{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre recognition: experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: Assess which distance metric is the more stable.\n",
    "\n",
    "Conclusion: `euclidean` is less stable but can potentially lead to better results.\n",
    "\n",
    "Observations:\n",
    "* All the `dm` leads to approximately the same accuracy.\n",
    "* `euclidean` is less stable, its accuracy changed by 1.5% between two runs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyper-parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter under test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Pname = 'dm'\n",
    "Pvalues = 2 * ['cosine_sim','euclidean','cosine_dist']\n",
    "\n",
    "# Regenerate the graph or the features at each iteration.\n",
    "regen_graph = True\n",
    "regen_features = True\n",
    "regen_baseline = False"
   ]
  },
  {
   "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['test_size'] = 0.1\n",
    "p['Ncv'] = 20\n",
    "p['dataset_classification'] = 'Z'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "\n",
    "texperiment = time.time()\n",
    "\n",
    "# Result dictionary.\n",
    "res = ['accuracy', 'accuracy_std']\n",
    "res += ['sparsity', 'atoms']\n",
    "res += ['objective_g', 'objective_h', 'objective_i', 'objective_j']\n",
    "res += ['time_features', 'iterations_inner', 'iterations_outer']\n",
    "res = dict.fromkeys(res)\n",
    "for key in res.keys():\n",
    "    res[key] = []\n",
    "\n",
    "def separator(name, parameter=False):\n",
    "    if parameter:\n",
    "        name += ', {} = {}'.format(Pname, p[Pname])\n",
    "    dashes = 20 * '-'\n",
    "    print('\\n {} {} {} \\n'.format(dashes, name, dashes))\n",
    "    # Fair comparison when tuning parameters.\n",
    "    # Randomnesses: dictionary initialization, training and testing sets.\n",
    "    np.random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " -------------------- Graph, dm = cosine_sim -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 523.94 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.66959297657, 1.0]\n",
      "w in [0.66959297657, 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    : (2430692,), float32\n",
      "  L_indices : (2430692,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2430692,), float32\n",
      "  W_indices : (2430692,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = cosine_sim\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Overall time: 533.73 seconds\n",
      "\n",
      " -------------------- Features, dm = cosine_sim -------------------- \n",
      "\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  Xa: (10, 100, 644, 2, 1024) , float32\n",
      "  Xs: (10, 100, 644, 2, 96)   , float32\n",
      "Full dataset:\n",
      "  size: N=1,288,000 x n=96 -> 123,648,000 floats\n",
      "  dim: 123,648 features per clip\n",
      "  shape: (10, 100, 644, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Data: (149000, 96), float32\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = cosine_sim\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2430692,), float32\n",
      "  L_indices : (2430692,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2430692,), float32\n",
      "  W_indices : (2430692,), 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: 779 seconds\n"
     ]
    },
    {
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       "T/b9Rdj5Sc/vIuwGvPxnizyMJd5LsH/PX1O7J6Jo+TPYxdARwALs4msCdl5W9tt/D2sSwW/b6pMP\n",
       "IiIi0ZqG3SQ4VrXy+OA47CJQhSTpGVWti0i3LcZKujuFDmSUWmnv3h04F7tPQURERERERERERERE\n",
       "RERERERERERERERERERERCSU/w+gGc4TcaRkigAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcfc944590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 314 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.391266e+05\n",
      "rdiff: 0.000265621767921\n",
      "i(Z) = ||Z||_1 = 7.507485e+04\n",
      "j(Z) = tr(Z^TLZ) = 2.182541e+04\n"
     ]
    },
    {
     "data": {
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efce1292950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 8.360269e+05\n"
     ]
    },
    {
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       "z2f4uRYREZEOrUmuBAyqQxn9uRT/Sq7MiPQVtTCIyCD6I7lPx8ujg3SoXYsP5MsRu5FvQRURERER\n",
       "ERERERERERERERERERERERERERERERERkd77/536Tr6kId9YAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efce118d8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 5 iterations\n",
      "\n",
      "Z in [-0.052473615855, 0.141105085611]\n",
      "Sparsity of Z: 8,656,904 non-zero entries out of 19,072,000 entries, i.e. 45.4%.\n"
     ]
    },
    {
