{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre recognition: experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: Test if the cosine similarity (with zero-mean data) or the euclidean distance with a Gaussian kernel (without zero-mean data) provides a better graph that the cosine distance with a Gaussian kernel (without zero-mean data).\n",
    "\n",
    "Conclusion: `cosine_sim` and `euclidean` metrics provide a 2% increase in accuracy with respect to `cosine_dist`.\n",
    "\n",
    "Observations:\n",
    "* With `Ncv`=20 (thanks to decreased variance), 10 minutes per classification (instead of 20).\n",
    "* With `lg`=100, 10 minutes for feature extraction.\n",
    "* 10 minutes per graph. Should run for 1h30.\n",
    "* `cosine_dist` failed because it was miss-spelled. Hopefully all the last experiments used this metric. Accuracy was 64.01% in `13c_novoting` and 64.03% in `13d_noise_level`.\n",
    "* `euclidean` is 7% faster than `cosine_sim` while `cosine_dist` is the fastest with an increse of 47% to `cosine_sim`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyper-parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter under test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Pname = 'dm'\n",
    "Pvalues = ['cosine_sim', 'cosine_dim', 'euclidean']\n",
    "\n",
    "# Regenerate the graph or the features at each iteration.\n",
    "regen_graph = True\n",
    "regen_features = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p = {}\n",
    "\n",
    "# Preprocessing.\n",
    "\n",
    "# Graph.\n",
    "p['K'] = 10 + 1  # 5 to 10 + 1 for self-reference\n",
    "p['dm'] = 'cosine'\n",
    "p['Csigma'] = 1\n",
    "p['diag'] = True\n",
    "p['laplacian'] = 'normalized'\n",
    "\n",
    "# Feature extraction.\n",
    "p['m'] = 128  # 64, 128, 512\n",
    "p['ls'] = 1\n",
    "p['ld'] = 10\n",
    "p['le'] = None\n",
    "p['lg'] = 100\n",
    "\n",
    "# Classification.\n",
    "p['scale'] = None\n",
    "p['Nvectors'] = 6\n",
    "p['svm_type'] = 'C'\n",
    "p['kernel'] = 'linear'\n",
    "p['C'] = 1\n",
    "p['nu'] = 0.5\n",
    "p['majority_voting'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# HDF5 data stores.\n",
    "p['folder'] = 'data'\n",
    "p['filename_gtzan'] = 'gtzan.hdf5'\n",
    "p['filename_audio'] = 'audio.hdf5'\n",
    "p['filename_graph'] = 'graph.hdf5'\n",
    "p['filename_features'] = 'features.hdf5'\n",
    "\n",
    "# Dataset (10,100,644 | 5,100,149 | 2,10,644).\n",
    "p['Ngenres'] = 5\n",
    "p['Nclips'] = 100\n",
    "p['Nframes'] = 149\n",
    "\n",
    "# Added white noise.\n",
    "p['noise_std'] = 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Graph.\n",
    "p['tol'] = 1e-5\n",
    "\n",
    "# Feature extraction.\n",
    "p['rtol'] = 1e-5  # 1e-3, 1e-5, 1e-7\n",
    "p['N_inner'] = 500\n",
    "p['N_outer'] = 50\n",
    "\n",
    "# Classification.\n",
    "p['Nfolds'] = 10\n",
    "p['Ncv'] = 20\n",
    "p['dataset_classification'] = 'Z'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "\n",
    "texperiment = time.time()\n",
    "\n",
    "# Result dictionary.\n",
    "res = ['accuracy', 'accuracy_std']\n",
    "res += ['sparsity', 'atoms']\n",
    "res += ['objective_g', 'objective_h', 'objective_i', 'objective_j']\n",
    "res += ['time_features', 'iterations_inner', 'iterations_outer']\n",
    "res = dict.fromkeys(res)\n",
    "for key in res.keys():\n",
    "    res[key] = []\n",
    "\n",
    "def separator(name, parameter=False):\n",
    "    if parameter:\n",
    "        name += ', {} = {}'.format(Pname, p[Pname])\n",
    "    dashes = 20 * '-'\n",
    "    print('\\n {} {} {} \\n'.format(dashes, name, dashes))\n",
    "    # Fair comparison when tuning parameters.\n",
    "    # Randomnesses: dictionary initialization, training and testing sets.\n",
