{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre recognition: experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: Test influence of number of neighbors $K$.\n",
    "\n",
    "Conclusion: Range 10-20 seems good. Decrease of 2% for 5 and 50.\n",
    "\n",
    "Observations:\n",
    "* Features extraction is 10 times slower for $K=50$.\n",
    "* Sparsity decreased with increasing $K$ due to increasing Dirichlet energy."
   ]
  },
  {
   "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 = 'K'\n",
    "Pvalues = [6, 11, 21, 51]\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"
   ]
  },
  {
   "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_D']\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, K = 6 -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 166.97 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 1.44633090496]\n",
      "w in [0.00253254896961, 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    : (1215822,), float32\n",
      "  L_indices : (1215822,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (1215822,), float32\n",
      "  W_indices : (1215822,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Attributes:\n",
      "  K = 6\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Overall time: 175.57 seconds\n",
      "\n",
      " -------------------- Features, K = 6 -------------------- \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 = 6\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (1215822,), float32\n",
      "  L_indices : (1215822,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (1215822,), float32\n",
      "  W_indices : (1215822,), 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: 2736 seconds\n"
     ]
    },
    {
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       "r2rk0clx2IWiClLSVVSNLyLttgQrMb/ZO5AxaqT9fR/gIuy+CRERERERERERERERERERERERERER\n",
       "ERERERERERHpTv8PyjaQPIhRArMAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f9c28c790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1410 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 6.054626e+04\n",
      "rdiff: 0.00214526373413\n",
      "i(Z) = ||Z||_1 = 6.122535e+04\n",
      "j(Z) = tr(Z^TLZ) = 6.740329e+03\n"
     ]
    },
    {
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       "hVVr345VZd/Q4Dy+xrrHflj8KMR2Ifb57wA+hyXUm8PjRjXOU+0qKon6BWx63h9in3011rkO4Ish\n",
       "3uvC/vI5LwHeQaVT3EfD+W4G/gAc1+Dz1fqs38K+B28BzsM6Aa5qsL/e9QDYkfH/1iIiIn1rbSxJ\n",
       "96qjqX9f/j9hPzZEolAJXUSm0nNYn4I3xA5kkmrVmIBVt++J3eInIiIiIiIiIiIiIiIiIiIiIiIi\n",
       "IiIiIiIiIiIi0uv+P7fL+bMlXd1rAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f955f7b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.285119e+05\n"
     ]
    },
    {
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       "voU1WkSiUY+BiHSaF7AxFx+MHWSQ6vXggF1G2B67tVNERERERERERERERERERERERERERERERERE\n",
       "REREpFP9fx0KeDn4FaONAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f95469390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 6 iterations\n",
      "\n",
      "Z in [-0.363849133253, 1.04555726051]\n",
      "Sparsity of Z: 2,705,168 non-zero entries out of 19,072,000 entries, i.e. 14.2%.\n"
     ]
    },
    {
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       "+0Lguxks1wcArxivv57BXX078M+Bbx+vfxr4uwyT5S3AzwPvdXS8mSG47dcY9uVfzrBQeCfwhcB/\n",
       "n6T3XPvHk9+OGY7WvZUL1vQnkzT/kMFbcDODK/4l4/Vnjd/fweCx+GmnPCGEEEIswLUMrnIhxIgs\n",
       "cSGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYRYF/8/PHII/6GGSD0AAAAASUVO\n",
       "RK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f953a3d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.425295501947, 0.918826341629]\n",