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       "Y24AAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efce1292210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.149452388287, 0.568888425827]\n",
      "d in [0.999999642372, 1.00000023842]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efce114a5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "QmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efce1420910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efce1000190>"
      ]
     },
     "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: 787 seconds\n",
      "\n",
      " -------------------- Classification, dm = cosine_sim -------------------- \n",
      "\n",
      "Software versions:\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcc8080090>"
      ]
     },
     "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: 63.5 %\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: 56.6 %\n",
      "Clips accuracy: 58.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 5400.0 training, 600.0 testing\n",
      "  63 (+/- 1.4) <- [63 62 64 63 61 63 63 66 64 61]\n",
      "  64 (+/- 2.0) <- [64 65 63 61 65 65 61 59 63 65]\n",
      "  66 (+/- 2.2) <- [65 66 67 64 68 67 67 61 66 62]\n",
      "  64 (+/- 2.5) <- [65 59 65 63 60 66 66 62 62 67]\n",
      "  64 (+/- 1.1) <- [63 64 63 66 65 65 63 64 63 62]\n",
      "  64 (+/- 1.2) <- [63 64 63 65 65 65 66 63 62 63]\n",
      "  64 (+/- 1.8) <- [64 63 65 65 65 59 63 62 62 63]\n",
      "  63 (+/- 2.0) <- [61 62 64 66 63 67 62 61 62 62]\n",
      "  64 (+/- 1.3) <- [64 63 62 65 61 62 62 65 64 65]\n",
      "  66 (+/- 1.8) <- [66 63 65 63 67 64 65 65 65 69]\n",
      "  66 (+/- 2.5) <- [69 66 66 59 65 65 68 63 65 65]\n",
      "  65 (+/- 1.3) <- [65 65 66 65 62 62 65 64 63 65]\n",
      "  64 (+/- 1.5) <- [65 65 63 66 63 63 61 64 63 62]\n",
      "  63 (+/- 2.8) <- [65 62 64 57 62 63 62 63 61 69]\n",
      "  64 (+/- 1.4) <- [63 63 65 65 62 64 63 65 67 63]\n",
      "  64 (+/- 1.5) <- [66 63 61 63 62 62 63 64 65 63]\n",
      "  64 (+/- 1.1) <- [64 64 65 64 64 63 64 63 67 63]\n",
      "  64 (+/- 2.0) <- [68 66 64 64 62 63 65 60 63 63]\n",
      "  64 (+/- 1.8) <- [62 66 64 63 67 63 66 64 61 64]\n",
      "  64 (+/- 1.1) <- [64 65 62 63 65 63 65 66 63 64]\n",
      "Accuracy: 64.2 (+/- 1.90)\n",
      "Mean time (20 cv): 24.69 seconds\n",
      "Overall time: 498.70 seconds\n",
      "\n",
      " -------------------- Graph, dm = euclidean -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 642.64 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 2.01710319519]\n",
      "w in [0.173404276371, 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    : (2913592,), float32\n",
      "  L_indices : (2913592,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2913592,), float32\n",
      "  W_indices : (2913592,), 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: 653.03 seconds\n",
      "\n",
      " -------------------- Features, dm = euclidean -------------------- \n",
      "\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  Xa: (10, 100, 644, 2, 1024) , float32\n",
      "  Xs: (10, 100, 644, 2, 96)   , float32\n",
      "Full dataset:\n",
      "  size: N=1,288,000 x n=96 -> 123,648,000 floats\n",
      "  dim: 123,648 features per clip\n",
      "  shape: (10, 100, 644, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Data: (149000, 96), float32\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2913592,), float32\n",
      "  L_indices : (2913592,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2913592,), float32\n",
      "  W_indices : (2913592,), 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: 769 seconds\n"
     ]
    },
    {
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       "Pj/AR7Fq92nYCco7gdumiO8i4BnUrnqvfv9a8RTnz8WOT358V2AX4FW/tp5/Yon3fOzv+Qsq10TU\n",
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       "AABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcc253b910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 287 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.469461e+05\n",
      "rdiff: 0.000393581782487\n",
      "i(Z) = ||Z||_1 = 7.001193e+04\n",
      "j(Z) = tr(Z^TLZ) = 2.276549e+04\n"
     ]
    },
    {
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       "IiIiIiIiIiIiIiK+/j/t7RgBNHaCGAAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcc806fe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 8.397235e+05\n"
     ]
    },
    {
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       "LnAb/R8BPFiO+nsnk/49vQW4gNTJ87lBlg/0fQC8hv7nWkRERNq0BqkSUFaHMfC4FMeTKjMiPUUt\n",
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       "/5gOcJ0SRbQAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcfc0590d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 5 iterations\n",
