Fish Bowl_old_for_feature_gen.ipynb

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  "name": ""
 },
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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is taken from: http://nbviewer.ipython.org/github/udibr/datasciencebowl/blob/master/141215-tutorial-submission.ipynb \n",
      "\n",
      "and http://nbviewer.ipython.org/github/udibr/datasciencebowl/blob/master/141215-tutorial.ipynb"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Import libraries for doing image analysis\n",
      "from skimage.io import imread\n",
      "from skimage.transform import resize\n",
      "from sklearn.ensemble import RandomForestClassifier as RF\n",
      "import glob\n",
      "import os\n",
      "from sklearn import cross_validation\n",
      "from sklearn.cross_validation import StratifiedKFold as KFold\n",
      "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
      "from sklearn.metrics import classification_report\n",
      "from matplotlib import pyplot as plt\n",
      "from matplotlib import colors\n",
      "from pylab import cm\n",
      "from skimage import segmentation\n",
      "from skimage.morphology import watershed\n",
      "from skimage import measure\n",
      "from skimage import morphology\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "from sklearn.ensemble import ExtraTreesClassifier, ExtraTreesRegressor, RandomForestClassifier, RandomForestRegressor, GradientBoostingRegressor, GradientBoostingClassifier\n",
      "from scipy import ndimage\n",
      "from skimage.feature import peak_local_max\n",
      "import mahotas\n",
      "import mahotas as mh\n",
      "from mahotas.features import surf\n",
      "# make graphics inline\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import warnings\n",
      "warnings.filterwarnings(\"ignore\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "directory_names = list(set(glob.glob(os.path.join(\"Data\",\"train\", \"*\"))\\\n",
      " ).difference(set(glob.glob(os.path.join(\"Data\",\"train\",\"*.*\")))))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "example_file = glob.glob(os.path.join(directory_names[5],\"*.jpg\"))[9]\n",
      "print example_file\n",
      "im = imread(example_file, as_grey=True)\n",
      "plt.imshow(im, cmap=cm.gray)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Data/train/trichodesmium_multiple/125555.jpg\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lqvViW48vyTowMJBwKLEeMO+JZB0ZGUFfX1/C4z4zM4Pp6elE9QwN53DjcyNZ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\ndSWehJZn/gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11b475450>"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "example_file = glob.glob(os.path.join(directory_names[5],\"*.jpg\"))[9]\n",
      "im = imread(example_file, as_grey=True)\n",
      "print \"SHAPE OF PICTURE = \",im.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "SHAPE OF PICTURE =  (65, 60)\n"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# First we threshold the image by only taking values greater than the mean to reduce noise in the image\n",
      "# to use later as a mask\n",
      "f = plt.figure(figsize=(12,3))\n",
      "imthr = im.copy()\n",
      "imthr = np.where(im > np.mean(im),0.,1.0)\n",
      "sub1 = plt.subplot(1,4,1)\n",
      "plt.imshow(im, cmap=cm.gray)\n",
      "sub1.set_title(\"Original Image\")\n",
      "\n",
      "sub2 = plt.subplot(1,4,2)\n",
      "plt.imshow(imthr, cmap=cm.gray_r)\n",
      "sub2.set_title(\"Thresholded Image\")\n",
      "\n",
      "imdilated = morphology.dilation(imthr, np.ones((4,4)))\n",
      "sub3 = plt.subplot(1, 4, 3)\n",
      "plt.imshow(imdilated, cmap=cm.gray_r)\n",
      "sub3.set_title(\"Dilated Image\")\n",
      "\n",
      "labels = measure.label(imdilated)\n",
      "labels = imthr*labels\n",
      "labels = labels.astype(int)\n",
      "sub4 = plt.subplot(1, 4, 4)\n",
      "sub4.set_title(\"Labeled Image\")\n",
      "plt.imshow(labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 89,
       "text": [
        "<matplotlib.image.AxesImage at 0x12c02aed0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cO3bswMsvv8zmd6bC5y1nINLVcYPP3RRh0gKLSoHvLgL6+5v61Umg0xVLLbhw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       "text": [
        "<matplotlib.figure.Figure at 0x11b4912d0>"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# calculate common region properties for each region within the segmentation\n",
      "regions = measure.regionprops(labels)\n",
      "# find the largest nonzero region\n",
      "def getLargestRegion(props=regions, labelmap=labels, imagethres=imthr):\n",
      "    regionmaxprop = None\n",
      "    for regionprop in props:\n",
      "        # check to see if the region is at least 50% nonzero\n",
      "        if sum(imagethres[labelmap == regionprop.label])*1.0/regionprop.area < 0.50:\n",
      "            continue\n",
      "        if regionmaxprop is None:\n",
      "            regionmaxprop = regionprop\n",
      "        if regionmaxprop.filled_area < regionprop.filled_area:\n",
      "            regionmaxprop = regionprop\n",
      "    return regionmaxprop"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "regionmax = getLargestRegion()\n",
      "plt.imshow(np.where(labels == regionmax.label,1.0,0.0))\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+8pdcunQJp9M575C3eOdz9ehf0WCFWJ/6yc/JwoqGtauri66uLu677z4AHnvs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\nSUhYhTAJCasQJvH/xlZzyR67bQUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x137610ad0>"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print regionmax.minor_axis_length/regionmax.major_axis_length"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.417720588954\n"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import mahotas as mh\n",
      "from mahotas.features import surf\n",
      "print example_file\n",
      "image = mh.imread(example_file, as_grey=True)\n",
      "\n",
      "print len(mh.features.haralick(image, ignore_zeros=False, preserve_haralick_bug=False, compute_14th_feature=False))\n",
      "print mh.features.haralick(image, ignore_zeros=False, preserve_haralick_bug=False, compute_14th_feature=False)\n",
      "\n",
      "print len(mh.features.lbp(image, radius=20, points=7, ignore_zeros=False))\n",
      "print mh.features.lbp(image, radius=20, points=7, ignore_zeros=False)\n",
      "\n",
      "print len(mh.features.pftas(image))\n",
      "print mh.features.pftas(image)\n",
      "\n",
      "print len(mh.features.zernike_moments(image, radius=20, degree=8))\n",
      "print mh.features.zernike_moments(image, radius=20, degree=8)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Data/train/trichodesmium_multiple/125555.jpg\n",
        "4\n",
        "[[  3.67485471e-01   1.45793481e+02   9.79720904e-01   3.59467412e+03\n",
        "    6.80897261e-01   4.49904563e+02   1.42329030e+04   3.74784439e+00\n",
        "    4.57471591e+00   1.55540020e-03   2.73555438e+00  -5.51154503e-01\n",
        "    9.84484088e-01]\n",
        " [  3.56825936e-01   3.41271186e+02   9.53077142e-01   3.63651321e+03\n",
        "    6.64199335e-01   4.48965572e+02   1.42047817e+04   3.86960057e+00\n",
        "    4.73275173e+00   1.49052036e-03   3.00148632e+00  -5.19878794e-01\n",
        "    9.81842965e-01]\n",
        " [  3.61973741e-01   1.61100521e+02   9.77568880e-01   3.59100477e+03\n",
        "    6.74623358e-01   4.49973437e+02   1.42029186e+04   3.76248837e+00\n",
        "    4.60354810e+00   1.51304987e-03   2.78669695e+00  -5.42118529e-01\n",
        "    9.83569074e-01]\n",
        " [  3.55443361e-01   1.91119174e+02   9.73722194e-01   3.63651321e+03\n",
        "    6.67464656e-01   4.48965572e+02   1.43549337e+04   3.83478115e+00\n",
        "    4.68849902e+00   1.48657635e-03   2.85829747e+00  -5.33718392e-01\n",
        "    9.83393895e-01]]\n",
        "20\n",
        "[  2.61400000e+03   4.30000000e+01   3.80000000e+01   6.40000000e+01\n",
        "   1.70000000e+01   6.00000000e+01   2.60000000e+01   6.90000000e+01\n",
        "   4.00000000e+01   1.08000000e+02   2.00000000e+00   2.80000000e+01\n",
        "   9.00000000e+01   2.20000000e+01   3.30000000e+01   1.40000000e+01\n",
        "   7.00000000e+01   9.80000000e+01   2.16000000e+02   2.48000000e+02]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "54\n",
        "[  7.63919822e-01   6.34743875e-02   5.56792873e-02   5.12249443e-02\n",
        "   2.56124722e-02   2.22717149e-02   1.11358575e-02   4.45434298e-03\n",
        "   2.22717149e-03   7.63919822e-01   6.34743875e-02   5.56792873e-02\n",
        "   5.12249443e-02   2.56124722e-02   2.22717149e-02   1.11358575e-02\n",
        "   4.45434298e-03   2.22717149e-03   7.58444217e-01   6.44831116e-02\n",
        "   5.11770727e-02   5.22006141e-02   2.35414534e-02   2.35414534e-02\n",
        "   1.02354145e-02   8.18833163e-03   8.18833163e-03   9.19720187e-01\n",
        "   2.56495670e-02   2.09860093e-02   1.76548967e-02   7.66155896e-03\n",
        "   4.99666889e-03   2.66489007e-03   6.66222518e-04   0.00000000e+00\n",
        "   9.19720187e-01   2.56495670e-02   2.09860093e-02   1.76548967e-02\n",
        "   7.66155896e-03   4.99666889e-03   2.66489007e-03   6.66222518e-04\n",
        "   0.00000000e+00   8.94628806e-01   4.31063975e-02   2.08689702e-02\n",
        "   2.32637701e-02   9.92131372e-03   4.44748546e-03   2.39479986e-03\n",
        "   1.36845706e-03   0.00000000e+00]\n",
        "25\n",
        "[ 0.31830989  0.00817567  0.11534658  0.11911607  0.01661388  0.0020055\n",
        "  0.02850377  0.09282183  0.05637887  0.02352703  0.00909106  0.00142288\n",
        "  0.00709389  0.02950931  0.00934524  0.00807931  0.00946011  0.0187795\n",
        "  0.01688652  0.00089718  0.00410385  0.01112799  0.00873078  0.01403707\n",
        "  0.00803531]\n"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import mahotas as mh\n",
      "import pylab\n",
      "from mahotas.features import surf\n",
      "print example_file\n",
      "image = mh.imread(example_file, as_grey=True)\n",
      " \n",
      "spoints = surf.surf(image, nr_octaves=8, nr_scales=8, initial_step_size=100, \n",
      "                    threshold=0.5, max_points=1024, descriptor_only=False)\n",
      "\n",
      "print(\"Nr points:\", len(spoints))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Data/train/trichodesmium_multiple/125555.jpg\n",
        "('Nr points:', 0)\n"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def getMinorMajorRatio_2(image):\n",
      "    image = image.copy()\n",
      "    # Create the thresholded image to eliminate some of the background\n",
      "    imagethr = np.where(image > np.mean(image),0.,1.0)\n",
      "\n",
      "    #Dilate the image\n",
      "    imdilated = morphology.dilation(imagethr, np.ones((4,4)))\n",
      "\n",
      "    # Create the label list\n",
      "    label_list = measure.label(imdilated)\n",
      "    label_list = imagethr*label_list\n",
      "    label_list = label_list.astype(int)\n",
      "    \n",
      "    region_list = measure.regionprops(label_list)\n",
      "    maxregion = getLargestRegion(region_list, label_list, imagethr)\n",
      "        \n",
      "    # guard against cases where the segmentation fails by providing zeros\n",
      "    ratio = 0.0\n",
      "    minor_axis_length = 0.0\n",
      "    major_axis_length = 0.0\n",
      "    area = 0.0\n",
      "    convex_area = 0.0\n",
      "    eccentricity = 0.0\n",
      "    equivalent_diameter = 0.0\n",
      "    euler_number = 0.0\n",
      "    extent = 0.0\n",
      "    filled_area = 0.0\n",
      "    orientation = 0.0\n",
      "    perimeter = 0.0\n",
      "    solidity = 0.0\n",
      "    centroid = []\n",
      "    if ((not maxregion is None) and  (maxregion.major_axis_length != 0.0)):\n",
      "        ratio = 0.0 if maxregion is None else  maxregion.minor_axis_length*1.0 / maxregion.major_axis_length\n",
      "        minor_axis_length = 0.0 if maxregion is None else maxregion.minor_axis_length \n",
      "        major_axis_length = 0.0 if maxregion is None else maxregion.major_axis_length  \n",
      "        area = 0.0 if maxregion is None else maxregion.area  \n",
      "        convex_area = 0.0 if maxregion is None else maxregion.convex_area  \n",
      "        eccentricity = 0.0 if maxregion is None else maxregion.eccentricity  \n",
      "        equivalent_diameter = 0.0 if maxregion is None else maxregion.equivalent_diameter  \n",
      "        euler_number = 0.0 if maxregion is None else maxregion.euler_number  \n",
      "        extent = 0.0 if maxregion is None else maxregion.extent \n",
      "        filled_area = 0.0 if maxregion is None else maxregion.filled_area  \n",
      "        orientation = 0.0 if maxregion is None else maxregion.orientation \n",
      "        perimeter = 0.0 if maxregion is None else maxregion.perimeter  \n",
      "        solidity = 0.0 if maxregion is None else maxregion.solidity\n",
      "        centroid = [0.0,0.0] if maxregion is None else maxregion.centroid\n",
      "\n",
      "    return ratio,minor_axis_length,major_axis_length,area,convex_area,eccentricity,\\\n",
      "           equivalent_diameter,euler_number,extent,filled_area,orientation,perimeter,solidity, centroid\n",
      "    \n",
      "getMinorMajorRatio_2(im)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 95,
       "text": [
        "(0.4177205889535195,\n",
        " 21.86176000429083,\n",
        " 52.335845017980255,\n",
        " 844.0,\n",
        " 1048,\n",
        " 0.9085755387221939,\n",
        " 32.78130832893156,\n",
        " 0,\n",
        " 0.49184149184149184,\n",
        " 844,\n",
        " 0.8926814412838353,\n",
        " 169.58073580374355,\n",
        " 0.80534351145038163,\n",
        " (31.578199052132703, 29.539099526066352))"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def getMinorMajorRatio(image):\n",
      "    image = image.copy()\n",
      "    # Create the thresholded image to eliminate some of the background\n",
      "    imagethr = np.where(image > np.mean(image),0.,1.0)\n",
      "\n",
      "    #Dilate the image\n",
      "    imdilated = morphology.dilation(imagethr, np.ones((4,4)))\n",
      "\n",
      "    # Create the label list\n",
      "    label_list = measure.label(imdilated)\n",
      "    label_list = imagethr*label_list\n",
      "    label_list = label_list.astype(int)\n",
      "    \n",
      "    region_list = measure.regionprops(label_list)\n",
      "    maxregion = getLargestRegion(region_list, label_list, imagethr)\n",
      "    \n",
      "    # guard against cases where the segmentation fails by providing zeros\n",
      "    ratio = 0.0\n",
      "    if ((not maxregion is None) and  (maxregion.major_axis_length != 0.0)):\n",
      "        ratio = 0.0 if maxregion is None else  maxregion.minor_axis_length*1.0 / maxregion.major_axis_length\n",
      "    return ratio"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Rescale the images and create the combined metrics and training labels\n",
      "\n",
      "#get the total training images\n",
      "numberofImages = 0\n",
      "for folder in directory_names:\n",
      "    for fileNameDir in os.walk(folder):   \n",
      "        for fileName in fileNameDir[2]:\n",
      "             # Only read in the images\n",
      "            if fileName[-4:] != \".jpg\":\n",
      "              continue\n",
      "            numberofImages += 1\n",
      "\n",
      "# We'll rescale the images to be 28x28\n",
      "#maxPixel = 48\n",
      "#maxPixel = 28\n",
      "maxPixel = 60\n",
      "imageSize = maxPixel * maxPixel\n",
      "#imageSize = 65 * 60\n",
      "num_rows = numberofImages # one row for each image in the training dataset\n",
      "num_features = imageSize + 2 + 16 + 4*13 + 20 + 54 + 25 # for our ratio + others\n",
      "#num_features = imageSize \n",
      "\n",
      "# X is the feature vector with one row of features per image\n",
      "# consisting of the pixel values and our metric\n",
      "X = np.zeros((num_rows, num_features), dtype=float)\n",
      "# y is the numeric class label \n",
      "y = np.zeros((num_rows))\n",
      "\n",
      "files = []\n",
      "# Generate training data\n",
      "i = 0    \n",
      "label = 0\n",
      "# List of string of class names\n",
      "namesClasses = list()\n",
      "\n",
      "print \"Reading images\"\n",
      "# Navigate through the list of directories\n",
      "for folder in directory_names:\n",
      "    # Append the string class name for each class\n",
      "    currentClass = folder.split(os.pathsep)[-1]\n",
      "    namesClasses.append(currentClass)\n",
      "    for fileNameDir in os.walk(folder):   \n",
      "        for fileName in fileNameDir[2]:\n",
      "            # Only read in the images\n",
      "            if fileName[-4:] != \".jpg\":\n",
      "              continue\n",
      "            \n",
      "            # Read in the images and create the features\n",
      "            nameFileImage = \"{0}{1}{2}\".format(fileNameDir[0], os.sep, fileName)            \n",
      "            image = imread(nameFileImage, as_grey=True)\n",
      "            files.append(nameFileImage)\n",
      "            \n",
      "            axisratio,minor_axis_length,major_axis_length,area,\\\n",
      "            convex_area,eccentricity,equivalent_diameter,euler_number,\\\n",
      "            extent,filled_area,orientation,perimeter,solidity, centroid = getMinorMajorRatio_2(image)\n",
      "\n",
      "            mean = np.mean(image)\n",
      "            std  = np.std(image)\n",
      "            size = os.path.getsize(nameFileImage)\n",
      "            \n",
      "            ### RESIZE THE IMAGE\n",
      "            image = resize(image, (maxPixel, maxPixel))\n",
      "            #image = resize(image, (65, 60))\n",
      "            \n",
      "            # Store the rescaled image pixels and the axis ratio\n",
      "            X[i, 0:imageSize] = np.reshape(image, (1, imageSize))\n",
      "            \n",
      "            X[i, imageSize] = axisratio\n",
      "            X[i, imageSize + 1] = minor_axis_length\n",
      "            X[i, imageSize + 2] = major_axis_length\n",
      "            X[i, imageSize + 3] = area\n",
      "            X[i, imageSize + 4] = convex_area\n",
      "            X[i, imageSize + 5] = eccentricity\n",
      "            X[i, imageSize + 6] = equivalent_diameter\n",
      "            X[i, imageSize + 7] = euler_number\n",
      "            X[i, imageSize + 8] = extent\n",
