{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre recognition: experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: observe the effect of $\\lambda_g$ on a small dataset.\n",
    "\n",
    "Conclusion: no ideal value for $\\lambda_g$ has been found. Gave the idea that we want to trade smoothness with sparsity.\n",
    "\n",
    "Observations:\n",
    "* Accuracy almost constant, drops for $\\lambda_g$ greater than 1000.\n",
    "* Constant running time. Increase of 50% compared to the model without a graph.\n",
    "* Sparsity increases linearly with $\\lambda_g$.\n",
    "* Compare atoms.\n",
    "* Lots of variance between cross-validation runs: e.g. from 70 to 73. We should take the mean of the various runs. Done for next experiment.\n",
    "* The gap between the Dirichlet energy and the other objectives is reduced from 2 orders of magnitude to 1 as $\\lambda_g$ becomes greater than 100.\n",
    "* $Z$ is much more constrained than $D$ (inner loop iterations).\n",
    "* We want to trade smoothness with sparsity, not reconstruction error (controlled by $\\lambda_d$). Try to set $\\lambda_d = \\lambda_g$, i.e. vary the redundant $\\lambda_s$.\n",
    "* Ran for 4h50."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x59ce190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f919144a390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x60a1850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x60a45d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9191ff0850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x609add0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_features = [1915, 3067, 3095, 3485, 3213, 916]\n",
    "inner_iterations = [1555, 1711, 1610, 1683, 1509, 425]\n",
    "outer_iterations = [26, 29, 14, 10, 12, 3]\n",
    "sparsity = [4.6, 5.1, 7.7, 18.4, 46.2, 88.8]\n",
    "objective_g = [4.156377e+04, 4.197443e+04, 4.792657e+04, 7.133035e+04, 1.145442e+05, 1.600268e+05]\n",
    "objective_i = [6.914378e+04, 6.879492e+04, 6.621795e+04, 5.868650e+04, 4.573782e+04, 3.421723e+04]\n",
    "objective_j = [0, 1.349397e+03, 6.038886e+03, 1.143229e+04, 1.177662e+04, 6.200758e+03]\n",
    "accuracy = [73, 74, 72, 73, 69, 53]\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\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",
    "    for var in args:\n",
    "        pltfunc(x, globals()[var], '.-', label=var)\n",
    "    plt.xlim(0, len(Pvalues)-1)\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",
    "plot('accuracy')\n",
    "plot('objective_g', 'objective_i', 'objective_j', log=True)\n",
    "plot('sparsity')\n",
    "plot('time_features')\n",
    "plot('inner_iterations')\n",
    "plot('outer_iterations')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyper-parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter under test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Pname = 'lg'\n",
    "Pvalues = [None, 1, 10, 1e2, 1e3, 1e4]\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['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['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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Dataset (10,100,644 | 5,100,149 | 2,10,644).\n",
    "p['Ngenres'] = 5\n",
    "p['Nclips'] = 100\n",
    "p['Nframes'] = 149\n",
    "\n",
    "# Graph.\n",
    "p['tol'] = 1e-5\n",
    "\n",
    "# Feature extraction.\n",
    "p['rtol'] = 1e-6  # 1e-3, 1e-5, 1e-7\n",
    "p['N_outer'] = 40  # 10, 15, 20\n",
    "\n",
    "# Classification.\n",
    "p['Ncv'] = 10\n",
    "p['dataset_classification'] = 'Z'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data: (149000, 96), float32\n",
      "Elapsed time: 174.13 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 0.418610453606]\n",
      "w in [0.0562636144459, 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    : (2367336,), float32\n",