      "\n",
      "Z in [-0.101718649268, 0.194122523069]\n",
      "Sparsity of Z: 8,337,151 non-zero entries out of 19,072,000 entries, i.e. 43.7%.\n"
     ]
    },
    {
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       "hO8KcwwNBUcAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcc2642490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.142467901111, 0.522177219391]\n",
      "d in [0.999999701977, 1.00000023842]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efce1289190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efce1292490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcc26b6cd0>"
      ]
     },
     "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: 779 seconds\n",
      "\n",
      " -------------------- Classification, dm = euclidean -------------------- \n",
      "\n",
      "Software versions:\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcf8a57690>"
      ]
     },
     "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: 63.0 %\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: 57.2 %\n",
      "Clips accuracy: 60.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 5400.0 training, 600.0 testing\n",
      "  63 (+/- 1.9) <- [63 63 62 62 59 64 62 65 66 60]\n",
      "  64 (+/- 2.1) <- [65 65 63 61 67 65 61 60 63 64]\n",
      "  65 (+/- 2.0) <- [64 65 66 64 66 67 67 60 65 62]\n",
      "  64 (+/- 2.3) <- [65 60 64 63 60 66 66 62 63 66]\n",
      "  64 (+/- 1.6) <- [62 64 63 67 64 64 62 64 62 60]\n",
      "  64 (+/- 1.0) <- [63 64 63 66 63 64 64 62 62 63]\n",
      "  64 (+/- 1.6) <- [64 64 63 64 65 59 65 63 63 63]\n",
      "  63 (+/- 1.6) <- [60 63 63 63 61 66 61 61 63 61]\n",
      "  64 (+/- 1.6) <- [65 62 62 64 61 61 62 64 66 65]\n",
      "  65 (+/- 1.4) <- [65 63 65 62 66 63 63 64 64 67]\n",
      "  65 (+/- 2.5) <- [66 66 64 58 63 65 67 63 65 65]\n",
      "  64 (+/- 1.5) <- [65 65 65 64 62 63 65 61 63 66]\n",
      "  64 (+/- 1.8) <- [65 64 63 66 63 62 63 65 63 59]\n",
      "  63 (+/- 2.7) <- [64 62 64 57 61 62 60 65 61 68]\n",
      "  64 (+/- 1.2) <- [63 62 64 64 62 64 63 66 66 63]\n",
      "  63 (+/- 1.6) <- [67 63 61 62 62 62 61 64 64 63]\n",
      "  64 (+/- 1.4) <- [62 64 65 64 64 62 63 63 67 63]\n",
      "  64 (+/- 1.9) <- [67 65 64 64 62 64 65 60 63 63]\n",
      "  64 (+/- 1.3) <- [62 64 63 63 66 65 65 65 62 63]\n",
      "  64 (+/- 1.1) <- [63 66 62 64 63 64 64 65 63 62]\n",
      "Accuracy: 63.8 (+/- 1.88)\n",
      "Mean time (20 cv): 24.30 seconds\n",
      "Overall time: 491.24 seconds\n",
      "\n",
      " -------------------- Graph, dm = cosine_dist -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 577.82 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 0.681089401245]\n",
      "w in [0.283994346857, 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    : (2789250,), float32\n",
      "  L_indices : (2789250,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2789250,), float32\n",
      "  W_indices : (2789250,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = cosine_dist\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Overall time: 588.28 seconds\n",
      "\n",
      " -------------------- Features, dm = cosine_dist -------------------- \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_dist\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2789250,), float32\n",
      "  L_indices : (2789250,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2789250,), float32\n",
      "  W_indices : (2789250,), 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: 657 seconds\n"
     ]
    },
    {
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       "eRRLvFdg/56/o3RPRK31L2AXQ8cAi7GLr/HYeVnVb/9DrEkEv22rTz6IiIiMWVOxmwQHVSuPDw5j\n",
       "F4EqJEnfqGpdRHrteayk+9bQgbSplfbufYCLsPsURERERERERERERERERERERERERERERERERERE\n",
       "REL5/8yE8P4AU0R5AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcfd9c44d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 246 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.462377e+05\n",
      "rdiff: 0.000593498029647\n",
      "i(Z) = ||Z||_1 = 7.127560e+04\n",
      "j(Z) = tr(Z^TLZ) = 2.056625e+04\n"
     ]
    },
    {
     "data": {
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcc2425f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 8.380796e+05\n"
     ]
    },
    {
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       "V4D/K637Lt5prtbBsfa+n8blc/GC+DP45YjGfdNkfrTf4dd4U/8dePP+7FH2Y02mDa+0XBSzXYj/\n",
       "/ncCn8cL67nx50ub7KfRr6lXAp4FjsA7Bd6KtyacEdedGPP+Li6v7fMy4J3UOzgeE/c3F7id4Y/x\n",
       "HS1Hbd138L+ntwLn4h06nx9l+UjnA+A1DP+3FhERkTasiVcAcnU4I4878WW8IiPSM9SyICI5+jPe\n",
