Solutions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OpenCV - used for image loading and processing\n",
    "import cv2\n",
    "# Numpy - used for calculations\n",
    "import numpy as np\n",
    "# Glob - for converting file lists to arrays\n",
    "import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating a dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate a file list using glob\n",
    "\n",
    "With glob you can pass paths to datasets and use wildcards to return a list of all the files into a useful array. Make an array containing the file names of all the galaxy images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = glob.glob('datasets/barred_galaxies/*.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read an image using OpenCV2\n",
    "\n",
    "To start this task you will need to load images into python arrays. One way of doing this is using OpenCV. Here is a link to the OpenCV introduction to image manipulation:\n",
    "\n",
    "https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_gui/py_image_display/py_image_display.html\n",
    "\n",
    "Load one of the images from the file name array you just created (in greyscale)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.imread(files[0], 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the image using matplotlib\n",
    "\n",
    "We can use OpenCV to plot images, however, it can be rather cumbersome for our needs. I would suggest using matplotlib to plot the image. Use matplotlib to display the image you have loaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 799.5, 799.5, -0.5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.imshow(image, cmap='Greys')\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log the flux\n",
    "\n",
    "In astronomy we often rescale the flux of an image to show details that would normally be washed out by the high intensity regions. One popular way to do this is to instead take the logarithm of the flux in each pixel. Rescale the image by taking the logarithm of each pixel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 799.5, 799.5, -0.5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = np.log10(image+1.).astype(float)\n",
    "\n",
    "plt.imshow(image, cmap='Greys')\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply a gaussian blur\n",
    "\n",
    "You might notice that this image has some background flux. To counteract this we can apply a random gaussian blur to distribute the flux between pixels more evenly. Apply a gaussian blur to the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 799.5, 799.5, -0.5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = cv2.GaussianBlur(image, (3, 3), 0)\n",
    "\n",
    "plt.imshow(image, cmap='Greys')\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Any further preprocessing\n",
    "\n",
    "Consider any further preprocessing steps you could make to increase the usefulness of the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some ideas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine the previous steps into a single function\n",
    "\n",
    "Now you have the makings of a preprocessing pipeline. Combine the previous steps into a single function that takes a file name as an input and returns a log scaled and blurred image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 799.5, 799.5, -0.5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def load_pre_pro(f_name):\n",
    "    image = cv2.imread(f_name, 0)\n",
    "    image = np.log10(image+1.).astype(float)\n",
    "    image = cv2.GaussianBlur(image, (3, 3), 0)\n",
    "    return image\n",
    "\n",
    "plt.imshow(load_pre_pro(files[1]), cmap='Greys')\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a fuction to calculate features\n",
    "\n",
    "You can now load and preprocess images. The next step is to consider possible features you might want to extract from the images. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Circular mask\n",
    "\n",
    "Before we can do anything we need to create a mask that we can use for selecting groups of pixels. Create a function that takes a shape array and a radius and returns a boolean array of the given shape with indexes inside the radius equal to True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False,  True, False, False],\n",
       "       [False,  True,  True,  True, False],\n",
       "       [ True,  True,  True,  True,  True],\n",
       "       [False,  True,  True,  True, False],\n",
       "       [False, False,  True, False, False]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def createCircularMask(shape, r):\n",
    "    \"\"\"Create a circular mask used for calcuating G inside the mask\"\"\"\n",
    "    Y, X = np.ogrid[:shape[0], :shape[1]]\n",
    "    center = [int(shape[0]/2), int(shape[1]/2)]\n",
    "    dist_from_center = np.sqrt((X - center[0])**2 + (Y-center[1])**2)\n",
    "    mask = dist_from_center <= r\n",
    "    return mask\n",
    "\n",
    "createCircularMask([5,5], 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gini coefficient\n",
    "\n",
    "As mentioned an interesting variable to consider is the Gini coefficient. Create a function that takes an array as an input and returns the Gini coefficient of the array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18064487872229434"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def gini(array):\n",