      "  L_indices : (2367336,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2367336,), float32\n",
      "  W_indices : (2367336,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = cosine\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Overall time: 183.07 seconds\n",
      "\n",
      "--------------------------------------------------\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\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2367336,), float32\n",
      "  L_indices : (2367336,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2367336,), float32\n",
      "  W_indices : (2367336,), 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: 1915 seconds\n"
     ]
    },
    {
     "data": {
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       "y5N+fB32+yn8fpdhJ+CVvreSJ7GK90bs73kVxXMiKq1/CTsYOhlYjB18Dcd+LxP99mdjQyL4beu9\n",
       "8kFERKRtjcdOEuxU9Vw+2IMdBKqRJMGoa11EWu1FrKW7V+wgQ1TPePf+wG+x8xRERERERERERERE\n",
       "REREREREREREREREREREREREYvn/S4qNGKn573kAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb038045890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1555 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 4.156377e+04\n",
      "rdiff: 0.00281476331612\n",
      "i(Z) = ||Z||_1 = 6.914378e+04\n"
     ]
    },
    {
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       "iIiI9Ob/ATPQdlJJswiUAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004c3b0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.107076e+05\n"
     ]
    },
    {
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       "iIiIiIiIiIiIiIiIiIiIiIg09v8Bf63YlLrmhn8AAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004825410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 26 iterations\n",
      "\n",
      "Z in [-1.25696384907, 1.62027561665]\n",
      "Sparsity of Z: 875,165 non-zero entries out of 19,072,000 entries, i.e. 4.6%.\n"
     ]
    },
    {
     "data": {
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       "zsNUlYtIRSVxERERERERERERERERERERERERERERERERERERERGRsvx//7M9W3OEsCMAAAAASUVO\n",
       "RK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004accd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.809242725372, 0.939565658569]\n",
      "d in [0.999999642372, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004949c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004b05210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "IgrvAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004a13410>"
      ]
     },
     "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: 1922 seconds\n",
      "\n",
      "--------------------------------------------------\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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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004934a10>"
      ]
     },
     "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: 74.1 %\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: 62.3 %\n",
      "Clips accuracy: 70.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (500, 1536), float64\n",
      "Labels: (500,), uint8\n",
      " 71 (+/-  7.5) <- [ 0.7   0.8   0.66  0.58  0.68  0.76  0.72  0.6   0.78  0.8 ]\n",
      " 70 (+/-  6.5) <- [ 0.66  0.68  0.74  0.56  0.76  0.72  0.76  0.74  0.62  0.76]\n",
      " 71 (+/-  5.5) <- [ 0.76  0.72  0.8   0.74  0.66  0.66  0.62  0.76  0.74  0.66]\n",
      " 73 (+/-  4.8) <- [ 0.7   0.7   0.72  0.7   0.68  0.72  0.7   0.82  0.82  0.7 ]\n",
      " 71 (+/-  4.8) <- [ 0.64  0.7   0.78  0.8   0.68  0.7   0.72  0.68  0.76  0.68]\n",
      " 71 (+/-  4.7) <- [ 0.66  0.8   0.72  0.62  0.7   0.76  0.7   0.74  0.7   0.72]\n",
      " 70 (+/-  5.4) <- [ 0.64  0.7   0.62  0.72  0.7   0.76  0.64  0.66  0.72  0.8 ]\n",
      " 72 (+/-  5.0) <- [ 0.76  0.68  0.64  0.7   0.82  0.7   0.74  0.72  0.74  0.66]\n",
      " 71 (+/-  5.1) <- [ 0.76  0.6   0.64  0.68  0.76  0.72  0.7   0.74  0.7   0.76]\n",