       "h2OP1EHa1KylB/wSxG74baYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiItJ5/w8S3q+WDDOr\n",
       "jQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcfc938fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 4 iterations\n",
      "\n",
      "Z in [-0.056636184454, 0.14264112711]\n",
      "Sparsity of Z: 8,786,613 non-zero entries out of 19,072,000 entries, i.e. 46.1%.\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcc1b73f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.145910441875, 0.507630586624]\n",
      "d in [0.999999761581, 1.00000023842]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcc22690d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcf89dc910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "RU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efd3e31ac90>"
      ]
     },
     "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: 667 seconds\n",
      "\n",
      " -------------------- Classification, dm = cosine_dist -------------------- \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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       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcb8c71250>"
      ]
     },
     "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: 63.2 %\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: 57.2 %\n",
      "Clips accuracy: 62.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 5400.0 training, 600.0 testing\n",
      "  63 (+/- 2.0) <- [63 63 62 61 59 63 62 65 66 60]\n",
      "  64 (+/- 1.8) <- [65 65 63 61 64 65 61 61 64 65]\n",
      "  66 (+/- 1.9) <- [65 66 67 64 66 67 66 62 66 62]\n",
      "  64 (+/- 2.1) <- [64 60 63 62 60 66 66 62 63 65]\n",
      "  64 (+/- 1.5) <- [62 64 64 65 63 64 62 65 62 61]\n",
      "  64 (+/- 1.1) <- [63 63 63 64 62 64 66 63 62 64]\n",
      "  64 (+/- 1.8) <- [65 64 64 64 65 58 63 63 62 64]\n",
      "  64 (+/- 1.5) <- [62 63 64 65 63 67 62 62 63 62]\n",
      "  64 (+/- 1.6) <- [64 63 62 65 61 61 61 65 65 64]\n",
      "  65 (+/- 2.2) <- [67 63 65 63 67 63 62 63 65 69]\n",
      "  65 (+/- 2.3) <- [67 64 65 59 63 65 68 63 66 66]\n",
      "  64 (+/- 1.1) <- [65 66 65 64 63 63 65 62 64 64]\n",
      "  64 (+/- 1.5) <- [64 66 63 65 63 63 62 65 63 61]\n",
      "  63 (+/- 2.7) <- [63 63 64 56 63 62 61 63 62 68]\n",
      "  64 (+/- 1.3) <- [62 62 64 64 65 64 64 64 67 62]\n",
      "  64 (+/- 1.4) <- [65 64 62 62 63 64 60 64 65 63]\n",
      "  64 (+/- 1.5) <- [62 64 65 64 63 62 64 63 67 61]\n",
      "  64 (+/- 2.2) <- [69 65 65 64 61 64 65 61 64 61]\n",
      "  64 (+/- 1.6) <- [63 64 63 62 67 64 64 65 61 62]\n",
      "  64 (+/- 1.3) <- [62 65 62 62 64 63 65 66 63 63]\n",
      "Accuracy: 64.0 (+/- 1.88)\n",
      "Mean time (20 cv): 24.58 seconds\n",
      "Overall time: 496.99 seconds\n",
      "\n",
      " -------------------- Graph, dm = cosine_sim -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 539.57 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.66959297657, 1.0]\n",
      "w in [0.66959297657, 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    : (2430692,), float32\n",
      "  L_indices : (2430692,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2430692,), float32\n",
      "  W_indices : (2430692,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = cosine_sim\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Overall time: 549.33 seconds\n",
      "\n",
      " -------------------- Features, dm = cosine_sim -------------------- \n",
      "\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  Xa: (10, 100, 644, 2, 1024) , float32\n",
      "  Xs: (10, 100, 644, 2, 96)   , float32\n",
      "Full dataset:\n",
      "  size: N=1,288,000 x n=96 -> 123,648,000 floats\n",
      "  dim: 123,648 features per clip\n",
      "  shape: (10, 100, 644, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Data: (149000, 96), float32\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = cosine_sim\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2430692,), float32\n",
      "  L_indices : (2430692,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2430692,), float32\n",
      "  W_indices : (2430692,), 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: 805 seconds\n"
     ]
    },
    {
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       "X6IrU8JLt7kcuMvHcrM/btF+qg3epwnv59hNYz/Gvj/AEVi1+zjsAuVjwL0jxHc5sBWNq97zx28U\n",
       "T/b9Rdj5Sc/vIuwGvPxnizyMJd5LsH/PX1O7J6Jo+TPYxdARwALs4msCdl5W9tt/D2sSwW/b6pMP\n",
       "IiIi0ZqG3SQ4VrXy+OA47CJQhSTpGVWti0i3LcZKujuFDmSUWmnv3h04F7tPQURERERERERERERE\n",
       "RERERERERERERERERERERCSU/w+gGc4TcaRkigAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcfc938e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 314 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.391266e+05\n",