    "    \"\"\"Calculate the Gini coefficient of a numpy array\"\"\"\n",
    "    array = array.flatten()\n",
    "    if np.amin(array) < 0:\n",
    "        array -= np.amin(array)\n",
    "    array += 0.0000001\n",
    "    array = np.sort(array)\n",
    "    index = np.arange(1,array.shape[0]+1)\n",
    "    n = array.shape[0]\n",
    "    return ((np.sum((2 * index - n  - 1) * array)) / (n * np.sum(array)))\n",
    "\n",
    "gini(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gini profile\n",
    "\n",
    "You can now calculate the Gini coefficient for a given array. Now using the circlar mask, calculate the Gini coefficient at different radii and produce a plot of radius versus Gini coefficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Gini')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ol4hIYB56dQPFuyv5w+cmkqZrVbRIS9csvgKMdvddQYYREQnaxp37ufeldVwwtg9njNLxWS3V0krdRPToahGRNsvdufWZ5WSkpvDDi8aFHadNaemaRRHwDzObC1QfHOnudwWSSkQkAE8tKeG1wl3ccfE4+ubqrLKHo6VlURz7yYj9iIi0KTsrqvnxc6spGNydqybmhx2nzWlRWbj7D4MOIiISpDv+tor91XX83yeO0/mfjkC8g/LucfebzexZ/vMkgLj7RU3MJiKSVF5Zs4O/vr2Fr5wzkpF9dNjYkYi3ZvFo7PbnDcYdLA1Vs4gkvf3VdXz36RUMz+vMl3Sk9hGLVxYDzWyyu98PYGZvAXlEC+PbQYcTETlad7+4ls17D/Cn608hM00XNTpS8b46+y0+fKbYDKKnCj8TuD6gTCIireLdkr08/NoGrpyUz8lDdPrxoxFvzSLD3Rte7e7V2IF5u2KnDhcRSUq19RFu+fNyeuVk8m2dfvyoxSuL7g3vuPuNDe7q0EcRSVoPv7qBVVvL+fVVJ5LbSScKPFrxNkMtNLPrGo80sy8AbwUTSUTk6BTvquTul9Zy3pg+TBnXN+w47UK8NYuvAs+Y2ZXA0ti4k4BM4OIgg4mIHAl353+eXk5aSgp3TBuHmb642RriXc9iB/ARMzsbGBsbPdfdXw48mYjIEfjL0s28WriTO6aN1Sk9WlFLj+B+GVBBiEhS21VRzY/mruLE/G5cNWlw2HHalUBP5G5mU8xsjZkVmtktTTyeaWazY48vNLMhDR473szeMLOVZrbczPQRQUQO6UdzV1NRXcdPPnm8TunRygIri9iV9e4HpgJjgCvMbEyjya4F9rj7COBu4M7YvGnAY8D17j6W6HEdtUFlFZG2b8HaUp5etpkvfnQ4o3RKj1YX5JrFRKDQ3YvcvQaYBUxrNM004JHY8FPAORbdG3U+8K67vwPg7rvcvT7ArCLShlXW1HHr08sZlteZL501Iuw47VKQZTGA6EWTDiqJjWtyGnevI3qBpZ7AKMDNbJ6ZLTWzbwWYU0TauHteWkfJngP83yXHkZWuU3oEoaXXszgSTW0wbHzm2uamSQNOA04GKoH5ZrbE3ed/aGazGcAMgPx8nZ9epCNaWLSLh17dwBUTBzFpWM+w47RbQa5ZlACDGtwfCGxpbprYfopcYHds/AJ33+nulcBzwImNn8DdZ7p7gbsX5OXpgHKRjmZ7eRU3PLGM/B7ZfOfCY8OO064FWRaLgJFmNtTMMoDpfPikhMTuXx0bvhR42d0dmAccb2bZsRL5KLAqwKwi0sbU1EX40uNL2V9dxwOfPomuWTqlR5AC2wzl7nVmdiPRN/5U4GF3X2lmtwOL3X0O8BDwqJkVEl2jmB6bd4+Z3UW0cBx4zt3nBpVVRNqe/31uNUve38Mvr5jA6L769lPQgtxngbs/R3QTUsNxtzUYrgIua2bex4h+fVZE5EOeXlbC71/fyOdOHcpFJ/QPO06HEOhBeSIirW3VlnK+85flTBzag+9cqFOPJ4rKQkTajLLKWq5/bAlds9K578oJpKfqLSxRAt0MJSLSWiIR52tPvs2WvQeY/YXJ9O6iMwAlkmpZRNqE+14pZP57O/jef43hpMG6RGqiqSxEJOm9smYHd7+0lksmDOCzp+hssmFQWYhIUiveVcnNs95mdJ8u/O8lx+liRiFRWYhI0qqqref6x5bg7jz4mZPolKHzPoVFO7hFJCm5O7c+vYJVW8t5+JoCBvfsHHakDk1rFiKSlB5fWMyfl5bw5XNGcvYxfcKO0+GpLEQk6Swt3sMPn13JmaPzuPmckWHHEVQWIpJkdlZU86XHltI3N4t7Lh+vy6MmCe2zEJGksauimuv+sJg9lTX8+YsfoVt2RtiRJEZlISJJYc22fVz7yCJK91Vz7/QJjBuQG3YkaUBlISKhe/m97dz0xDI6Z6Yx+wunMH5Qt7AjSSMqCxEJjbvz239t4H+fX83Y/l35zWcL6JfbKexY0gSVhYiEoqYuwnefWc6Ti0u48Li+/PyyE8jO0FtSstJfRkQSbvf+Gq5/bAlvbdjNl88ewc3njtK3npKcykJEEmrt9uiO7O3l1dw7fTzTxg8IO5K0gMpCRBLmlfd2cNMfl9EpI5XZMyYzIb972JGkhQI9KM/MppjZGjMrNLNbmng808xmxx5faGZDGj2eb2YVZvaNIHOKSLCiO7KLuPaRReT3yOavN5yqomhjAluzMLNU4H7gPKAEWGRmc9x9VYPJrgX2uPsIM5sO3Alc3uDxu4Hng8ooIsGrqYvwvWdWMHvxJqaM7ctdl2tHdlsU5F9sIlDo7kUAZjYLmAY0LItpwA9iw08B95mZubub2cVAEbA/wIwiEqAtew9w8+y3eWvDbm48awRfO087stuqIMtiALCpwf0SYFJz07h7nZmVAT3N7ADwbaJrJdoEJdKGRCLOa+t38ugb7zP/vR2kpph2ZLcDQZZFUx8fvIXT/BC4290rDnVVLDObAcwAyM/PP8KYIi1TWx9h3fYKSvZUUlvv1NTXU1vnVNdHqK2LUNPgtqY+Qk1dhNr6CLV1TqeMVHrlZNAzJ5OenaO3vXIy6JWTSXZGaru4+tveyhqeWlLC4wuL2bBzPz06Z3Dd6cO4alI+g3pkhx1PjlKQZVECDGpwfyCwpZlpSswsDcgFdhNdA7nUzH4KdAMiZlbl7vc1nNndZwIzAQoKChoXkcgRq62PsHb7PlZsLmP55jKWby5n9dZyauoicec1g4zUlOhPWgppqUZlTT37quqanD4rPYWenTM/KJODJTKkZ2eG5XVmeF4O3Tsn7wn13tm0l0fffJ9n39lCdV2EkwZ35yvnjGTqcX3JTNOV7dqLIMtiETDSzIYCm4HpwJWNppkDXA28AVwKvOzuDpx+cAIz+wFQ0bgoRFpLTd2Hi2HF5jJWb9v3QTF0yUxj7ICuXH3KYMYNyGVYrxwy06NlkJ6W8qFiSE81UlOsyTWFqtp6du+vYVdFDTv3V7OrooZdFdXs2l/DzopqdlbUsL28ilVbytlZUU1d5N+ff7pnpzM8L+eD8hiWl8PwvM4M6pFNemrirzRwoKaeZ9/ZwqNvvs/yzWVkZ6TyyZMG8ulJgxnTv2vC80jwAiuL2D6IG4F5QCrwsLuvNLPbgcXuPgd4CHjUzAqJrlFMDyqPSEN7K2v489LNzHlnC6u3lFNTHyuGrDTG9c/lmo8MYdyAXI4bkMvgHtmtslM2Kz2V/t060b9b/HMf1Ueckj2VFJXuZ31pBetjty+/V8qTi0s+mC4txRjcM5theTkM7dWZvl2z6JebRZ/c6G1eTiZprVAm7k5VbYTi3ZXMXrSJp5ZsoryqjlF9crh92lgumTCALlnpR/08krws+kG+7SsoKPDFixeHHUOSmLuz5P09PLGwmL8t30pNXYQTBuYyeVjPD4ohv5WKIUhlB2opihVI9LaCotL9vL+78j82k6UY5HXJpG9uJ/p2zaRfbif65mbRt2sWfXOzANhbWcveyhr2HqhlT2UNZZXR2+j4WvYeqGFPZe0HvzstxZgyri+fmTyYiUN7tIv9LR2ZmS1x94J40+nLztLulVXW8vSyEp54q5i12yvIyUzjUwUDuXJi29xkktspnQn53f/joDZ3Z09lLVvLDrC9vIqtZVVsO/hTXkVR6X5eL9zFvuqm951AdF9Lt+z02E8Gg3tmc0J2Lt2zM8jNTqdX50zOPCaP3l2ygv5nSpJRWUi75O4sLd7LEwuLmbt8C1W10bWIn3ziOD5+Qn86Z7a/l76Z0aNzBj06ZzC2f/MXDqqorvugRMz4oBi6Z6fTKb19fDNLWl/7+x8jHVp5VS3PLNvMEwuLeW/bPjpnpHLJhIFcNSlfV16LyclMY0TvHEb0zgk7irQhKgtpF3aUV3HP/HU8vXQzB2rrGdu/Kz++ZBzTxg8gpx2uRYgkmv4XSZt2oKae3/yriAcWrKe2PsInJgzkykn5HD8wV5tTRFqRykLapEjEeXrZZn42bw3byqu48Li+fHvKMQzu2TnsaCLtkspC2pw3i3bxo7mrWLG5nOMH5vKrKydw8pAeYccSaddUFtJmbNi5n588v5p5K7fTLzeLey4fz0Un9E/64yJE2gOVhSS9vZU1/HJ+IY++uZGM1BS+cf4orj1tGJ0ydN4hkURRWUjSqqmL8Nib73Pv/HXsq6rl8pMH8dXzRumAMJEQqCwkKc1fvZ0fzV3Nhp37OW1EL2792LEc26/tHW0t0l6oLCSpbNpdyQ+fXclLq3cwPK8zv7vmZM4cnaevwYqETGUhSaGqtp6Z/yzi/lcKSU0x/ufCY/jvU4eGcvptEflPKgsJ3YK1pXz/ryvYuKuSjx3fj+9