{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre recognition: experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: assess the usefulness of the auto-encoder with and without the graph when faced with noisy data. The euclidean distance metric is used to construct the graph.\n",
    "\n",
    "Conclusion: In a noisy setting, our structuring auto-encoder improves the accuracy by 7.2% while a sparse auto-encoder degrades the performance by 1.4% compared to the baseline (raw spectrograms).\n",
    "\n",
    "Observations:\n",
    "* Comparison with results who should be similar.\n",
    "    * Baseline is the same as `13e_dm`, `13c_novoting` and `13b_noise_lg` (voting).\n",
    "    * Graph-less is the same as `13c_novoting` and `13b_noise_lg` (voting).\n",
    "    * Graph-based is 1% less than `13e_dm`. The only difference I see is a different kNN from FLANN (see the numbers in `dist` or `w`)."
   ]
  },
  {
   "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 = 'lg'\n",
    "Pvalues = [None, 100]\n",
    "\n",
    "# Regenerate the graph or the features at each iteration.\n",
    "regen_graph = False\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['data_scaling_graph'] = 'features'\n",
    "p['K'] = 10 + 1  # 5 to 10 + 1 for self-reference\n",
    "p['dm'] = 'euclidean'\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 -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 1156.15 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 2.06495928764]\n",
      "w in [0.202578514814, 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    : (2921688,), float32\n",
      "  L_indices : (2921688,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2921688,), float32\n",
      "  W_indices : (2921688,), 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: 1165.99 seconds\n",
      "\n",
      " -------------------- Features, lg = None -------------------- \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    : (2921688,), float32\n",
      "  L_indices : (2921688,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2921688,), float32\n",
      "  W_indices : (2921688,), 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: 372 seconds\n"
     ]
    },
    {
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       "jQ5j1Z/HFQdhN4XKNEkwKooXkUZbgOWEPxU6kBr1p758J+AyrJ2DiIiIiIiIiIiIiIiIiIiIiIiI\n",
       "iIiIiIiIiIiISKf6/weLe+Heoh26AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f943078cf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 304 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 3.556715e+05\n",
      "rdiff: 0.000847538508528\n",
      "i(Z) = ||Z||_1 = 3.337448e+05\n"
     ]
    },
    {
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       "nc/7/y9sx0MkOLXcRWSQnsSOQdg+dJAJyutJAeuS3xo7bVBERERERERERERERERERERERERERERE\n",
       "REREREREuvs/uErh03UBW5sAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93ee8062d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 6.894163e+05\n"
     ]
    },
    {
     "data": {
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       "+yqsAyTAySnv9Wl+sc+fAK+j3nHx2LS/m7HHM79znO/X7Luejv1/egtwHtZR85lx5o91PABezMg/\n",
       "axEREWnDOtiJP1dHMva4EZ/GKjAi7tSSICI5+hPWR+OV3kEmqVnLDtilhj2x20VFRERERERERERE\n",
       "RERERERERERERERERERERERERGS0/w8nUi5U5Ggb/gAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93ee689e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 13 iterations\n",
      "\n",
      "Z in [-0.925734937191, 0.85433703661]\n",
      "Sparsity of Z: 5,470,082 non-zero entries out of 19,072,000 entries, i.e. 28.7%.\n"
     ]
    },
    {
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       "fwzTefRB4DpM59L3nH//o6T/3TF9UPY+TOfIz8b5DPaFAL4XU+Z6PwAvm9vfgKlcfSuA/wvAd87t\n",
       "nwHw9zG9LG8C8IsA3i9kvBHTx22/gelc/qWYAoV3A/hiAP/Z9Fel/Y15tsH0p3Vvx/ls+lOuzz/G\n",
       "VC24EVMpfvmvtZ45378LU8XiZwW/RqPRaDQah4BXYCqVNxqNGZ2JNxqNRqPRaDQajUaj0Wg0Go1G\n",
       "o9FoNBqNRqPRaDQajUaj0Wg0Go1Go9FoNBqNRqPRaDQajcZu4f8H51F20nX0vJsAAAAASUVORK5C\n",
       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93ee7a6710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.296960860491, 0.514295816422]\n",
      "d in [0.999999701977, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93ee65e590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93ee81c890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "B2dnLkZfnSAAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93ee6a2a90>"
      ]
     },
