13a_noise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre recognition: experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: Verify the hypothesis that our structuring auto-encoder behaves better in a noisy setting that a sparse auto-encoder.\n",
    "\n",
    "Conclusion: Hypothesis verified by an accuracy increase of 5% with 10% of added Gaussian noise.\n",
    "\n",
    "Observations:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyper-parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter under test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Pname = 'lg'\n",
    "Pvalues = [None, 100]\n",
    "\n",
    "# Regenerate the graph or the features at each iteration.\n",
    "regen_graph = False\n",
    "regen_features = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p = {}\n",
    "\n",
    "# Preprocessing.\n",
    "\n",
    "# Graph.\n",
    "p['K'] = 10 + 1  # 5 to 10 + 1 for self-reference\n",
    "p['dm'] = 'cosine'\n",
    "p['Csigma'] = 1\n",
    "p['diag'] = True\n",
    "p['laplacian'] = 'normalized'\n",
    "\n",
    "# Feature extraction.\n",
    "p['m'] = 128  # 64, 128, 512\n",
    "p['ls'] = 1\n",
    "p['ld'] = 10\n",
    "p['le'] = None\n",
    "p['lg'] = 100\n",
    "\n",
    "# Classification.\n",
    "p['scale'] = None\n",
    "p['Nvectors'] = 6\n",
    "p['svm_type'] = 'C'\n",
    "p['kernel'] = 'linear'\n",
    "p['C'] = 1\n",
    "p['nu'] = 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# HDF5 data stores.\n",
    "p['folder'] = 'data'\n",
    "p['filename_gtzan'] = 'gtzan.hdf5'\n",
    "p['filename_audio'] = 'audio.hdf5'\n",
    "p['filename_graph'] = 'graph.hdf5'\n",
    "p['filename_features'] = 'features.hdf5'\n",
    "\n",
    "# Dataset (10,100,644 | 5,100,149 | 2,10,644).\n",
    "p['Ngenres'] = 5\n",
    "p['Nclips'] = 100\n",
    "p['Nframes'] = 149\n",
    "\n",
    "# Added white noise.\n",
    "p['noise_std'] = 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Graph.\n",
    "p['tol'] = 1e-5\n",
    "\n",
    "# Feature extraction.\n",
    "p['rtol'] = 1e-5  # 1e-3, 1e-5, 1e-7\n",
    "p['N_inner'] = 500\n",
    "p['N_outer'] = 50\n",
    "\n",
    "# Classification.\n",
    "p['Nfolds'] = 10\n",
    "p['Ncv'] = 40\n",
    "p['dataset_classification'] = 'Z'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "\n",
    "texperiment = time.time()\n",
    "\n",
    "# Result dictionary.\n",
    "res = ['accuracy', 'accuracy_std']\n",
    "res += ['sparsity', 'atoms']\n",
    "res += ['objective_g', 'objective_h', 'objective_i', 'objective_j']\n",
    "res += ['time_features', 'iterations_inner', 'iterations_outer']\n",
    "res = dict.fromkeys(res)\n",
    "for key in res.keys():\n",
    "    res[key] = []\n",
    "\n",
    "def separator(name, parameter=False):\n",
    "    if parameter:\n",
    "        name += ', {} = {}'.format(Pname, p[Pname])\n",
    "    dashes = 20 * '-'\n",
    "    print('\\n {} {} {} \\n'.format(dashes, name, dashes))\n",
    "    # Fair comparison when tuning parameters.\n",
    "    # Randomnesses: dictionary initialization, training and testing sets.\n",
    "    np.random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " -------------------- Graph -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 614.88 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\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Overall time: 625.28 seconds\n",