      "            X[i, imageSize + 9] = filled_area\n",
      "            X[i, imageSize + 10] = orientation\n",
      "            X[i, imageSize + 11] = perimeter\n",
      "            X[i, imageSize + 12] = solidity\n",
      "            X[i, imageSize + 13] = mean\n",
      "            X[i, imageSize + 14] = std\n",
      "            X[i, imageSize + 15] = size\n",
      "            \n",
      "            image2 = mh.imread(nameFileImage, as_grey=True)\n",
      "            \n",
      "            haralick = mh.features.haralick(image2, ignore_zeros=False, preserve_haralick_bug=False, compute_14th_feature=False)\n",
      "            lbp      =  mh.features.lbp(image2, radius=20, points=7, ignore_zeros=False)\n",
      "            pftas    = mh.features.pftas(image2)\n",
      "            zernike_moments = mh.features.zernike_moments(image2, radius=20, degree=8)\n",
      "            \n",
      "            X[i, (imageSize + 15) :  (imageSize + 28)]   = haralick[0]\n",
      "            X[i, (imageSize + 28) :  (imageSize + 41) ]  = haralick[1]\n",
      "            X[i, (imageSize + 41) :  (imageSize + 54) ]  = haralick[2]\n",
      "            X[i, (imageSize + 54) :  (imageSize + 67) ]  = haralick[3]\n",
      "            X[i, (imageSize + 67) :  (imageSize + 87) ]  = lbp\n",
      "            X[i, (imageSize + 87) :  (imageSize + 141) ] = pftas\n",
      "            X[i, (imageSize + 141) : (imageSize + 166) ] = zernike_moments\n",
      "            \n",
      "            if len(centroid) == 2:\n",
      "                X[i, (imageSize + 166) ] = centroid[0]\n",
      "                X[i, (imageSize + 167) ] = centroid[1]\n",
      "            else:\n",
      "                X[i, (imageSize + 166) ] = 0.0\n",
      "                X[i, (imageSize + 167) ] = 0.0\n",
      "            \n",
      "            \n",
      "            # Store the classlabel\n",
      "            y[i] = label\n",
      "            i += 1\n",
      "            # report progress for each 5% done  \n",
      "            report = [int((j+1)*num_rows/20.) for j in range(20)]\n",
      "            if i in report: print np.ceil(i *100.0 / num_rows), \"% done\"\n",
      "    label += 1\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Reading images\n",
        "5.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "10.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "15.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "20.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "25.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "30.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "35.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "40.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "45.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "50.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "55.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "60.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "65.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "70.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "75.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "80.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "85.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "90.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "95.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "100.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n"
       ]
      }
     ],
     "prompt_number": 143
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(np.shape(X))\n",
      "pd.DataFrame(X).head(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(30336, 3769)\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>...</th>\n",
        "      <th>3759</th>\n",
        "      <th>3760</th>\n",
        "      <th>3761</th>\n",
        "      <th>3762</th>\n",
        "      <th>3763</th>\n",
        "      <th>3764</th>\n",
        "      <th>3765</th>\n",
        "      <th>3766</th>\n",
        "      <th>3767</th>\n",
        "      <th>3768</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 0.787222</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.003607</td>\n",
        "      <td> 0.001537</td>\n",
        "      <td> 0.016294</td>\n",
        "      <td> 0.010317</td>\n",
        "      <td> 0.002834</td>\n",
        "      <td> 0.011323</td>\n",
        "      <td> 0.007867</td>\n",
        "      <td> 20.893491</td>\n",
        "      <td> 21.076923</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 0.933333</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.003600</td>\n",
        "      <td> 0.004621</td>\n",
        "      <td> 0.009172</td>\n",
        "      <td> 0.003295</td>\n",
        "      <td> 0.002224</td>\n",
        "      <td> 0.016087</td>\n",
        "      <td> 0.007073</td>\n",
        "      <td> 47.603448</td>\n",
        "      <td> 25.713793</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 0.799583</td>\n",
        "      <td> 0.950000</td>\n",
        "      <td> 0.950000</td>\n",
        "      <td> 0.950000</td>\n",
        "      <td> 0.950000</td>\n",
        "      <td> 0.950000</td>\n",
        "      <td> 0.950000</td>\n",
        "      <td> 0.950000</td>\n",
        "      <td> 0.950000</td>\n",
        "      <td> 0.950000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000860</td>\n",
        "      <td> 0.000162</td>\n",
        "      <td> 0.014894</td>\n",
        "      <td> 0.011192</td>\n",
        "      <td> 0.003978</td>\n",
        "      <td> 0.016480</td>\n",
        "      <td> 0.012719</td>\n",
        "      <td> 26.853261</td>\n",
        "      <td> 19.271739</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.900000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000278</td>\n",
        "      <td> 0.000232</td>\n",
        "      <td> 0.013201</td>\n",
        "      <td> 0.002771</td>\n",
        "      <td> 0.009340</td>\n",
        "      <td> 0.002711</td>\n",
        "      <td> 0.004510</td>\n",
        "      <td> 23.726141</td>\n",
        "      <td> 38.340249</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.002856</td>\n",
        "      <td> 0.009315</td>\n",
        "      <td> 0.004854</td>\n",
        "      <td> 0.004901</td>\n",
        "      <td> 0.014154</td>\n",
        "      <td> 0.003574</td>\n",
        "      <td> 0.005008</td>\n",
        "      <td> 28.002710</td>\n",
        "      <td> 25.303523</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 0.671667</td>\n",
        "      <td> 0.775000</td>\n",
        "      <td> 0.775000</td>\n",
        "      <td> 0.775000</td>\n",
        "      <td> 0.775000</td>\n",
        "      <td> 0.775000</td>\n",
        "      <td> 0.775000</td>\n",
        "      <td> 0.775000</td>\n",
        "      <td> 0.775000</td>\n",
        "      <td> 0.775000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000068</td>\n",
        "      <td> 0.000019</td>\n",
        "      <td> 0.014005</td>\n",
        "      <td> 0.019504</td>\n",
        "      <td> 0.032933</td>\n",
        "      <td> 0.018782</td>\n",
        "      <td> 0.020049</td>\n",
        "      <td> 16.357143</td>\n",
        "      <td> 21.508929</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 0.743056</td>\n",
        "      <td> 0.833333</td>\n",
        "      <td> 0.833333</td>\n",
        "      <td> 0.833333</td>\n",
        "      <td> 0.833333</td>\n",
        "      <td> 0.833333</td>\n",
        "      <td> 0.833333</td>\n",
        "      <td> 0.833333</td>\n",
        "      <td> 0.833333</td>\n",
        "      <td> 0.833333</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000153</td>\n",
        "      <td> 0.000055</td>\n",
        "      <td> 0.015882</td>\n",
        "      <td> 0.008629</td>\n",
        "      <td> 0.003219</td>\n",
        "      <td> 0.020728</td>\n",
        "      <td> 0.013313</td>\n",
        "      <td> 20.048193</td>\n",
        "      <td> 23.144578</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 0.861250</td>\n",
        "      <td> 0.883333</td>\n",
        "      <td> 0.883333</td>\n",
        "      <td> 0.883333</td>\n",
        "      <td> 0.883333</td>\n",
        "      <td> 0.883333</td>\n",
        "      <td> 0.883333</td>\n",
        "      <td> 0.883333</td>\n",
        "      <td> 0.883333</td>\n",
        "      <td> 0.883333</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.004878</td>\n",
        "      <td> 0.004546</td>\n",
        "      <td> 0.015500</td>\n",
        "      <td> 0.006185</td>\n",
        "      <td> 0.005965</td>\n",
        "      <td> 0.019139</td>\n",
        "      <td> 0.009019</td>\n",
        "      <td> 21.930380</td>\n",
        "      <td> 27.550633</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 0.729444</td>\n",
        "      <td> 0.866667</td>\n",
        "      <td> 0.866667</td>\n",
        "      <td> 0.866667</td>\n",
        "      <td> 0.866667</td>\n",
        "      <td> 0.866667</td>\n",
        "      <td> 0.866667</td>\n",
        "      <td> 0.866667</td>\n",
        "      <td> 0.866667</td>\n",
        "      <td> 0.866667</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000253</td>\n",
        "      <td> 0.000055</td>\n",
        "      <td> 0.012963</td>\n",
        "      <td> 0.009261</td>\n",
        "      <td> 0.003207</td>\n",
        "      <td> 0.019014</td>\n",
        "      <td> 0.013318</td>\n",
        "      <td> 23.411111</td>\n",
        "      <td> 19.550000</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 0.708333</td>\n",
        "      <td> 0.850000</td>\n",
        "      <td> 0.850000</td>\n",
        "      <td> 0.850000</td>\n",
        "      <td> 0.850000</td>\n",
        "      <td> 0.850000</td>\n",
        "      <td> 0.850000</td>\n",
        "      <td> 0.850000</td>\n",
        "      <td> 0.850000</td>\n",
        "      <td> 0.850000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000220</td>\n",
        "      <td> 0.000416</td>\n",
        "      <td> 0.014700</td>\n",
        "      <td> 0.001687</td>\n",
        "      <td> 0.007420</td>\n",
        "      <td> 0.000679</td>\n",
        "      <td> 0.001745</td>\n",
        "      <td> 19.815534</td>\n",
        "      <td> 18.067961</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 0.731111</td>\n",
        "      <td> 0.783333</td>\n",
        "      <td> 0.783333</td>\n",
        "      <td> 0.783333</td>\n",
        "      <td> 0.783333</td>\n",
        "      <td> 0.783333</td>\n",
        "      <td> 0.783333</td>\n",
        "      <td> 0.783333</td>\n",
        "      <td> 0.783333</td>\n",
        "      <td> 0.783333</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.001672</td>\n",
        "      <td> 0.000940</td>\n",
        "      <td> 0.023323</td>\n",
        "      <td> 0.009140</td>\n",
        "      <td> 0.003282</td>\n",
        "      <td> 0.016543</td>\n",
        "      <td> 0.016747</td>\n",
        "      <td> 20.412037</td>\n",
        "      <td> 22.842593</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 0.756250</td>\n",
        "      <td> 0.825000</td>\n",
        "      <td> 0.825000</td>\n",
        "      <td> 0.825000</td>\n",
        "      <td> 0.825000</td>\n",
        "      <td> 0.825000</td>\n",
        "      <td> 0.825000</td>\n",
        "      <td> 0.825000</td>\n",
        "      <td> 0.825000</td>\n",
        "      <td> 0.825000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000127</td>\n",
        "      <td> 0.000060</td>\n",
        "      <td> 0.012465</td>\n",
        "      <td> 0.009296</td>\n",
        "      <td> 0.004498</td>\n",
        "      <td> 0.018477</td>\n",
        "      <td> 0.013109</td>\n",
        "      <td> 19.333333</td>\n",
        "      <td> 23.777778</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 0.658056</td>\n",
        "      <td> 0.858333</td>\n",
        "      <td> 0.858333</td>\n",
        "      <td> 0.858333</td>\n",
        "      <td> 0.858333</td>\n",
        "      <td> 0.858333</td>\n",
        "      <td> 0.858333</td>\n",
        "      <td> 0.858333</td>\n",
        "      <td> 0.858333</td>\n",
        "      <td> 0.858333</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000148</td>\n",
        "      <td> 0.000037</td>\n",
        "      <td> 0.016035</td>\n",
        "      <td> 0.037431</td>\n",
        "      <td> 0.023055</td>\n",
        "      <td> 0.048487</td>\n",
        "      <td> 0.010552</td>\n",
        "      <td> 21.963415</td>\n",
        "      <td> 17.000000</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 0.893194</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000095</td>\n",
        "      <td> 0.000285</td>\n",
        "      <td> 0.012948</td>\n",
        "      <td> 0.009544</td>\n",
        "      <td> 0.003625</td>\n",
        "      <td> 0.018579</td>\n",
        "      <td> 0.014114</td>\n",
        "      <td> 22.380282</td>\n",
        "      <td> 27.292254</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.004688</td>\n",
        "      <td> 0.005058</td>\n",
        "      <td> 0.010819</td>\n",
        "      <td> 0.006512</td>\n",
        "      <td> 0.001888</td>\n",
        "      <td> 0.012468</td>\n",
        "      <td> 0.008618</td>\n",
        "      <td> 37.110256</td>\n",
        "      <td> 55.976923</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> 0.793333</td>\n",
        "      <td> 0.800000</td>\n",
        "      <td> 0.800000</td>\n",
        "      <td> 0.800000</td>\n",
        "      <td> 0.800000</td>\n",
        "      <td> 0.800000</td>\n",
        "      <td> 0.800000</td>\n",
        "      <td> 0.800000</td>\n",
        "      <td> 0.800000</td>\n",
        "      <td> 0.800000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.004818</td>\n",
        "      <td> 0.003744</td>\n",
        "      <td> 0.018939</td>\n",
        "      <td> 0.007318</td>\n",
        "      <td> 0.012176</td>\n",
        "      <td> 0.004024</td>\n",
        "      <td> 0.020606</td>\n",
        "      <td> 16.644351</td>\n",
        "      <td> 28.527197</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.001643</td>\n",
        "      <td> 0.002282</td>\n",
        "      <td> 0.012051</td>\n",
        "      <td> 0.006527</td>\n",
        "      <td> 0.002030</td>\n",
        "      <td> 0.009920</td>\n",
        "      <td> 0.007670</td>\n",
        "      <td> 54.252207</td>\n",
        "      <td> 48.322825</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> 0.753333</td>\n",
        "      <td> 0.941667</td>\n",
        "      <td> 0.941667</td>\n",
        "      <td> 0.941667</td>\n",
        "      <td> 0.941667</td>\n",
        "      <td> 0.941667</td>\n",
        "      <td> 0.941667</td>\n",
        "      <td> 0.941667</td>\n",
        "      <td> 0.941667</td>\n",
        "      <td> 0.941667</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000076</td>\n",
        "      <td> 0.000065</td>\n",
        "      <td> 0.023460</td>\n",
        "      <td> 0.001912</td>\n",
        "      <td> 0.016800</td>\n",
        "      <td> 0.008161</td>\n",
        "      <td> 0.025944</td>\n",
        "      <td> 22.649547</td>\n",
        "      <td> 15.848943</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> 0.696389</td>\n",
        "      <td> 0.766667</td>\n",
        "      <td> 0.766667</td>\n",
        "      <td> 0.766667</td>\n",
        "      <td> 0.766667</td>\n",
        "      <td> 0.766667</td>\n",
        "      <td> 0.766667</td>\n",
        "      <td> 0.766667</td>\n",
        "      <td> 0.766667</td>\n",
        "      <td> 0.766667</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000102</td>\n",
        "      <td> 0.000024</td>\n",
        "      <td> 0.016114</td>\n",
        "      <td> 0.038275</td>\n",
        "      <td> 0.022039</td>\n",
        "      <td> 0.048131</td>\n",
        "      <td> 0.010558</td>\n",
        "      <td> 15.560606</td>\n",
        "      <td> 23.628788</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> 0.933333</td>\n",
        "      <td> 0.933333</td>\n",
        "      <td> 0.933333</td>\n",
        "      <td> 0.933333</td>\n",
        "      <td> 0.933333</td>\n",
        "      <td> 0.933333</td>\n",
        "      <td> 0.933333</td>\n",
        "      <td> 0.933333</td>\n",
        "      <td> 0.933333</td>\n",
        "      <td> 0.933333</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.003754</td>\n",
        "      <td> 0.004747</td>\n",
        "      <td> 0.018936</td>\n",
        "      <td> 0.001095</td>\n",
        "      <td> 0.003504</td>\n",
        "      <td> 0.008514</td>\n",
        "      <td> 0.000459</td>\n",
        "      <td> 24.650224</td>\n",
        "      <td> 33.704036</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>20 rows \u00d7 3769 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 144,
       "text": [
        "        0         1         2         3         4         5         6     \\\n",
        "0   0.787222  0.908333  0.908333  0.908333  0.908333  0.908333  0.908333   \n",
        "1   0.933333  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
        "2   0.799583  0.950000  0.950000  0.950000  0.950000  0.950000  0.950000   \n",
        "3   0.900000  0.900000  0.900000  0.900000  0.900000  0.900000  0.900000   \n",
        "4   0.908333  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
        "5   0.671667  0.775000  0.775000  0.775000  0.775000  0.775000  0.775000   \n",
        "6   0.743056  0.833333  0.833333  0.833333  0.833333  0.833333  0.833333   \n",
        "7   0.861250  0.883333  0.883333  0.883333  0.883333  0.883333  0.883333   \n",
        "8   0.729444  0.866667  0.866667  0.866667  0.866667  0.866667  0.866667   \n",
        "9   0.708333  0.850000  0.850000  0.850000  0.850000  0.850000  0.850000   \n",
        "10  0.731111  0.783333  0.783333  0.783333  0.783333  0.783333  0.783333   \n",
        "11  0.756250  0.825000  0.825000  0.825000  0.825000  0.825000  0.825000   \n",
        "12  0.658056  0.858333  0.858333  0.858333  0.858333  0.858333  0.858333   \n",
        "13  0.893194  0.908333  0.908333  0.908333  0.908333  0.908333  0.908333   \n",
        "14  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
        "15  0.793333  0.800000  0.800000  0.800000  0.800000  0.800000  0.800000   \n",
        "16  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
        "17  0.753333  0.941667  0.941667  0.941667  0.941667  0.941667  0.941667   \n",