     "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: 380 seconds\n",
      "\n",
      " -------------------- Classification, lg = None -------------------- \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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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93e1e39a90>"
      ]
     },
     "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: 54.8 %\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: 48.4 %\n",
      "Clips accuracy: 57.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "  58 (+/- 1.8) <- [56 56 56 59 58 59 54 56 57 60]\n",
      "  57 (+/- 1.4) <- [57 58 59 55 57 55 57 55 56 55]\n",
      "  57 (+/- 1.8) <- [56 56 54 58 56 55 59 57 56 60]\n",
      "  57 (+/- 2.0) <- [54 60 56 57 57 56 59 53 58 56]\n",
      "  57 (+/- 1.4) <- [57 56 56 56 56 56 60 59 57 56]\n",
      "  57 (+/- 1.8) <- [56 54 57 60 56 55 56 59 56 54]\n",
      "  57 (+/- 1.4) <- [60 54 57 57 57 56 55 58 58 56]\n",
      "  57 (+/- 1.7) <- [58 58 56 57 56 56 56 60 59 54]\n",
      "  57 (+/- 2.2) <- [57 57 54 57 53 57 58 61 54 55]\n",
      "  57 (+/- 1.7) <- [58 56 57 58 56 60 57 56 56 53]\n",
      "  57 (+/- 2.1) <- [59 57 58 58 52 57 55 58 58 57]\n",
      "  56 (+/- 1.9) <- [55 56 57 55 55 60 57 57 55 53]\n",
      "  57 (+/- 1.5) <- [57 59 55 59 56 59 55 55 57 56]\n",
      "  57 (+/- 2.0) <- [55 53 58 59 55 54 55 56 59 57]\n",
      "  57 (+/- 2.6) <- [54 59 53 55 58 54 58 55 61 56]\n",
      "  57 (+/- 2.1) <- [56 58 53 60 54 58 57 57 54 58]\n",
      "  57 (+/- 2.0) <- [56 54 54 58 60 59 57 57 55 57]\n",
      "  57 (+/- 2.9) <- [56 52 61 58 58 61 54 59 55 57]\n",
      "  58 (+/- 2.2) <- [58 56 56 52 56 59 58 59 58 59]\n",
      "  57 (+/- 2.1) <- [58 61 54 58 56 55 55 56 55 58]\n",
      "Accuracy: 57.1 (+/- 1.99)\n",
      "Mean time (20 cv): 42.90 seconds\n",
      "Overall time: 864.97 seconds\n",
      "\n",
      " -------------------- Features, lg = 100 -------------------- \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    : (2921688,), float32\n",
      "  L_indices : (2921688,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2921688,), float32\n",
      "  W_indices : (2921688,), 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: 931 seconds\n"
     ]
    },
    {
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       "bDP/wArey7Df82Jq50Q0m/8EdjD0SWAxdvA1Cfte1onLfx3rEiEu2+mVDyIiIuPWTOwkwUHVyeWD\n",
       "E7CDQFWSpG/UtC4ieVuG1XRf6R1kjDrp794f+Al2noKIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiI\n",
       "iIiIl/8PhFbV2E3FBBIAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93eb057f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 366 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.395061e+05\n",
      "rdiff: 0.000187625228747\n",
      "i(Z) = ||Z||_1 = 7.325671e+04\n",
      "j(Z) = tr(Z^TLZ) = 2.222949e+04\n"
     ]
    },
    {
     "data": {
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       "sdsNRURERERERERERERERERERERERERERERERERERCSs/w+eMEQI3HZJ7AAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93e18b44d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 8.349923e+05\n"
     ]
    },
    {
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       "DP+tRUREpEPLY5WAQfV+Rh+X4stYZUakr6iFQUQG0d+xPh2vjw7SoZFafMAuR2yD3YIqIiIiIiIi\n",
       "IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiK99/8B7R87ClK3FLcAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93e05e9b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 5 iterations\n",
      "\n",
      "Z in [-0.106827653944, 0.253815919161]\n",
      "Sparsity of Z: 8,161,347 non-zero entries out of 19,072,000 entries, i.e. 42.8%.\n"
     ]
    },
    {
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       "wWAw2Bf+fz5nNc9B03WiAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93e18cc5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.135560333729, 0.695128142834]\n",
      "d in [0.999999701977, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f942fecf1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93e051d2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93e18cc590>"
      ]
     },
     "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: 940 seconds\n",
      "\n",
      " -------------------- Classification, lg = 100 -------------------- \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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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f942c8ab1d0>"