      "rdiff: 0.000265621767921\n",
      "i(Z) = ||Z||_1 = 7.507485e+04\n",
      "j(Z) = tr(Z^TLZ) = 2.182541e+04\n"
     ]
    },
    {
     "data": {
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcb83de610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 8.360269e+05\n"
     ]
    },
    {
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       "ERERERERERERERERERERERERERERERERkd77/536Tr6kId9YAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcc17fd350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 5 iterations\n",
      "\n",
      "Z in [-0.052473615855, 0.141105085611]\n",
      "Sparsity of Z: 8,656,904 non-zero entries out of 19,072,000 entries, i.e. 45.4%.\n"
     ]
    },
    {
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       "Y24AAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcb83d3a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.149452388287, 0.568888425827]\n",
      "d in [0.999999642372, 1.00000023842]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcc1ce9650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "QmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcc1e19910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcb8c65fd0>"
      ]
     },
     "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: 817 seconds\n",
      "\n",
      " -------------------- Classification, dm = cosine_sim -------------------- \n",
      "\n",
      "Software versions:\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcb0bc8810>"
      ]
     },
     "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: 63.5 %\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: 56.6 %\n",
      "Clips accuracy: 58.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 5400.0 training, 600.0 testing\n",
      "  63 (+/- 1.4) <- [63 62 64 63 61 63 63 66 64 61]\n",
      "  64 (+/- 2.0) <- [64 65 63 61 65 65 61 59 63 65]\n",
      "  66 (+/- 2.2) <- [65 66 67 64 68 67 67 61 66 62]\n",
      "  64 (+/- 2.5) <- [65 59 65 63 60 66 66 62 62 67]\n",
      "  64 (+/- 1.1) <- [63 64 63 66 65 65 63 64 63 62]\n",
      "  64 (+/- 1.2) <- [63 64 63 65 65 65 66 63 62 63]\n",
      "  64 (+/- 1.8) <- [64 63 65 65 65 59 63 62 62 63]\n",
      "  63 (+/- 2.0) <- [61 62 64 66 63 67 62 61 62 62]\n",
      "  64 (+/- 1.3) <- [64 63 62 65 61 62 62 65 64 65]\n",
      "  66 (+/- 1.8) <- [66 63 65 63 67 64 65 65 65 69]\n",
      "  66 (+/- 2.5) <- [69 66 66 59 65 65 68 63 65 65]\n",
      "  65 (+/- 1.3) <- [65 65 66 65 62 62 65 64 63 65]\n",
      "  64 (+/- 1.5) <- [65 65 63 66 63 63 61 64 63 62]\n",
      "  63 (+/- 2.8) <- [65 62 64 57 62 63 62 63 61 69]\n",
      "  64 (+/- 1.4) <- [63 63 65 65 62 64 63 65 67 63]\n",
      "  64 (+/- 1.5) <- [66 63 61 63 62 62 63 64 65 63]\n",
      "  64 (+/- 1.1) <- [64 64 65 64 64 63 64 63 67 63]\n",
      "  64 (+/- 2.0) <- [68 66 64 64 62 63 65 60 63 63]\n",
      "  64 (+/- 1.8) <- [62 66 64 63 67 63 66 64 61 64]\n",
      "  64 (+/- 1.1) <- [64 65 62 63 65 63 65 66 63 64]\n",
      "Accuracy: 64.2 (+/- 1.90)\n",
      "Mean time (20 cv): 24.42 seconds\n",
      "Overall time: 493.99 seconds\n",
      "\n",
      " -------------------- Graph, dm = euclidean -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 1489.04 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 2.08512282372]\n",
      "w in [0.199098289013, 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    : (2923166,), float32\n",
      "  L_indices : (2923166,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2923166,), float32\n",
      "  W_indices : (2923166,), 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: 1498.97 seconds\n",
      "\n",
      " -------------------- Features, dm = euclidean -------------------- \n",
      "\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  Xa: (10, 100, 644, 2, 1024) , float32\n",
      "  Xs: (10, 100, 644, 2, 96)   , float32\n",
      "Full dataset:\n",
      "  size: N=1,288,000 x n=96 -> 123,648,000 floats\n",
      "  dim: 123,648 features per clip\n",
      "  shape: (10, 100, 644, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Data: (149000, 96), float32\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2923166,), float32\n",
      "  L_indices : (2923166,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2923166,), float32\n",
      "  W_indices : (2923166,), 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: 944 seconds\n"
     ]
    },
    {
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       "se8n/X6XYDfl1e7byENY8p2N/Z4XUblHotH6p7ELoi8BC7ELsEnY97Jq3P67WPMIcduxPhEhIiIy\n",
       "rs3AbhwcVGN5tHACdiGogpL0larZRaTXnsJKvK/zDqRNY2n/3hM4D7tvQURERERERERERERERERE\n",