+7Fj65cY/jbeIJI7KQkKzee8B7nh2FX9fuY1hvTrz6LUTOX1kXtixRKQJKgtJuJq6CL99tYhfzS/Ecb55wWg+f/pQXYJTJImpLCShXi/cyff+uoL1pfs5f0wfbvv4GAZ2zw47lojEobKQhNhWVsWPn1vNs+9sYXDPbH53zcmcdUzvsGOJSAupLCRQ60srePzNYmYvKqY24tx87kiu/+hwstK1yUmkLQm0LMxsCnAvkAr81t1/0ujxTOAPwEnALuByd99oZucBPwEygBrgm+7+cpBZpfXU1kd4adV2Hlv4Pq8V7iI91Zgyrh/fOH+Uzgor0kYFVhZmlgrcD5wHlACLzGyOu69qMNm1wB53H2Fm04E7gcuBncDH3X2LmY0D5gEDgsoqrWNbWRV/fKuYWYuK2V5ezYBunfjmBaP5VMEg8rpkhh1PRI5CkGsWE4FCdy8CMLNZwDSgYVlMA34QG34KuM/MzN2XNZhmJZBlZpnuXh1gXjkC7s7r63fx6Bvv8+Lq7UTcOWNkHj++eDBnHdObVJ0RVqRdCLIsBgCbGtwvASY1N42715lZGdCT6JrFQZ8EljVVFGY2A5gBkJ+f33rJJa6yylqeWlrC42++T9HO/XTPTufzpw/lqomDye+pbzeJtDdBlkVTHyn9cKYxs7FEN02d39QTuPtMYCZAQUFB498traCuPsK28io27T7Apj2VlOw5QFFpBS+t3k5VbYQT87tx9+UnMHVcP+20FmnHgiyLEmBQg/sDgS3NTFNiZmlALrAbwMwGAk8Dn3X39QHmTEr1EWfF5jLKq2pxh4h7tEUPDnu0VQ8OgxOJ1WV6agrpqRa7bXo4Iy2FtBQjPS2Fyup6SvZUsmlPJZt2H4gO7z5Ayd5Ktu6toi7y7x5OMeiX24lLJgzk05PzGds/N/ELR0QSLsiyWASMNLOhwGZgOnBlo2nmAFcDbwCXAi+7u5tZN2Au8B13fy3AjEmltj7CwqLdPL9iKy+s2k7pvnB20fTuksnA7p04Mb87A0/oxKDu2Qzqkc3A7p3ol9uJjDSd3E+kowmsLGL7IG4k+k2mVOBhd19pZrcDi919DvAQ8KiZFRJdo5gem/1GYATwPTP7Xmzc+e6+I6i8Yamuq+fVdTt5fsU2Xlq9nb2VtXRKT+WsY/K4YGxf+nfrhAFmhll0u11KbDgldtrug8MHz+JdV+/U1EeorYtQF/n3cG29UxeJUBMbrq2PUFsfITM9lUHdOzGwe7QQtDlJRBoz9/axqb+goMAXL14cdowWqaypY8GaUp5fsY2X39tBRXUdXbLSOPfYPlwwti8fHZWnS4aKSEKY2RJ3L4g3nY7gTpCK6jpeWrWd51dsZcHaUqpqI3TPTudjx/VjynF9OXV4L23eEZGkpbIIWCTi/GnJJn769zXs2l9D7y6ZfKpgEFPG9WXikB6k6eI+ItIGqCwCtLR4Dz+Ys5J3S8o4aXB3/t9VJ3LykB6k6EA1EWljVBYB2LGvijufX8Ofl5bQu0sm91w+nmnj++s60iLSZqksWlFNXYRHXt/IvfPXUV1Xz/UfHc6NZ48gJ1OLWUTaNr2LtZJ/ri3lB8+upKh0P2eNzuO2j49laC+dYVVE2geVxVEq3lXJHXNX8eKq7Qzpmc3D1xRw9jF9wo4lItKqVBZHqLKmjl//Yz0P/rOItBTj21OO4XOnDdF1pEWkXVJZHIEl7+/hpieWsqWsiovH9+eWqcfSNzcr7FgiIoFRWRympcV7uPrht+guCrWNAAAIS0lEQVSZk8Gfrj+Fk4f0CDuSiEjgVBaH4Z1Ne7n6oWhRzJ5xitYmRKTD0OHDLbS8pIzPPLSQbp3T+eN1k1UUItKhqCxaYOWWMj790EK6ZEWLon+3TmFHEhFJKJVFHKu3lvPp3y6kc0Yqs2ZMZmB3XTJURDoelcUhrNm2j6t+u5Cs9FT+OGMyg3qoKESkY1JZNKNwxz6u+u2bpKcaT1w3mcE9dTS2iHRcKosmrC+t4IrfLMQsWhQ6bYeIdHQqi0Y27NzPFTPfxN3543WTGJ6XE3YkEZHQBVoWZjbFzNaYWaGZ3dLE45lmNjv2+EIzG9Lgse/Exq8xswuCzHnQ+7uiRVEXcZ64bjIjendJxNOKiCS9wMrCzFKB+4GpwBjgCjMb02iya4E97j4CuBu4MzbvGGA6MBaYAvy/2O8LzKbdlVwx802q6+p5/POTGNVHRSEiclCQaxYTgUJ3L3L3GmAWMK3RNNOAR2LDTwHnWPQKQdOAWe5e7e4bgMLY7wtEyZ5KrvjNm+yvqeexz0/i2H5dg3oqEZE2KciyGABsanC/JDauyWncvQ4oA3q2cN5Wsa2siit/s5DyA7U8/vlJjO2fG8TTiIi0aUGeG6qpa4h6C6dpybyY2QxgBkB+fv7h5gMgJyuNkb1z+PI5Ixk3QEUhItKUINcsSoBBDe4PBLY0N42ZpQG5wO4Wzou7z3T3AncvyMvLO6KQOZlpPHTNyZwwqNsRzS8i0hEEWRaLgJFmNtTMMojusJ7TaJo5wNWx4UuBl93dY+Onx74tNRQYCbwVYFYRETmEwDZDuXudmd0IzANSgYfdfaWZ3Q4sdvc5wEPAo2ZWSHSNYnps3pVm9iSwCqgDbnD3+qCyiojIoVn0g3zbV1BQ4IsXLw47hohIm2JmS9y9IN50OoJbRETiUlmIiEhcKgsREYlLZSEiInGpLEREJK52820oMysF3j+KX9EL2NlKcVqTch0e5To8ynV42mOuwe4e96jmdlMWR8vMFrfk62OJplyHR7kOj3Idno6cS5uhREQkLpWFiIjEpbL4t5lhB2iGch0e5To8ynV4Omwu7bMQEZG4tGYhIiJxdfiyMLMpZrbGzArN7JYQcwwys1fMbLWZrTSzr8TG/8DMNpvZ27GfC0PIttHMlseef3FsXA8ze9HM1sVuuyc40+gGy+RtMys3s5vDWl5m9rCZ7TCzFQ3GNbmMLOqXsdfcu2Z2YgIz/czM3os979Nm1i02foiZHWiw3B4IIlOcbM3+7czsO7HltcbMLkhwrtkNMm00s7dj4xOyzA7x3pDY15e7d9gfoqdOXw8MAzKAd4AxIWXpB5wYG+4CrAXGAD8AvhHyctoI9Go07qfALbHhW4A7Q/47bgMGh7W8gDOAE4EV8ZYRcCHwPNErQk4GFiYw0/lAWmz4zgaZhjScLqTl1eTfLvb/4B0gExga+z+bmqhcjR7/BXBbIpfZId4bEvr66uhrFhOBQncvcvcaYBYwLYwg7r7V3ZfGhvcBqwnouuOtZBrwSGz4EeDiELOcA6x396M5KPOouPs/iV6TpaHmltE04A8e9SbQzcz6JSKTu7/g0evdA7xJ9CqUCdfM8mrONGCWu1e7+wagkOj/3YTmMjMDPgX8MYjnPkSm5t4bEvr66uhlMQDY1OB+CUnwBm1mQ4AJwMLYqBtjq5MPJ3pzT4wDL5jZEote9xygj7tvheiLGegdQq6DpvPh/8BhL6+DmltGyfK6+xzRT6AHDTWzZWa2wMxODyEPNP23S5bldTqw3d3XNRiX0GXW6L0hoa+vjl4W1sS4UL8eZmY5wJ+Bm929HPg1MBwYD2wluhqcaKe6+4nAVOAGMzsjhAxNsugley8C/hQblQzLK57QX3dmdivRq1A+Hhu1Fch39wnA14AnzKxrIjPR/N8u9OUVcwUf/lCS0GXWxHtDs5M2Me6ol1dHL4sSYFCD+wOBLSFlwczSib4YHnf3vwC4+3Z3r3f3CPAbAlr9PhR33xK73QE8Hcuw/eCqbex2R6JzxUwFlrr79ljG0JdXA80to1Bfd2Z2NfBfwFUe28gd28SzKza8hOh+gVGJyhR73ub+dqH/PzWzNOATwOyD4xK5zJp6byDBr6+OXhaLgJFmNjT2CXU6MCeMILHtoQ8Bq939rgbjG25rvARY0XjegHN1NrMuB4eJ7iBdQXQ5XR2b7Grgr4nM1cCHPu2FvbwaaW4ZzQE+G/vWymSg7ODmhKCZ2RTg28BF7l7ZYHyemaXGhocBI4GiRGRqkKG5v90cYLqZZZrZ0Fi2txKZDTgXeM/dSw6OSNQya+69gUS/voLek5/sP0S/ObCW6KeCW0PMcRrRVcV3gbdjPxcCjwLLY+PnAP0SnGsY0W+ivAOsPLiMgJ7AfGBd7LZHCMssG9gF5DYYF8ryIlpYW4Faop/srm1uGRHdTHB/7DW3HChIYKZCotuzD77GHohN+8nY3/cdYCnw8RCWV7N/O+DW2PJaA0xNZK7Y+N8D1zeaNiHL7BDvDQl9fekIbhERiaujb4YSEZEWUFmIiEhcKgsREYlLZSEiInGpLEREJK60sAOItFdmVk/0q4tpwAbgM+6+N9xUIkdGaxYiwTng7uPdfRzRk9PdEHYgkSOlshBJjDdIgpNUihwplYVIwGKnhDiHkE4lI9IaVBYiwekUu6raLqAH8GLIeUSOmMpCJDgH3H080Sv4ZaB9FtKG6dxQIgExswp3z4kNTyB6VtDh7l4bbjKRw6c1C5EEcPdlRM9OOj3sLCJHQmsWIiISl9YsREQkLpWFiIjEpbIQEZG4VBYiIhKXykJEROJSWYiISFwqCxERiUtlISIicf1/e9Hi1lbbkckAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def giniProf(image, max_R=200, nbins=25):\n",
    "    ginis = np.zeros((nbins))\n",
    "    for i in range(0,nbins):\n",
    "        ginis[i] = gini(np.array(image[createCircularMask(np.shape(image), r=i*(max_R/nbins))], dtype='float'))\n",
    "    return ginis\n",
    "        \n",
    "plt.plot(np.linspace(0,200,25), giniProf(image, 200, 25))\n",
    "plt.xlabel('R')\n",
    "plt.ylabel('Gini')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gini gradient\n",
    "\n",
    "The next thing to consider is the derivative of this profile. Calculate the gradient of the profile you just produced and display it using matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'d/d$R$ Gini')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gini_grad = np.gradient(giniProf(image))\n",