      "\n",
      " -------------------- Features, lg = None -------------------- \n",
      "\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  Xa: (10, 100, 644, 2, 1024) , float32\n",
      "  Xs: (10, 100, 644, 2, 96)   , float32\n",
      "Full dataset:\n",
      "  size: N=1,288,000 x n=96 -> 123,648,000 floats\n",
      "  dim: 123,648 features per clip\n",
      "  shape: (10, 100, 644, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Data: (149000, 96), float32\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = cosine\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: 388 seconds\n"
     ]
    },
    {
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       "FK/vfKxBX/m+1byEJcQ3YX/Paym1qai2/g3s5ugIYBZ2MzYEuy5j/fYnYlUo+G37+2SFiIhINEZj\n",
       "jQ5j1Z/HFQdhN4XKNEkwKooXkUZbgOWEPxU6kBr1p758J+AyrJ2DiIiIiIiIiIiIiIiIiIiIiIiI\n",
       "iIiIiIiIiIiISKf6/weLe+Heoh26AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f859b7549d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 304 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 3.556715e+05\n",
      "rdiff: 0.000847538508528\n",
      "i(Z) = ||Z||_1 = 3.337448e+05\n"
     ]
    },
    {
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       "nc/7/y9sx0MkOLXcRWSQnsSOQdg+dJAJyutJAeuS3xo7bVBERERERERERERERERERERERERERERE\n",
       "REREREREuvs/uErh03UBW5sAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85598312d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 6.894163e+05\n"
     ]
    },
    {
     "data": {
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       "+yqsAyTAySnv9Wl+sc+fAK+j3nHx2LS/m7HHM79znO/X7Luejv1/egtwHtZR85lx5o91PABezMg/\n",
       "axEREWnDOtiJP1dHMva4EZ/GKjAi7tSSICI5+hPWR+OV3kEmqVnLDtilhj2x20VFRERERERERERE\n",
       "RERERERERERERERERERERERERGS0/w8nUi5U5Ggb/gAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8559729c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 13 iterations\n",
      "\n",
      "Z in [-0.925734937191, 0.85433703661]\n",
      "Sparsity of Z: 5,470,082 non-zero entries out of 19,072,000 entries, i.e. 28.7%.\n"
     ]
    },
    {
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       "fwzTefRB4DpM59L3nH//o6T/3TF9UPY+TOfIz8b5DPaFAL4XU+Z6PwAvm9vfgKlcfSuA/wvAd87t\n",
       "nwHw9zG9LG8C8IsA3i9kvBHTx22/gelc/qWYAoV3A/hiAP/Z9Fel/Y15tsH0p3Vvx/ls+lOuzz/G\n",
       "VC24EVMpfvmvtZ45378LU8XiZwW/RqPRaDQah4BXYCqVNxqNGZ2JNxqNRqPRaDQajUaj0Wg0Go1G\n",
       "o9FoNBqNRqPRaDQajUaj0Wg0Go1Go9FoNBqNRqPRaDQajcZu4f8H51F20nX0vJsAAAAASUVORK5C\n",
       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f856052fed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.296960860491, 0.514295816422]\n",
      "d in [0.999999701977, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8559690e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85597d1650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "B2dnLkZfnSAAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f856052fdd0>"
      ]
     },
     "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: 396 seconds\n",
      "\n",
      " -------------------- Classification, lg = None -------------------- \n",