        "18  0.696389  0.766667  0.766667  0.766667  0.766667  0.766667  0.766667   \n",
        "19  0.933333  0.933333  0.933333  0.933333  0.933333  0.933333  0.933333   \n",
        "\n",
        "        7         8         9       ...         3759      3760      3761  \\\n",
        "0   0.908333  0.908333  0.908333    ...     0.003607  0.001537  0.016294   \n",
        "1   1.000000  1.000000  1.000000    ...     0.003600  0.004621  0.009172   \n",
        "2   0.950000  0.950000  0.950000    ...     0.000860  0.000162  0.014894   \n",
        "3   0.900000  0.900000  0.900000    ...     0.000278  0.000232  0.013201   \n",
        "4   1.000000  1.000000  1.000000    ...     0.002856  0.009315  0.004854   \n",
        "5   0.775000  0.775000  0.775000    ...     0.000068  0.000019  0.014005   \n",
        "6   0.833333  0.833333  0.833333    ...     0.000153  0.000055  0.015882   \n",
        "7   0.883333  0.883333  0.883333    ...     0.004878  0.004546  0.015500   \n",
        "8   0.866667  0.866667  0.866667    ...     0.000253  0.000055  0.012963   \n",
        "9   0.850000  0.850000  0.850000    ...     0.000220  0.000416  0.014700   \n",
        "10  0.783333  0.783333  0.783333    ...     0.001672  0.000940  0.023323   \n",
        "11  0.825000  0.825000  0.825000    ...     0.000127  0.000060  0.012465   \n",
        "12  0.858333  0.858333  0.858333    ...     0.000148  0.000037  0.016035   \n",
        "13  0.908333  0.908333  0.908333    ...     0.000095  0.000285  0.012948   \n",
        "14  1.000000  1.000000  1.000000    ...     0.004688  0.005058  0.010819   \n",
        "15  0.800000  0.800000  0.800000    ...     0.004818  0.003744  0.018939   \n",
        "16  1.000000  1.000000  1.000000    ...     0.001643  0.002282  0.012051   \n",
        "17  0.941667  0.941667  0.941667    ...     0.000076  0.000065  0.023460   \n",
        "18  0.766667  0.766667  0.766667    ...     0.000102  0.000024  0.016114   \n",
        "19  0.933333  0.933333  0.933333    ...     0.003754  0.004747  0.018936   \n",
        "\n",
        "        3762      3763      3764      3765       3766       3767  3768  \n",
        "0   0.010317  0.002834  0.011323  0.007867  20.893491  21.076923     0  \n",
        "1   0.003295  0.002224  0.016087  0.007073  47.603448  25.713793     0  \n",
        "2   0.011192  0.003978  0.016480  0.012719  26.853261  19.271739     0  \n",
        "3   0.002771  0.009340  0.002711  0.004510  23.726141  38.340249     0  \n",
        "4   0.004901  0.014154  0.003574  0.005008  28.002710  25.303523     0  \n",
        "5   0.019504  0.032933  0.018782  0.020049  16.357143  21.508929     0  \n",
        "6   0.008629  0.003219  0.020728  0.013313  20.048193  23.144578     0  \n",
        "7   0.006185  0.005965  0.019139  0.009019  21.930380  27.550633     0  \n",
        "8   0.009261  0.003207  0.019014  0.013318  23.411111  19.550000     0  \n",
        "9   0.001687  0.007420  0.000679  0.001745  19.815534  18.067961     0  \n",
        "10  0.009140  0.003282  0.016543  0.016747  20.412037  22.842593     0  \n",
        "11  0.009296  0.004498  0.018477  0.013109  19.333333  23.777778     0  \n",
        "12  0.037431  0.023055  0.048487  0.010552  21.963415  17.000000     0  \n",
        "13  0.009544  0.003625  0.018579  0.014114  22.380282  27.292254     0  \n",
        "14  0.006512  0.001888  0.012468  0.008618  37.110256  55.976923     0  \n",
        "15  0.007318  0.012176  0.004024  0.020606  16.644351  28.527197     0  \n",
        "16  0.006527  0.002030  0.009920  0.007670  54.252207  48.322825     0  \n",
        "17  0.001912  0.016800  0.008161  0.025944  22.649547  15.848943     0  \n",
        "18  0.038275  0.022039  0.048131  0.010558  15.560606  23.628788     0  \n",
        "19  0.001095  0.003504  0.008514  0.000459  24.650224  33.704036     0  \n",
        "\n",
        "[20 rows x 3769 columns]"
       ]
      }
     ],
     "prompt_number": 144
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.DataFrame(y).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 145,
       "text": [
        "   0\n",
        "0  0\n",
        "1  0\n",
        "2  0\n",
        "3  0\n",
        "4  0"
       ]
      }
     ],
     "prompt_number": 145
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 0-1 scaling for training\n",
      "min_max_scaler = MinMaxScaler(feature_range=(0, 1))\n",
      "X = min_max_scaler.fit_transform(X)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 146
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Mean and STD scaling, For Images Use this\n",
      "'''\n",
      "from sklearn import preprocessing\n",
      "scaler = preprocessing.StandardScaler()\n",
      "X = scaler.fit_transform(X)\n",
      "'''"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 161
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.DataFrame(X).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
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        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>...</th>\n",
        "      <th>3759</th>\n",
        "      <th>3760</th>\n",
        "      <th>3761</th>\n",
        "      <th>3762</th>\n",
        "      <th>3763</th>\n",
        "      <th>3764</th>\n",
        "      <th>3765</th>\n",
        "      <th>3766</th>\n",
        "      <th>3767</th>\n",
        "      <th>3768</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.787222</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.899237</td>\n",
        "      <td> 0.904384</td>\n",
        "      <td> 0.908136</td>\n",
        "      <td> 0.906439</td>\n",
        "      <td> 0.908017</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.038909</td>\n",
        "      <td> 0.019204</td>\n",
        "      <td> 0.103424</td>\n",
        "      <td> 0.118952</td>\n",
        "      <td> 0.030941</td>\n",
        "      <td> 0.149901</td>\n",
        "      <td> 0.093879</td>\n",
        "      <td> 0.071856</td>\n",
        "      <td> 0.069114</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.933333</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.038828</td>\n",
        "      <td> 0.057761</td>\n",
        "      <td> 0.058220</td>\n",
        "      <td> 0.037680</td>\n",
        "      <td> 0.024106</td>\n",
        "      <td> 0.213264</td>\n",
        "      <td> 0.084391</td>\n",
        "      <td> 0.163715</td>\n",
        "      <td> 0.084319</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.799583</td>\n",
        "      <td> 0.950000</td>\n",
        "      <td> 0.950000</td>\n",
        "      <td> 0.950000</td>\n",
        "      <td> 0.950000</td>\n",
        "      <td> 0.945038</td>\n",
        "      <td> 0.947846</td>\n",
        "      <td> 0.949893</td>\n",
        "      <td> 0.948967</td>\n",
        "      <td> 0.949827</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.009115</td>\n",
        "      <td> 0.002011</td>\n",
        "      <td> 0.094538</td>\n",
        "      <td> 0.129089</td>\n",
        "      <td> 0.043776</td>\n",
        "      <td> 0.218490</td>\n",
        "      <td> 0.151799</td>\n",
        "      <td> 0.092352</td>\n",
        "      <td> 0.063195</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.890077</td>\n",
        "      <td> 0.895691</td>\n",
        "      <td> 0.899785</td>\n",
        "      <td> 0.897934</td>\n",
        "      <td> 0.899654</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.002801</td>\n",
        "      <td> 0.002884</td>\n",
        "      <td> 0.083796</td>\n",
        "      <td> 0.031613</td>\n",
        "      <td> 0.103913</td>\n",
        "      <td> 0.035383</td>\n",
        "      <td> 0.053802</td>\n",
        "      <td> 0.081598</td>\n",
        "      <td> 0.125723</td>\n",
        "      <td> 0</td>\n",
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        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
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        "      <td> 1.000000</td>\n",
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        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
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        "      <td> 0.030809</td>\n",
        "      <td> 0.056266</td>\n",
        "      <td> 0.157899</td>\n",
        "      <td> 0.046850</td>\n",
        "      <td> 0.059741</td>\n",
        "      <td> 0.096305</td>\n",
        "      <td> 0.082974</td>\n",
        "      <td> 0</td>\n",
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        "<p>5 rows \u00d7 3769 columns</p>\n",
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       ]
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     ],
     "prompt_number": 147
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "from scipy import linalg\n",
      "from sklearn.utils import array2d, as_float_array\n",
      "from sklearn.base import TransformerMixin, BaseEstimator\n",
      " \n",
      "class ZCA(BaseEstimator, TransformerMixin):\n",
      " \n",
      "    def __init__(self, regularization=10**-5, copy=False):\n",
      "        self.regularization = regularization\n",
      "        self.copy = copy\n",
      " \n",
      "    def fit(self, X, y=None):\n",
      "        X = array2d(X)\n",
      "        X = as_float_array(X, copy = self.copy)\n",
      "        self.mean_ = np.mean(X, axis=0)\n",
      "        X -= self.mean_\n",
      "        sigma = np.dot(X.T,X) / X.shape[1]\n",
      "        U, S, V = linalg.svd(sigma)\n",
      "        tmp = np.dot(U, np.diag(1/np.sqrt(S+self.regularization)))\n",
      "        self.components_ = np.dot(tmp, U.T)\n",
      "        return self\n",
      " \n",
      "    def transform(self, X):\n",
      "        X = array2d(X)\n",
      "        X_transformed = X - self.mean_\n",
      "        X_transformed = np.dot(X_transformed, self.components_.T)\n",
      "        return X_transformed"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 148
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.decomposition import PCA, RandomizedPCA\n",
      "# Whiten the Data\n",
      "pca = PCA(n_components=0.99, whiten=True).fit(X)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 300
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pylab as pl\n",
      "pl.figure(1, figsize=(4, 3))\n",
      "pl.clf()\n",
      "pl.axes([.2, .2, .7, .7])\n",
      "pl.plot(np.cumsum(pca.explained_variance_ratio_), linewidth=2)\n",
      "#pl.xlim([0,n_features])\n",
      "pl.xlabel('n_components')\n",
      "pl.ylabel('Cumalative explained_variance_')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 301,
       "text": [
        "<matplotlib.text.Text at 0x10edf7bd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10df38750>"
       ]
      }
     ],
     "prompt_number": 301
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_pca = pca.transform(X)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 302
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.DataFrame(X_pca).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>...</th>\n",
        "      <th>562</th>\n",
        "      <th>563</th>\n",
        "      <th>564</th>\n",
        "      <th>565</th>\n",
        "      <th>566</th>\n",
        "      <th>567</th>\n",
        "      <th>568</th>\n",
        "      <th>569</th>\n",
        "      <th>570</th>\n",
        "      <th>571</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1.397276</td>\n",
        "      <td> 0.475565</td>\n",
        "      <td> 0.333027</td>\n",
        "      <td> 0.389094</td>\n",
        "      <td> 0.111853</td>\n",
        "      <td>-0.008370</td>\n",
        "      <td> 0.358451</td>\n",
        "      <td> 0.251509</td>\n",
        "      <td> 0.111265</td>\n",
        "      <td>-0.005614</td>\n",
        "      <td>...</td>\n",
        "      <td> 1.225658</td>\n",
        "      <td> 0.132695</td>\n",
        "      <td>-0.481429</td>\n",
        "      <td>-0.034229</td>\n",
        "      <td> 0.201011</td>\n",
        "      <td>-0.426413</td>\n",
        "      <td>-0.174860</td>\n",
        "      <td> 0.461204</td>\n",
        "      <td>-0.072863</td>\n",
        "      <td>-0.052770</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1.412498</td>\n",
        "      <td> 0.578094</td>\n",
        "      <td> 0.353454</td>\n",
        "      <td> 0.390612</td>\n",
        "      <td> 0.001612</td>\n",
        "      <td> 0.119356</td>\n",
        "      <td> 0.339560</td>\n",
        "      <td> 0.116622</td>\n",
        "      <td>-0.064834</td>\n",
        "      <td> 0.373151</td>\n",
        "      <td>...</td>\n",
        "      <td> 1.250889</td>\n",
        "      <td>-0.347898</td>\n",
        "      <td> 0.939390</td>\n",
        "      <td>-0.275882</td>\n",
        "      <td>-0.148364</td>\n",
        "      <td>-0.603949</td>\n",
        "      <td> 0.371673</td>\n",
        "      <td> 0.535243</td>\n",
        "      <td> 0.661244</td>\n",
        "      <td>-0.088048</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1.311391</td>\n",
        "      <td> 0.491578</td>\n",
        "      <td>-0.121733</td>\n",
        "      <td> 0.395560</td>\n",
        "      <td> 0.032087</td>\n",
        "      <td> 0.049861</td>\n",
        "      <td> 0.224718</td>\n",
        "      <td>-0.026843</td>\n",
        "      <td>-0.246868</td>\n",
        "      <td>-0.002289</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.737265</td>\n",
        "      <td> 0.079322</td>\n",
        "      <td> 0.253334</td>\n",
        "      <td> 0.056986</td>\n",
        "      <td>-0.196609</td>\n",
        "      <td>-0.336182</td>\n",
        "      <td>-0.100596</td>\n",
        "      <td> 0.037012</td>\n",
        "      <td> 0.095202</td>\n",
        "      <td>-0.124741</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1.407345</td>\n",
        "      <td> 0.558591</td>\n",
        "      <td>-0.066945</td>\n",
        "      <td> 0.278227</td>\n",
        "      <td> 0.040859</td>\n",
        "      <td> 0.050266</td>\n",
        "      <td> 0.438117</td>\n",
        "      <td> 0.055979</td>\n",
        "      <td>-0.202950</td>\n",
        "      <td> 0.389838</td>\n",
        "      <td>...</td>\n",
        "      <td> 1.446812</td>\n",
        "      <td>-0.235878</td>\n",
        "      <td> 0.403788</td>\n",
        "      <td>-0.570928</td>\n",
        "      <td>-0.110020</td>\n",
        "      <td>-0.193582</td>\n",
        "      <td>-0.139901</td>\n",
        "      <td>-0.085820</td>\n",
        "      <td> 0.344570</td>\n",
        "      <td> 0.278683</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1.435721</td>\n",
        "      <td> 0.483207</td>\n",
        "      <td> 0.327055</td>\n",
        "      <td> 0.330713</td>\n",
        "      <td> 0.011736</td>\n",
        "      <td> 0.036982</td>\n",
        "      <td> 0.391743</td>\n",
        "      <td> 0.399359</td>\n",
        "      <td>-0.020987</td>\n",
        "      <td> 0.248780</td>\n",
        "      <td>...</td>\n",
        "      <td> 3.142474</td>\n",
        "      <td>-0.688433</td>\n",
        "      <td> 1.254018</td>\n",
        "      <td>-2.111024</td>\n",
        "      <td>-0.136815</td>\n",
        "      <td>-1.583568</td>\n",
        "      <td>-0.171189</td>\n",
        "      <td> 0.277397</td>\n",
        "      <td> 1.056891</td>\n",
        "      <td>-0.447138</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 572 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 303,
       "text": [
        "        0         1         2         3         4         5         6    \\\n",
        "0  1.397276  0.475565  0.333027  0.389094  0.111853 -0.008370  0.358451   \n",
        "1  1.412498  0.578094  0.353454  0.390612  0.001612  0.119356  0.339560   \n",
        "2  1.311391  0.491578 -0.121733  0.395560  0.032087  0.049861  0.224718   \n",
        "3  1.407345  0.558591 -0.066945  0.278227  0.040859  0.050266  0.438117   \n",
        "4  1.435721  0.483207  0.327055  0.330713  0.011736  0.036982  0.391743   \n",
        "\n",
        "        7         8         9      ...          562       563       564  \\\n",
        "0  0.251509  0.111265 -0.005614    ...     1.225658  0.132695 -0.481429   \n",
        "1  0.116622 -0.064834  0.373151    ...     1.250889 -0.347898  0.939390   \n",
        "2 -0.026843 -0.246868 -0.002289    ...     0.737265  0.079322  0.253334   \n",
        "3  0.055979 -0.202950  0.389838    ...     1.446812 -0.235878  0.403788   \n",
        "4  0.399359 -0.020987  0.248780    ...     3.142474 -0.688433  1.254018   \n",
        "\n",
        "        565       566       567       568       569       570       571  \n",
        "0 -0.034229  0.201011 -0.426413 -0.174860  0.461204 -0.072863 -0.052770  \n",
        "1 -0.275882 -0.148364 -0.603949  0.371673  0.535243  0.661244 -0.088048  \n",
        "2  0.056986 -0.196609 -0.336182 -0.100596  0.037012  0.095202 -0.124741  \n",
        "3 -0.570928 -0.110020 -0.193582 -0.139901 -0.085820  0.344570  0.278683  \n",
        "4 -2.111024 -0.136815 -1.583568 -0.171189  0.277397  1.056891 -0.447138  \n",
        "\n",
        "[5 rows x 572 columns]"
       ]
      }
     ],
     "prompt_number": 303
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_zca = X[:,:3600]\n",
      "zca = ZCA(copy=True).fit(X_zca)\n",
      "X_zca = zca.transform(X_zca)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 149
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import copy\n",
      "X_final = copy.copy(X)\n",
      "print X_final.shape\n",
      "X_final[:,:3600] = X_zca\n",
      "print X_final.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(30336, 3769)\n",
        "(30336, 3769)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 150
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.DataFrame(X).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>...</th>\n",
        "      <th>3759</th>\n",
        "      <th>3760</th>\n",
        "      <th>3761</th>\n",
        "      <th>3762</th>\n",
        "      <th>3763</th>\n",
        "      <th>3764</th>\n",
        "      <th>3765</th>\n",
        "      <th>3766</th>\n",
        "      <th>3767</th>\n",
        "      <th>3768</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.787222</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 0.908333</td>\n",
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        "      <td> 0.093879</td>\n",
        "      <td> 0.071856</td>\n",
        "      <td> 0.069114</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.933333</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.038828</td>\n",