      "d in [0.999999701977, 1.00000023842]\n",
      "Constraints on D: True\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f954847d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f95539950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f9c32fed0>"
      ]
     },
     "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: 2743 seconds\n",
      "\n",
      " -------------------- Classification, K = 6 -------------------- \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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       "ngAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f88c19090>"
      ]
     },
     "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: 74.4 %\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: 60.5 %\n",
      "Clips accuracy: 65.0 %\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",
      "  75 (+/- 2.0) <- [73 76 76 73 70 75 77 75 76 77]\n",
      "  75 (+/- 1.9) <- [77 74 75 71 75 76 73 77 77 77]\n",
      "  76 (+/- 1.4) <- [77 75 76 78 76 76 78 74 77 74]\n",
      "  76 (+/- 1.5) <- [76 72 73 77 74 76 75 76 77 74]\n",
      "  76 (+/- 0.7) <- [77 75 75 75 75 75 76 76 75 75]\n",
      "  75 (+/- 1.1) <- [76 76 74 75 77 74 74 75 74 74]\n",
      "  76 (+/- 1.5) <- [76 74 76 75 75 72 78 75 74 76]\n",
      "  75 (+/- 1.2) <- [72 76 76 75 74 75 75 74 73 76]\n",
      "  76 (+/- 1.2) <- [74 77 74 76 77 74 74 76 75 75]\n",
      "  75 (+/- 1.3) <- [74 75 75 74 76 75 73 74 74 78]\n",
      "  76 (+/- 1.9) <- [76 77 74 75 77 75 76 71 77 77]\n",
      "  76 (+/- 1.4) <- [76 74 77 73 75 77 74 75 76 75]\n",
      "  76 (+/- 1.2) <- [76 73 76 76 76 77 77 75 75 74]\n",
      "  75 (+/- 1.8) <- [77 75 75 73 72 74 72 74 76 76]\n",
      "  76 (+/- 1.0) <- [75 75 76 74 75 76 78 76 77 75]\n",
      "  76 (+/- 1.7) <- [77 75 76 76 73 75 75 76 79 74]\n",
      "  75 (+/- 1.8) <- [73 74 73 72 76 76 76 77 77 74]\n",
      "  75 (+/- 1.3) <- [75 74 74 74 72 76 76 76 76 74]\n",
      "  75 (+/- 1.2) <- [76 73 76 74 75 74 77 73 75 73]\n",
      "  75 (+/- 1.1) <- [76 75 74 73 76 76 75 76 75 73]\n",
      "Accuracy: 75.5 (+/- 1.51)\n",
      "Mean time (20 cv): 25.08 seconds\n",
      "Overall time: 505.95 seconds\n",
      "\n",
      " -------------------- Graph, K = 11 -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 157.62 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 1.5599988699]\n",
      "w in [0.00449881749228, 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    : (2417250,), float32\n",
      "  L_indices : (2417250,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2417250,), float32\n",
      "  W_indices : (2417250,), 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: 167.49 seconds\n",
      "\n",
      " -------------------- Features, K = 11 -------------------- \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    : (2417250,), float32\n",
      "  L_indices : (2417250,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2417250,), float32\n",
      "  W_indices : (2417250,), 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: 2726 seconds\n"
     ]
    },
    {
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       "sT7l7HO/AbaP31nBLuK6F+sTLtboWqnhZe/5C3BbzHJd/N5G+6nUWc4KvF9jF439FPv9AIdize7T\n",
       "sBOUDwN3TJLvL8A21G96L35/vTz55fOw45Md3yXYBXjFzzbyL6zgvQD79zyD6jURjbY/jp0MHQos\n",
       "xk6+ZmDHZeX4/u9gXSLE97Z754OIiEiy5mIXCQ6qdm4fnIadBKqSJH2jpnUR6bXHsJruzt5Bpqid\n",
       "/u7dgVOw6xREREREREREREREREREREREREREREREREREREREvPz/SAYWlHCLp6sAAAAASUVORK5C\n",
       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f8861d2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1292 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.209134e+04\n",