    "\n",
    "plt.plot(np.linspace(0,200,25), gini_grad)\n",
    "plt.xlabel('R')\n",
    "plt.ylabel('d/d$R$ Gini')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating features\n",
    "\n",
    "Using the Gini profiles you have just created create a function that takes a file name as an input and outputs some features of the Gini profle. For example: the minimum values of the Gini gradient, the radius of the mask at this point, the maximum gini coeffient, the radius at this point. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300.0, 0.18219775622288198, 0.18684647457765005, 264.0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def gini_grab(f_name, nbins=25, max_R=300):\n",
    "    \"\"\"Return some interesting values of an image\"\"\"\n",
    "    image = load_pre_pro(f_name)\n",
    "    shape = np.shape(image)\n",
    "    R = np.linspace(max_R/nbins,max_R,nbins)\n",
    "    ginis = giniProf(image, max_R, nbins)\n",
    "    gini_grad = np.gradient(ginis)\n",
    "    return R[gini_grad.argmin()], ginis[gini_grad.argmin()], np.max(ginis), R[np.argmax(ginis)]\n",
    "    \n",
    "gini_grab(files[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate a feature dataset\n",
    "\n",
    "Now modify the function to write the values to a file and loop over all images in the dataset. Make sure you include the file name as one of your outputs (you will need this!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gini_grab(f_name, nbins=25, verbose=False, max_R=300):\n",
    "    \"\"\"Return some interesting values of an image\"\"\"\n",
    "    if verbose: print('Reading image...')\n",
    "    image = load_pre_pro(f_name)\n",
    "    shape = np.shape(image)\n",
    "    R = np.linspace(max_R/nbins,max_R,nbins)\n",
    "    if verbose: print 'Calculating ginis...'\n",
    "    ginis = giniProf(image, max_R, nbins)\n",
    "    if verbose: print 'Computing gradient...'\n",
    "    gini_grad = np.gradient(ginis)\n",
    "    if verbose: print 'Writing to file...'\n",
    "    f = open(outfile,'a')\n",
    "    f.write('%s,%s,%s,%s,%s\\n'%(f_name,                    # file name\n",
    "                                R[gini_grad.argmin()],     # radius at minimum gini\n",
    "                                ginis[gini_grad.argmin()], # gini at minimum gini\n",
    "                                np.max(ginis),             # maximum gini\n",
    "                                R[np.argmax(ginis)]))      # radius at maximum gini\n",
    "    f.close()\n",
    "    return None\n",
    "\n",
    "\n",
    "# output\n",
    "outfile = 'barred_galaxies_new.csv'\n",
    "\n",
    "# open the file\n",
    "f = open(outfile,'w')\n",
    "# add a header to the file\n",
    "f.write('%s,%s,%s,%s,%s\\n'%('f_name', 'R_at_min_dG/dR', 'min_dG/dR', 'max_G', 'R_at_max_G'))\n",
    "# save and close\n",
    "f.close()\n",
    "    \n",
    "# loop over all the images\n",
    "for file in files:\n",
    "    gini_grab(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the output into a pandas dataframe\n",
    "\n",
    "Now you have created your dataset, load it as a dataframe using pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f_name</th>\n",
       "      <th>R_at_min_dG/dR</th>\n",
       "      <th>min_dG/dR</th>\n",
       "      <th>max_G</th>\n",
       "      <th>R_at_max_G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run6...</td>\n",
       "      <td>120</td>\n",
       "      <td>0.037482</td>\n",
       "      <td>0.228998</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run6...</td>\n",
       "      <td>300</td>\n",
       "      <td>0.182198</td>\n",
       "      <td>0.186846</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run7...</td>\n",
       "      <td>252</td>\n",
       "      <td>0.109867</td>\n",
       "      <td>0.121911</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run6...</td>\n",
       "      <td>84</td>\n",
       "      <td>0.156365</td>\n",
       "      <td>0.173761</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_star...</td>\n",
       "      <td>192</td>\n",
       "      <td>0.207611</td>\n",
       "      <td>0.232584</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_star...</td>\n",
       "      <td>144</td>\n",
       "      <td>0.209928</td>\n",
       "      <td>0.237699</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run6...</td>\n",
       "      <td>108</td>\n",
       "      <td>0.035878</td>\n",
       "      <td>0.221691</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run....</td>\n",
       "      <td>72</td>\n",
       "      <td>0.012677</td>\n",
       "      <td>0.285708</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run6...</td>\n",
       "      <td>252</td>\n",
       "      <td>0.210685</td>\n",
       "      <td>0.238696</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run6...</td>\n",
       "      <td>240</td>\n",
       "      <td>0.142264</td>\n",
       "      <td>0.172346</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              f_name  R_at_min_dG/dR  \\\n",
       "0  datasets/barred_galaxies/poisson_noise_bg_run6...             120   \n",