        "      <td> 0.057761</td>\n",
        "      <td> 0.058220</td>\n",
        "      <td> 0.037680</td>\n",
        "      <td> 0.024106</td>\n",
        "      <td> 0.213264</td>\n",
        "      <td> 0.084391</td>\n",
        "      <td> 0.163715</td>\n",
        "      <td> 0.084319</td>\n",
        "      <td> 0</td>\n",
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        "    <tr>\n",
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        "      <td> 0.799583</td>\n",
        "      <td> 0.950000</td>\n",
        "      <td> 0.950000</td>\n",
        "      <td> 0.950000</td>\n",
        "      <td> 0.950000</td>\n",
        "      <td> 0.945038</td>\n",
        "      <td> 0.947846</td>\n",
        "      <td> 0.949893</td>\n",
        "      <td> 0.948967</td>\n",
        "      <td> 0.949827</td>\n",
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        "      <td> 0.009115</td>\n",
        "      <td> 0.002011</td>\n",
        "      <td> 0.094538</td>\n",
        "      <td> 0.129089</td>\n",
        "      <td> 0.043776</td>\n",
        "      <td> 0.218490</td>\n",
        "      <td> 0.151799</td>\n",
        "      <td> 0.092352</td>\n",
        "      <td> 0.063195</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.900000</td>\n",
        "      <td> 0.890077</td>\n",
        "      <td> 0.895691</td>\n",
        "      <td> 0.899785</td>\n",
        "      <td> 0.897934</td>\n",
        "      <td> 0.899654</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.002801</td>\n",
        "      <td> 0.002884</td>\n",
        "      <td> 0.083796</td>\n",
        "      <td> 0.031613</td>\n",
        "      <td> 0.103913</td>\n",
        "      <td> 0.035383</td>\n",
        "      <td> 0.053802</td>\n",
        "      <td> 0.081598</td>\n",
        "      <td> 0.125723</td>\n",
        "      <td> 0</td>\n",
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        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.908333</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
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        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
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        "      <td> 0.157899</td>\n",
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        "      <td> 0.059741</td>\n",
        "      <td> 0.096305</td>\n",
        "      <td> 0.082974</td>\n",
        "      <td> 0</td>\n",
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        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3769 columns</p>\n",
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       "metadata": {},
       "output_type": "pyout",
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        "\n",
        "[5 rows x 3769 columns]"
       ]
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     ],
     "prompt_number": 151
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.DataFrame(X_final).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>...</th>\n",
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        "      <th>3764</th>\n",
        "      <th>3765</th>\n",
        "      <th>3766</th>\n",
        "      <th>3767</th>\n",
        "      <th>3768</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>-0.020671</td>\n",
        "      <td> 0.061787</td>\n",
        "      <td> 0.027481</td>\n",
        "      <td>-0.026546</td>\n",
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        "      <td>-0.060474</td>\n",
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        "      <td> 0.009770</td>\n",
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        "      <td> 0.093879</td>\n",
        "      <td> 0.071856</td>\n",
        "      <td> 0.069114</td>\n",
        "      <td> 0</td>\n",
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        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.052424</td>\n",
        "      <td> 0.049682</td>\n",
        "      <td>-0.067491</td>\n",
        "      <td> 0.031538</td>\n",
        "      <td> 0.017199</td>\n",
        "      <td> 0.044899</td>\n",
        "      <td>-0.018087</td>\n",
        "      <td>-0.048782</td>\n",
        "      <td> 0.043013</td>\n",
        "      <td> 0.059295</td>\n",
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        "      <td> 0.057761</td>\n",
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        "      <td> 0.084391</td>\n",
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        "      <td> 0.084319</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>-0.109670</td>\n",
        "      <td> 0.094826</td>\n",
        "      <td> 0.039041</td>\n",
        "      <td> 0.035318</td>\n",
        "      <td> 0.051648</td>\n",
        "      <td> 0.030429</td>\n",
        "      <td> 0.035524</td>\n",
        "      <td> 0.028203</td>\n",
        "      <td> 0.048178</td>\n",
        "      <td> 0.057259</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.009115</td>\n",
        "      <td> 0.002011</td>\n",
        "      <td> 0.094538</td>\n",
        "      <td> 0.129089</td>\n",
        "      <td> 0.043776</td>\n",
        "      <td> 0.218490</td>\n",
        "      <td> 0.151799</td>\n",
        "      <td> 0.092352</td>\n",
        "      <td> 0.063195</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-0.049248</td>\n",
        "      <td>-0.039965</td>\n",
        "      <td> 0.020855</td>\n",
        "      <td>-0.040127</td>\n",
        "      <td>-0.002179</td>\n",
        "      <td>-0.069333</td>\n",
        "      <td>-0.015743</td>\n",
        "      <td>-0.052435</td>\n",
        "      <td> 0.039952</td>\n",
        "      <td> 0.022341</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.002801</td>\n",
        "      <td> 0.002884</td>\n",
        "      <td> 0.083796</td>\n",
        "      <td> 0.031613</td>\n",
        "      <td> 0.103913</td>\n",
        "      <td> 0.035383</td>\n",
        "      <td> 0.053802</td>\n",
        "      <td> 0.081598</td>\n",
        "      <td> 0.125723</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>-0.121176</td>\n",
        "      <td> 0.147288</td>\n",
        "      <td>-0.030314</td>\n",
        "      <td> 0.022530</td>\n",
        "      <td> 0.239216</td>\n",
        "      <td> 0.127131</td>\n",
        "      <td> 0.163162</td>\n",
        "      <td> 0.077072</td>\n",
        "      <td> 0.048554</td>\n",
        "      <td>-0.004433</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.030760</td>\n",
        "      <td> 0.116445</td>\n",
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      }
     ],
     "prompt_number": 152
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.DataFrame(X).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### Write out the arrays just in case we need them later\n",
      "pd.DataFrame(X).to_csv(\"/Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Data/X_train_fullImage.csv\")\n",
      "pd.DataFrame(y).to_csv(\"/Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Data/Y_train_fullImage.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#X_CNN = X[:,:784]\n",
      "import copy\n",
      "X_CNN = copy.copy(X)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_CNN.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 104,
       "text": [
        "(30336, 3600)"
       ]
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pickle \n",
      "from sklearn.cross_validation import train_test_split\n",
      "\n",
      "### Create the training set with 70% of data\n",
      "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
      "#X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.3, random_state=42)\n",
      "#X_train, X_test, y_train, y_test = train_test_split(X_zca, y, test_size=0.3, random_state=42)\n",
      "\n",
      "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10, random_state=42)\n",
      "#X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.15, random_state=42)\n",
      "#X_train, X_test, y_train, y_test = train_test_split(X_zca, y, test_size=0.15, random_state=42)\n",
      "\n",
      "#X_train, X_test, y_train, y_test = train_test_split(X_CNN, y, test_size=0.10, random_state=42)\n",
      "#X_train, X_test, y_train, y_test = train_test_split(X_zca, y, test_size=0.10, random_state=42)\n",
      "X_train, X_test, y_train, y_test = train_test_split(X_final, y, test_size=0.10, random_state=42)\n",
      "\n",
      "### Split the testing set to 50/50 for validation and testing\n",
      "X_val, X_test, y_val, y_test = train_test_split(X_test, y_test, test_size=0.5, random_state=42)\n",
      "\n",
      "print(np.shape(X_train))\n",
      "print(np.shape(X_val))\n",
      "print(np.shape(X_test))\n",
      "print(np.shape(y_train))\n",
      "print(np.shape(y_val))\n",
      "print(np.shape(y_test))\n",
      "\n",
      "#PIK = \"/Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels.pkl\"\n",
      "#PIK = \"/Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels_CNN.pkl\"\n",
      "\n",
      "#PIK = \"/Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_3600_pixels_CNN.pkl\"\n",
      "#PIK = \"/Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_3600_pixels_CNN_zca.pkl\"\n",
      "\n",
      "PIK = \"/Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_3600_pixels_zca.pkl\"\n",
      "\n",
      "#PIK = \"/Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels_pca.pkl\"\n",
      "#PIK = \"/Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels_zca.pkl\"\n",
      "data = [[X_train,y_train],[X_val,y_val],[X_test,y_test]]\n",
      "with open(PIK, \"wb\") as f:\n",
      "    pickle.dump(data, f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(27302, 3769)\n",
        "(1517, 3769)\n",
        "(1517, 3769)\n",
        "(27302,)\n",
        "(1517,)\n",
        "(1517,)\n"
       ]
      }
     ],
     "prompt_number": 153
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#!rm  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels.pkl.gz\n",
      "#!gzip  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels.pkl\n",
      "\n",
      "#!rm  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels_largeTrain.pkl.gz\n",
      "#!gzip  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels_largeTrain.pkl\n",
      "\n",
      "#!rm  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels_pca.pkl.gz\n",
      "#!gzip  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels_pca.pkl\n",
      "\n",
      "#!rm  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels_zca.pkl.gz\n",
      "#!gzip  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels_zca.pkl\n",
      "\n",
      "#!rm  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels_CNN.pkl.gz\n",
      "#!gzip  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels_CNN.pkl\n",
      "#!rm  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_3600_pixels_CNN.pkl.gz\n",
      "#!gzip  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_3600_pixels_CNN.pkl\n",
      "#!rm  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_3600_pixels_CNN_zca.pkl.gz\n",
      "#!gzip  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_3600_pixels_CNN_zca.pkl\n",
      "\n",
      "!rm  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_3600_pixels_zca.pkl.gz\n",
      "!gzip  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_3600_pixels_zca.pkl"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "rm: /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_3600_pixels_zca.pkl.gz: No such file or directory\r\n"
       ]
      }
     ],
     "prompt_number": 154
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#!ls -l /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels.pkl.gz\n",
      "#!ls -l /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels_largeTrain.pkl.gz\n",
      "#!ls -l /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels_pca.pkl.gz\n",
      "#!ls -l /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels_zca.pkl.gz\n",
      "\n",
      "#!ls -l /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_784_pixels_CNN.pkl.gz\n",
      "#!ls -l /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_3600_pixels_CNN.pkl.gz\n",
      "#!ls -l /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_3600_pixels_CNN_zca.pkl.gz\n",
      "\n",
      "!ls -l /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_3600_pixels_zca.pkl.gz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-rw-r--r--  1 msegala  msegala  1194929358 Jan 16 18:30 /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Train_Data_3600_pixels_zca.pkl.gz\r\n"
       ]
      }
     ],
     "prompt_number": 155
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#df = pd.DataFrame({\"class\": y[:], \"ratio\": X[:, num_features-1]})\n",
      "df = pd.DataFrame({\"class\": y[:], \"ratio\": X[:, 785]})\n",
      "\n",
      "gp3 = df.groupby('class')\n",
      "means = gp3.mean()\n",
      "errors = gp3.std()\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "means.plot(yerr=errors, ax=ax)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 156,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x249b87c10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OnjetyV5ateJhoE2acCFw7pwUFhosHCUAhO0aMK8BdO3Kl7OzeZiVHKUTWI1A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\nz3T+LHkQLFJfX8969uzJSktL2dmzZ9mAAQPYzp0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       "text": [
        "<matplotlib.figure.Figure at 0x2484f47d0>"
       ]
      }
     ],
     "prompt_number": 156
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Loop through the classes two at a time and compare their distributions of the Width/Length Ratio\n",
      "\n",
      "#Create a DataFrame object to make subsetting the data on the class \n",
      "df = pd.DataFrame({\"class\": y[:], \"ratio\": X[:, num_features-1]})\n",
      "\n",
      "f = plt.figure(figsize=(30, 20))\n",
      "#we suppress zeros and choose a few large classes to better highlight the distributions.\n",
      "df = df.loc[df[\"ratio\"] > 0]\n",
      "minimumSize = 20 \n",
      "counts = df[\"class\"].value_counts()\n",
      "largeclasses = [int(x) for x in list(counts.loc[counts > minimumSize].index)]\n",
      "# Loop through 40 of the classes \n",
      "for j in range(0,40,2):\n",
      "    subfig = plt.subplot(4, 5, j/2 +1)\n",
      "    # Plot the normalized histograms for two classes\n",
      "    classind1 = largeclasses[j]\n",
      "    classind2 = largeclasses[j+1]\n",
      "    n, bins,p = plt.hist(df.loc[df[\"class\"] == classind1][\"ratio\"].values,\\\n",
      "                         alpha=0.5, bins=[x*0.01 for x in range(100)], \\\n",
      "                         label=namesClasses[classind1].split(os.sep)[-1], normed=1)\n",
      "\n",
      "    n2, bins,p = plt.hist(df.loc[df[\"class\"] == (classind2)][\"ratio\"].values,\\\n",
      "                          alpha=0.5, bins=bins, label=namesClasses[classind2].split(os.sep)[-1],normed=1)\n",
      "    subfig.set_ylim([0.,10.])\n",
      "    plt.legend(loc='upper right')\n",
      "    plt.xlabel(\"Width/Length Ratio\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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j7Nmz+Pnnn9GvXz8hTU1l1jQJt6HgwBhRLS1duhShoaGQyWTCsyOqqvxaREQE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\nUhJEIhGWLFkiHC91fU/aUBf7VLVJa3p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xZBRRD4/cHlXAaYAZVxBub1fDx4PIY52+ecmfmc813O92yyGyuAay8+Pl5JSUmaOXOmqlWrpipV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\nNXToUB04cMCYvnr1a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1Resu3jazVVnkHG/1PdYQf99v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       "text": [
        "<matplotlib.figure.Figure at 0x102d8a3d0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def multiclass_log_loss(y_true, y_pred, eps=1e-15):\n",
      "    \"\"\"Multi class version of Logarithmic Loss metric.\n",
      "    https://www.kaggle.com/wiki/MultiClassLogLoss\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    y_true : array, shape = [n_samples]\n",
      "            true class, intergers in [0, n_classes - 1)\n",
      "    y_pred : array, shape = [n_samples, n_classes]\n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    loss : float\n",
      "    \"\"\"\n",
      "    predictions = np.clip(y_pred, eps, 1 - eps)\n",
      "\n",
      "    # normalize row sums to 1\n",
      "    predictions /= predictions.sum(axis=1)[:, np.newaxis]\n",
      "\n",
      "    actual = np.zeros(y_pred.shape)\n",
      "    n_samples = actual.shape[0]\n",
      "    actual[np.arange(n_samples), y_true.astype(int)] = 1\n",
      "    vectsum = np.sum(actual * np.log(predictions))\n",
      "    loss = -1.0 / n_samples * vectsum\n",
      "    return loss"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the probability predictions for computing the log-loss function\n",
      "kf = KFold(y, n_folds=5)\n",
      "#kf = KFold(y, n_folds=2)\n",
      "# prediction probabilities number of samples, by number of classes\n",
      "y_pred = np.zeros((len(y),len(set(y))))\n",
      "for train, test in kf:\n",
      "    _X_train, _X_test, _y_train, _y_test = X[train,:], X[test,:], y[train], y[test]\n",
      "    #clf = RF(n_estimators=1000, n_jobs=7)\n",
      "    clf = ExtraTreesClassifier(n_estimators=2000, n_jobs=7)\n",
      "    #clf = ExtraTreesClassifier(n_estimators=200, n_jobs=7)\n",
      "    clf.fit(_X_train, _y_train)\n",
      "    y_pred[test] = clf.predict_proba(_X_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "multiclass_log_loss(y, y_pred)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[   0.    0.    0. ...,  120.  120.  120.]\n",
        "(30336,)\n",
        "\n",
        "[[  5.77500000e-01   0.00000000e+00   4.00000000e-03 ...,   0.00000000e+00\n",
        "    4.50000000e-03   4.00000000e-03]\n",
        " [  6.85500000e-01   5.00000000e-04   8.00000000e-03 ...,   0.00000000e+00\n",
        "    2.00000000e-03   0.00000000e+00]\n",
        " [  7.14500000e-01   0.00000000e+00   2.50000000e-03 ...,   0.00000000e+00\n",
        "    1.00000000e-03   0.00000000e+00]\n",
        " ..., \n",
        " [  2.00000000e-03   8.50000000e-03   2.85000000e-02 ...,   0.00000000e+00\n",
        "    3.70000000e-02   1.08500000e-01]\n",
        " [  1.50000000e-02   4.00000000e-03   1.70000000e-02 ...,   5.00000000e-04\n",
        "    1.17000000e-01   5.00000000e-03]\n",