      "\n",
      "Software versions:\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f854ce63a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 54.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 48.4 %\n",
      "Clips accuracy: 57.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (500, 1536), float64\n",
      "Labels: (500,), uint8\n",
      "  56 (+/- 7.0) <- [54 68 54 54 54 68 50 46 50 60]\n",
      "  56 (+/- 4.8) <- [60 57 57 48 57 57 60 60 56 46]\n",
      "  57 (+/- 5.2) <- [52 60 57 57 48 54 54 60 68 60]\n",
      "  59 (+/- 4.4) <- [50 62 60 62 62 57 64 54 62 54]\n",
      "  57 (+/- 8.8) <- [40 60 52 64 44 62 66 57 68 54]\n",
      "  57 (+/- 5.4) <- [57 56 57 57 62 52 60 54 44 64]\n",
      "  56 (+/- 6.5) <- [50 57 40 60 57 57 60 50 62 60]\n",
      "  57 (+/- 5.0) <- [62 56 60 54 66 48 54 52 56 60]\n",
      "  57 (+/- 6.8) <- [62 54 56 52 70 66 46 52 56 57]\n",
      "  56 (+/- 8.0) <- [66 54 68 46 66 52 56 44 50 57]\n",
      "  55 (+/- 5.9) <- [57 50 54 54 50 56 70 57 56 48]\n",
      "  57 (+/- 9.4) <- [57 52 57 74 57 57 50 57 66 36]\n",
      "  59 (+/- 9.1) <- [76 52 54 66 64 44 57 57 50 68]\n",
      "  56 (+/- 7.5) <- [52 52 64 57 72 48 54 60 48 48]\n",
      "  56 (+/- 7.8) <- [62 54 68 42 48 68 57 54 52 54]\n",
      "  58 (+/- 7.3) <- [60 70 52 52 48 64 66 56 48 60]\n",
      "  58 (+/- 5.9) <- [56 56 56 57 46 64 70 60 56 60]\n",
      "  60 (+/- 4.7) <- [66 66 54 54 60 57 66 54 60 57]\n",
      "  58 (+/- 8.3) <- [46 70 60 52 64 62 62 56 64 42]\n",
      "  55 (+/- 5.1) <- [56 54 50 64 57 60 52 57 48 48]\n",
      "  57 (+/- 5.2) <- [56 54 60 62 56 56 48 64 64 50]\n",
      "  57 (+/- 4.8) <- [56 56 64 50 48 60 62 60 57 60]\n",
      "  58 (+/- 3.5) <- [62 57 60 60 60 62 57 50 54 57]\n",
      "  58 (+/- 6.1) <- [54 70 56 60 62 54 62 56 46 60]\n",
      "  58 (+/- 6.7) <- [62 54 52 54 60 56 57 48 66 72]\n",
      "  57 (+/- 7.0) <- [66 57 46 60 56 60 66 56 44 62]\n",
      "  56 (+/- 6.9) <- [52 52 54 66 44 56 48 62 66 56]\n",
      "  58 (+/- 7.3) <- [60 64 66 64 40 62 52 56 54 57]\n",
      "  58 (+/- 5.7) <- [56 52 50 62 52 57 56 68 56 66]\n",
      "  55 (+/- 4.3) <- [57 52 54 57 56 52 48 60 50 62]\n",
      "  57 (+/- 4.2) <- [57 48 62 54 52 57 60 62 56 56]\n",
      "  56 (+/- 7.8) <- [44 57 64 68 52 52 48 54 52 68]\n",
      "  57 (+/- 2.7) <- [56 56 57 57 62 57 56 54 60 52]\n",
      "  56 (+/- 6.8) <- [72 60 64 52 50 50 50 54 56 54]\n",
      "  58 (+/- 7.7) <- [64 64 54 44 70 52 62 62 60 48]\n",
      "  57 (+/- 5.9) <- [57 56 44 54 57 56 62 54 66 64]\n",
      "  55 (+/- 9.1) <- [48 60 42 66 56 50 62 50 46 72]\n",
      "  56 (+/- 6.8) <- [56 66 50 60 56 44 66 56 48 54]\n",
      "  57 (+/- 5.7) <- [62 48 60 68 60 56 52 57 52 52]\n",
      "  58 (+/- 8.0) <- [57 56 50 70 56 60 66 64 60 40]\n",
      "Accuracy: 56.9 (+/- 6.68)\n",
      "Mean time (40 cv): 43.31 seconds\n",
      "Overall time: 1739.68 seconds\n",
      "\n",
      " -------------------- Features, lg = 100 -------------------- \n",
      "\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  Xa: (10, 100, 644, 2, 1024) , float32\n",
      "  Xs: (10, 100, 644, 2, 96)   , float32\n",
      "Full dataset:\n",
      "  size: N=1,288,000 x n=96 -> 123,648,000 floats\n",
      "  dim: 123,648 features per clip\n",