       "RERERERERERERERERGS8+v+MnOaUvryfhQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcd3240e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 357 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.395080e+05\n",
      "rdiff: 0.000191850515487\n",
      "i(Z) = ||Z||_1 = 7.341332e+04\n",
      "j(Z) = tr(Z^TLZ) = 2.210159e+04\n"
     ]
    },
    {
     "data": {
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       "47kWEREZKKtgibus5tB63ID/wi5ARApDJXkR6aelWB+F3WIH0qWsmhWwqvqdsdsNRURERERERERE\n",
       "RERERERERERERERERERERERERCSs/w9fSTumI2vt8gAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcb85e7a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 8.350230e+05\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcb8122fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 5 iterations\n",
      "\n",
      "Z in [-0.100512064993, 0.248378291726]\n",
      "Sparsity of Z: 8,152,165 non-zero entries out of 19,072,000 entries, i.e. 42.7%.\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcb035e850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.139279812574, 0.677405714989]\n",
      "d in [0.999999642372, 1.00000023842]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcc1b31290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "RK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efca3f8fcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcb3faa2d0>"
      ]
     },
     "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: 956 seconds\n",
      "\n",
      " -------------------- Classification, dm = euclidean -------------------- \n",
      "\n",
      "Software versions:\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc9c1c94d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 65.5 %\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: 58.9 %\n",
      "Clips accuracy: 64.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 5400.0 training, 600.0 testing\n",
      "  65 (+/- 1.9) <- [63 64 66 66 63 67 63 68 66 63]\n",
      "  66 (+/- 1.5) <- [67 66 66 64 67 67 63 64 64 66]\n",
      "  68 (+/- 2.4) <- [66 69 69 66 69 71 70 63 68 64]\n",
      "  66 (+/- 2.6) <- [66 60 66 64 63 68 69 66 64 69]\n",
      "  66 (+/- 1.0) <- [66 65 65 68 68 66 65 65 66 65]\n",
      "  67 (+/- 1.1) <- [65 67 65 66 66 67 68 66 65 66]\n",
      "  66 (+/- 1.3) <- [66 66 66 66 66 62 66 65 66 65]\n",
      "  66 (+/- 1.5) <- [65 64 65 67 65 68 65 66 65 62]\n",
      "  66 (+/- 1.2) <- [67 65 65 68 63 66 66 66 67 67]\n",
      "  67 (+/- 1.2) <- [66 65 65 66 69 66 66 66 67 68]\n",
      "  67 (+/- 2.1) <- [69 69 67 62 67 66 69 65 67 69]\n",
      "  67 (+/- 1.3) <- [69 69 67 65 66 67 68 65 66 68]\n",
      "  66 (+/- 2.1) <- [69 67 65 68 66 64 64 66 65 62]\n",
      "  66 (+/- 2.7) <- [68 65 66 62 66 64 63 67 61 70]\n",
      "  67 (+/- 1.8) <- [67 64 66 66 65 69 65 67 70 65]\n",
      "  66 (+/- 1.6) <- [69 66 64 66 64 65 64 67 67 64]\n",
      "  67 (+/- 1.5) <- [67 66 66 66 67 65 67 66 70 65]\n",
      "  67 (+/- 1.5) <- [67 68 67 66 66 68 66 62 66 66]\n",
      "  66 (+/- 1.7) <- [63 67 66 65 69 65 67 67 62 66]\n",
      "  66 (+/- 1.3) <- [65 67 64 66 66 65 67 68 65 64]\n",
      "Accuracy: 66.3 (+/- 1.85)\n",
      "Mean time (20 cv): 24.16 seconds\n",
      "Overall time: 488.71 seconds\n",
      "\n",
      " -------------------- Graph, dm = cosine_dist -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 551.52 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 0.681089401245]\n",
      "w in [0.283994346857, 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    : (2789250,), float32\n",
      "  L_indices : (2789250,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2789250,), float32\n",
      "  W_indices : (2789250,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = cosine_dist\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Overall time: 561.39 seconds\n",
      "\n",
      " -------------------- Features, dm = cosine_dist -------------------- \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_dist\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2789250,), float32\n",
      "  L_indices : (2789250,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2789250,), float32\n",
      "  W_indices : (2789250,), 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: 648 seconds\n"
     ]
    },
    {
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       "1l/p9xsG3u/nP4z9uw1h370AHAmsCkz120ynvKZGpKdUIhcxaQnqVqx0ewtwJnAiVtq7Gfvxfgwb\n",
       "y7pIeanrUuAz2JjM92A3PKVOwaphb8LalNP9LgZ28p9ZxG7iegxrE64s0TVTwku3uRp40Mdyt//c\n",
       "WscpVplPE94vsZvGzsG+P8AxWLX7MHaB8q/AQw3iuxrYjupV75WfXy2e7Pxl2PlJz+8S7Aa8yn1r\n",
