n",
    "    if i < 5:\n",
    "        fig.text(.05, (1 - (i + 1) * .183),\n",
    "               \"model %d\\n\\nfit with\\n%d labels\" %\n",
    "               ((i + 1), i * 10 + 10), size=10)\n",
    "    for index, image_index in enumerate(uncertainty_index):\n",
    "        image = digits['images'][image_index]\n",
    "\n",
    "        # for more than 5 iterations, visualize the gain only on the first 5\n",
    "        if i < 5:\n",
    "            sub = fig.add_subplot(5, 10, index + 1 + (10 * i))\n",
    "            sub.imshow(image)\n",
    "            sub.set_title(\"predict: %i\\ntrue: %i\" % (\n",
    "                lp_model.transduction_[image_index], digits['target'][image_index]), size=10)\n",
    "            sub.axis('off')\n",
    "\n",
    "        # labeling 5 points, remote from labeled set\n",
    "        delete_index, = np.where(unlabeled_indices == image_index)\n",
    "        delete_indices = np.concatenate((delete_indices, delete_index))\n",
    "\n",
    "    unlabeled_indices = np.delete(unlabeled_indices, delete_indices)\n",
    "    n_labeled_points += len(uncertainty_index)\n",
    "\n",
    "fig.suptitle(\"Active learning with Label Propagation.\\nRows show the 10 most \"\n",
    "           \"uncertain labels to learn with the next model.\", y=1.02)\n",
    "plt.subplots_adjust(left=0.2, bottom=0.03, right=0.9, top=0.9, wspace=0.2, hspace=0.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 12.4: Active Learning Application\n",
    "\n",
    "Describe a medical application of active learning that was not discussed in class or in our readings. Be sure to describe why the active learning paradigm is a good fit for this application. For example, you might describe why it's expensive or difficult to acquire labels.\n",
    "\n",
    "--- \n",
    "\n",
    "**YOUR RESPONSE HERE**"
   ]
  }
 ],
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