        " [  2.00000000e-03   1.40000000e-02   1.80000000e-02 ...,   5.00000000e-04\n",
        "    3.40000000e-02   1.81500000e-01]]\n",
        "(30336, 121)\n",
        "\n",
        "[[  5.77500000e-01   1.00000000e-15   4.00000000e-03 ...,   1.00000000e-15\n",
        "    4.50000000e-03   4.00000000e-03]\n",
        " [  6.85500000e-01   5.00000000e-04   8.00000000e-03 ...,   1.00000000e-15\n",
        "    2.00000000e-03   1.00000000e-15]\n",
        " [  7.14500000e-01   1.00000000e-15   2.50000000e-03 ...,   1.00000000e-15\n",
        "    1.00000000e-03   1.00000000e-15]\n",
        " ..., \n",
        " [  2.00000000e-03   8.50000000e-03   2.85000000e-02 ...,   1.00000000e-15\n",
        "    3.70000000e-02   1.08500000e-01]\n",
        " [  1.50000000e-02   4.00000000e-03   1.70000000e-02 ...,   5.00000000e-04\n",
        "    1.17000000e-01   5.00000000e-03]\n",
        " [  2.00000000e-03   1.40000000e-02   1.80000000e-02 ...,   5.00000000e-04\n",
        "    3.40000000e-02   1.81500000e-01]]\n",
        "(30336, 121)\n",
        "\n",
        "[[  5.77500000e-01   1.00000000e-15   4.00000000e-03 ...,   1.00000000e-15\n",
        "    4.50000000e-03   4.00000000e-03]\n",
        " [  6.85500000e-01   5.00000000e-04   8.00000000e-03 ...,   1.00000000e-15\n",
        "    2.00000000e-03   1.00000000e-15]\n",
        " [  7.14500000e-01   1.00000000e-15   2.50000000e-03 ...,   1.00000000e-15\n",
        "    1.00000000e-03   1.00000000e-15]\n",
        " ..., \n",
        " [  2.00000000e-03   8.50000000e-03   2.85000000e-02 ...,   1.00000000e-15\n",
        "    3.70000000e-02   1.08500000e-01]\n",
        " [  1.50000000e-02   4.00000000e-03   1.70000000e-02 ...,   5.00000000e-04\n",
        "    1.17000000e-01   5.00000000e-03]\n",
        " [  2.00000000e-03   1.40000000e-02   1.80000000e-02 ...,   5.00000000e-04\n",
        "    3.40000000e-02   1.81500000e-01]]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "2.1683480365507952"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#2.177"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#clf_full = ExtraTreesClassifier(n_estimators=2000, n_jobs=7)\n",
      "#clf_full.fit(X, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": "*"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "header = \"acantharia_protist_big_center,acantharia_protist_halo,acantharia_protist,amphipods,\\\n",
      "appendicularian_fritillaridae,appendicularian_s_shape,appendicularian_slight_curve,appendicularian_straight,\\\n",
      "artifacts_edge,artifacts,chaetognath_non_sagitta,chaetognath_other,chaetognath_sagitta,chordate_type1,\\\n",
      "copepod_calanoid_eggs,copepod_calanoid_eucalanus,copepod_calanoid_flatheads,copepod_calanoid_frillyAntennae,\\\n",
      "copepod_calanoid_large_side_antennatucked,copepod_calanoid_large,copepod_calanoid_octomoms,\\\n",
      "copepod_calanoid_small_longantennae,copepod_calanoid,copepod_cyclopoid_copilia,copepod_cyclopoid_oithona_eggs,\\\n",
      "copepod_cyclopoid_oithona,copepod_other,crustacean_other,ctenophore_cestid,ctenophore_cydippid_no_tentacles,\\\n",
      "ctenophore_cydippid_tentacles,ctenophore_lobate,decapods,detritus_blob,detritus_filamentous,detritus_other,\\\n",
      "diatom_chain_string,diatom_chain_tube,echinoderm_larva_pluteus_brittlestar,echinoderm_larva_pluteus_early,\\\n",
      "echinoderm_larva_pluteus_typeC,echinoderm_larva_pluteus_urchin,echinoderm_larva_seastar_bipinnaria,\\\n",
      "echinoderm_larva_seastar_brachiolaria,echinoderm_seacucumber_auricularia_larva,echinopluteus,ephyra,\\\n",
      "euphausiids_young,euphausiids,fecal_pellet,fish_larvae_deep_body,fish_larvae_leptocephali,fish_larvae_medium_body,\\\n",
      "fish_larvae_myctophids,fish_larvae_thin_body,fish_larvae_very_thin_body,heteropod,hydromedusae_aglaura,\\\n",
      "hydromedusae_bell_and_tentacles,hydromedusae_h15,hydromedusae_haliscera_small_sideview,hydromedusae_haliscera,\\\n",
      "hydromedusae_liriope,hydromedusae_narco_dark,hydromedusae_narco_young,hydromedusae_narcomedusae,hydromedusae_other,\\\n",
      "hydromedusae_partial_dark,hydromedusae_shapeA_sideview_small,hydromedusae_shapeA,hydromedusae_shapeB,\\\n",
      "hydromedusae_sideview_big,hydromedusae_solmaris,hydromedusae_solmundella,hydromedusae_typeD_bell_and_tentacles,\\\n",
      "hydromedusae_typeD,hydromedusae_typeE,hydromedusae_typeF,invertebrate_larvae_other_A,invertebrate_larvae_other_B,\\\n",
      "jellies_tentacles,polychaete,protist_dark_center,protist_fuzzy_olive,protist_noctiluca,protist_other,protist_star,\\\n",
      "pteropod_butterfly,pteropod_theco_dev_seq,pteropod_triangle,radiolarian_chain,radiolarian_colony,shrimp_caridean,\\\n",
      "shrimp_sergestidae,shrimp_zoea,shrimp-like_other,siphonophore_calycophoran_abylidae,\\\n",
      "siphonophore_calycophoran_rocketship_adult,siphonophore_calycophoran_rocketship_young,\\\n",
      "siphonophore_calycophoran_sphaeronectes_stem,siphonophore_calycophoran_sphaeronectes_young,\\\n",
      "siphonophore_calycophoran_sphaeronectes,siphonophore_other_parts,siphonophore_partial,\\\n",
      "siphonophore_physonect_young,siphonophore_physonect,stomatopod,tornaria_acorn_worm_larvae,trichodesmium_bowtie,\\\n",
      "trichodesmium_multiple,trichodesmium_puff,trichodesmium_tuft,trochophore_larvae,tunicate_doliolid_nurse,\\\n",
      "tunicate_doliolid,tunicate_partial,tunicate_salp_chains,tunicate_salp,unknown_blobs_and_smudges,unknown_sticks,\\\n",
      "unknown_unclassified\".split(',')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 114
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "labels = map(lambda s: s.split('/')[-1], namesClasses)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 116,
       "text": [
        "121"
       ]
      }
     ],
     "prompt_number": 116
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#get the total test images\n",
      "fnames = glob.glob(os.path.join(\"Data\", \"test\", \"*.jpg\"))\n",
      "numberofTestImages = len(fnames)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numberofTestImages"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 118,
       "text": [
        "130400"
       ]
      }
     ],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_test = np.zeros((numberofTestImages, num_features), dtype=float)\n",
      "y_test = np.zeros(numberofTestImages, dtype=float)\n",
      "print X_test.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(130400, 3600)\n"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "images = map(lambda fileName: fileName.split('/')[-1], fnames)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = 0\n",
      "# report progress for each 5% done  \n",
      "report = [int((j+1)*numberofTestImages/20.) for j in range(20)]\n",
      "for fileName in fnames:\n",
      "    # Read in the images and create the features\n",
      "    image = imread(fileName, as_grey=True)\n",
      "    #axisratio = getMinorMajorRatio(image)\n",
      "    \n",
      "    axisratio,minor_axis_length,major_axis_length,area,\\\n",
      "    convex_area,eccentricity,equivalent_diameter,euler_number,\\\n",
      "    extent,filled_area,orientation,perimeter,solidity,centroid = getMinorMajorRatio_2(image)\n",
      "    \n",
      "    mean = np.mean(image)\n",
      "    std  = np.std(image)\n",
      "    size = os.path.getsize(fileName)\n",
      "    \n",
      "    ### RESIZE THE IMAGE\n",
      "    image = resize(image, (maxPixel, maxPixel))\n",
      "    #image = resize(image, (65, 60))\n",
      "    \n",
      "    # Store the rescaled image pixels and the axis ratio\n",
      "    X_test[i, 0:imageSize] = np.reshape(image, (1, imageSize))\n",
      "    X_test[i, imageSize] = axisratio\n",
      "    X_test[i, imageSize + 1] = minor_axis_length\n",
      "    X_test[i, imageSize + 2] = major_axis_length\n",
      "    X_test[i, imageSize + 3] = area\n",
      "    X_test[i, imageSize + 4] = convex_area\n",
      "    X_test[i, imageSize + 5] = eccentricity\n",
      "    X_test[i, imageSize + 6] = equivalent_diameter\n",
      "    X_test[i, imageSize + 7] = euler_number\n",
      "    X_test[i, imageSize + 8] = extent\n",
      "    X_test[i, imageSize + 9] = filled_area\n",
      "    X_test[i, imageSize + 10] = orientation\n",
      "    X_test[i, imageSize + 11] = perimeter\n",
      "    X_test[i, imageSize + 12] = solidity\n",
      "    X_test[i, imageSize + 13] = mean\n",
      "    X_test[i, imageSize + 14] = std\n",
      "    X_test[i, imageSize + 15] = size\n",
      "    \n",
      "    image2 = mh.imread(fileName, as_grey=True)\n",
      "            \n",
      "    haralick = mh.features.haralick(image2, ignore_zeros=False, preserve_haralick_bug=False, compute_14th_feature=False)\n",
      "    lbp      =  mh.features.lbp(image2, radius=20, points=7, ignore_zeros=False)\n",
      "    pftas    = mh.features.pftas(image2)\n",
      "    zernike_moments = mh.features.zernike_moments(image2, radius=20, degree=8)\n",
      "    \n",
      "    X_test[i, (imageSize + 15) :  (imageSize + 28)]   = haralick[0]    \n",
      "    X_test[i, (imageSize + 28) :  (imageSize + 41) ]  = haralick[1]\n",
      "    X_test[i, (imageSize + 41) :  (imageSize + 54) ]  = haralick[2]\n",
      "    X_test[i, (imageSize + 54) :  (imageSize + 67) ]  = haralick[3]\n",
      "    X_test[i, (imageSize + 67) :  (imageSize + 87) ]  = lbp\n",
      "    X_test[i, (imageSize + 87) :  (imageSize + 141) ] = pftas\n",
      "    X_test[i, (imageSize + 141) : (imageSize + 166) ] = zernike_moments\n",
      "    \n",
      "    if len(centroid) == 2:\n",
      "        X_test[i, (imageSize + 166) ] = centroid[0]\n",
      "        X_test[i, (imageSize + 167) ] = centroid[1]\n",
      "    else:\n",
      "        X_test[i, (imageSize + 166) ] = 0.0\n",
      "        X_test[i, (imageSize + 167) ] = 0.0\n",
      "\n",
      "\n",
      "    i += 1\n",
      "    if i in report: print np.ceil(i *100.0 / numberofTestImages), \"% done\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "5.0 % done\n",
        "10.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "15.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "20.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "25.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "30.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "35.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "40.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "45.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "50.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "55.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "60.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "65.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "70.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "75.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "80.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "85.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "90.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "95.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "100.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.DataFrame(X_test).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
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        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>...</th>\n",
        "      <th>943</th>\n",
        "      <th>944</th>\n",
        "      <th>945</th>\n",
        "      <th>946</th>\n",
        "      <th>947</th>\n",
        "      <th>948</th>\n",
        "      <th>949</th>\n",
        "      <th>950</th>\n",
        "      <th>951</th>\n",
        "      <th>952</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.998277</td>\n",
        "      <td> 0.999707</td>\n",
        "      <td> 0.998667</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.996961</td>\n",
        "      <td> 0.997411</td>\n",
        "      <td> 0.997074</td>\n",
        "      <td> 0.999175</td>\n",
        "      <td> 0.996429</td>\n",
        "      <td> 0.998179</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.003423</td>\n",
        "      <td> 0.000821</td>\n",
        "      <td> 0.009710</td>\n",
        "      <td> 0.023542</td>\n",
        "      <td> 0.038705</td>\n",
        "      <td> 0.028607</td>\n",
        "      <td> 0.032953</td>\n",
        "      <td> 28.337349</td>\n",
        "      <td> 15.084337</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.006973</td>\n",
        "      <td> 0.005002</td>\n",
        "      <td> 0.031817</td>\n",
        "      <td> 0.015820</td>\n",
        "      <td> 0.011946</td>\n",
        "      <td> 0.007181</td>\n",
        "      <td> 0.009510</td>\n",
        "      <td> 25.097222</td>\n",
        "      <td> 27.763889</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.003192</td>\n",
        "      <td> 0.016169</td>\n",
        "      <td> 0.001996</td>\n",
        "      <td> 0.012404</td>\n",
        "      <td> 0.023802</td>\n",
        "      <td> 0.011342</td>\n",
        "      <td> 0.008236</td>\n",
        "      <td> 40.596603</td>\n",
        "      <td> 46.559802</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.003675</td>\n",
        "      <td> 0.006002</td>\n",
        "      <td> 0.034921</td>\n",
        "      <td> 0.006374</td>\n",
        "      <td> 0.006086</td>\n",
        "      <td> 0.017690</td>\n",
        "      <td> 0.016164</td>\n",
        "      <td> 22.557971</td>\n",
        "      <td> 15.123188</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
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        "      <td> 1</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
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        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
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        "      <td> 0.018431</td>\n",
        "      <td> 0.015155</td>\n",
        "      <td> 30.897354</td>\n",
        "      <td> 31.449735</td>\n",
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        "<p>5 rows \u00d7 953 columns</p>\n",
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       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "        0         1         2    3         4         5         6         7    \\\n",
        "0  0.998277  0.999707  0.998667    1  0.996961  0.997411  0.997074  0.999175   \n",
        "1  1.000000  1.000000  1.000000    1  1.000000  1.000000  1.000000  1.000000   \n",
        "2  1.000000  1.000000  1.000000    1  1.000000  1.000000  1.000000  1.000000   \n",
        "3  1.000000  1.000000  1.000000    1  1.000000  1.000000  1.000000  1.000000   \n",
        "4  1.000000  1.000000  1.000000    1  1.000000  1.000000  1.000000  1.000000   \n",
        "\n",
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        "0  0.996429  0.998179    ...     0.003423  0.000821  0.009710  0.023542   \n",
        "1  1.000000  1.000000    ...     0.006973  0.005002  0.031817  0.015820   \n",
        "2  1.000000  1.000000    ...     0.003192  0.016169  0.001996  0.012404   \n",
        "3  1.000000  1.000000    ...     0.003675  0.006002  0.034921  0.006374   \n",
        "4  1.000000  1.000000    ...     0.019139  0.012200  0.021966  0.026231   \n",
        "\n",
        "        947       948       949        950        951  952  \n",
        "0  0.038705  0.028607  0.032953  28.337349  15.084337    0  \n",
        "1  0.011946  0.007181  0.009510  25.097222  27.763889    0  \n",
        "2  0.023802  0.011342  0.008236  40.596603  46.559802    0  \n",
        "3  0.006086  0.017690  0.016164  22.557971  15.123188    0  \n",
        "4  0.010651  0.018431  0.015155  30.897354  31.449735    0  \n",
        "\n",
        "[5 rows x 953 columns]"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(np.shape(X_test))\n",
      "pd.DataFrame(X_test).head(15)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(130400, 1135)\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>...</th>\n",
        "      <th>1125</th>\n",
        "      <th>1126</th>\n",
        "      <th>1127</th>\n",
        "      <th>1128</th>\n",
        "      <th>1129</th>\n",
        "      <th>1130</th>\n",
        "      <th>1131</th>\n",
        "      <th>1132</th>\n",
        "      <th>1133</th>\n",
        "      <th>1134</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 0.998277</td>\n",
        "      <td> 0.999707</td>\n",
        "      <td> 0.998667</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.996961</td>\n",
        "      <td> 0.997411</td>\n",
        "      <td> 0.997074</td>\n",
        "      <td> 0.999175</td>\n",
        "      <td> 0.996429</td>\n",
        "      <td> 0.998179</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.003824</td>\n",
        "      <td> 0.001238</td>\n",
        "      <td> 0.023009</td>\n",
        "      <td> 0.008094</td>\n",
        "      <td> 0.010532</td>\n",
        "      <td> 0.015707</td>\n",
        "      <td> 0.034497</td>\n",
        "      <td> 0.011339</td>\n",
        "      <td> 0.002849</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.037258</td>\n",
        "      <td> 0.013751</td>\n",
        "      <td> 0.014714</td>\n",
        "      <td> 0.010355</td>\n",
        "      <td> 0.036614</td>\n",
        "      <td> 0.011194</td>\n",
        "      <td> 0.014724</td>\n",
        "      <td> 0.009608</td>\n",
        "      <td> 0.028023</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.015627</td>\n",
        "      <td> 0.007321</td>\n",
        "      <td> 0.011909</td>\n",
        "      <td> 0.034768</td>\n",
        "      <td> 0.021798</td>\n",
        "      <td> 0.045485</td>\n",
        "      <td> 0.021706</td>\n",
        "      <td> 0.012733</td>\n",
        "      <td> 0.008117</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.008672</td>\n",
        "      <td> 0.001514</td>\n",
        "      <td> 0.020739</td>\n",
        "      <td> 0.007046</td>\n",
        "      <td> 0.012109</td>\n",
        "      <td> 0.011233</td>\n",
        "      <td> 0.035482</td>\n",
        "      <td> 0.009222</td>\n",
        "      <td> 0.001532</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.030366</td>\n",
        "      <td> 0.054045</td>\n",
        "      <td> 0.035649</td>\n",
        "      <td> 0.041810</td>\n",
        "      <td> 0.034360</td>\n",
        "      <td> 0.038001</td>\n",
        "      <td> 0.017819</td>\n",
        "      <td> 0.036830</td>\n",
        "      <td> 0.030872</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 0.975428</td>\n",
        "      <td> 0.993870</td>\n",
        "      <td> 0.993660</td>\n",