      " 70 (+/-  5.3) <- [ 0.74  0.66  0.74  0.64  0.76  0.74  0.68  0.6   0.68  0.76]\n",
      "Mean time (10 cv): 29.66 seconds\n",
      "Overall time: 301.45 seconds\n",
      "\n",
      "--------------------------------------------------\n",
      "\n",
      "The memory_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext memory_profiler\n",
      "The line_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext line_profiler\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\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2367336,), float32\n",
      "  L_indices : (2367336,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2367336,), float32\n",
      "  W_indices : (2367336,), 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: 3067 seconds\n"
     ]
    },
    {
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       "RLz8f/SH4dUlaZ4cAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb089944a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1711 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 4.197443e+04\n",
      "rdiff: 0.00267776003901\n",
      "i(Z) = ||Z||_1 = 6.879492e+04\n",
      "j(Z) = tr(Z^TLZ) = 1.349397e+03\n"
     ]
    },
    {
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       "sXmkS53djvb7uxO4BziqyvfL+q4/xn4H7wIuwDr3La2yPe94AGzB8H9rERGRnrMKlpxjNYf8++q/\n",
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       "EQnt/wPRYVj76hB+ZQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb0018d2610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.121188e+05\n"
     ]
    },
    {
     "data": {
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       "3wx8CLsgL06vWzU5TqNfUr/QP4I9Tvo87Gd/HOucCPDRlPfqtL445o+xZ9gXnQrfnY63GLgReHub\n",
       "n6/Zz3oy9n/p9cDZWCfKx9qsb3U+AJ7D+N+1iIiItLAhdpHP1WG0Hpfhk1hhRWTKqYZARHLzENan\n",
       "Yh/vIF1qVmMD1lywJ3aLpoiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIl/8Par7JbsuhhScA\n",
       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb0019ace90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 29 iterations\n",
      "\n",
      "Z in [-1.05637359619, 1.47232508659]\n",
      "Sparsity of Z: 970,772 non-zero entries out of 19,072,000 entries, i.e. 5.1%.\n"
     ]
    },
    {
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       "QgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQoiz+P303Qk5wNeJmAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb00490a990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.818693161011, 0.937262773514]\n",
      "d in [0.999999582767, 1.00000047684]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb00133e410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004a764d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb0899444d0>"
      ]
     },
     "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: 3074 seconds\n",
      "\n",
      "--------------------------------------------------\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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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004ac6990>"
      ]
     },
     "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: 74.1 %\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: 61.7 %\n",
      "Clips accuracy: 69.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (500, 1536), float64\n",
      "Labels: (500,), uint8\n",
      " 72 (+/-  6.9) <- [ 0.64  0.82  0.68  0.6   0.72  0.8   0.68  0.68  0.76  0.78]\n",
      " 71 (+/-  5.9) <- [ 0.66  0.72  0.7   0.64  0.78  0.84  0.72  0.68  0.66  0.66]\n",
      " 71 (+/-  4.7) <- [ 0.74  0.72  0.76  0.74  0.7   0.68  0.62  0.74  0.78  0.66]\n",
      " 74 (+/-  5.5) <- [ 0.72  0.72  0.72  0.72  0.66  0.76  0.72  0.8   0.86  0.68]\n",
      " 72 (+/-  7.6) <- [ 0.56  0.66  0.76  0.78  0.66  0.72  0.78  0.68  0.84  0.72]\n",
      " 72 (+/-  4.6) <- [ 0.68  0.82  0.72  0.64  0.76  0.72  0.76  0.7   0.72  0.72]\n",