       "eRRLvFdg/56/o3RPRK31L2AXQ8cAi7GLr/HYeVnVb/9DrEkEv22rTz6IiIiMWVOxmwQHVSuPDw5j\n",
       "F4EqJEnfqGpdRHrteayk+9bQgbSplfbufYCLsPsURERERERERERERERERERERERERERERERERERE\n",
       "REL5/8yE8P4AU0R5AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc9bc585d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 246 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.462377e+05\n",
      "rdiff: 0.000593498029647\n",
      "i(Z) = ||Z||_1 = 7.127560e+04\n",
      "j(Z) = tr(Z^TLZ) = 2.056625e+04\n"
     ]
    },
    {
     "data": {
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       "CgeGDqRDWTUrYFX1+2KXG4qIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhIsf4/9TQvnyOs2JIA\n",
       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcb06b42d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 8.380796e+05\n"
     ]
    },
    {
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       "V4D/K637Lt5prtbBsfa+n8blc/GC+DP45YjGfdNkfrTf4dd4U/8dePP+7FH2Y02mDa+0XBSzXYj/\n",
       "/ncCn8cL67nx50ub7KfRr6lXAp4FjsA7Bd6KtyacEdedGPP+Li6v7fMy4J3UOzgeE/c3F7id4Y/x\n",
       "HS1Hbd138L+ntwLn4h06nx9l+UjnA+A1DP+3FhERkTasiVcAcnU4I4878WW8IiPSM9SyICI5+jPe\n",
       "h2OP1EHa1KylB/wSxG74baYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiItJ5/w8S3q+WDDOr\n",
       "jQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcb00ae550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 4 iterations\n",
      "\n",
      "Z in [-0.056636184454, 0.14264112711]\n",
      "Sparsity of Z: 8,786,613 non-zero entries out of 19,072,000 entries, i.e. 46.1%.\n"
     ]
    },
    {
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       "451Yftz2K1jeyz8by0HhFQA+BsB/Cv1daT/+D07nsfzVupfhApt+N/X5p1iqBXdiKcXfemh/yuH7\n",
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       "DAaDwWAwGAwGg8FgMBgMBoPBYDAY7Av/P9maY1PY7L9BAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc9b8092d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.145910441875, 0.507630586624]\n",
      "d in [0.999999761581, 1.00000023842]\n",
      "Constraints on D: True\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcb09226d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcb002db10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "RU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcf0066510>"
      ]
     },
     "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: 661 seconds\n",
      "\n",
      " -------------------- Classification, dm = cosine_dist -------------------- \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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       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc9fb28050>"
      ]
     },
     "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: 63.2 %\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: 57.2 %\n",
      "Clips accuracy: 62.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 5400.0 training, 600.0 testing\n",
      "  63 (+/- 2.0) <- [63 63 62 61 59 63 62 65 66 60]\n",
      "  64 (+/- 1.8) <- [65 65 63 61 64 65 61 61 64 65]\n",
      "  66 (+/- 1.9) <- [65 66 67 64 66 67 66 62 66 62]\n",
      "  64 (+/- 2.1) <- [64 60 63 62 60 66 66 62 63 65]\n",
      "  64 (+/- 1.5) <- [62 64 64 65 63 64 62 65 62 61]\n",
      "  64 (+/- 1.1) <- [63 63 63 64 62 64 66 63 62 64]\n",
      "  64 (+/- 1.8) <- [65 64 64 64 65 58 63 63 62 64]\n",
      "  64 (+/- 1.5) <- [62 63 64 65 63 67 62 62 63 62]\n",
      "  64 (+/- 1.6) <- [64 63 62 65 61 61 61 65 65 64]\n",
      "  65 (+/- 2.2) <- [67 63 65 63 67 63 62 63 65 69]\n",
      "  65 (+/- 2.3) <- [67 64 65 59 63 65 68 63 66 66]\n",
      "  64 (+/- 1.1) <- [65 66 65 64 63 63 65 62 64 64]\n",
      "  64 (+/- 1.5) <- [64 66 63 65 63 63 62 65 63 61]\n",
      "  63 (+/- 2.7) <- [63 63 64 56 63 62 61 63 62 68]\n",
      "  64 (+/- 1.3) <- [62 62 64 64 65 64 64 64 67 62]\n",
      "  64 (+/- 1.4) <- [65 64 62 62 63 64 60 64 65 63]\n",
      "  64 (+/- 1.5) <- [62 64 65 64 63 62 64 63 67 61]\n",
      "  64 (+/- 2.2) <- [69 65 65 64 61 64 65 61 64 61]\n",
      "  64 (+/- 1.6) <- [63 64 63 62 67 64 64 65 61 62]\n",
      "  64 (+/- 1.3) <- [62 65 62 62 64 63 65 66 63 63]\n",
      "Accuracy: 64.0 (+/- 1.88)\n",
      "Mean time (20 cv): 24.16 seconds\n",
      "Overall time: 489.05 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 0x7efc9ef50f90>"
      ]
     },
     "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",
      "Ratio: 5400.0 training, 600.0 testing\n",
      "  58 (+/- 1.8) <- [57 56 61 57 53 59 57 59 57 56]\n",
      "  59 (+/- 2.0) <- [59 61 58 57 60 56 60 57 57 64]\n",
      "  60 (+/- 2.0) <- [60 62 60 58 59 62 62 57 60 56]\n",
      "  57 (+/- 2.2) <- [57 51 57 57 56 59 57 58 56 59]\n",
      "  59 (+/- 2.1) <- [61 57 60 59 57 63 59 61 59 55]\n",