        "      <td> 0.999247</td>\n",
        "      <td> 0.994800</td>\n",
        "      <td> 0.990621</td>\n",
        "      <td> 0.997527</td>\n",
        "      <td> 0.995513</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.994330</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.026300</td>\n",
        "      <td> 0.029114</td>\n",
        "      <td> 0.006595</td>\n",
        "      <td> 0.024120</td>\n",
        "      <td> 0.019751</td>\n",
        "      <td> 0.018492</td>\n",
        "      <td> 0.011048</td>\n",
        "      <td> 0.025545</td>\n",
        "      <td> 0.002481</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.019361</td>\n",
        "      <td> 0.016889</td>\n",
        "      <td> 0.020117</td>\n",
        "      <td> 0.019915</td>\n",
        "      <td> 0.003006</td>\n",
        "      <td> 0.037276</td>\n",
        "      <td> 0.015632</td>\n",
        "      <td> 0.029200</td>\n",
        "      <td> 0.019845</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.029468</td>\n",
        "      <td> 0.005380</td>\n",
        "      <td> 0.039630</td>\n",
        "      <td> 0.007519</td>\n",
        "      <td> 0.027762</td>\n",
        "      <td> 0.009277</td>\n",
        "      <td> 0.007272</td>\n",
        "      <td> 0.010339</td>\n",
        "      <td> 0.044473</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 0.996083</td>\n",
        "      <td> 0.958996</td>\n",
        "      <td> 0.988858</td>\n",
        "      <td> 0.981793</td>\n",
        "      <td> 0.964188</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.985544</td>\n",
        "      <td> 0.956783</td>\n",
        "      <td> 0.972201</td>\n",
        "      <td> 0.996376</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.012776</td>\n",
        "      <td> 0.013267</td>\n",
        "      <td> 0.012150</td>\n",
        "      <td> 0.023546</td>\n",
        "      <td> 0.009693</td>\n",
        "      <td> 0.049653</td>\n",
        "      <td> 0.028316</td>\n",
        "      <td> 0.024644</td>\n",
        "      <td> 0.005269</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000254</td>\n",
        "      <td> 0.014349</td>\n",
        "      <td> 0.017165</td>\n",
        "      <td> 0.015188</td>\n",
        "      <td> 0.011329</td>\n",
        "      <td> 0.001231</td>\n",
        "      <td> 0.011802</td>\n",
        "      <td> 0.025298</td>\n",
        "      <td> 0.008825</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.004816</td>\n",
        "      <td> 0.008857</td>\n",
        "      <td> 0.018757</td>\n",
        "      <td> 0.028749</td>\n",
        "      <td> 0.020432</td>\n",
        "      <td> 0.031341</td>\n",
        "      <td> 0.032801</td>\n",
        "      <td> 0.001450</td>\n",
        "      <td> 0.005456</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.020449</td>\n",
        "      <td> 0.032956</td>\n",
        "      <td> 0.020433</td>\n",
        "      <td> 0.029217</td>\n",
        "      <td> 0.011803</td>\n",
        "      <td> 0.003172</td>\n",
        "      <td> 0.019681</td>\n",
        "      <td> 0.016883</td>\n",
        "      <td> 0.016900</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 0.996772</td>\n",
        "      <td> 0.998968</td>\n",
        "      <td> 0.998677</td>\n",
        "      <td> 0.995378</td>\n",
        "      <td> 0.997648</td>\n",
        "      <td> 0.998878</td>\n",
        "      <td> 0.998202</td>\n",
        "      <td> 0.997690</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.023588</td>\n",
        "      <td> 0.022777</td>\n",
        "      <td> 0.026474</td>\n",
        "      <td> 0.037315</td>\n",
        "      <td> 0.027038</td>\n",
        "      <td> 0.024345</td>\n",
        "      <td> 0.013050</td>\n",
        "      <td> 0.010599</td>\n",
        "      <td> 0.008440</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.010990</td>\n",
        "      <td> 0.014171</td>\n",
        "      <td> 0.020815</td>\n",
        "      <td> 0.006656</td>\n",
        "      <td> 0.017103</td>\n",
        "      <td> 0.020217</td>\n",
        "      <td> 0.033231</td>\n",
        "      <td> 0.028220</td>\n",
        "      <td> 0.008430</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.002504</td>\n",
        "      <td> 0.020044</td>\n",
        "      <td> 0.030852</td>\n",
        "      <td> 0.015908</td>\n",
        "      <td> 0.013891</td>\n",
        "      <td> 0.019435</td>\n",
        "      <td> 0.015670</td>\n",
        "      <td> 0.026109</td>\n",
        "      <td> 0.018495</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>15 rows \u00d7 1135 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 383,
       "text": [
        "        0         1         2         3         4         5         6     \\\n",
        "0   0.998277  0.999707  0.998667  1.000000  0.996961  0.997411  0.997074   \n",
        "1   1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
        "2   1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
        "3   1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
        "4   1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
        "5   0.975428  0.993870  0.993660  0.999247  0.994800  0.990621  0.997527   \n",
        "6   1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
        "7   1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
        "8   0.996083  0.958996  0.988858  0.981793  0.964188  1.000000  0.985544   \n",
        "9   1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
        "10  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
        "11  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
        "12  0.996772  0.998968  0.998677  0.995378  0.997648  0.998878  0.998202   \n",
        "13  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
        "14  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
        "\n",
        "        7         8         9       ...         1125      1126      1127  \\\n",
        "0   0.999175  0.996429  0.998179    ...     0.003824  0.001238  0.023009   \n",
        "1   1.000000  1.000000  1.000000    ...     0.037258  0.013751  0.014714   \n",
        "2   1.000000  1.000000  1.000000    ...     0.015627  0.007321  0.011909   \n",
        "3   1.000000  1.000000  1.000000    ...     0.008672  0.001514  0.020739   \n",
        "4   1.000000  1.000000  1.000000    ...     0.030366  0.054045  0.035649   \n",
        "5   0.995513  1.000000  0.994330    ...     0.026300  0.029114  0.006595   \n",
        "6   1.000000  1.000000  1.000000    ...     0.019361  0.016889  0.020117   \n",
        "7   1.000000  1.000000  1.000000    ...     0.029468  0.005380  0.039630   \n",
        "8   0.956783  0.972201  0.996376    ...     0.012776  0.013267  0.012150   \n",
        "9   1.000000  1.000000  1.000000    ...     0.000254  0.014349  0.017165   \n",
        "10  1.000000  1.000000  1.000000    ...     0.004816  0.008857  0.018757   \n",
        "11  1.000000  1.000000  1.000000    ...     0.020449  0.032956  0.020433   \n",
        "12  0.997690  1.000000  1.000000    ...     0.023588  0.022777  0.026474   \n",
        "13  1.000000  1.000000  1.000000    ...     0.010990  0.014171  0.020815   \n",
        "14  1.000000  1.000000  1.000000    ...     0.002504  0.020044  0.030852   \n",
        "\n",
        "        1128      1129      1130      1131      1132      1133  1134  \n",
        "0   0.008094  0.010532  0.015707  0.034497  0.011339  0.002849     0  \n",
        "1   0.010355  0.036614  0.011194  0.014724  0.009608  0.028023     0  \n",
        "2   0.034768  0.021798  0.045485  0.021706  0.012733  0.008117     0  \n",
        "3   0.007046  0.012109  0.011233  0.035482  0.009222  0.001532     0  \n",
        "4   0.041810  0.034360  0.038001  0.017819  0.036830  0.030872     0  \n",
        "5   0.024120  0.019751  0.018492  0.011048  0.025545  0.002481     0  \n",
        "6   0.019915  0.003006  0.037276  0.015632  0.029200  0.019845     0  \n",
        "7   0.007519  0.027762  0.009277  0.007272  0.010339  0.044473     0  \n",
        "8   0.023546  0.009693  0.049653  0.028316  0.024644  0.005269     0  \n",
        "9   0.015188  0.011329  0.001231  0.011802  0.025298  0.008825     0  \n",
        "10  0.028749  0.020432  0.031341  0.032801  0.001450  0.005456     0  \n",
        "11  0.029217  0.011803  0.003172  0.019681  0.016883  0.016900     0  \n",
        "12  0.037315  0.027038  0.024345  0.013050  0.010599  0.008440     0  \n",
        "13  0.006656  0.017103  0.020217  0.033231  0.028220  0.008430     0  \n",
        "14  0.015908  0.013891  0.019435  0.015670  0.026109  0.018495     0  \n",
        "\n",
        "[15 rows x 1135 columns]"
       ]
      }
     ],
     "prompt_number": 383
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 0-1 scaling for testing\n",
      "X_test = min_max_scaler.transform(X_test)\n",
      "\n",
      "## Mean 0 Varaince 1 \n",
      "#X_test = scaler.transform(X_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_test_pca = pca.transform(X_test)\n",
      "X_test_zca = zca.transform(X_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 277
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_test_pca = pd.DataFrame(X_test_pca)\n",
      "X_test_zca = pd.DataFrame(X_test_zca)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 278
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_test_pca.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "      <td>-0.512512</td>\n",
        "      <td>-0.115726</td>\n",
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        "      <td> 0.208747</td>\n",
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        "      <td> 0.012138</td>\n",
        "      <td> 0.133296</td>\n",
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        "      <td>-0.169288</td>\n",
        "      <td>-0.151698</td>\n",
        "      <td>-0.100305</td>\n",
        "      <td>-0.247559</td>\n",
        "      <td> 0.327404</td>\n",
        "      <td> 0.719458</td>\n",
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        "      <td>-2.639054</td>\n",
        "      <td> 0.451607</td>\n",
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        "      <td>-0.329587</td>\n",
        "      <td>-0.846240</td>\n",
        "      <td> 0.364924</td>\n",
        "      <td> 1.297726</td>\n",
        "      <td> 0.566092</td>\n",
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        "      <td>-0.442774</td>\n",
        "      <td> 0.300215</td>\n",
        "      <td>-0.311885</td>\n",
        "      <td> 0.714249</td>\n",
        "      <td>-1.106207</td>\n",
        "      <td>-0.204057</td>\n",
        "      <td> 0.851777</td>\n",
        "      <td> 0.343107</td>\n",
        "      <td>-0.156003</td>\n",
        "      <td> 0.216713</td>\n",
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        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-0.492077</td>\n",
        "      <td>-0.213381</td>\n",
        "      <td> 0.540160</td>\n",
        "      <td> 0.342050</td>\n",
        "      <td>-0.051944</td>\n",
        "      <td> 0.447368</td>\n",
        "      <td> 0.314058</td>\n",
        "      <td> 0.223167</td>\n",
        "      <td> 0.377174</td>\n",
        "      <td>-0.353439</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.242826</td>\n",
        "      <td>-0.265217</td>\n",
        "      <td>-0.199928</td>\n",
        "      <td>-0.113058</td>\n",
        "      <td>-1.504244</td>\n",
        "      <td>-0.393762</td>\n",
        "      <td> 0.017057</td>\n",
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        "      <td>-0.445363</td>\n",
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        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.137891</td>\n",
        "      <td> 3.005717</td>\n",
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        "      <td>-1.171985</td>\n",
        "      <td> 0.594212</td>\n",
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        "      <td>-0.424142</td>\n",
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        "      <td>-0.222506</td>\n",
        "      <td> 0.308849</td>\n",
        "      <td>-0.298658</td>\n",
        "      <td> 0.326227</td>\n",
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        "</table>\n",
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    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_test_zca.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "      <th>6</th>\n",
        "      <th>7</th>\n",
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        "      <td>-0.183223</td>\n",
        "      <td> 0.012318</td>\n",
        "      <td> 0.338665</td>\n",
        "      <td> 0.059752</td>\n",
        "      <td> 0.263512</td>\n",
        "      <td> 0</td>\n",
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        "      <td> 0.014297</td>\n",
        "      <td>-0.000766</td>\n",
        "      <td>-0.001202</td>\n",
        "      <td>-0.016751</td>\n",
        "      <td> 0.000074</td>\n",
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        "      <td> 0.016420</td>\n",
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        "      <td>-0.056656</td>\n",
        "      <td> 0.222173</td>\n",
        "      <td> 0.013357</td>\n",
        "      <td> 0.008409</td>\n",
        "      <td>-0.048621</td>\n",
        "      <td> 0.041689</td>\n",
        "      <td> 0</td>\n",
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        "      <td>-0.012287</td>\n",
        "      <td>-0.015491</td>\n",
        "      <td>...</td>\n",
        "      <td>-0.066032</td>\n",
        "      <td> 0.041485</td>\n",
        "      <td>-0.261107</td>\n",
        "      <td> 0.193970</td>\n",
        "      <td>-0.142229</td>\n",
        "      <td>-0.043486</td>\n",
        "      <td> 0.244691</td>\n",
        "      <td>-0.018391</td>\n",
        "      <td>-0.100155</td>\n",
        "      <td> 0</td>\n",
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        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.006336</td>\n",
        "      <td> 0.004255</td>\n",
        "      <td>-0.004369</td>\n",
        "      <td> 0.011567</td>\n",
        "      <td>-0.002050</td>\n",
        "      <td>-0.006573</td>\n",
        "      <td>-0.007801</td>\n",
        "      <td>-0.022246</td>\n",
        "      <td> 0.007342</td>\n",
        "      <td> 0.010725</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.281349</td>\n",
        "      <td>-0.069582</td>\n",
        "      <td>-0.136511</td>\n",
        "      <td>-0.008463</td>\n",
        "      <td> 0.208204</td>\n",
        "      <td>-0.271921</td>\n",
        "      <td>-0.199422</td>\n",
        "      <td> 0.076775</td>\n",
        "      <td> 0.037339</td>\n",
        "      <td> 0</td>\n",
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        "      <td>-0.027203</td>\n",
        "      <td>-0.013015</td>\n",
        "      <td> 0</td>\n",
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    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_test = pd.DataFrame(X_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### Write out the arrays just in case we need them later\n",
      "X_test.to_csv(\"/Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Data/X_test.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": "*"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_test = pd.read_csv(\"/Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Data/X_test.csv\")\n",
      "X_test.drop('Unnamed: 0', axis=1, inplace=True)\n",
      "X_test.head()"
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     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_test.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 249,
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        "       ..., \n",
        "       [ 1.        ,  1.        ,  1.        , ...,  0.6147017 ,\n",
        "         0.52788997,  0.        ],\n",
        "       [ 0.98439694,  0.99351631,  0.99202859, ...,  0.18769087,\n",
        "         0.14053628,  0.        ],\n",
        "       [ 1.        ,  1.        ,  1.        , ...,  0.12878364,\n",
        "         0.02445404,  0.        ]])"
       ]
      }
     ],
     "prompt_number": 249
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pickle \n",
      "PIK = \"/Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Test_Data_784_pixels.pkl\"\n",
      "#PIK = \"/Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Test_Data_784_pixels_zca.pkl\"\n",
      "#PIK = \"/Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Test_Data_784_pixels_pca.pkl\"\n",
      "#data = [X_test, y_test]\n",
      "data = [[X_test.head(100).values, y_test[0:100]],  ## just fill with blanks\n",
      "        [X_test.head(100).values, y_test[0:100]],  ## just fill with blanks\n",
      "        [X_test.values, y_test]]                   ## This is the real stuff\n",
      "with open(PIK, \"wb\") as f:\n",
      "    pickle.dump(data, f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!rm  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Test_Data_784_pixels.pkl.gz\n",
      "!gzip  /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Test_Data_784_pixels.pkl"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "rm: /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Test_Data_784_pixels.pkl.gz: No such file or directory\r\n"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls -l /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Test_Data_784_pixels.pkl.gz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-rw-r--r--  1 msegala  msegala  740325475 Jan 15 20:49 /Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Theano/Test_Data_784_pixels.pkl.gz\r\n"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'''\n",
      "Make a Submission using FR\n",
      "'''"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_pred = clf.predict_proba(X_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": "*"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_pred.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": "*"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(y_pred, columns=labels, index=images)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.index.name = 'image'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = df[header]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>acantharia_protist_big_center</th>\n",
        "      <th>acantharia_protist_halo</th>\n",
        "      <th>acantharia_protist</th>\n",