      "rdiff: 0.000108572654307\n",
      "i(Z) = ||Z||_1 = 5.659263e+04\n",
      "j(Z) = tr(Z^TLZ) = 8.596201e+03\n"
     ]
    },
    {
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       "ERdqoYtIJz2HHVOwvXeQJlXbYwK2u31r7BQ/ERERERERERERERERERERERERERERERERERERkV73\n",
       "/xfod+xGRAb9AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8fd327f490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.372802e+05\n"
     ]
    },
    {
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       "hZ1SGLht6kwP9R1+i3XX34d10d85xHZinXLEGijXpGxXY9//fuBUrGK+J72vU2c7A/2WaoX/Avbo\n",
       "8Kuw7/4yNogR4MyU9/Y0P9vmDcA7qA4+PCFt7x7gj8CHh/h+9b7redi/pXOpPut+2RDzBzseANtQ\n",
       "+7cWERGRBlbFKvtudRSD39fhC1ijRcSNegxEpNv8HRtz8VbvIMNUrwcH7DTCrtilnSIiIiIiIiIi\n",
       "IiIiIiIiIiIiIiIiIiIiIiIiIiIi3er/AyugzFWXgMQoAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8fd68ffb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 8 iterations\n",
      "\n",
      "Z in [-0.201653152704, 1.06266367435]\n",
      "Sparsity of Z: 3,790,319 non-zero entries out of 19,072,000 entries, i.e. 19.9%.\n"
     ]
    },
    {
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       "hBBCCCGEEEIIIYQQQgghhBBCCFkW/x8AyMaOwMZC8gAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f9549d3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.0327109843493, 0.894884943962]\n",
      "d in [0.999999642372, 1.00000047684]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8fd68f5e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "lA4lAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f8872d050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "+P86fqh/rIoEagAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f8861d690>"
      ]
     },
     "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: 2734 seconds\n",
      "\n",
      " -------------------- Classification, K = 11 -------------------- \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 0x7f8fd3374fd0>"
      ]
     },
     "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: 76.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 63.6 %\n",
      "Clips accuracy: 68.5 %\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",
      "  76 (+/- 2.4) <- [79 74 77 75 70 77 77 78 78 75]\n",
      "  78 (+/- 1.3) <- [76 78 77 75 78 76 79 79 76 79]\n",
      "  79 (+/- 1.4) <- [81 77 78 78 79 80 78 76 80 77]\n",
      "  78 (+/- 2.2) <- [78 72 77 78 79 77 79 78 80 77]\n",
      "  78 (+/- 1.3) <- [79 78 80 77 77 77 79 78 77 75]\n",
      "  79 (+/- 1.4) <- [79 77 78 80 76 80 77 79 77 77]\n",
      "  78 (+/- 1.3) <- [78 76 79 76 78 76 79 76 77 79]\n",
      "  78 (+/- 1.2) <- [75 78 79 77 77 79 77 76 76 77]\n",
      "  79 (+/- 1.1) <- [77 79 77 80 80 78 78 79 78 78]\n",
      "  78 (+/- 1.7) <- [79 77 79 76 78 77 75 76 80 82]\n",
      "  78 (+/- 1.9) <- [80 80 78 75 79 78 79 74 78 77]\n",
      "  78 (+/- 2.0) <- [79 79 79 79 75 75 75 78 81 78]\n",
      "  78 (+/- 1.5) <- [79 76 78 77 78 79 78 74 78 75]\n",
      "  77 (+/- 1.7) <- [79 77 79 76 76 76 74 75 78 78]\n",
      "  78 (+/- 1.5) <- [76 76 78 78 78 79 81 78 79 76]\n",
      "  78 (+/- 1.8) <- [80 77 78 79 76 76 78 77 82 76]\n",
      "  78 (+/- 1.3) <- [78 80 76 78 79 76 77 79 78 76]\n",
      "  78 (+/- 1.0) <- [77 78 76 78 76 79 77 77 77 77]\n",
      "  78 (+/- 1.2) <- [77 77 77 77 79 78 78 79 75 77]\n",
      "  77 (+/- 1.6) <- [77 79 79 73 76 77 78 77 78 75]\n",
      "Accuracy: 78.0 (+/- 1.67)\n",
      "Mean time (20 cv): 20.05 seconds\n",
      "Overall time: 405.18 seconds\n",
      "\n",