      "  shape: (10, 100, 644, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Data: (149000, 96), float32\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = cosine\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: 650 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 0x7f859d5755d0>"
      ]
     },
     "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 0x7f854c7b0090>"
      ]
     },
     "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 0x7f8560525c90>"
      ]
     },
     "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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       "DAaDwWAwGAwGg8FgMBgMBoPBYDAY7Av/P9maY1PY7L9BAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f854c8f8b10>"
      ]
     },
     "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 0x7f855977a890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85557f0250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "RU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8559572310>"
      ]
     },
     "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: 660 seconds\n",
      "\n",
      " -------------------- Classification, lg = 100 -------------------- \n",
      "\n",
      "Software versions:\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
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       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8597859850>"
      ]
     },
     "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: (500, 1536), float64\n",
      "Labels: (500,), uint8\n",
      "  64 (+/- 7.1) <- [68 70 57 62 74 70 60 48 64 64]\n",
      "  61 (+/- 4.7) <- [62 60 56 57 72 66 64 57 62 56]\n",
      "  62 (+/- 5.3) <- [60 64 68 62 57 66 56 54 72 60]\n",
      "  62 (+/- 4.4) <- [54 60 66 68 64 62 64 57 68 57]\n",
      "  61 (+/- 8.7) <- [52 68 70 64 48 68 68 62 68 46]\n",
      "  63 (+/- 7.8) <- [66 68 70 56 76 50 68 57 54 64]\n",
      "  63 (+/- 6.2) <- [50 72 64 64 56 72 62 62 64 64]\n",
      "  61 (+/- 7.0) <- [60 50 56 68 76 54 64 64 62 60]\n",
      "  63 (+/- 6.8) <- [72 54 56 60 74 70 62 62 56 68]\n",
      "  61 (+/- 3.1) <- [66 62 62 57 66 62 62 62 56 57]\n",
      "  62 (+/- 5.8) <- [68 60 62 66 68 66 66 60 57 48]\n",
      "  62 (+/- 6.4) <- [66 57 60 78 62 57 56 56 66 57]\n",
      "  63 (+/- 8.1) <- [76 60 54 70 70 54 60 66 50 68]\n",
      "  62 (+/- 6.2) <- [64 62 74 57 66 64 57 68 50 60]\n",
      "  61 (+/- 8.3) <- [68 62 74 50 57 72 64 52 54 52]\n",
      "  62 (+/- 6.1) <- [60 74 60 60 57 68 62 62 50 66]\n",
      "  61 (+/- 5.3) <- [62 72 56 56 57 66 68 57 57 57]\n",
      "  63 (+/- 7.0) <- [56 66 68 50 74 60 70 60 66 56]\n",
      "  62 (+/-10.0) <- [50 68 68 54 66 56 76 57 78 48]\n",
      "  62 (+/- 7.2) <- [60 66 62 74 70 62 60 56 62 46]\n",
      "  62 (+/- 5.2) <- [64 64 64 68 60 57 54 70 66 54]\n",
      "  62 (+/- 4.5) <- [64 64 66 57 60 56 70 66 56 60]\n",
      "  62 (+/- 3.2) <- [60 57 64 60 66 68 62 57 62 60]\n",
      "  63 (+/- 6.5) <- [57 72 56 62 62 56 74 57 57 70]\n",
      "  61 (+/- 6.1) <- [64 62 57 56 60 60 60 50 72 70]\n",
      "  60 (+/- 9.0) <- [60 57 54 62 57 64 72 57 40 74]\n",
      "  60 (+/- 4.5) <- [57 62 62 62 57 60 52 68 66 56]\n",
      "  61 (+/- 6.3) <- [56 57 70 70 48 62 57 64 57 64]\n",