       "1  datasets/barred_galaxies/poisson_noise_bg_run6...             300   \n",
       "2  datasets/barred_galaxies/poisson_noise_bg_run7...             252   \n",
       "3  datasets/barred_galaxies/poisson_noise_bg_run6...              84   \n",
       "4  datasets/barred_galaxies/poisson_noise_bg_star...             192   \n",
       "5  datasets/barred_galaxies/poisson_noise_bg_star...             144   \n",
       "6  datasets/barred_galaxies/poisson_noise_bg_run6...             108   \n",
       "7  datasets/barred_galaxies/poisson_noise_bg_run....              72   \n",
       "8  datasets/barred_galaxies/poisson_noise_bg_run6...             252   \n",
       "9  datasets/barred_galaxies/poisson_noise_bg_run6...             240   \n",
       "\n",
       "   min_dG/dR     max_G  R_at_max_G  \n",
       "0   0.037482  0.228998         300  \n",
       "1   0.182198  0.186846         264  \n",
       "2   0.109867  0.121911         168  \n",
       "3   0.156365  0.173761          60  \n",
       "4   0.207611  0.232584         144  \n",
       "5   0.209928  0.237699         108  \n",
       "6   0.035878  0.221691         300  \n",
       "7   0.012677  0.285708         300  \n",
       "8   0.210685  0.238696         180  \n",
       "9   0.142264  0.172346         156  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('barred_galaxies_new.csv')\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unsupervised clustering\n",
    "\n",
    "Start by trying to use unsupervised clustering algorithms to try to seperate barred and unbarred galaxies using your features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate a dataframe without the file names\n",
    "\n",
    "First, you will need to drop the file names from the dataframe. Make a new dataframe which only contains the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['f_name'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize features\n",
    "\n",
    "Because the features all have different scales you will need to standardize them, otherwise the fitting will be biased. Use the inbuilt scikit-learn StandardScaler function to standardize your features.\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Scale all of the variables\n",
    "X = StandardScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pick a clustering algorthm\n",
    "\n",
    "Go ahead and pick a clustering algorthm to use. You might want to do some plotting to see how which ones might be best to use. \n",
    "\n",
    "You can see a comparison of different methods on the scikit-learn website:\n",
    "https://scikit-learn.org/stable/modules/clustering.html\n",
    "\n",
    "Once you have picked your algorthm(s), import and initialize them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spectral \n",
    "\n",
    "https://scikit-learn.org/stable/modules/clustering.html#spectral-clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import cluster\n",
    "\n",
    "spectral = cluster.SpectralClustering(n_clusters=2, eigen_solver='arpack',affinity=\"nearest_neighbors\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Birch \n",
    "\n",
    "https://scikit-learn.org/stable/modules/clustering.html#birch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import cluster\n",
    "\n",
    "birch = cluster.Birch(n_clusters=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GMM <-\n",
    "\n",
    "https://scikit-learn.org/stable/modules/mixture.html#mixture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import mixture\n",
    "\n",
    "gmm = mixture.GMM(n_components=2, covariance_type='full')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit using your selected classifier\n",
    "\n",
    "Now go ahead and fit your chosen algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GMM(covariance_type='full', init_params='wmc', min_covar=0.001,\n",
       "  n_components=2, n_init=1, n_iter=100, params='wmc', random_state=None,\n",
       "  thresh=None, tol=0.001)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets use GMM, start by fitting the data\n",
    "gmm.fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use your model to predict the labels\n",
    "\n",
    "Use your model to predict the different classes from your feature space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now create labels for the galaxies\n",
    "y_pred = gmm.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare the clusters\n",
    "\n",
    "Use matplotlib to compare the clusters your algorithm has identified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = df.drop(['f_name'], axis=1).columns.values\n",
    "\n",
    "cols = 2.\n",
    "rows = round(len(predictors)/cols)\n",
    "fig = plt.figure()\n",
    "\n",
    "for i,label in enumerate(predictors):\n",
    "    ax = fig.add_subplot(rows, cols, i+1)\n",
    "    bins = np.linspace(min(df[label]), max(df[label]), 40)\n",
    "    ax.hist(df[label][y_pred == 0], bins, alpha=0.5, label='0')\n",
    "    ax.hist(df[label][y_pred == 1], bins, alpha=0.5, label='1')\n",
    "    ax.hist(df[label][y_pred == 2], bins, alpha=0.5, label='2')\n",
    "    ax.set_xlabel(label)\n",
    "    ax.set_ylabel('N')\n",
    "    ax.set_xlim(min(df[label]), max(df[label]))\n",