      " -------------------- Graph, K = 21 -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 222.16 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 1.57787835598]\n",
      "w in [0.00692198285833, 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    : (4860042,), float32\n",
      "  L_indices : (4860042,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (4860042,), float32\n",
      "  W_indices : (4860042,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Attributes:\n",
      "  K = 21\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Overall time: 233.53 seconds\n",
      "\n",
      " -------------------- Features, K = 21 -------------------- \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 = 21\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (4860042,), float32\n",
      "  L_indices : (4860042,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (4860042,), float32\n",
      "  W_indices : (4860042,), 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: 2740 seconds\n"
     ]
    },
    {
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       "JiJr/VPYydCRwGLs5GsMdlym+e1/gHWJ4Ldt9coHERGRnjUZGyRYVK1cPjgKOwlUJUm6Rk3rIjLS\n",
       "nsRquruGDjJMrfR37wWcjY1TEBEREREREREREREREREREREREREREREREREREQnl/wMlc9BwUqZY\n",
       "dQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f9b4840d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1111 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.942663e+04\n",
      "rdiff: 0.000274427809718\n",
      "i(Z) = ||Z||_1 = 5.415536e+04\n",
      "j(Z) = tr(Z^TLZ) = 1.026438e+04\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f88681710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.438464e+05\n"
     ]
    },
    {
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       "n/LkwyPD/uYB9wOH9fH7VfpdT8f+L70HuAibbPlGH/XVjgfAtvT8sxYREZEa1sYa+1QdSvX7OvwI\n",
       "67SIRKMRAxFJzcvYnIsPxg7ST5VGcMBOI+yMXdopIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi\n",
       "IpKq/w+D9uQCcz9GpQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f88a1b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 7 iterations\n",
      "\n",
      "Z in [-0.0817069783807, 0.914247214794]\n",
      "Sparsity of Z: 4,382,076 non-zero entries out of 19,072,000 entries, i.e. 23.0%.\n"
     ]
    },
    {
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       "hBBCCCGEEEIIIYQQQgghhBCyLP4/LRDLFoIWMFYAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f9b79e150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.0374212861061, 0.891595244408]\n",
      "d in [0.999999463558, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f91593e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "SUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8fd68f5590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "juM4juM4juN8wfj/mPqph1SoNJMAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8fd72334d0>"
      ]
     },
     "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: 2750 seconds\n",
      "\n",
      " -------------------- Classification, K = 21 -------------------- \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 0x7f8f97ec9d10>"
      ]
     },
     "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: 75.9 %\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: 65.0 %\n",
      "Clips accuracy: 71.0 %\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",
      "  77 (+/- 1.4) <- [77 76 76 77 73 77 74 78 77 77]\n",
      "  78 (+/- 1.7) <- [77 78 79 74 81 76 77 77 76 78]\n",
      "  79 (+/- 1.4) <- [78 77 81 76 78 79 80 77 79 77]\n",
      "  78 (+/- 2.5) <- [79 71 77 79 78 80 78 78 77 76]\n",
      "  78 (+/- 0.7) <- [78 77 78 77 78 77 79 77 79 77]\n",