      ]
     },
     "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: 64.7 %\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: 58.2 %\n",
      "Clips accuracy: 65.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "  66 (+/- 1.7) <- [63 65 62 67 68 67 64 65 66 65]\n",
      "  66 (+/- 2.7) <- [60 70 67 67 62 65 68 66 64 67]\n",
      "  66 (+/- 2.0) <- [66 65 61 67 67 66 66 65 65 68]\n",
      "  66 (+/- 1.6) <- [65 66 64 66 63 67 69 64 64 66]\n",
      "  66 (+/- 2.1) <- [62 64 65 65 65 65 67 70 64 65]\n",
      "  66 (+/- 2.2) <- [64 65 64 69 67 65 65 70 63 64]\n",
      "  66 (+/- 1.7) <- [66 63 65 66 67 64 64 64 64 69]\n",
      "  66 (+/- 1.6) <- [66 66 66 67 63 66 65 68 68 63]\n",
      "  66 (+/- 2.1) <- [68 64 65 67 63 62 68 68 65 66]\n",
      "  66 (+/- 2.0) <- [68 69 64 68 65 65 63 66 66 63]\n",
      "  66 (+/- 1.7) <- [67 66 67 66 66 69 63 65 64 63]\n",
      "  66 (+/- 2.3) <- [62 64 69 67 64 68 66 64 63 68]\n",
      "  66 (+/- 2.2) <- [66 69 68 66 66 65 66 61 63 66]\n",
      "  66 (+/- 2.1) <- [62 66 66 67 64 66 64 64 67 70]\n",
      "  66 (+/- 2.5) <- [64 67 63 66 68 60 66 68 67 63]\n",
      "  66 (+/- 1.5) <- [65 66 62 68 65 67 64 66 64 66]\n",
      "  66 (+/- 2.1) <- [66 60 65 66 68 66 65 65 67 63]\n",
      "  66 (+/- 2.7) <- [66 60 69 66 67 68 64 67 61 65]\n",
      "  66 (+/- 1.8) <- [63 66 67 63 64 65 64 68 67 68]\n",
      "  66 (+/- 1.6) <- [65 67 66 67 67 66 65 64 62 66]\n",
      "Accuracy: 65.9 (+/- 2.04)\n",
      "Mean time (20 cv): 21.87 seconds\n",
      "Overall time: 442.39 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 0x7f93f45cf090>"
      ]
     },
     "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): 19.50 seconds\n",
      "Overall time: 394.70 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": [
      "lg = [None, 100]\n",
      "res['accuracy_std'] = [1.9911106267050498, 2.0446812777980292]\n",
      "res['objective_j'] = [0, 22229.493713378906]\n",
      "res['objective_i'] = [333744.78125, 73256.7109375]\n",
      "res['objective_h'] = [0, 0]\n",
      "res['objective_g'] = [355671.5234375, 739506.09375]\n",
      "res['baseline'] = [58.740000000000023, 58.740000000000023]\n",
      "res['time_features'] = [372.1276490688324, 931.3005359172821]\n",
      "res['baseline_std'] = 2.07362002305\n",
      "res['sparsity'] = [28.681218540268457, 42.792297609060405]\n",
      "res['iterations_inner'] = [304, 366]\n",
      "res['iterations_outer'] = [13, 5]\n",
      "res['accuracy'] = [57.097499999999989, 65.890833333333347]\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93f4ac7950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python2.7/dist-packages/numpy/ma/core.py:3847: UserWarning: Warning: converting a masked element to nan.\n",
      "  warnings.warn(\"Warning: converting a masked element to nan.\")\n"
     ]
    },
    {
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       "G2D/mFsBW2PfEEREBtrqWFcG7ufq7vpoSovnX8aG9ksFSh4kj4XAX4Djy7ZvD1zmrl8C7OyuR8C1\n",
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       "Ivo6sCbwpttePox/LbdNKlDLg+Q1D7gK+G+SrPw+4HPu+pHA3VX2cSvwZWC4uz0GGNW/YYqIZLqe\n",
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       "ioj0p3i4/gckw/VXwQq7s4Zqfh8rlHwa+GRdIxURERERERERERERERERERERERERERERERERERER\n",
       "ERHxb5HvAESkZ5phUkQakdYWEGlgSh5EpJG1Y6u6PoXNCngTpUu8i4iIiAC2kivA4VjCALY661xK\n",
       "pz4XEQ/U8iAijWxnbEE2sFURJ3mMRUQcJQ8i0sgiel72XUQ8UfIgIo3sXqzGoQ3rtgi8RiMiAAz2\n",
       "HYCISIZ4tMU1wJ7ANGxp5UeBt30FJSIiIsUw3P1cFZgOrOYxFhFBLQ8i0vhuBFYChgKnAG/6DUdE\n",
       "RERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERE\n",
       "RERERERERERERERERERERERERERERERExK//D19A5DgVglEMAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f942fee9c10>"
      ]
     },
     "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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       "SUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93f4532ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93f4640810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f942c88b6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93f4bf9990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93f4d9cc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary atoms for lg = None\n",
      "Dictionary atoms for lg = 100\n",
      "Experiment time: 4195 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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