    "    np.random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " -------------------- Graph, dm = cosine_sim -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 1257.85 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.560503005981, 1.0]\n",
      "w in [0.560503005981, 1.0]\n",
      "Ones on the diagonal: 149000 (over 149000)\n",
      "assert: True\n",
      "W in [0.0, 1.0]\n",
      "Datasets:\n",
      "  L_data    : (2925296,), float32\n",
      "  L_indices : (2925296,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2925296,), float32\n",
      "  W_indices : (2925296,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = cosine_sim\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Overall time: 1267.74 seconds\n",
      "\n",
      " -------------------- Features, dm = cosine_sim -------------------- \n",
      "\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  Xa: (10, 100, 644, 2, 1024) , float32\n",
      "  Xs: (10, 100, 644, 2, 96)   , float32\n",
      "Full dataset:\n",
      "  size: N=1,288,000 x n=96 -> 123,648,000 floats\n",
      "  dim: 123,648 features per clip\n",
      "  shape: (10, 100, 644, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Data: (149000, 96), float32\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = cosine_sim\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2925296,), float32\n",
      "  L_indices : (2925296,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2925296,), float32\n",
      "  W_indices : (2925296,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Size X: 13.6 M --> 54.6 MiB\n",
      "Size Z: 18.2 M --> 72.8 MiB\n",
      "Size D: 12.0 k --> 48.0 kiB\n",
      "Size E: 12.0 k --> 48.0 kiB\n",
      "Elapsed time: 982 seconds\n"
     ]
    },
    {
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       "rrMxtS01Ij2lGrmIyWtQV2O126uAE4BvYrW9K7A/3vcAb4jrF2tdvwX+FfgrdtLVJYXXvoM1w16O\n",
       "9Snn7zsDeEn8zAp2Etc9WJ9wfY2ulRpevs5FwG0xy/Xxc8u2U2nwPC/wfoadNPZDbP8BDsOa3adh\n",
       "ByjvB+6YJN9FwA40bnqv//xGeYrPz8W+n/z7XY6dgFf/3jJ/xwre87Hf81dUz4koW/4IdjB0GLAU\n",
       "O/iagX0va8f1v4F1iRDXbffKBxERkb41FztJcFC1c/ngNOwgUJUkSUZN6yLSaw9jNd2XeQeZonb6\n",
       "u18DnI6dpyAiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLi5f8DncMY9KCFpSYAAAAASUVORK5C\n",
       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1068ea1cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 384 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.365278e+05\n",
      "rdiff: 0.000154546947268\n",
      "i(Z) = ||Z||_1 = 7.460725e+04\n",
      "j(Z) = tr(Z^TLZ) = 2.092982e+04\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f104dbc7110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 8.320649e+05\n"
     ]
    },
    {
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       "axEREWnRCqRKQLc6kP6fS/FDUmVGpFTUwiAi3eg1Up+Oj3oHaVFfLT6QLkdsS7oFVURERERERERE\n",
       "REREREREREREREREREREREREREREOu//A83vN03fw0vSAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f104d618cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 5 iterations\n",
      "\n",
      "Z in [-0.0605589039624, 0.252947568893]\n",
      "Sparsity of Z: 8,314,786 non-zero entries out of 19,072,000 entries, i.e. 43.6%.\n"
     ]
    },
    {
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       "/x+mSpx3+7gcxQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f104d743990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.138522788882, 0.704355895519]\n",
      "d in [0.999999701977, 1.00000023842]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f104d6a4210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "gg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f104d814250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "rkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f104d683450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasets:\n",