      "  79 (+/- 1.7) <- [78 77 75 79 78 81 78 81 76 79]\n",
      "  78 (+/- 1.1) <- [78 77 79 78 76 76 78 76 77 78]\n",
      "  78 (+/- 1.3) <- [75 79 78 79 76 79 76 77 76 78]\n",
      "  79 (+/- 1.2) <- [76 80 77 80 78 76 79 79 78 78]\n",
      "  78 (+/- 1.4) <- [79 77 78 75 77 77 76 76 78 80]\n",
      "  78 (+/- 1.9) <- [78 79 77 77 79 79 80 73 78 77]\n",
      "  78 (+/- 1.6) <- [78 76 80 78 75 77 77 76 80 78]\n",
      "  78 (+/- 0.8) <- [77 77 77 77 78 78 79 76 78 77]\n",
      "  77 (+/- 1.4) <- [78 77 79 74 76 76 76 76 76 78]\n",
      "  78 (+/- 1.4) <- [79 76 80 76 77 80 79 77 77 76]\n",
      "  78 (+/- 1.9) <- [79 76 78 80 76 76 79 76 81 75]\n",
      "  77 (+/- 1.0) <- [77 77 77 78 78 78 76 77 78 75]\n",
      "  77 (+/- 1.1) <- [76 78 76 78 77 79 77 75 77 77]\n",
      "  77 (+/- 1.3) <- [78 78 76 78 76 78 79 77 74 76]\n",
      "  77 (+/- 1.8) <- [77 79 79 74 74 76 79 76 77 75]\n",
      "Accuracy: 77.8 (+/- 1.57)\n",
      "Mean time (20 cv): 19.80 seconds\n",
      "Overall time: 400.37 seconds\n",
      "\n",
      " -------------------- Graph, K = 51 -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 167.10 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 1.83867681026]\n",
      "w in [0.0215746201575, 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    : (13161952,), float32\n",
      "  L_indices : (13161952,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (13161952,), float32\n",
      "  W_indices : (13161952,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Attributes:\n",
      "  K = 51\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Overall time: 182.70 seconds\n",
      "\n",
      " -------------------- Features, K = 51 -------------------- \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 = 51\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (13161952,), float32\n",
      "  L_indices : (13161952,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (13161952,), float32\n",
      "  W_indices : (13161952,), 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: 3170 seconds\n"
     ]
    },
    {
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       "iIiIiIiX/w8IOn2wfqRfjQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f9be1d990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 848 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 8.904239e+04\n",
      "rdiff: 0.000695222748206\n",
      "i(Z) = ||Z||_1 = 5.141387e+04\n",
      "j(Z) = tr(Z^TLZ) = 1.205900e+04\n"
     ]
    },
    {
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       "ERERERERERERERERERERERGRbvf/ASMMZjGqI+O4AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f9bc50610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.525153e+05\n"
     ]
    },
    {
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       "/9DPwJ8a+BX+HPYd+K73M/GNvFH7LfHasPwe4HvAP3Lv/QI/oC0bfJh97vdh+Ux8o/s1/CmF/tum\n",
       "zvxQv8Nf8d319+C76G8fYjtWZ9rwBcrvQrbL8b//vcC38A3zzPBzzTrb6e+vVBv8F/GP9L4M/7u/\n",
       "ih/ECHB8yHtbWJ5t82rgo1QHHx4WtjcTuBs4aIjfr97vegb+b+mdwMX4wZavDLF8sOMBsCW1/61F\n",
       "RESkgZXwjX232p/B7+vwXXzRIhKNegxEpNs8ix9zsUvsIMNUrwcH/GmEHfGXdoqIiIiIiIiIiIiI\n",
       "iIiIiIiIiIiIiIiIiIiIiIiIdKv/D2Q69Ut3cpzNAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f9b479510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 6 iterations\n",
      "\n",
      "Z in [-0.0230858866125, 0.52791005373]\n",
      "Sparsity of Z: 5,014,500 non-zero entries out of 19,072,000 entries, i.e. 26.3%.\n"
     ]
    },
    {
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       "/VXxbIXup3XvxBFr+vMizc+j8xZcg84VP/xXU0/pv78Xncfit43yCCGEEDIDr0LnKieE9NASJ4QQ\n",