        "      <th>amphipods</th>\n",
        "      <th>appendicularian_fritillaridae</th>\n",
        "      <th>appendicularian_s_shape</th>\n",
        "      <th>appendicularian_slight_curve</th>\n",
        "      <th>appendicularian_straight</th>\n",
        "      <th>artifacts_edge</th>\n",
        "      <th>artifacts</th>\n",
        "      <th>...</th>\n",
        "      <th>trichodesmium_tuft</th>\n",
        "      <th>trochophore_larvae</th>\n",
        "      <th>tunicate_doliolid_nurse</th>\n",
        "      <th>tunicate_doliolid</th>\n",
        "      <th>tunicate_partial</th>\n",
        "      <th>tunicate_salp_chains</th>\n",
        "      <th>tunicate_salp</th>\n",
        "      <th>unknown_blobs_and_smudges</th>\n",
        "      <th>unknown_sticks</th>\n",
        "      <th>unknown_unclassified</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>image</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
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        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
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        "      <th>1.jpg</th>\n",
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        "      <td> 0.0000</td>\n",
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        "      <td> 0.0180</td>\n",
        "      <td> 0.0245</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10.jpg</th>\n",
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        "      <td> 0.0000</td>\n",
        "      <td> 0.0175</td>\n",
        "      <td> 0.0000</td>\n",
        "      <td> 0.0005</td>\n",
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        "      <td> 0.0020</td>\n",
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        "      <td> 0.0005</td>\n",
        "      <td> 0.0005</td>\n",
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        "      <td> 0.0000</td>\n",
        "      <td> 0.0050</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100.jpg</th>\n",
        "      <td> 0.000</td>\n",
        "      <td> 0.0065</td>\n",
        "      <td> 0.0145</td>\n",
        "      <td> 0.0010</td>\n",
        "      <td> 0.0015</td>\n",
        "      <td> 0.0085</td>\n",
        "      <td> 0.0085</td>\n",
        "      <td> 0.006</td>\n",
        "      <td> 0.0030</td>\n",
        "      <td> 0.0095</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.0065</td>\n",
        "      <td> 0.0015</td>\n",
        "      <td> 0.0085</td>\n",
        "      <td> 0.0070</td>\n",
        "      <td> 0.0015</td>\n",
        "      <td> 0.0010</td>\n",
        "      <td> 0.0120</td>\n",
        "      <td> 0.0025</td>\n",
        "      <td> 0.0005</td>\n",
        "      <td> 0.0065</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1000.jpg</th>\n",
        "      <td> 0.001</td>\n",
        "      <td> 0.0015</td>\n",
        "      <td> 0.0235</td>\n",
        "      <td> 0.0005</td>\n",
        "      <td> 0.0005</td>\n",
        "      <td> 0.0560</td>\n",
        "      <td> 0.0425</td>\n",
        "      <td> 0.004</td>\n",
        "      <td> 0.0000</td>\n",
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        "      <td>...</td>\n",
        "      <td> 0.0275</td>\n",
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        "      <td> 0.0000</td>\n",
        "      <td> 0.0010</td>\n",
        "      <td> 0.0010</td>\n",
        "      <td> 0.0100</td>\n",
        "      <td> 0.0190</td>\n",
        "      <td> 0.0080</td>\n",
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        "    <tr>\n",
        "      <th>10000.jpg</th>\n",
        "      <td> 0.000</td>\n",
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        "      <td> 0.0000</td>\n",
        "      <td> 0.0010</td>\n",
        "      <td> 0.0000</td>\n",
        "      <td> 0.0000</td>\n",
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        "      <td> 0.0000</td>\n",
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        "      <td> 0.0000</td>\n",
        "      <td> 0.0000</td>\n",
        "      <td> 0.0000</td>\n",
        "      <td> 0.0000</td>\n",
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        "      <td> 0.0000</td>\n",
        "      <td> 0.0020</td>\n",
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        "</table>\n",
        "<p>5 rows \u00d7 121 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "           acantharia_protist_big_center  acantharia_protist_halo  \\\n",
        "image                                                               \n",
        "1.jpg                              0.001                   0.0020   \n",
        "10.jpg                             0.000                   0.0000   \n",
        "100.jpg                            0.000                   0.0065   \n",
        "1000.jpg                           0.001                   0.0015   \n",
        "10000.jpg                          0.000                   0.0000   \n",
        "\n",
        "           acantharia_protist  amphipods  appendicularian_fritillaridae  \\\n",
        "image                                                                     \n",
        "1.jpg                  0.0215     0.0000                         0.0005   \n",
        "10.jpg                 0.0175     0.0000                         0.0005   \n",
        "100.jpg                0.0145     0.0010                         0.0015   \n",
        "1000.jpg               0.0235     0.0005                         0.0005   \n",
        "10000.jpg              0.0000     0.0010                         0.0000   \n",
        "\n",
        "           appendicularian_s_shape  appendicularian_slight_curve  \\\n",
        "image                                                              \n",
        "1.jpg                       0.0380                        0.0210   \n",
        "10.jpg                      0.0185                        0.0050   \n",
        "100.jpg                     0.0085                        0.0085   \n",
        "1000.jpg                    0.0560                        0.0425   \n",
        "10000.jpg                   0.0000                        0.0000   \n",
        "\n",
        "           appendicularian_straight  artifacts_edge  artifacts    ...      \\\n",
        "image                                                             ...       \n",
        "1.jpg                         0.020          0.0005     0.0210    ...       \n",
        "10.jpg                        0.002          0.0000     0.0020    ...       \n",
        "100.jpg                       0.006          0.0030     0.0095    ...       \n",
        "1000.jpg                      0.004          0.0000     0.0005    ...       \n",
        "10000.jpg                     0.000          0.0000     0.0000    ...       \n",
        "\n",
        "           trichodesmium_tuft  trochophore_larvae  tunicate_doliolid_nurse  \\\n",
        "image                                                                        \n",
        "1.jpg                  0.0130              0.0000                   0.0210   \n",
        "10.jpg                 0.0045              0.0010                   0.0005   \n",
        "100.jpg                0.0065              0.0015                   0.0085   \n",
        "1000.jpg               0.0275              0.0020                   0.0060   \n",
        "10000.jpg              0.0000              0.0000                   0.0000   \n",
        "\n",
        "           tunicate_doliolid  tunicate_partial  tunicate_salp_chains  \\\n",
        "image                                                                  \n",
        "1.jpg                 0.0140            0.0000                0.0035   \n",
        "10.jpg                0.0040            0.0000                0.0005   \n",
        "100.jpg               0.0070            0.0015                0.0010   \n",
        "1000.jpg              0.0095            0.0000                0.0010   \n",
        "10000.jpg             0.0000            0.0000                0.0000   \n",
        "\n",
        "           tunicate_salp  unknown_blobs_and_smudges  unknown_sticks  \\\n",
        "image                                                                 \n",
        "1.jpg             0.0000                     0.0110          0.0180   \n",
        "10.jpg            0.0005                     0.0210          0.0000   \n",
        "100.jpg           0.0120                     0.0025          0.0005   \n",
        "1000.jpg          0.0010                     0.0100          0.0190   \n",
        "10000.jpg         0.0000                     0.0335          0.0000   \n",
        "\n",
        "           unknown_unclassified  \n",
        "image                            \n",
        "1.jpg                    0.0245  \n",
        "10.jpg                   0.0050  \n",
        "100.jpg                  0.0065  \n",
        "1000.jpg                 0.0080  \n",
        "10000.jpg                0.0020  \n",
        "\n",
        "[5 rows x 121 columns]"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.to_csv('submission.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!gzip submission.csv "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls -l submission.csv.gz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-rw-r--r--  1 msegala  2022  15733739 Dec 17 20:52 submission.csv.gz\r\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'''\n",
      "Make a Submission using DBN which is trained elsewhere and results are fed in\n",
      "'''"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_pred = pd.read_csv(\"/Users/msegala/Documents/Personal/Kaggle/National_Data_Science_Bowl/Data/DBN_prediction_out.csv\")\n",
      "y_pred.drop('Unnamed: 0', axis=1, inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 121
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print np.shape(y_pred)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(130400, 121)\n"
       ]
      }
     ],
     "prompt_number": 122
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_pred.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 4.209204e-02</td>\n",
        "      <td> 0.001844</td>\n",
        "      <td> 2.754721e-05</td>\n",
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        "      <td> 8.486231e-07</td>\n",
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        "      <td> 2.433538e-05</td>\n",
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        "      <th>1</th>\n",
        "      <td> 1.744004e-05</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1.504915e-07</td>\n",
        "      <td> 0.000622</td>\n",
        "      <td> 8.163467e-08</td>\n",
        "      <td> 1.052215e-05</td>\n",
        "      <td> 9.570957e-06</td>\n",
        "      <td> 5.516410e-09</td>\n",
        "      <td> 6.755651e-09</td>\n",
        "      <td> 2.900165e-09</td>\n",
        "      <td> 0.001304</td>\n",
        "      <td> 7.456432e-07</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000007</td>\n",
        "      <td> 1.683236e-04</td>\n",
        "      <td> 5.564707e-08</td>\n",
        "      <td> 0.000012</td>\n",
        "      <td> 0.000003</td>\n",
        "      <td> 1.277566e-02</td>\n",
        "      <td> 9.110948e-08</td>\n",
        "      <td> 3.310113e-07</td>\n",
        "      <td> 4.266259e-08</td>\n",
        "      <td> 1.517999e-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 9.041952e-06</td>\n",
        "      <td> 0.000007</td>\n",
        "      <td> 2.561925e-06</td>\n",
        "      <td> 2.713777e-07</td>\n",
        "      <td> 3.589415e-07</td>\n",
        "      <td> 4.325502e-06</td>\n",
        "      <td> 7.613880e-04</td>\n",
        "      <td> 5.266941e-06</td>\n",
        "      <td> 0.000084</td>\n",
        "      <td> 2.126428e-06</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 3.079181e-08</td>\n",
        "      <td> 7.810617e-08</td>\n",
        "      <td> 0.000006</td>\n",
        "      <td> 0.000033</td>\n",
        "      <td> 1.054424e-08</td>\n",
        "      <td> 1.170541e-06</td>\n",
        "      <td> 1.606289e-07</td>\n",
        "      <td> 3.184167e-03</td>\n",
        "      <td> 2.086126e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 4.046911e-08</td>\n",
        "      <td> 0.000001</td>\n",
        "      <td> 4.404523e-09</td>\n",
        "      <td> 8.904023e-08</td>\n",
        "      <td> 1.567397e-08</td>\n",
        "      <td> 1.818184e-03</td>\n",
        "      <td> 6.549651e-06</td>\n",
        "      <td> 2.120695e-09</td>\n",
        "      <td> 0.000479</td>\n",
        "      <td> 1.951769e-05</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 7.657513e-08</td>\n",
        "      <td> 4.024586e-07</td>\n",
        "      <td> 0.000025</td>\n",
        "      <td> 0.000012</td>\n",
        "      <td> 6.461930e-07</td>\n",
        "      <td> 1.429574e-07</td>\n",
        "      <td> 8.457068e-07</td>\n",
        "      <td> 4.119918e-08</td>\n",
        "      <td> 3.035439e-08</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 121 columns</p>\n",
        "</div>"
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       "metadata": {},
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       "prompt_number": 123,
       "text": [
        "              0         1             2             3             4  \\\n",
        "0  4.209204e-02  0.001844  2.754721e-05  4.313973e-06  8.289287e-08   \n",
        "1  1.744004e-05  0.000085  1.578234e-05  2.965745e-07  5.260508e-07   \n",
        "2  1.504915e-07  0.000622  8.163467e-08  1.052215e-05  9.570957e-06   \n",
        "3  9.041952e-06  0.000007  2.561925e-06  2.713777e-07  3.589415e-07   \n",
        "4  4.046911e-08  0.000001  4.404523e-09  8.904023e-08  1.567397e-08   \n",
        "\n",
        "              5             6             7         8             9  \\\n",
        "0  3.244722e-05  3.643864e-04  7.261858e-05  0.001281  1.495125e-08   \n",
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        "2  5.516410e-09  6.755651e-09  2.900165e-09  0.001304  7.456432e-07   \n",
        "3  4.325502e-06  7.613880e-04  5.266941e-06  0.000084  2.126428e-06   \n",
        "4  1.818184e-03  6.549651e-06  2.120695e-09  0.000479  1.951769e-05   \n",
        "\n",
        "       ...            111           112           113       114       115  \\\n",
        "0      ...       0.000001  4.391015e-08  8.149540e-07  0.000001  0.000000   \n",
        "1      ...       0.000000  1.202999e-08  1.408844e-07  0.000022  0.000012   \n",
        "2      ...       0.000007  1.683236e-04  5.564707e-08  0.000012  0.000003   \n",
        "3      ...       0.000000  3.079181e-08  7.810617e-08  0.000006  0.000033   \n",
        "4      ...       0.000000  7.657513e-08  4.024586e-07  0.000025  0.000012   \n",
        "\n",
        "            116           117           118           119           120  \n",
        "0  1.065995e-07  8.486231e-07  2.431051e-07  1.096124e-04  2.433538e-05  \n",
        "1  1.959607e-08  4.388895e-08  2.525571e-08  5.688950e-02  1.663799e-03  \n",
        "2  1.277566e-02  9.110948e-08  3.310113e-07  4.266259e-08  1.517999e-06  \n",
        "3  1.054424e-08  1.170541e-06  1.606289e-07  3.184167e-03  2.086126e-04  \n",
        "4  6.461930e-07  1.429574e-07  8.457068e-07  4.119918e-08  3.035439e-08  \n",
        "\n",
        "[5 rows x 121 columns]"
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     ],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 124,
       "text": [
        "121"
       ]
      }
     ],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(images)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 125,
       "text": [
        "130400"
       ]
      }
     ],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(y_pred)\n",
      "df.columns = labels\n",
      "df.index = images"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>diatom_chain_string</th>\n",
        "      <th>protist_noctiluca</th>\n",
        "      <th>appendicularian_slight_curve</th>\n",
        "      <th>hydromedusae_haliscera_small_sideview</th>\n",
        "      <th>hydromedusae_aglaura</th>\n",
        "      <th>trichodesmium_multiple</th>\n",
        "      <th>crustacean_other</th>\n",
        "      <th>appendicularian_straight</th>\n",
        "      <th>protist_other</th>\n",
        "      <th>hydromedusae_narco_dark</th>\n",
        "      <th>...</th>\n",
        "      <th>siphonophore_physonect</th>\n",
        "      <th>hydromedusae_sideview_big</th>\n",
        "      <th>fish_larvae_leptocephali</th>\n",
        "      <th>hydromedusae_partial_dark</th>\n",
        "      <th>pteropod_butterfly</th>\n",
        "      <th>hydromedusae_haliscera</th>\n",
        "      <th>stomatopod</th>\n",
        "      <th>fish_larvae_medium_body</th>\n",
        "      <th>copepod_cyclopoid_oithona</th>\n",
        "      <th>echinoderm_larva_pluteus_urchin</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1.jpg</th>\n",
        "      <td> 4.209204e-02</td>\n",
        "      <td> 0.001844</td>\n",
        "      <td> 2.754721e-05</td>\n",
        "      <td> 4.313973e-06</td>\n",
        "      <td> 8.289287e-08</td>\n",
        "      <td> 3.244722e-05</td>\n",
        "      <td> 3.643864e-04</td>\n",
        "      <td> 7.261858e-05</td>\n",
        "      <td> 0.001281</td>\n",
        "      <td> 1.495125e-08</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000001</td>\n",
        "      <td> 4.391015e-08</td>\n",
        "      <td> 8.149540e-07</td>\n",
        "      <td> 0.000001</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 1.065995e-07</td>\n",
        "      <td> 8.486231e-07</td>\n",
        "      <td> 2.431051e-07</td>\n",
        "      <td> 1.096124e-04</td>\n",
        "      <td> 2.433538e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10.jpg</th>\n",
        "      <td> 1.744004e-05</td>\n",
        "      <td> 0.000085</td>\n",
        "      <td> 1.578234e-05</td>\n",
        "      <td> 2.965745e-07</td>\n",
        "      <td> 5.260508e-07</td>\n",
        "      <td> 7.433321e-05</td>\n",
        "      <td> 1.586424e-04</td>\n",
        "      <td> 4.443415e-07</td>\n",
        "      <td> 0.000391</td>\n",
        "      <td> 2.948902e-07</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 1.202999e-08</td>\n",
        "      <td> 1.408844e-07</td>\n",
        "      <td> 0.000022</td>\n",
        "      <td> 0.000012</td>\n",
        "      <td> 1.959607e-08</td>\n",