      "  D : (128, 96)             , float32\n",
      "  X : (5, 100, 149, 2, 96)  , float32\n",
      "  Z : (5, 100, 149, 2, 128) , float32\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Overall time: 990 seconds\n",
      "\n",
      " -------------------- Classification, dm = cosine_sim -------------------- \n",
      "\n",
      "Software versions:\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
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       "xhhjjDHGGGOMMcYYY4yZIDf32i6NruVwpwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1034138a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 65.3 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 60.0 %\n",
      "Clips accuracy: 67.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "  67 (+/- 1.5) <- [65 65 64 68 68 68 66 64 66 67]\n",
      "  66 (+/- 3.0) <- [60 70 68 67 64 64 71 66 65 66]\n",
      "  66 (+/- 1.7) <- [67 66 62 67 67 66 67 65 65 68]\n",
      "  66 (+/- 1.1) <- [64 67 66 67 64 66 68 65 66 65]\n",
      "  66 (+/- 2.1) <- [63 66 68 66 65 64 66 71 65 66]\n",
      "  67 (+/- 2.1) <- [65 65 65 71 68 66 65 68 63 65]\n",
      "  67 (+/- 1.6) <- [66 65 66 68 68 65 67 63 68 67]\n",
      "  67 (+/- 1.4) <- [67 66 65 68 66 68 64 67 68 64]\n",
      "  66 (+/- 2.1) <- [68 65 66 68 64 61 68 68 67 66]\n",
      "  67 (+/- 1.8) <- [70 69 65 67 68 67 65 65 66 64]\n",
      "  67 (+/- 1.6) <- [66 68 66 67 68 69 66 66 66 62]\n",
      "  67 (+/- 1.9) <- [63 67 67 68 65 68 67 66 62 68]\n",
      "  67 (+/- 2.3) <- [67 68 68 68 66 65 68 61 63 66]\n",
      "  67 (+/- 1.9) <- [63 65 67 67 67 66 65 65 67 71]\n",
      "  67 (+/- 2.7) <- [65 66 63 66 70 62 67 68 71 64]\n",
      "  66 (+/- 1.4) <- [66 65 65 69 64 66 65 67 65 67]\n",
      "  66 (+/- 1.7) <- [67 63 66 66 68 68 64 66 65 64]\n",
      "  66 (+/- 2.4) <- [66 62 67 66 67 71 64 66 64 66]\n",
      "  67 (+/- 1.6) <- [65 66 67 64 65 66 64 69 67 69]\n",
      "  66 (+/- 1.7) <- [65 67 65 67 68 66 67 64 62 67]\n",
      "Accuracy: 66.5 (+/- 1.94)\n",
      "Mean time (20 cv): 21.83 seconds\n",
      "Overall time: 441.52 seconds\n",
      "\n",
      " -------------------- Graph, dm = cosine_dim -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 1294.85 seconds\n",
      "All self-referenced in the first column: True\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-a8e755cefddd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mdist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mdist\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m4.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'dist in [{}, {}]'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " -------------------- Features, dm = cosine_dim -------------------- \n",
      "\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  Xa: (10, 100, 644, 2, 1024) , float32\n",
      "  Xs: (10, 100, 644, 2, 96)   , float32\n",
      "Full dataset:\n",
      "  size: N=1,288,000 x n=96 -> 123,648,000 floats\n",
      "  dim: 123,648 features per clip\n",
      "  shape: (10, 100, 644, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Data: (149000, 96), float32\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = cosine_sim\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2925296,), float32\n",
      "  L_indices : (2925296,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2925296,), float32\n",
      "  W_indices : (2925296,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Size X: 13.6 M --> 54.6 MiB\n",
      "Size Z: 18.2 M --> 72.8 MiB\n",
      "Size D: 12.0 k --> 48.0 kiB\n",
      "Size E: 12.0 k --> 48.0 kiB\n",
      "Elapsed time: 973 seconds\n"
     ]
    },
    {
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       "ntnCtvIug274b2xUuTlxftNCnslUsO93d6pN6G/BauKNll8Q3zcNeFN8fgD2u41h+z6BDe+5NjA3\n",