       "QgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhJBl8f8BmL4rrh2TQUgAAAAASUVORK5C\n",
       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f88876f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.0224548019469, 0.910473465919]\n",
      "d in [0.999999582767, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f9bdfdbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f88860b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f88bb88d0>"
      ]
     },
     "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: 3181 seconds\n",
      "\n",
      " -------------------- Classification, K = 51 -------------------- \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 0x7f8f7cbe8910>"
      ]
     },
     "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: 73.4 %\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: 64.7 %\n",
      "Clips accuracy: 70.0 %\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",
      "  75 (+/- 2.2) <- [75 74 77 77 69 76 75 74 74 72]\n",
      "  76 (+/- 1.6) <- [76 76 74 72 78 77 75 76 74 75]\n",
      "  77 (+/- 2.1) <- [75 78 80 74 76 78 78 73 77 74]\n",
      "  76 (+/- 1.1) <- [77 73 74 76 75 77 75 75 76 76]\n",
      "  76 (+/- 1.2) <- [76 74 77 76 76 77 77 74 73 77]\n",
      "  77 (+/- 1.3) <- [77 77 75 78 78 78 75 77 78 76]\n",
      "  76 (+/- 1.2) <- [75 75 77 77 74 73 75 75 77 77]\n",
      "  76 (+/- 1.5) <- [76 77 75 73 75 79 75 74 75 76]\n",
      "  76 (+/- 1.6) <- [75 76 74 77 75 71 75 75 75 77]\n",
      "  76 (+/- 1.7) <- [75 73 77 73 75 75 76 75 75 79]\n",
      "  75 (+/- 1.8) <- [77 75 77 71 76 74 77 73 75 75]\n",
      "  76 (+/- 1.3) <- [76 77 77 74 74 75 77 75 78 76]\n",
      "  75 (+/- 1.5) <- [74 75 76 73 77 77 76 73 75 73]\n",
      "  75 (+/- 1.6) <- [76 75 77 72 74 73 74 73 75 77]\n",
      "  76 (+/- 1.2) <- [76 74 77 76 75 76 76 74 75 78]\n",
      "  76 (+/- 1.9) <- [76 77 76 78 72 76 76 78 77 73]\n",
      "  76 (+/- 1.7) <- [74 75 75 75 76 75 76 77 79 72]\n",
      "  76 (+/- 1.5) <- [76 75 78 74 74 77 74 73 78 76]\n",
      "  76 (+/- 1.4) <- [75 76 77 73 78 76 76 74 74 74]\n",
      "  76 (+/- 1.4) <- [75 76 76 74 76 73 78 76 75 73]\n",
      "Accuracy: 75.8 (+/- 1.66)\n",
      "Mean time (20 cv): 19.95 seconds\n",
      "Overall time: 403.72 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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       "rwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f7bfe3890>"
      ]
     },
     "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: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 5400.0 training, 600.0 testing\n",
      "  69 (+/- 2.2) <- [70 68 71 67 64 67 70 68 72 66]\n",
      "  69 (+/- 1.4) <- [71 68 67 67 72 69 67 70 69 70]\n",
      "  70 (+/- 2.0) <- [71 69 73 68 68 71 71 68 71 66]\n",
      "  69 (+/- 1.4) <- [70 67 66 67 67 70 68 70 70 68]\n",
      "  70 (+/- 2.4) <- [71 69 72 68 70 75 71 68 68 66]\n",
      "  71 (+/- 0.7) <- [71 71 70 70 71 71 70 69 70 70]\n",
      "  70 (+/- 1.4) <- [70 68 72 69 69 67 71 69 70 69]\n",
      "  70 (+/- 1.5) <- [69 70 68 71 65 70 70 69 70 69]\n",
      "  69 (+/- 1.7) <- [68 69 68 69 68 65 70 70 69 72]\n",
      "  69 (+/- 1.8) <- [72 69 66 68 69 66 69 69 70 71]\n",
      "  70 (+/- 1.4) <- [71 70 69 69 69 69 73 70 71 68]\n",
      "  70 (+/- 1.6) <- [71 70 71 68 69 69 73 68 69 71]\n",
      "  69 (+/- 1.3) <- [69 70 66 68 69 70 68 71 68 68]\n",
      "  69 (+/- 2.0) <- [71 66 68 66 67 69 66 69 69 72]\n",
      "  70 (+/- 1.1) <- [69 70 72 68 69 70 69 71 71 69]\n",
      "  70 (+/- 2.0) <- [72 71 69 71 67 68 71 72 69 67]\n",
      "  70 (+/- 1.6) <- [67 71 69 69 68 70 71 71 72 69]\n",