      "  59 (+/- 1.6) <- [59 61 58 61 60 59 57 60 56 60]\n",
      "  59 (+/- 1.7) <- [62 56 57 59 57 56 59 59 59 58]\n",
      "  58 (+/- 1.4) <- [57 58 56 56 58 61 60 57 58 57]\n",
      "  58 (+/- 2.6) <- [60 58 55 58 53 55 59 60 60 62]\n",
      "  59 (+/- 1.2) <- [59 58 60 56 58 57 57 59 59 60]\n",
      "  59 (+/- 1.4) <- [59 61 59 55 60 58 59 59 59 60]\n",
      "  59 (+/- 2.1) <- [61 61 58 56 55 57 61 60 60 60]\n",
      "  58 (+/- 2.2) <- [58 61 57 57 60 57 54 58 60 54]\n",
      "  57 (+/- 1.5) <- [59 56 58 54 56 58 56 56 57 59]\n",
      "  59 (+/- 0.9) <- [60 59 58 60 59 57 58 59 58 57]\n",
      "  59 (+/- 1.3) <- [61 58 57 60 59 58 58 59 59 57]\n",
      "  59 (+/- 1.7) <- [56 60 60 59 59 57 60 57 60 56]\n",
      "  59 (+/- 1.3) <- [61 57 60 58 61 59 58 58 57 59]\n",
      "  59 (+/- 1.6) <- [59 61 58 57 61 61 58 60 59 56]\n",
      "  59 (+/- 1.2) <- [57 60 60 58 59 59 57 60 60 59]\n",
      "Accuracy: 58.8 (+/- 1.90)\n",
      "Mean time (20 cv): 21.96 seconds\n",
      "Overall time: 444.55 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 regen_baseline:\n",
    "    res['baseline'] = []\n",
    "    res['baseline_std'] = []\n",
    "    for p[Pname] in Pvalues:\n",
    "        separator('Baseline', True)\n",
    "        %run audio_classification.ipynb\n",
    "        res['baseline'].append(accuracy)\n",
    "        res['baseline_std'].append(accuracy_std)\n",
    "else:\n",
    "    separator('Baseline')\n",
    "    %run audio_classification.ipynb\n",
    "    res['baseline'] = len(Pvalues) * [accuracy]\n",
    "    res['baseline_std'] = accuracy_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dm = ['cosine_sim', 'euclidean', 'cosine_dist', 'cosine_sim', 'euclidean', 'cosine_dist']\n",
      "res['accuracy_std'] = [1.9028042171723516, 1.8752331336544679, 1.8835921651048679, 1.9028042171723516, 1.852325115451749, 1.8835921651048679]\n",
      "res['objective_j'] = [21825.411987304688, 22765.493774414062, 20566.252136230469, 21825.411987304688, 22101.589965820312, 20566.252136230469]\n",
      "res['objective_i'] = [75074.8515625, 70011.9296875, 71275.6015625, 75074.8515625, 73413.3203125, 71275.6015625]\n",
      "res['objective_h'] = [0, 0, 0, 0, 0, 0]\n",
      "res['objective_g'] = [739126.640625, 746946.09375, 746237.734375, 739126.640625, 739508.046875, 746237.734375]\n",
      "res['baseline'] = [58.794999999999995, 58.794999999999995, 58.794999999999995, 58.794999999999995, 58.794999999999995, 58.794999999999995]\n",
      "res['time_features'] = [778.8208689689636, 769.245001077652, 656.6257529258728, 805.0615928173065, 943.803286075592, 647.862753868103]\n",
      "res['baseline_std'] = 1.89807724348\n",
      "res['sparsity'] = [45.39064597315436, 43.71408871644295, 46.07074769295302, 45.39064597315436, 42.744153733221474, 46.07074769295302]\n",
      "res['iterations_inner'] = [314, 287, 246, 314, 357, 246]\n",
      "res['iterations_outer'] = [5, 5, 4, 5, 5, 4]\n",
      "res['accuracy'] = [64.198333333333323, 63.80916666666667, 63.988333333333323, 64.198333333333323, 66.334999999999937, 63.988333333333323]\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efca1eebf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "fhZCCCGEEEIIIYQoOf3Wp9hPXAWsmAs7GBuWJUSv0HdYDEpH0Q/oOxRCCCGEEEIIIYQQQgghhBBC\n",
       "CCGEEG3R9zOBCSH6ghFs6uM/B5ZDCFES+n0mTyFEfxB69kUhRMmQgiGEqMfnsNkF/4BNBw02VfF3\n",
       "sbUQbgW2w1ae/BtwTO9FFEIIIUSZ2BablnglbK2DO7BVOucBX/PXHIatdbEmsAJwP7b0tRBCyIIh\n",
       "hKjJLtgiYM9iCyr9MnMu2b/Jb//AVpS8m/oLOgkhBgwpGEKIWtRb0hqqly9/LhO+HDmOCyE8UjCE\n",
       "ELX4PbAfaRfJPplzWmJACNGUSaEFEEL0JdcD52BLQf8TuMaHV0hHlGT3hRBCCCGEEEIIIYQQQggh\n",
       "hBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCiPj5f/no3xHou5RWAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcb071eb50>"
      ]
     },
     "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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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc9f185250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efca294eb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efcb03e7610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efca3fb6310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc9b6ac550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary atoms for dm = cosine_sim\n",
      "Dictionary atoms for dm = euclidean\n",
      "Dictionary atoms for dm = cosine_dist\n",
      "Dictionary atoms for dm = cosine_sim\n",
      "Dictionary atoms for dm = euclidean\n",
      "Dictionary atoms for dm = cosine_dist\n",
      "Experiment time: 12479 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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