       "rrMxtS01Ij2lGrmIyWtQV2O126uAE4BvYrW9K7A/3vcAb4jrF2tdvwX+FfgrdtLVJYXXvoM1w16O\n",
       "9Snn7zsDeEn8zAp2Etc9WJ9wfY2ulRpevs5FwG0xy/Xxc8u2U2nwPC/wfoadNPZDbP8BDsOa3adh\n",
       "ByjvB+6YJN9FwA40bnqv//xGeYrPz8W+n/z7XY6dgFf/3jJ/xwre87Hf81dUz4koW/4IdjB0GLAU\n",
       "O/iagX0va8f1v4F1iRDXbffKBxERkb41FztJcFC1c/ngNOwgUJUkSUZN6yLSaw9jNd2XeQeZonb6\n",
       "u18DnI6dpyAiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLi5f8DncMY9KCFpSYAAAAASUVORK5C\n",
       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1064f448d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 384 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.365278e+05\n",
      "rdiff: 0.000154546947268\n",
      "i(Z) = ||Z||_1 = 7.460725e+04\n",
      "j(Z) = tr(Z^TLZ) = 2.092982e+04\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1064e69550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 8.320649e+05\n"
     ]
    },
    {
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       "axEREWnRCqRKQLc6kP6fS/FDUmVGpFTUwiAi3eg1Up+Oj3oHaVFfLT6QLkdsS7oFVURERERERERE\n",
       "REREREREREREREREREREREREREREOu//A83vN03fw0vSAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1064d2f0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 5 iterations\n",
      "\n",
      "Z in [-0.0605589039624, 0.252947568893]\n",
      "Sparsity of Z: 8,314,786 non-zero entries out of 19,072,000 entries, i.e. 43.6%.\n"
     ]
    },
    {
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       "f1faPwr3jrD81bpX4iKbfi/1+SdYqgW3YSnFP3ttf/L6/VVYKhY/ZPQNBoPBYDA4ATwfS6l8MBis\n",
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       "/x+mSpx3+7gcxQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10abdd6a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.138522788882, 0.704355895519]\n",
      "d in [0.999999701977, 1.00000023842]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10685c3250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "gg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f102e737150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "rkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f104d8140d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasets:\n",
      "  D : (128, 96)             , float32\n",
      "  X : (5, 100, 149, 2, 96)  , float32\n",
      "  Z : (5, 100, 149, 2, 128) , float32\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Overall time: 982 seconds\n",
      "\n",
      " -------------------- Classification, dm = cosine_dim -------------------- \n",
      "\n",
      "Software versions:\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
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       "xhhjjDHGGGOMMcYYY4yZIDf32i6NruVwpwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1064fad3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 65.3 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 60.0 %\n",
      "Clips accuracy: 67.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "  67 (+/- 1.5) <- [65 65 64 68 68 68 66 64 66 67]\n",
      "  66 (+/- 3.0) <- [60 70 68 67 64 64 71 66 65 66]\n",
      "  66 (+/- 1.7) <- [67 66 62 67 67 66 67 65 65 68]\n",
      "  66 (+/- 1.1) <- [64 67 66 67 64 66 68 65 66 65]\n",
      "  66 (+/- 2.1) <- [63 66 68 66 65 64 66 71 65 66]\n",
      "  67 (+/- 2.1) <- [65 65 65 71 68 66 65 68 63 65]\n",
      "  67 (+/- 1.6) <- [66 65 66 68 68 65 67 63 68 67]\n",
      "  67 (+/- 1.4) <- [67 66 65 68 66 68 64 67 68 64]\n",
      "  66 (+/- 2.1) <- [68 65 66 68 64 61 68 68 67 66]\n",
      "  67 (+/- 1.8) <- [70 69 65 67 68 67 65 65 66 64]\n",
      "  67 (+/- 1.6) <- [66 68 66 67 68 69 66 66 66 62]\n",
      "  67 (+/- 1.9) <- [63 67 67 68 65 68 67 66 62 68]\n",
      "  67 (+/- 2.3) <- [67 68 68 68 66 65 68 61 63 66]\n",
      "  67 (+/- 1.9) <- [63 65 67 67 67 66 65 65 67 71]\n",
      "  67 (+/- 2.7) <- [65 66 63 66 70 62 67 68 71 64]\n",