    "    ax.legend(loc='best')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare to human classification\n",
    "\n",
    "In this task we actually have human classifications for all the galaxies. Let's compare the clusters that you have found to the actual distribution of barred and unbarred galaxies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the labeled dataframe\n",
    "\n",
    "The barred_unbarred.csv file contains the file names and flags for each file, 1 denotes barred and 0 unbarred. Load the csv file into a new pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f_name</th>\n",
       "      <th>barred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>datasets/barred_galaxies/poisson_noise_bg_run....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              f_name  barred\n",
       "0  datasets/barred_galaxies/poisson_noise_bg_run....       1\n",
       "1  datasets/barred_galaxies/poisson_noise_bg_run....       1\n",
       "2  datasets/barred_galaxies/poisson_noise_bg_run....       1\n",
       "3  datasets/barred_galaxies/poisson_noise_bg_run....       1\n",
       "4  datasets/barred_galaxies/poisson_noise_bg_run....       1\n",
       "5  datasets/barred_galaxies/poisson_noise_bg_run....       1\n",
       "6  datasets/barred_galaxies/poisson_noise_bg_run....       1\n",
       "7  datasets/barred_galaxies/poisson_noise_bg_run....       1\n",
       "8  datasets/barred_galaxies/poisson_noise_bg_run....       1\n",
       "9  datasets/barred_galaxies/poisson_noise_bg_run....       1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('datasets/barred_galaxies/barred_unbarred.csv')\n",
    "\n",
    "df2.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change the index to the file name\n",
    "\n",
    "You will need to join the two pandas dataframes. To do this first you need to change the index of each dataframe to the file name column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.set_index('f_name')\n",
    "df = df.set_index('f_name')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join the two dataframes\n",
    "\n",
    "Next you can simply join the two dataframes into one new dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R_at_min_dG/dR</th>\n",
       "      <th>min_dG/dR</th>\n",
       "      <th>max_G</th>\n",
       "      <th>R_at_max_G</th>\n",
       "      <th>barred</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>datasets/barred_galaxies/poisson_noise_bg_run.201.std_1_highres.fits.png</th>\n",
       "      <td>300</td>\n",
       "      <td>0.279797</td>\n",
       "      <td>0.291865</td>\n",
       "      <td>252</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasets/barred_galaxies/poisson_noise_bg_run.201.std_2_highres.fits.png</th>\n",
       "      <td>300</td>\n",
       "      <td>0.243587</td>\n",
       "      <td>0.285907</td>\n",
       "      <td>216</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasets/barred_galaxies/poisson_noise_bg_run.201.std_3_highres.fits.png</th>\n",
       "      <td>72</td>\n",
       "      <td>0.012003</td>\n",
       "      <td>0.293822</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasets/barred_galaxies/poisson_noise_bg_run.201.std_4_highres.fits.png</th>\n",
       "      <td>264</td>\n",
       "      <td>0.253109</td>\n",
       "      <td>0.294296</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasets/barred_galaxies/poisson_noise_bg_run.240.std_1_highres.fits.png</th>\n",
       "      <td>72</td>\n",
       "      <td>0.013397</td>\n",
       "      <td>0.293225</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasets/barred_galaxies/poisson_noise_bg_run.240.std_2_highres.fits.png</th>\n",
       "      <td>72</td>\n",
       "      <td>0.012677</td>\n",
       "      <td>0.285708</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasets/barred_galaxies/poisson_noise_bg_run.240.std_3_highres.fits.png</th>\n",
       "      <td>300</td>\n",
       "      <td>0.267561</td>\n",
       "      <td>0.299163</td>\n",
       "      <td>228</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasets/barred_galaxies/poisson_noise_bg_run.240.std_4_highres.fits.png</th>\n",
       "      <td>72</td>\n",
       "      <td>0.013204</td>\n",
       "      <td>0.292551</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasets/barred_galaxies/poisson_noise_bg_run.252.s2.std_1_highres.fits.png</th>\n",
       "      <td>300</td>\n",
       "      <td>0.248489</td>\n",
       "      <td>0.299986</td>\n",
       "      <td>204</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasets/barred_galaxies/poisson_noise_bg_run.252.s2.std_2_highres.fits.png</th>\n",
       "      <td>72</td>\n",
       "      <td>0.012815</td>\n",
       "      <td>0.296627</td>\n",
       "      <td>288</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    R_at_min_dG/dR  min_dG/dR  \\\n",