      "  70 (+/- 0.9) <- [70 68 71 70 68 70 69 69 71 69]\n",
      "  70 (+/- 1.2) <- [72 71 69 69 71 69 72 70 68 69]\n",
      "  69 (+/- 1.7) <- [67 70 71 67 67 72 67 70 70 69]\n",
      "Accuracy: 69.7 (+/- 1.71)\n",
      "Mean time (20 cv): 16.77 seconds\n",
      "Overall time: 339.64 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": [
      "K = [6, 11, 21, 51]\n",
      "res['accuracy_std'] = [1.5132709238232562, 1.6719813180109941, 1.5700707698133303, 1.6648748701729308]\n",
      "res['objective_j'] = [6740.3289794921875, 8596.2005615234375, 10264.377593994141, 12059.004211425781]\n",
      "res['objective_i'] = [61225.3515625, 56592.6328125, 54155.359375, 51413.87109375]\n",
      "res['objective_h'] = [0, 0, 0, 0]\n",
      "res['objective_g'] = [60546.259765625, 72091.3427734375, 79426.6259765625, 89042.392578125]\n",
      "res['baseline'] = [69.729166666666742, 69.729166666666742, 69.729166666666742, 69.729166666666742]\n",
      "res['time_features'] = [2735.5493268966675, 2725.535964012146, 2740.2448320388794, 3169.648545026779]\n",
      "res['baseline_std'] = 1.71029866625\n",
      "res['sparsity'] = [14.183976510067113, 19.873736367449663, 22.97648909395973, 26.292470637583893]\n",
      "res['iterations_inner'] = [1410, 1292, 1111, 848]\n",
      "res['iterations_outer'] = [6, 8, 7, 6]\n",
      "res['accuracy'] = [75.496666666666655, 77.964166666666614, 77.776666666666628, 75.845000000000041]\n"
     ]
    },
    {
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f7c798e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python2.7/dist-packages/numpy/ma/core.py:3847: UserWarning: Warning: converting a masked element to nan.\n",
      "  warnings.warn(\"Warning: converting a masked element to nan.\")\n"
     ]
    },
    {
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       "4GrgACfKIUiSJBVzHPgO2JodRNJwWShP0qg6BrwN3JUdRJIk1WkWWA28w4miYZIkScXMDWA9g6hi\n",
       "Op2YRZIkVeiHvu0NwBfADUlZJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\n",
       "JEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEkC/gIVdVh0UaNasAAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f7c426c10>"
      ]
     },
     "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 0x7f8f8841c450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "QmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f7c076910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f88434990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f7c801f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "MuBrxKtKThrlNVJP6wHrp/tTgPmsfeIq6O/fV5oHmdHZ5vV2DrAKeI74xe39wDvS/d8Rv9Bd4ZZO\n",
       "irQr8CJxKOZb0m0B+vsPijnEv/2txEsFj0zL34n+/iIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi\n",
       "IiJSjsZhQd9GnGFrllMWERERacMz6eebgV8SB7AQERGRCnsGeBNwN8PDA4uIiEiFPQ+sAV7rHURE\n",
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       "6ZRFRERERERERERERERERERERERERERERERERERERERERERERESkff8fkZ7OAYTIOZUAAAAASUVO\n",
       "RK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8f7c77e990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary atoms for K = 6\n",
      "Dictionary atoms for K = 11\n",
      "Dictionary atoms for K = 21\n",
      "Dictionary atoms for K = 51\n",
      "Experiment time: 14237 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_D':\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_h', '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_h_un'] = res['objective_h'] / div('le')\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_h_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_D']):\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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