      "  66 (+/- 1.4) <- [66 65 65 69 64 66 65 67 65 67]\n",
      "  66 (+/- 1.7) <- [67 63 66 66 68 68 64 66 65 64]\n",
      "  66 (+/- 2.4) <- [66 62 67 66 67 71 64 66 64 66]\n",
      "  67 (+/- 1.6) <- [65 66 67 64 65 66 64 69 67 69]\n",
      "  66 (+/- 1.7) <- [65 67 65 67 68 66 67 64 62 67]\n",
      "Accuracy: 66.5 (+/- 1.94)\n",
      "Mean time (20 cv): 21.77 seconds\n",
      "Overall time: 440.49 seconds\n",
      "\n",
      " -------------------- Graph, dm = euclidean -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 1183.53 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 2.08629345894]\n",
      "w in [0.198544532061, 1.0]\n",
      "Ones on the diagonal: 149000 (over 149000)\n",
      "assert: True\n",
      "W in [0.0, 1.0]\n",
      "Datasets:\n",
      "  L_data    : (2919394,), float32\n",
      "  L_indices : (2919394,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2919394,), float32\n",
      "  W_indices : (2919394,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Overall time: 1193.83 seconds\n",
      "\n",
      " -------------------- Features, dm = euclidean -------------------- \n",
      "\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  Xa: (10, 100, 644, 2, 1024) , float32\n",
      "  Xs: (10, 100, 644, 2, 96)   , float32\n",
      "Full dataset:\n",
      "  size: N=1,288,000 x n=96 -> 123,648,000 floats\n",
      "  dim: 123,648 features per clip\n",
      "  shape: (10, 100, 644, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Data: (149000, 96), float32\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2919394,), float32\n",
      "  L_indices : (2919394,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2919394,), float32\n",
      "  W_indices : (2919394,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Size X: 13.6 M --> 54.6 MiB\n",
      "Size Z: 18.2 M --> 72.8 MiB\n",
      "Size D: 12.0 k --> 48.0 kiB\n",
      "Size E: 12.0 k --> 48.0 kiB\n",
      "Elapsed time: 905 seconds\n"
     ]
    },
    {
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       "eyJqLX8EOxn6PLAUO/mahP1e1onrfxe7JEJct9UnH0RERMatWdhNgoOqlccHJ2AngaokSd+oaV1E\n",
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       "WPQAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f102de36e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 358 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.395564e+05\n",
      "rdiff: 0.000189513942163\n",
      "i(Z) = ||Z||_1 = 7.337558e+04\n",
      "j(Z) = tr(Z^TLZ) = 2.211262e+04\n"
     ]
    },
    {
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       "/1//3hd8vFf7+bV1XgS8m3rHuyP9+m4A/kLj7TjbxVF779vY/8EbgXOwjoYvtpnf6vsA2IbGYy0i\n",
       "IjJQVscSd1EtoPW4AV/BTkBEckMleRHpp2VYH4XdYgfSpbSaFbCq+p2xyw1FRERERERERERERERE\n",
       "REREREREREREREREREREJKz/DxDBNI+77ovLAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10650a8050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 8.350446e+05\n"
     ]
    },
    {
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       "kwvsReXn00fZzki/oFUR+BtwKLmj4HXkVoUzynsnlry/Lsub27wI2J9Wp8ejy/YWAb9l+COAx8vR\n",
       "fO808u/T64BzyJ08nxhn+VjnA+CFDP+uRUREpENrkysBg+oQxh6X4pPkyoxIX1ELg4gMor+Q+3S8\n",
       "IjpIh0Zr8YF8OWJ38i2oIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLSe/8f8GWojdoHIgkA\n",
       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f102ec65110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 5 iterations\n",
      "\n",
      "Z in [-0.108043551445, 0.236873194575]\n",
      "Sparsity of Z: 8,158,022 non-zero entries out of 19,072,000 entries, i.e. 42.8%.\n"
     ]
    },
    {
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       "g8FgMBgM9gv/P5E+FugFCw0cAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f102e5d8d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.135033249855, 0.678504288197]\n",