        "      <td> 4.388895e-08</td>\n",
        "      <td> 2.525571e-08</td>\n",
        "      <td> 5.688950e-02</td>\n",
        "      <td> 1.663799e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100.jpg</th>\n",
        "      <td> 1.504915e-07</td>\n",
        "      <td> 0.000622</td>\n",
        "      <td> 8.163467e-08</td>\n",
        "      <td> 1.052215e-05</td>\n",
        "      <td> 9.570957e-06</td>\n",
        "      <td> 5.516410e-09</td>\n",
        "      <td> 6.755651e-09</td>\n",
        "      <td> 2.900165e-09</td>\n",
        "      <td> 0.001304</td>\n",
        "      <td> 7.456432e-07</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000007</td>\n",
        "      <td> 1.683236e-04</td>\n",
        "      <td> 5.564707e-08</td>\n",
        "      <td> 0.000012</td>\n",
        "      <td> 0.000003</td>\n",
        "      <td> 1.277566e-02</td>\n",
        "      <td> 9.110948e-08</td>\n",
        "      <td> 3.310113e-07</td>\n",
        "      <td> 4.266259e-08</td>\n",
        "      <td> 1.517999e-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1000.jpg</th>\n",
        "      <td> 9.041952e-06</td>\n",
        "      <td> 0.000007</td>\n",
        "      <td> 2.561925e-06</td>\n",
        "      <td> 2.713777e-07</td>\n",
        "      <td> 3.589415e-07</td>\n",
        "      <td> 4.325502e-06</td>\n",
        "      <td> 7.613880e-04</td>\n",
        "      <td> 5.266941e-06</td>\n",
        "      <td> 0.000084</td>\n",
        "      <td> 2.126428e-06</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 3.079181e-08</td>\n",
        "      <td> 7.810617e-08</td>\n",
        "      <td> 0.000006</td>\n",
        "      <td> 0.000033</td>\n",
        "      <td> 1.054424e-08</td>\n",
        "      <td> 1.170541e-06</td>\n",
        "      <td> 1.606289e-07</td>\n",
        "      <td> 3.184167e-03</td>\n",
        "      <td> 2.086126e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10000.jpg</th>\n",
        "      <td> 4.046911e-08</td>\n",
        "      <td> 0.000001</td>\n",
        "      <td> 4.404523e-09</td>\n",
        "      <td> 8.904023e-08</td>\n",
        "      <td> 1.567397e-08</td>\n",
        "      <td> 1.818184e-03</td>\n",
        "      <td> 6.549651e-06</td>\n",
        "      <td> 2.120695e-09</td>\n",
        "      <td> 0.000479</td>\n",
        "      <td> 1.951769e-05</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 7.657513e-08</td>\n",
        "      <td> 4.024586e-07</td>\n",
        "      <td> 0.000025</td>\n",
        "      <td> 0.000012</td>\n",
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        "      <td> 8.457068e-07</td>\n",
        "      <td> 4.119918e-08</td>\n",
        "      <td> 3.035439e-08</td>\n",
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        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 121 columns</p>\n",
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       "prompt_number": 127,
       "text": [
        "           diatom_chain_string  protist_noctiluca  \\\n",
        "1.jpg             4.209204e-02           0.001844   \n",
        "10.jpg            1.744004e-05           0.000085   \n",
        "100.jpg           1.504915e-07           0.000622   \n",
        "1000.jpg          9.041952e-06           0.000007   \n",
        "10000.jpg         4.046911e-08           0.000001   \n",
        "\n",
        "           appendicularian_slight_curve  \\\n",
        "1.jpg                      2.754721e-05   \n",
        "10.jpg                     1.578234e-05   \n",
        "100.jpg                    8.163467e-08   \n",
        "1000.jpg                   2.561925e-06   \n",
        "10000.jpg                  4.404523e-09   \n",
        "\n",
        "           hydromedusae_haliscera_small_sideview  hydromedusae_aglaura  \\\n",
        "1.jpg                               4.313973e-06          8.289287e-08   \n",
        "10.jpg                              2.965745e-07          5.260508e-07   \n",
        "100.jpg                             1.052215e-05          9.570957e-06   \n",
        "1000.jpg                            2.713777e-07          3.589415e-07   \n",
        "10000.jpg                           8.904023e-08          1.567397e-08   \n",
        "\n",
        "           trichodesmium_multiple  crustacean_other  appendicularian_straight  \\\n",
        "1.jpg                3.244722e-05      3.643864e-04              7.261858e-05   \n",
        "10.jpg               7.433321e-05      1.586424e-04              4.443415e-07   \n",
        "100.jpg              5.516410e-09      6.755651e-09              2.900165e-09   \n",
        "1000.jpg             4.325502e-06      7.613880e-04              5.266941e-06   \n",
        "10000.jpg            1.818184e-03      6.549651e-06              2.120695e-09   \n",
        "\n",
        "           protist_other  hydromedusae_narco_dark           ...             \\\n",
        "1.jpg           0.001281             1.495125e-08           ...              \n",
        "10.jpg          0.000391             2.948902e-07           ...              \n",
        "100.jpg         0.001304             7.456432e-07           ...              \n",
        "1000.jpg        0.000084             2.126428e-06           ...              \n",
        "10000.jpg       0.000479             1.951769e-05           ...              \n",
        "\n",
        "           siphonophore_physonect  hydromedusae_sideview_big  \\\n",
        "1.jpg                    0.000001               4.391015e-08   \n",
        "10.jpg                   0.000000               1.202999e-08   \n",
        "100.jpg                  0.000007               1.683236e-04   \n",
        "1000.jpg                 0.000000               3.079181e-08   \n",
        "10000.jpg                0.000000               7.657513e-08   \n",
        "\n",
        "           fish_larvae_leptocephali  hydromedusae_partial_dark  \\\n",
        "1.jpg                  8.149540e-07                   0.000001   \n",
        "10.jpg                 1.408844e-07                   0.000022   \n",
        "100.jpg                5.564707e-08                   0.000012   \n",
        "1000.jpg               7.810617e-08                   0.000006   \n",
        "10000.jpg              4.024586e-07                   0.000025   \n",
        "\n",
        "           pteropod_butterfly  hydromedusae_haliscera    stomatopod  \\\n",
        "1.jpg                0.000000            1.065995e-07  8.486231e-07   \n",
        "10.jpg               0.000012            1.959607e-08  4.388895e-08   \n",
        "100.jpg              0.000003            1.277566e-02  9.110948e-08   \n",
        "1000.jpg             0.000033            1.054424e-08  1.170541e-06   \n",
        "10000.jpg            0.000012            6.461930e-07  1.429574e-07   \n",
        "\n",
        "           fish_larvae_medium_body  copepod_cyclopoid_oithona  \\\n",
        "1.jpg                 2.431051e-07               1.096124e-04   \n",
        "10.jpg                2.525571e-08               5.688950e-02   \n",
        "100.jpg               3.310113e-07               4.266259e-08   \n",
        "1000.jpg              1.606289e-07               3.184167e-03   \n",
        "10000.jpg             8.457068e-07               4.119918e-08   \n",
        "\n",
        "           echinoderm_larva_pluteus_urchin  \n",
        "1.jpg                         2.433538e-05  \n",
        "10.jpg                        1.663799e-03  \n",
        "100.jpg                       1.517999e-06  \n",
        "1000.jpg                      2.086126e-04  \n",
        "10000.jpg                     3.035439e-08  \n",
        "\n",
        "[5 rows x 121 columns]"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.index.name = 'image'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = df[header]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 129
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>acantharia_protist_big_center</th>\n",
        "      <th>acantharia_protist_halo</th>\n",
        "      <th>acantharia_protist</th>\n",
        "      <th>amphipods</th>\n",
        "      <th>appendicularian_fritillaridae</th>\n",
        "      <th>appendicularian_s_shape</th>\n",
        "      <th>appendicularian_slight_curve</th>\n",
        "      <th>appendicularian_straight</th>\n",
        "      <th>artifacts_edge</th>\n",
        "      <th>artifacts</th>\n",
        "      <th>...</th>\n",
        "      <th>trichodesmium_tuft</th>\n",
        "      <th>trochophore_larvae</th>\n",
        "      <th>tunicate_doliolid_nurse</th>\n",
        "      <th>tunicate_doliolid</th>\n",
        "      <th>tunicate_partial</th>\n",
        "      <th>tunicate_salp_chains</th>\n",
        "      <th>tunicate_salp</th>\n",
        "      <th>unknown_blobs_and_smudges</th>\n",
        "      <th>unknown_sticks</th>\n",
        "      <th>unknown_unclassified</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>image</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></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>1.jpg</th>\n",
        "      <td> 0.000001</td>\n",
        "      <td> 0.000002</td>\n",
        "      <td> 0.000126</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 2.822413e-05</td>\n",
        "      <td> 7.345952e-04</td>\n",
        "      <td> 2.754721e-05</td>\n",
        "      <td> 7.261858e-05</td>\n",
        "      <td> 0.000295</td>\n",
        "      <td> 0.002911</td>\n",
        "      <td>...</td>\n",
        "      <td> 2.803320e-04</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 5.823364e-05</td>\n",
        "      <td> 0.000091</td>\n",
        "      <td> 4.971785e-05</td>\n",
        "      <td> 1.430399e-06</td>\n",
        "      <td> 0.000012</td>\n",
        "      <td> 0.051860</td>\n",
        "      <td> 0.024934</td>\n",
        "      <td> 0.000196</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10.jpg</th>\n",
        "      <td> 0.000027</td>\n",
        "      <td> 0.000044</td>\n",
        "      <td> 0.001874</td>\n",
        "      <td> 0.000001</td>\n",
        "      <td> 3.626214e-06</td>\n",
        "      <td> 3.621871e-07</td>\n",
        "      <td> 1.578234e-05</td>\n",
        "      <td> 4.443415e-07</td>\n",
        "      <td> 0.000016</td>\n",
        "      <td> 0.000093</td>\n",
        "      <td>...</td>\n",
        "      <td> 6.625888e-04</td>\n",
        "      <td> 0.000015</td>\n",
        "      <td> 8.108461e-08</td>\n",
        "      <td> 0.000005</td>\n",
        "      <td> 9.914502e-07</td>\n",
        "      <td> 1.376727e-08</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.140667</td>\n",
        "      <td> 0.000015</td>\n",
        "      <td> 0.000277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100.jpg</th>\n",
        "      <td> 0.000007</td>\n",
        "      <td> 0.000427</td>\n",
        "      <td> 0.000010</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 4.144632e-07</td>\n",
        "      <td> 1.081621e-08</td>\n",
        "      <td> 8.163467e-08</td>\n",
        "      <td> 2.900165e-09</td>\n",
        "      <td> 0.000019</td>\n",
        "      <td> 0.000009</td>\n",
        "      <td>...</td>\n",
        "      <td> 3.253605e-09</td>\n",
        "      <td> 0.000011</td>\n",
        "      <td> 3.207374e-06</td>\n",
        "      <td> 0.000038</td>\n",
        "      <td> 5.211473e-07</td>\n",
        "      <td> 2.413651e-07</td>\n",
        "      <td> 0.000013</td>\n",
        "      <td> 0.000011</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000098</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1000.jpg</th>\n",
        "      <td> 0.000035</td>\n",
        "      <td> 0.000008</td>\n",
        "      <td> 0.000143</td>\n",
        "      <td> 0.000030</td>\n",
        "      <td> 3.430862e-05</td>\n",
        "      <td> 1.595678e-06</td>\n",
        "      <td> 2.561925e-06</td>\n",
        "      <td> 5.266941e-06</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 3.043980e-04</td>\n",
        "      <td> 0.000057</td>\n",
        "      <td> 9.389372e-08</td>\n",
        "      <td> 0.000001</td>\n",
        "      <td> 1.554755e-08</td>\n",
        "      <td> 9.494240e-09</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.002569</td>\n",
        "      <td> 0.000494</td>\n",
        "      <td> 0.002566</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10000.jpg</th>\n",
        "      <td> 0.000007</td>\n",
        "      <td> 0.000003</td>\n",
        "      <td> 0.000019</td>\n",
        "      <td> 0.000007</td>\n",
        "      <td> 4.164263e-08</td>\n",
        "      <td> 2.520597e-10</td>\n",
        "      <td> 4.404523e-09</td>\n",
        "      <td> 2.120695e-09</td>\n",
        "      <td> 0.000005</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 3.494301e-05</td>\n",
        "      <td> 0.000010</td>\n",
        "      <td> 1.387667e-08</td>\n",
        "      <td> 0.000001</td>\n",
        "      <td> 3.836712e-06</td>\n",
        "      <td> 3.294610e-07</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.002931</td>\n",
        "      <td> 0.000001</td>\n",
        "      <td> 0.000050</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 121 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 130,
       "text": [
        "           acantharia_protist_big_center  acantharia_protist_halo  \\\n",
        "image                                                               \n",
        "1.jpg                           0.000001                 0.000002   \n",
        "10.jpg                          0.000027                 0.000044   \n",
        "100.jpg                         0.000007                 0.000427   \n",
        "1000.jpg                        0.000035                 0.000008   \n",
        "10000.jpg                       0.000007                 0.000003   \n",
        "\n",
        "           acantharia_protist  amphipods  appendicularian_fritillaridae  \\\n",
        "image                                                                     \n",
        "1.jpg                0.000126   0.000000                   2.822413e-05   \n",
        "10.jpg               0.001874   0.000001                   3.626214e-06   \n",
        "100.jpg              0.000010   0.000000                   4.144632e-07   \n",
        "1000.jpg             0.000143   0.000030                   3.430862e-05   \n",
        "10000.jpg            0.000019   0.000007                   4.164263e-08   \n",
        "\n",
        "           appendicularian_s_shape  appendicularian_slight_curve  \\\n",
        "image                                                              \n",
        "1.jpg                 7.345952e-04                  2.754721e-05   \n",
        "10.jpg                3.621871e-07                  1.578234e-05   \n",
        "100.jpg               1.081621e-08                  8.163467e-08   \n",
        "1000.jpg              1.595678e-06                  2.561925e-06   \n",
        "10000.jpg             2.520597e-10                  4.404523e-09   \n",
        "\n",
        "           appendicularian_straight  artifacts_edge  artifacts    ...      \\\n",
        "image                                                             ...       \n",
        "1.jpg                  7.261858e-05        0.000295   0.002911    ...       \n",
        "10.jpg                 4.443415e-07        0.000016   0.000093    ...       \n",
        "100.jpg                2.900165e-09        0.000019   0.000009    ...       \n",
        "1000.jpg               5.266941e-06        0.000000   0.000000    ...       \n",
        "10000.jpg              2.120695e-09        0.000005   0.000000    ...       \n",
        "\n",
        "           trichodesmium_tuft  trochophore_larvae  tunicate_doliolid_nurse  \\\n",
        "image                                                                        \n",
        "1.jpg            2.803320e-04            0.000000             5.823364e-05   \n",
        "10.jpg           6.625888e-04            0.000015             8.108461e-08   \n",
        "100.jpg          3.253605e-09            0.000011             3.207374e-06   \n",
        "1000.jpg         3.043980e-04            0.000057             9.389372e-08   \n",
        "10000.jpg        3.494301e-05            0.000010             1.387667e-08   \n",
        "\n",
        "           tunicate_doliolid  tunicate_partial  tunicate_salp_chains  \\\n",
        "image                                                                  \n",
        "1.jpg               0.000091      4.971785e-05          1.430399e-06   \n",
        "10.jpg              0.000005      9.914502e-07          1.376727e-08   \n",
        "100.jpg             0.000038      5.211473e-07          2.413651e-07   \n",
        "1000.jpg            0.000001      1.554755e-08          9.494240e-09   \n",
        "10000.jpg           0.000001      3.836712e-06          3.294610e-07   \n",
        "\n",
        "           tunicate_salp  unknown_blobs_and_smudges  unknown_sticks  \\\n",
        "image                                                                 \n",
        "1.jpg           0.000012                   0.051860        0.024934   \n",
        "10.jpg          0.000000                   0.140667        0.000015   \n",
        "100.jpg         0.000013                   0.000011        0.000000   \n",
        "1000.jpg        0.000000                   0.002569        0.000494   \n",
        "10000.jpg       0.000000                   0.002931        0.000001   \n",
        "\n",
        "           unknown_unclassified  \n",
        "image                            \n",
        "1.jpg                  0.000196  \n",
        "10.jpg                 0.000277  \n",
        "100.jpg                0.000098  \n",
        "1000.jpg               0.002566  \n",
        "10000.jpg              0.000050  \n",
        "\n",
        "[5 rows x 121 columns]"
       ]
      }
     ],
     "prompt_number": 130
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.to_csv('submission_DBN.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!rm submission_DBN.csv.gz \n",
      "!gzip submission_DBN.csv "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls -lrth submission_DBN.csv.gz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-rw-r--r--  1 msegala  msegala   147M Jan 16 13:40 submission_DBN.csv.gz\r\n"
       ]
      }
     ],
     "prompt_number": 133
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}