      " 71 (+/-  6.9) <- [ 0.6   0.8   0.62  0.76  0.72  0.74  0.6   0.74  0.74  0.76]\n",
      " 73 (+/-  4.2) <- [ 0.78  0.74  0.7   0.72  0.82  0.72  0.76  0.68  0.72  0.68]\n",
      " 70 (+/-  5.4) <- [ 0.7   0.64  0.68  0.68  0.76  0.74  0.58  0.7   0.74  0.76]\n",
      " 70 (+/-  4.3) <- [ 0.74  0.7   0.74  0.66  0.72  0.74  0.66  0.62  0.68  0.76]\n",
      "Mean time (10 cv): 28.96 seconds\n",
      "Overall time: 294.16 seconds\n",
      "\n",
      "--------------------------------------------------\n",
      "\n",
      "The memory_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext memory_profiler\n",
      "The line_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext line_profiler\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\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2367336,), float32\n",
      "  L_indices : (2367336,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2367336,), float32\n",
      "  W_indices : (2367336,), 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: 3095 seconds\n"
     ]
    },
    {
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       "pv8Pr7PHbWQkAtsAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb001345750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1610 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 4.792657e+04\n",
      "rdiff: 0.00346171679269\n",
      "i(Z) = ||Z||_1 = 6.621795e+04\n",
      "j(Z) = tr(Z^TLZ) = 6.038886e+03\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb000b25090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.201834e+05\n"
     ]
    },
    {
     "data": {
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       "TdjJ9tPYpYOB+6bO8lC/w++wbvlbsK7464fYT6xTjljD5Ocp28+w3/9W4AvYCfmm9HOzOvsZ6HdU\n",
       "T/R/wx63+1Psd38eG5wIcGLKe12qL/Z5KfBuqoMKj077uwl7lPERQ/x+9X7X07H/S28GLsQGUT43\n",
       "RH2j4wGwA7V/1iIiItLA+thJfqw6jMbzMnwVa6yIdJ16CERkrHkSG1PxFu8go1SvxwbscsHu2C2a\n",
       "IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiJe/j8eCDdA4tXBMAAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb000d0bad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 14 iterations\n",
      "\n",
      "Z in [-0.477136224508, 1.17480039597]\n",
      "Sparsity of Z: 1,475,033 non-zero entries out of 19,072,000 entries, i.e. 7.7%.\n"
     ]
    },
    {
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       "RU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004a29a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.740027308464, 0.912485957146]\n",
      "d in [0.999999642372, 1.00000023842]\n",
      "Constraints on D: True\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb000c43250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "JOX3/wHKYBd5Gg1mqwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb000ae2e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb00133e590>"
      ]
     },
     "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: 3102 seconds\n",
      "\n",
      "--------------------------------------------------\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 0x7fb000a75510>"
      ]
     },
     "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: 75.0 %\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: 61.1 %\n",
      "Clips accuracy: 70.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (500, 1536), float64\n",
      "Labels: (500,), uint8\n",
      " 70 (+/-  4.2) <- [ 0.68  0.76  0.7   0.66  0.64  0.74  0.68  0.64  0.74  0.74]\n",
      " 69 (+/-  8.4) <- [ 0.62  0.7   0.76  0.54  0.82  0.8   0.72  0.68  0.6   0.68]\n",
      " 71 (+/-  4.3) <- [ 0.74  0.68  0.74  0.66  0.7   0.7   0.64  0.78  0.76  0.74]\n",
      " 71 (+/-  7.5) <- [ 0.56  0.68  0.7   0.7   0.62  0.7   0.74  0.82  0.82  0.72]\n",
      " 71 (+/-  4.2) <- [ 0.62  0.68  0.7   0.76  0.74  0.7   0.76  0.7   0.76  0.7 ]\n",
      " 72 (+/-  5.0) <- [ 0.66  0.74  0.72  0.66  0.82  0.72  0.74  0.76  0.68  0.66]\n",