       "f_name                                                                          \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...             300   0.279797   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...             300   0.243587   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...              72   0.012003   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...             264   0.253109   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...              72   0.013397   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...              72   0.012677   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...             300   0.267561   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...              72   0.013204   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...             300   0.248489   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...              72   0.012815   \n",
       "\n",
       "                                                       max_G  R_at_max_G  \\\n",
       "f_name                                                                     \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...  0.291865         252   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...  0.285907         216   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...  0.293822         300   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...  0.294296         192   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...  0.293225         300   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...  0.285708         300   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...  0.299163         228   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...  0.292551         300   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...  0.299986         204   \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...  0.296627         288   \n",
       "\n",
       "                                                    barred  \n",
       "f_name                                                      \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...       1  \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...       1  \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...       1  \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...       1  \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...       1  \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...       1  \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...       1  \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...       1  \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...       1  \n",
       "datasets/barred_galaxies/poisson_noise_bg_run.2...       1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.join(df2)\n",
    "\n",
    "df2.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the real distribution\n",
    "\n",
    "You now have your features and the actual label for the galaxy (barred/unbarred). Now using matplotlib to compare the distribution of barred and unbarred galaxies in your feature space and compare the the clusters you found before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = 20,10\n",
    "\n",
    "predictors2 = df2.drop(['barred'], axis=1).columns.values\n",
    "\n",
    "cols = 2.\n",
    "rows = round(len(predictors2)/cols)\n",
    "fig = plt.figure()\n",
    "\n",
    "for i,label in enumerate(predictors2):\n",
    "    ax = fig.add_subplot(rows, cols, i+1)\n",
    "    bins = np.linspace(min(df2[label]), max(df2[label]), 40)\n",
    "    ax.hist(df2[label][df2.barred == 0], bins, alpha=0.5, label='non-barred')\n",
    "    ax.hist(df2[label][df2.barred == 1], bins, alpha=0.5, label='barred')\n",
    "    ax.set_xlabel(label)\n",
    "    ax.set_ylabel('N')\n",
    "    ax.set_xlim(min(df2[label]), max(df2[label]))\n",
    "    ax.legend(loc='best')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supervised machine learning\n",
    "\n",
    "Clearly for this take unsupervised machine learning can only get us so far. But because we have the actual labels we can go ahead and use supervised machine learning instead."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop any NaNs\n",
    "\n",
    "Start by dropping all the NaNs in your dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.dropna(how='any')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seperate observations from features\n",
    "\n",
    "Now seperate your dataframe into observations (features) and targets (actual labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_y = df2['barred']\n",
    "observations_X = df2.drop(['barred'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize features \n",
    "\n",
    "Again you will need to standardize your features to avoid bias.\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "observations_X = StandardScaler().fit_transform(observations_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data into training and test data\n",
    "\n",
    "Next split your datasets into a training set and a test set. You can hard code this if you want, but I would suggest using the scikit-learn function train_test_split\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "# split the data into two sets training and testing\n",
    "train_x, test_x, train_y, test_y = train_test_split(observations_X, target_y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression\n",
    "\n",
    "Let's start with a vary widely used algorithm, logistic regression. Initialize, train and evaluate a logistic regression model.\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss = 0.37403901913 & accuracy = 0.821917808219\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn import linear_model\n",