      "d in [0.999999701977, 1.00000023842]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f102cd0b150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f102e073210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "EARBEARBEARBEARBEARBEARBEARBEARB8A7j/wFVqhL9t5kKqwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f102de4be90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasets:\n",
      "  D : (128, 96)             , float32\n",
      "  X : (5, 100, 149, 2, 96)  , float32\n",
      "  Z : (5, 100, 149, 2, 128) , float32\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Overall time: 915 seconds\n",
      "\n",
      " -------------------- Classification, dm = euclidean -------------------- \n",
      "\n",
      "Software versions:\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
     "data": {
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       "OurlHCsAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10268e5a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 65.3 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 59.3 %\n",
      "Clips accuracy: 66.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "  67 (+/- 1.6) <- [65 69 63 68 68 68 66 66 68 67]\n",
      "  67 (+/- 2.7) <- [62 70 69 67 63 67 69 64 64 68]\n",
      "  67 (+/- 2.1) <- [67 66 62 68 69 66 68 66 64 69]\n",
      "  67 (+/- 1.6) <- [66 67 65 68 64 67 70 65 66 66]\n",
      "  67 (+/- 2.3) <- [63 65 65 66 64 66 68 72 66 67]\n",
      "  67 (+/- 2.4) <- [66 65 66 71 68 65 65 71 64 64]\n",
      "  67 (+/- 1.8) <- [67 63 67 68 67 67 66 64 66 69]\n",
      "  67 (+/- 1.2) <- [66 65 67 66 65 66 66 68 69 65]\n",
      "  67 (+/- 1.8) <- [68 65 66 67 65 63 68 67 66 69]\n",
      "  67 (+/- 1.2) <- [69 67 66 67 66 67 66 65 67 65]\n",
      "  67 (+/- 1.9) <- [68 66 67 64 66 70 66 65 67 64]\n",
      "  67 (+/- 1.9) <- [64 68 68 68 65 68 65 67 64 68]\n",
      "  67 (+/- 2.2) <- [68 69 68 67 67 65 66 61 64 68]\n",
      "  67 (+/- 2.5) <- [65 64 66 69 66 66 65 63 67 72]\n",
      "  67 (+/- 3.1) <- [65 68 63 65 70 60 68 69 70 64]\n",
      "  66 (+/- 2.1) <- [68 65 63 70 66 67 64 68 65 64]\n",
      "  66 (+/- 1.9) <- [66 62 66 66 70 67 64 67 66 65]\n",
      "  66 (+/- 2.2) <- [65 62 67 67 67 69 65 67 62 67]\n",
      "  67 (+/- 1.7) <- [64 66 66 64 67 66 65 67 68 70]\n",
      "  67 (+/- 2.0) <- [66 67 65 68 68 67 67 64 62 69]\n",
      "Accuracy: 66.8 (+/- 2.08)\n",
      "Mean time (20 cv): 21.58 seconds\n",
      "Overall time: 436.91 seconds\n",
      "\n",
      " -------------------- Baseline -------------------- \n",
      "\n",
      "Software versions:\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 298, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=96 -> 12,960,000 floats\n",
      "  dim: 25,920 features per clip\n",
      "  shape: (5, 100, 6, 45, 96)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=96 -> 576,000 floats\n",
      "  dim: 1,152 features per clip\n",
      "  shape: (5, 100, 6, 2, 96)\n"
     ]
    },
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1025d1a7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 59.3 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 54.8 %\n",
      "Clips accuracy: 62.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "  58 (+/- 1.5) <- [57 59 55 59 59 59 57 55 59 59]\n",
      "  59 (+/- 2.7) <- [52 61 62 58 59 58 63 58 57 58]\n",
      "  59 (+/- 2.4) <- [60 57 53 61 59 58 59 58 56 62]\n",
      "  59 (+/- 1.8) <- [57 60 56 61 56 59 60 60 59 56]\n",
      "  59 (+/- 2.2) <- [55 57 56 58 57 59 60 62 58 61]\n",
      "  59 (+/- 1.7) <- [56 59 57 61 60 56 59 59 58 55]\n",
      "  59 (+/- 1.0) <- [59 57 58 61 58 58 59 57 59 59]\n",
      "  59 (+/- 2.1) <- [57 57 57 57 57 58 59 61 63 55]\n",
      "  59 (+/- 2.3) <- [59 56 57 57 55 55 59 61 59 62]\n",
      "  59 (+/- 1.9) <- [59 58 60 62 56 62 58 59 59 56]\n",