      " 70 (+/-  5.4) <- [ 0.64  0.74  0.68  0.74  0.66  0.68  0.62  0.72  0.78  0.78]\n",
      " 70 (+/-  4.6) <- [ 0.66  0.68  0.68  0.74  0.78  0.64  0.72  0.74  0.64  0.74]\n",
      " 71 (+/-  4.7) <- [ 0.78  0.66  0.66  0.76  0.7   0.72  0.62  0.72  0.74  0.7 ]\n",
      " 70 (+/-  5.0) <- [ 0.8   0.68  0.74  0.7   0.68  0.68  0.66  0.6   0.72  0.72]\n",
      "Mean time (10 cv): 24.68 seconds\n",
      "Overall time: 251.12 seconds\n",
      "\n",
      "--------------------------------------------------\n",
      "\n",
      "The memory_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext memory_profiler\n",
      "The line_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext line_profiler\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\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2367336,), float32\n",
      "  L_indices : (2367336,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2367336,), float32\n",
      "  W_indices : (2367336,), 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: 3485 seconds\n"
     ]
    },
    {
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       "2A14jd9t5W6s4D0f+32eRf2eiFbLH8FOhj4NLMZOvqZhP5c14/rfxC6JENft9MkHERGRgTUHu0lw\n",
       "WHXy+OAU7CRQlSTpGzWti0hqD2M13Zd6B5mgTq537wWcht2nICIiIiIiIiIiIiIiIiIiIiIiIiIi\n",
       "IiIiIiIiIuLl/wOJiNauDvPgywAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb00b7a93d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1683 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.133035e+04\n",
      "rdiff: 0.00123024645659\n",
      "i(Z) = ||Z||_1 = 5.868650e+04\n",
      "j(Z) = tr(Z^TLZ) = 1.143229e+04\n"
     ]
    },
    {
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       "/Srefx3wX8Cfc48dj+18Vd1Rrvq6M+P9V2IF88NYN3z9ummw3Gobzse6uK/DurUva7GerMHtDPtx\n",
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       "iZ2a8rAW29doW7+JfQ9eRe3c4c+0uL/Z5wGwNeP/1iIiIkNtdaxQl9XBND9u/z+xHxwibtRSF5F+\n",
       "WortY7Cbd5AONeo5Aet63xk7/E9ERERERERERERERERERERERERERERERERERERk2P1/USWM2Jlo\n",
       "pTgAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb000d18650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.414491e+05\n"
     ]
    },
    {
     "data": {
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       "+Ai5YL62PD+ryXnGuoJGgf8Yecrxb5I/+1PkTowAp5a8vy7b6+f8PnAojc6Hx5fzXUuelvbYCT5f\n",
       "s896Bvn/0uuA88idLZ+cYPt43wfAzoz+sxYREZEWNiQX9oPqaMYf1+GT5EqLSBi1GIjIoHmY3Odi\n",
       "3+ggHWrWggP5MsJe5Fs7RUREREREREREREREREREREREREREREREREREREQG1f8P2TzCw/ITfSMA\n",
       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb000f24d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 10 iterations\n",
      "\n",
      "Z in [-0.0622419342399, 1.01885557175]\n",
      "Sparsity of Z: 3,506,138 non-zero entries out of 19,072,000 entries, i.e. 18.4%.\n"
     ]
    },
    {
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       "hGyL/w/jALfZt8FAFQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb000b18c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.0309991780668, 0.920943379402]\n",
      "d in [0.999999582767, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb0018f05d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb00b46bd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "Z/w/+ne7zy8h8JIAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004a764d0>"
      ]
     },
     "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: 3492 seconds\n",
      "\n",
      "--------------------------------------------------\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 0x7fb000ede0d0>"
      ]
     },
     "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: 76.0 %\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: 63.7 %\n",
      "Clips accuracy: 70.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (500, 1536), float64\n",
      "Labels: (500,), uint8\n",