    "\n",
    "logistic = linear_model.LogisticRegression(multi_class='ovr', solver='lbfgs')\n",
    "\n",
    "logistic.fit(train_x, train_y)\n",
    "\n",
    "# Log loss score for the model\n",
    "print('Loss = {} & accuracy = {}'.format(metrics.log_loss(test_y, logistic.predict_proba(test_x)),\n",
    "                                         metrics.accuracy_score(test_y, logistic.predict(test_x))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SGDC\n",
    "\n",
    "Compare to annother widely used classifier, Stochastic Gradient Decient Classifier.\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss = 0.517721472393 & accuracy = 0.77397260274\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "clf = SGDClassifier(loss=\"log\", penalty=\"l2\", alpha=0.001)\n",
    "\n",
    "clf = clf.fit(train_x, train_y)\n",
    "\n",
    "# Log loss score for the model\n",
    "print('Loss = {} & accuracy = {}'.format(metrics.log_loss(test_y, clf.predict_proba(test_x)),\n",
    "                                         metrics.accuracy_score(test_y, clf.predict(test_x))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suport vector machine\n",
    "\n",
    "Next, compare to a suport vector machine approach.\n",
    "\n",
    "https://scikit-learn.org/stable/modules/svm.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss = 0.356040098492 & accuracy = 0.883561643836\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "clf = svm.SVC(probability=True)\n",
    "\n",
    "clf = clf.fit(train_x, train_y)\n",
    "\n",
    "# Log loss score for the model\n",
    "print('Loss = {} & accuracy = {}'.format(metrics.log_loss(test_y, clf.predict_proba(test_x)),\n",
    "                                         metrics.accuracy_score(test_y, clf.predict(test_x))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pick one more\n",
    "\n",
    "Pick a further classifier, train a model and compare to LG, SGDC and SVM.\n",
    "\n",
    "https://scikit-learn.org/stable/supervised_learning.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a GridSearch pipeline\n",
    "\n",
    "With machine learning techniques come hyperparameters. These parameters need to be tuned to get the optimal model. Create a grid search pipeline to explore teh hyperparameter space and find the optimal model.\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 96 candidates, totalling 288 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   1 jobs       | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  50 jobs       | elapsed:    0.3s\n",
      "[Parallel(n_jobs=-1)]: Done 200 jobs       | elapsed:    1.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss = 0.361196742054 & accuracy = 0.876712328767\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 282 out of 288 | elapsed:    1.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 288 out of 288 | elapsed:    1.4s finished\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "pipe = Pipeline([('SVM', svm.SVC(C=1, kernel='rbf', probability=True, max_iter=-1))])\n",
    "params = {'SVM__C': (0.1, 1, 10, 100),\n",
    "          # 'SVM__kernel': ('linear', 'poly', 'rbf', 'sigmoid'),\n",
    "           'SVM__degree': (1, 2, 3, 4),\n",
    "          'SVM__gamma': (0.01, 0.1, 1),\n",
    "          # 'SVM__coef': (0.0, 0.5, 1),\n",
    "          # 'SVM__shrinking': (True, False),\n",
    "          'SVM__tol': (0.00001, 0.0001)}\n",
    "\n",
    "# Start the GridSearch\n",
    "grid_s = GridSearchCV(pipe, params, n_jobs=-1, verbose=1, refit=True, scoring='log_loss')\n",
    "# Fit model\n",
    "grid_s.fit(train_x, train_y)\n",
    "# Return the best estimator\n",
    "best_p = grid_s.best_estimator_.get_params()\n",
    "\n",
    "print('Loss = {} & accuracy = {}'.format(metrics.log_loss(test_y, grid_s.best_estimator_.predict_proba(test_x)),\n",
    "                                         metrics.accuracy_score(test_y, grid_s.best_estimator_.predict(test_x))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Futher model evaulation\n",
    "\n",
    "One of the most important steps in machine learning is evaluation. Does a model really do a good job? Is it overfit? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate a confusion matrix\n",
    "\n",
    "One quick tool is the confusion matrix. This is often used for spare feature spaces, but can still be a useful tool in this project. Create a confusion matrix for your best model.\n",
    "\n",
    "https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[117   7]\n",
      " [ 11  11]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\n",
    "\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.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(test_y, grid_s.best_estimator_.predict(test_x))\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['unbarred', 'barred'],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next steps\n",
    "\n",
    "Now you have a way to create models for the feature space you have generated. As is always the case with machine learning, the quality of the feature space is the most important part of the process. Go back and try to find better features and get better and better models."
   ]
  }
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