      "  59 (+/- 2.5) <- [57 61 58 60 55 64 56 56 58 55]\n",
      "  59 (+/- 1.5) <- [60 59 61 59 56 60 59 59 57 56]\n",
      "  59 (+/- 2.8) <- [59 62 57 58 52 62 60 55 59 58]\n",
      "  59 (+/- 2.0) <- [59 55 58 58 56 59 57 57 62 60]\n",
      "  58 (+/- 2.6) <- [57 60 54 59 60 54 58 57 62 58]\n",
      "  59 (+/- 2.1) <- [56 61 55 61 56 61 57 59 57 58]\n",
      "  59 (+/- 1.8) <- [58 57 59 61 60 61 55 59 60 56]\n",
      "  59 (+/- 1.4) <- [59 58 60 58 58 57 58 57 55 60]\n",
      "  58 (+/- 1.3) <- [57 57 56 57 58 59 59 60 59 59]\n",
      "  59 (+/- 2.4) <- [58 60 57 61 62 61 56 54 58 59]\n",
      "Accuracy: 58.7 (+/- 2.07)\n",
      "Mean time (20 cv): 19.52 seconds\n",
      "Overall time: 395.24 seconds\n"
     ]
    }
   ],
   "source": [
    "#%run gtzan.ipynb\n",
    "#%run audio_preprocessing.ipynb\n",
    "if not regen_graph:\n",
    "    separator('Graph')\n",
    "    %run audio_graph.ipynb\n",
    "if not regen_features:\n",
    "    separator('Features')\n",
    "    %run audio_features.ipynb\n",
    "\n",
    "# Hyper-parameter under test.\n",
    "for p[Pname] in Pvalues:\n",
    "\n",
    "    if regen_graph:\n",
    "        separator('Graph', True)\n",
    "        %run audio_graph.ipynb\n",
    "    if regen_features:\n",
    "        separator('Features', True)\n",
    "        p['filename_features'] = 'features_{}_{}.hdf5'.format(Pname, p[Pname])\n",
    "        %run audio_features.ipynb\n",
    "    separator('Classification', True)\n",
    "    %run audio_classification.ipynb\n",
    "    \n",
    "    # Collect results.\n",
    "    for key in res:\n",
    "        res[key].append(globals()[key])\n",
    "\n",
    "# Baseline, i.e. classification with spectrograms.\n",
    "p['dataset_classification'] = 'X'\n",
    "p['scale'] = 'minmax'  # Todo: should be done in pre-processing.\n",
    "if not regen_graph and not regen_features:\n",
    "    # Classifier parameters are being tested.\n",
    "    for p[Pname] in Pvalues:\n",
    "        separator('Baseline', True)\n",
    "        %run audio_classification.ipynb\n",
    "else:\n",
    "    separator('Baseline')\n",
    "    %run audio_classification.ipynb\n",
    "res['baseline'] = len(Pvalues) * [accuracy]\n",
    "res['baseline_std'] = accuracy_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dm = ['cosine_sim', 'cosine_dim', 'euclidean']\n",
      "res['accuracy_std'] = [1.9390674663066498, 1.9390674663066498, 2.0771909450559005]\n",
      "res['objective_j'] = [20929.82177734375, 20929.82177734375, 22112.623596191406]\n",
      "res['objective_i'] = [74607.25, 74607.25, 73375.578125]\n",
      "res['objective_h'] = [0, 0, 0]\n",
      "res['objective_g'] = [736527.8125, 736527.8125, 739556.40625]\n",
      "res['baseline'] = [58.740000000000023, 58.740000000000023, 58.740000000000023]\n",
      "res['time_features'] = [981.9027650356293, 973.1077239513397, 905.0378549098969]\n",
      "res['baseline_std'] = 2.07362002305\n",
      "res['sparsity'] = [43.596822567114096, 43.596822567114096, 42.77486367449664]\n",
      "res['iterations_inner'] = [384, 384, 358]\n",
      "res['iterations_outer'] = [5, 5, 5]\n",
      "res['accuracy'] = [66.520833333333357, 66.520833333333357, 66.76666666666668]\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10273b2ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "7R48X4w144qIiIiIiIiItAC1w8V1P7Bq0bojsdtORLJC6VhEREREREREREREREREREREJBKNsiUi\n",
       "tejFhkC9L3IcIpISjQQoIrXQAD0ibUYFABEp55vYDIP3YEOhgqbvFRERaWvbYEMErwasgU1v+jU0\n",
       "fa9I21ANgIiUsgs2IdUb2FzqNwXbSk3f+2/y0/eKSAaoACAipZSbXhg0fa9IW1ABQERKuRubVS1p\n",
       "Atg/2KYhxEXagKYDFpFSZgBXY/Oh/wN40K8Pp+zV9L0iIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi\n",