      " 73 (+/-  4.4) <- [ 0.76  0.72  0.68  0.7   0.7   0.82  0.7   0.68  0.78  0.72]\n",
      " 72 (+/-  3.5) <- [ 0.72  0.76  0.76  0.64  0.74  0.72  0.72  0.74  0.72  0.68]\n",
      " 73 (+/-  5.1) <- [ 0.76  0.82  0.78  0.74  0.78  0.66  0.66  0.74  0.7   0.7 ]\n",
      " 72 (+/-  4.6) <- [ 0.64  0.78  0.68  0.74  0.68  0.74  0.74  0.74  0.8   0.7 ]\n",
      " 71 (+/-  4.9) <- [ 0.74  0.78  0.64  0.78  0.7   0.7   0.76  0.64  0.72  0.68]\n",
      " 73 (+/-  3.4) <- [ 0.7   0.8   0.74  0.72  0.74  0.72  0.74  0.68  0.74  0.68]\n",
      " 73 (+/-  5.0) <- [ 0.7   0.76  0.64  0.76  0.7   0.68  0.72  0.78  0.74  0.82]\n",
      " 73 (+/-  5.7) <- [ 0.68  0.68  0.68  0.8   0.82  0.68  0.8   0.78  0.72  0.68]\n",
      " 71 (+/-  5.0) <- [ 0.76  0.62  0.68  0.78  0.72  0.76  0.68  0.76  0.66  0.7 ]\n",
      " 73 (+/-  3.7) <- [ 0.78  0.74  0.78  0.72  0.78  0.7   0.72  0.72  0.66  0.74]\n",
      "Mean time (10 cv): 18.83 seconds\n",
      "Overall time: 192.12 seconds\n",
      "\n",
      "--------------------------------------------------\n",
      "\n",
      "The memory_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext memory_profiler\n",
      "The line_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext line_profiler\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\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2367336,), float32\n",
      "  L_indices : (2367336,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2367336,), float32\n",
      "  W_indices : (2367336,), 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: 3213 seconds\n"
     ]
    },
    {
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       "gh0EqpIkyahpXUS6bTlW032td5Apmkx/927AT7HzFERERERERERERERERERERERERERERERERERE\n",
       "RES8/H/zaumYFJ0pwwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004a1ed50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1509 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 1.145442e+05\n",
      "rdiff: 0.000884876305308\n",
      "i(Z) = ||Z||_1 = 4.573782e+04\n",
      "j(Z) = tr(Z^TLZ) = 1.177662e+04\n"
     ]
    },
    {
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       "IiIiIiIiIiIiIiIiIqH9f7lkmPwFWRdOAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004c93c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.720586e+05\n"
     ]
    },
    {
     "data": {
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       "SUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004829ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 12 iterations\n",
      "\n",
      "Z in [-0.0212081614882, 0.280105501413]\n",
      "Sparsity of Z: 8,819,964 non-zero entries out of 19,072,000 entries, i.e. 46.2%.\n"
     ]
    },
    {
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       "/x830pKoKVK1SQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb000dcfad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.0482563264668, 0.844439685345]\n",
      "d in [0.999999642372, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb00b7fba50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb0018e0ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb0010053d0>"
      ]
     },
     "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: 3220 seconds\n",
      "\n",
      "--------------------------------------------------\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 0x7fb001380650>"
      ]
     },
     "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: 70.0 %\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: 62.4 %\n",
      "Clips accuracy: 67.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (500, 1536), float64\n",
      "Labels: (500,), uint8\n",
      " 68 (+/-  8.2) <- [ 0.64  0.82  0.7   0.68  0.66  0.82  0.58  0.56  0.66  0.7 ]\n",
      " 68 (+/-  3.6) <- [ 0.6   0.68  0.68  0.66  0.72  0.7   0.7   0.74  0.66  0.68]\n",
      " 69 (+/-  5.7) <- [ 0.68  0.74  0.82  0.62  0.66  0.64  0.72  0.64  0.72  0.68]\n",
      " 68 (+/-  4.6) <- [ 0.6   0.64  0.76  0.68  0.7   0.68  0.74  0.66  0.64  0.7 ]\n",
      " 67 (+/-  4.2) <- [ 0.72  0.68  0.7   0.62  0.66  0.66  0.68  0.62  0.76  0.64]\n",
      " 67 (+/-  6.1) <- [ 0.66  0.68  0.76  0.72  0.64  0.54  0.74  0.68  0.62  0.7 ]\n",
      " 68 (+/-  4.0) <- [ 0.68  0.7   0.6   0.7   0.6   0.7   0.68  0.68  0.72  0.7 ]\n",
      " 68 (+/-  7.1) <- [ 0.6   0.64  0.72  0.64  0.84  0.6   0.74  0.68  0.62  0.7 ]\n",
      " 69 (+/-  4.9) <- [ 0.72  0.68  0.64  0.7   0.74  0.78  0.6   0.66  0.7   0.72]\n",
      " 67 (+/-  5.9) <- [ 0.76  0.64  0.8   0.68  0.66  0.64  0.64  0.62  0.6   0.68]\n",
      "Mean time (10 cv): 18.98 seconds\n",
      "Overall time: 194.10 seconds\n",
      "\n",
      "--------------------------------------------------\n",
      "\n",
      "The memory_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext memory_profiler\n",
      "The line_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext line_profiler\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\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2367336,), float32\n",
      "  L_indices : (2367336,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2367336,), float32\n",
      "  W_indices : (2367336,), 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: 916 seconds\n"
     ]
    },
    {
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       "m3UR+48AAACSSURBVNO86b1x/c3yFF9fhH0/te/3SewEvMbPtvIgVvBegu3PX1I/J6LV9Kexg6FD\n",
       "gYewg69p2PeyXD7/97EuEfJ5O73yQUREpG/Nwk4SLKtOLh+cgh0EqpIkPaOmdRFJ7Smspru9d5AJ\n",
       "6qS/ezfgHOw8BREREREREREREREREREREREREREREREREREREREv/x9ESsu9qDgPtQAAAABJRU5E\n",
       "rkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb000c7a0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 425 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 1.600268e+05\n",
      "rdiff: 0.000294506304058\n",
      "i(Z) = ||Z||_1 = 3.421723e+04\n",
      "j(Z) = tr(Z^TLZ) = 6.200758e+03\n"
     ]
    },
    {
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       "aVkAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004c3bbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 2.004448e+05\n"
     ]
    },
    {
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       "7zvxbSyRESkM9SyISBm9gI3h2Ct2IBnV6+kBOwUxDbvMVERERERERERERERERERERERERERERERE\n",
       "REREREREWu//A0o1IOgiZBikAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb00475e310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 3 iterations\n",
      "\n",
      "Z in [-1.27479197545e-05, 0.0141088813543]\n",
      "Sparsity of Z: 16,930,816 non-zero entries out of 19,072,000 entries, i.e. 88.8%.\n"
     ]
    },
    {
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       "/onx+fWEFotPTlifJEmqwbmEpnJJkZm4JEmSJEmSJEmSJEmSJEmSJEmSJEmSJKXl/wOmASuccaAj\n",
       "2wAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004c85d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [0.0195936579257, 0.24978607893]\n",
      "d in [0.999999642372, 1.00000023842]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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       "SUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004c859d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004794790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "/wfwxjCOGRTUcwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004990110>"
      ]
     },
     "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: 923 seconds\n",
      "\n",
      "--------------------------------------------------\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 0x7fb000f0a350>"
      ]
     },
     "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: 52.4 %\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: 50.7 %\n",
      "Clips accuracy: 51.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (500, 1536), float64\n",