       "IiIiIiIiIiIiIiIiK+P/A7g9GZykci+gAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1026d061d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "g(Z) = ||X-DZ||_2^2, h(Z) = ||Z-EX||_2^2, i(Z) = ||Z||_1, j(Z) = tr(Z^TLZ)\n"
     ]
    },
    {
     "data": {
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       "Gf8fMZYZYmvpZ10AAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f102e6f0150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f102650da10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10262eaa50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1034138b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1025c42150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary atoms for dm = cosine_sim\n",
      "Dictionary atoms for dm = cosine_dim\n",
      "Dictionary atoms for dm = euclidean\n",
      "Experiment time: 8369 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/pymodules/python2.7/matplotlib/figure.py:371: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    }
   ],
   "source": [
    "print('{} = {}'.format(Pname, Pvalues))\n",
    "for key, value in res.items():\n",
    "    if key is not 'atoms':\n",
    "        print('res[\\'{}\\'] = {}'.format(key, value))\n",
    "\n",
    "def plot(*args, **kwargs):\n",
    "    plt.figure(figsize=(8,5))\n",
    "    x = range(len(Pvalues))\n",
    "    log = 'log' in kwargs and kwargs['log'] is True\n",
    "    pltfunc = plt.semilogy if log else plt.plot\n",
    "    params = {}\n",
    "    params['linestyle'] = '-'\n",
    "    params['marker'] = '.'\n",
    "    params['markersize'] = 10\n",
    "    for i, var in enumerate(args):\n",
    "        if 'err' in kwargs:\n",
    "            pltfunc = plt.errorbar\n",
    "            params['yerr'] = res[kwargs['err'][i]]\n",
    "            params['capsize'] = 5\n",
    "        pltfunc(x, res[var], label=var, **params)\n",
    "        for i,j in zip(x, res[var]):\n",
    "            plt.annotate('{:.2f}'.format(j), xy=(i,j), xytext=(5,5), textcoords='offset points')\n",
    "    margin = 0.25\n",
    "    params['markersize'] = 10\n",
    "    plt.xlim(-margin, len(Pvalues)-1+margin)\n",
    "    if 'ylim' in kwargs:\n",
    "        plt.ylim(kwargs['ylim'])\n",
    "    plt.title('{} vs {}'.format(', '.join(args), Pname))\n",
    "    plt.xlabel(Pname)\n",
    "    plt.ylabel(' ,'.join(args))\n",
    "    plt.xticks(x, Pvalues)\n",
    "    plt.grid(True); plt.legend(loc='best'); plt.show()\n",
    "\n",
    "def div(l):\n",
    "    div = Pvalues if Pname is l else [p[l]]\n",
    "    return np.array([1 if v is None else v for v in div])\n",
    "\n",
    "# Classification results.\n",
    "res['chance'] = len(Pvalues) * [100./p['Ngenres']]\n",
    "res['chance_std'] = 0\n",
    "err=['accuracy_std', 'baseline_std', 'chance_std']\n",
    "plot('accuracy', 'baseline', 'chance', err=err, ylim=[0,100])\n",
    "\n",
    "# Features extraction results.\n",
    "if regen_features:\n",
    "    plot('objective_g', 'objective_i', 'objective_j', log=True)\n",
    "    # Unweighted objectives.\n",
    "    print('g(Z) = ||X-DZ||_2^2, h(Z) = ||Z-EX||_2^2, i(Z) = ||Z||_1, j(Z) = tr(Z^TLZ)')\n",
    "    res['objective_g_un'] = res['objective_g'] / div('ld')\n",
    "    res['objective_i_un'] = res['objective_i'] / div('ls')\n",
    "    res['objective_j_un'] = res['objective_j'] / div('lg')\n",
    "    plot('objective_g_un', 'objective_i_un', 'objective_j_un', log=True)\n",
    "    plot('sparsity', ylim=[0,100])\n",
    "    plot('time_features')\n",
    "    plot('iterations_inner')\n",
    "    plot('iterations_outer')\n",
    "\n",
    "    for i, fig in enumerate(res['atoms']):\n",
    "        print('Dictionary atoms for {} = {}'.format(Pname, Pvalues[i]))\n",
    "        fig.show()\n",
    "\n",
    "print('Experiment time: {:.0f} seconds'.format(time.time() - texperiment))"
   ]
  }
 ],
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