      "Labels: (500,), uint8\n",
      " 51 (+/-  7.1) <- [ 0.6   0.52  0.44  0.4   0.56  0.54  0.6   0.48  0.58  0.42]\n",
      " 52 (+/-  4.6) <- [ 0.44  0.54  0.58  0.46  0.56  0.5   0.58  0.56  0.52  0.5 ]\n",
      " 52 (+/-  7.4) <- [ 0.52  0.46  0.56  0.54  0.4   0.48  0.46  0.66  0.62  0.5 ]\n",
      " 52 (+/-  5.9) <- [ 0.48  0.4   0.56  0.5   0.54  0.58  0.54  0.5   0.62  0.48]\n",
      " 51 (+/-  6.9) <- [ 0.5   0.54  0.54  0.56  0.46  0.42  0.6   0.4   0.62  0.48]\n",
      " 51 (+/-  8.6) <- [ 0.4   0.6   0.58  0.54  0.46  0.4   0.48  0.66  0.42  0.56]\n",
      " 52 (+/-  6.5) <- [ 0.42  0.6   0.46  0.46  0.58  0.54  0.56  0.52  0.44  0.6 ]\n",
      " 53 (+/-  5.1) <- [ 0.5   0.5   0.62  0.48  0.6   0.46  0.48  0.56  0.54  0.54]\n",
      " 52 (+/-  8.3) <- [ 0.66  0.34  0.48  0.54  0.6   0.52  0.44  0.56  0.52  0.5 ]\n",
      " 52 (+/-  7.2) <- [ 0.46  0.48  0.56  0.42  0.6   0.6   0.6   0.4   0.56  0.5 ]\n",
      "Mean time (10 cv): 26.70 seconds\n",
      "Overall time: 272.53 seconds\n",
      "\n",
      "--------------------------------------------------\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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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb000ef85d0>"
      ]
     },
     "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: 68.8 %\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: 62.1 %\n",
      "Clips accuracy: 68.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (500, 1152), float64\n",
      "Labels: (500,), uint8\n",
      " 67 (+/-  8.0) <- [ 0.72  0.74  0.6   0.48  0.62  0.74  0.72  0.66  0.66  0.74]\n",
      " 66 (+/-  6.5) <- [ 0.7   0.6   0.62  0.52  0.64  0.7   0.72  0.68  0.66  0.76]\n",
      " 68 (+/-  6.7) <- [ 0.74  0.68  0.84  0.62  0.68  0.7   0.62  0.62  0.7   0.62]\n",
      " 68 (+/-  5.5) <- [ 0.6   0.72  0.62  0.66  0.74  0.74  0.72  0.64  0.76  0.64]\n",
      " 67 (+/-  5.1) <- [ 0.58  0.74  0.66  0.7   0.68  0.64  0.72  0.62  0.74  0.64]\n",
      " 68 (+/-  6.1) <- [ 0.68  0.78  0.74  0.64  0.72  0.7   0.68  0.58  0.58  0.66]\n",
      " 66 (+/-  3.7) <- [ 0.62  0.7   0.6   0.68  0.68  0.66  0.6   0.68  0.7   0.68]\n",
      " 67 (+/-  5.9) <- [ 0.68  0.6   0.58  0.68  0.8   0.64  0.64  0.68  0.64  0.72]\n",
      " 65 (+/-  6.0) <- [ 0.68  0.54  0.68  0.66  0.76  0.62  0.68  0.7   0.62  0.58]\n",
      " 66 (+/-  6.3) <- [ 0.72  0.62  0.78  0.64  0.74  0.64  0.62  0.58  0.6   0.7 ]\n",
      "Mean time (10 cv): 14.97 seconds\n",
      "Overall time: 153.30 seconds\n",
      "\n",
      "--------------------------------------------------\n",
      "\n",
      "Experiment time: 17581 seconds\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "\n",
    "texperiment = time.time()\n",
    "\n",
    "def separator():\n",
    "    print('\\n' + 50 * '-' + '\\n')\n",
    "    # Fair comparison when tuning parameters.\n",
    "    np.random.seed(1)\n",
    "\n",
    "#%run gtzan.ipynb\n",
    "#%run audio_preprocessing.ipynb\n",
    "if not regen_graph:\n",
    "    %run audio_graph.ipynb\n",
    "    separator()\n",
    "if not regen_features:\n",
    "    %run audio_features.ipynb\n",
    "    separator()\n",
    "\n",
    "# Hyper-parameter under test.\n",
    "for p[Pname] in Pvalues:\n",
    "\n",
    "    if regen_graph:\n",
    "        %run audio_graph.ipynb\n",
    "        separator()\n",
    "    if regen_features:\n",
    "        %run audio_features.ipynb\n",
    "        separator()\n",
    "    %run audio_classification.ipynb\n",
    "    separator()\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",
    "        %run audio_classification.ipynb\n",
    "        separator()\n",
    "else:\n",
    "    %run audio_classification.ipynb\n",
    "    separator()\n",
    "\n",
    "print('Experiment time: {:.0f} seconds'.format(time.time() - texperiment))"
   ]
  }
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