      "  61 (+/- 4.2) <- [57 60 57 68 54 57 62 68 60 62]\n",
      "  61 (+/- 4.8) <- [64 60 62 57 56 54 57 72 62 64]\n",
      "  61 (+/- 7.0) <- [66 54 62 68 68 50 66 68 62 50]\n",
      "  61 (+/- 5.7) <- [56 66 62 68 50 62 52 62 62 66]\n",
      "  61 (+/- 3.8) <- [66 57 66 60 60 66 57 56 62 56]\n",
      "  63 (+/- 7.9) <- [78 62 76 54 56 57 56 60 60 66]\n",
      "  63 (+/- 6.8) <- [74 68 64 56 62 54 70 64 66 52]\n",
      "  62 (+/- 7.8) <- [64 56 48 68 62 62 66 54 78 62]\n",
      "  62 (+/- 7.9) <- [57 64 52 74 72 50 62 60 57 72]\n",
      "  61 (+/- 7.5) <- [54 70 64 66 70 56 70 52 54 52]\n",
      "  62 (+/- 5.9) <- [64 62 60 68 64 72 54 66 57 52]\n",
      "  62 (+/- 7.3) <- [62 60 57 76 66 62 57 72 50 56]\n",
      "Accuracy: 61.8 (+/- 6.58)\n",
      "Mean time (40 cv): 21.89 seconds\n",
      "Overall time: 880.75 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 0x7f855f5dcd90>"
      ]
     },
     "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: (500, 1152), float64\n",
      "Labels: (500,), uint8\n",
      "  58 (+/- 7.0) <- [64 60 60 42 52 68 64 56 56 56]\n",
      "  59 (+/- 6.6) <- [64 62 52 46 70 66 60 57 56 60]\n",
      "  61 (+/- 6.2) <- [60 66 72 54 57 62 56 54 70 56]\n",
      "  58 (+/- 6.4) <- [50 56 57 68 68 57 64 48 57 56]\n",
      "  57 (+/- 5.6) <- [48 60 54 60 62 56 68 57 54 50]\n",
      "  58 (+/- 4.0) <- [54 57 64 60 64 56 60 57 50 57]\n",
      "  58 (+/- 5.8) <- [54 56 54 62 57 66 60 52 52 70]\n",
      "  60 (+/- 7.2) <- [57 48 60 64 70 48 68 57 66 57]\n",
      "  58 (+/- 5.0) <- [66 48 57 57 60 64 52 60 60 56]\n",
      "  59 (+/- 5.6) <- [72 54 66 60 57 60 57 56 56 52]\n",
      "  58 (+/- 5.7) <- [62 50 54 66 64 57 62 54 48 57]\n",
      "  57 (+/- 8.5) <- [57 54 68 68 57 50 46 60 68 44]\n",
      "  59 (+/- 7.0) <- [64 60 64 62 52 52 56 66 44 66]\n",
      "  59 (+/- 7.2) <- [66 64 62 64 70 57 56 57 44 52]\n",
      "  60 (+/- 5.8) <- [62 57 68 50 57 68 64 60 56 52]\n",
      "  59 (+/- 5.7) <- [54 70 64 56 62 48 60 56 57 62]\n",
      "  58 (+/- 7.0) <- [57 60 60 52 46 60 74 57 52 57]\n",
      "  59 (+/- 5.0) <- [54 68 66 54 64 57 57 54 62 56]\n",
      "  57 (+/- 9.8) <- [44 62 60 56 70 44 60 54 74 46]\n",
      "  61 (+/- 5.0) <- [60 60 64 66 62 64 56 52 68 54]\n",
      "  59 (+/- 5.4) <- [60 66 60 62 60 56 54 68 60 48]\n",
      "  58 (+/- 8.8) <- [48 64 76 52 48 66 56 56 64 50]\n",
      "  60 (+/- 4.6) <- [66 62 60 52 66 56 66 57 56 60]\n",
      "  59 (+/- 7.5) <- [54 62 54 57 56 52 76 64 48 62]\n",
      "  57 (+/- 5.7) <- [62 54 62 54 50 60 54 48 66 62]\n",
      "  58 (+/- 7.0) <- [54 50 57 66 50 64 60 60 48 70]\n",
      "  59 (+/- 8.0) <- [74 60 54 64 57 68 48 64 50 52]\n",
      "  58 (+/- 7.7) <- [60 57 68 66 40 54 52 54 60 64]\n",
      "  57 (+/- 4.6) <- [57 57 52 64 50 56 56 64 54 62]\n",
      "  60 (+/- 5.0) <- [60 62 54 57 54 54 56 64 66 68]\n",
      "  58 (+/- 6.7) <- [68 52 68 52 62 57 66 54 50 54]\n",
      "  57 (+/- 7.5) <- [46 54 68 64 52 62 57 54 46 66]\n",
      "  58 (+/- 6.2) <- [62 57 64 52 66 64 62 46 52 56]\n",
      "  59 (+/- 7.8) <- [72 68 54 46 48 62 56 56 62 62]\n",
      "  58 (+/- 9.7) <- [74 66 52 42 64 52 70 50 60 52]\n",
      "  59 (+/- 4.2) <- [56 62 52 56 62 60 64 62 62 52]\n",
      "  59 (+/- 7.1) <- [60 66 48 60 72 52 62 50 56 62]\n",
      "  59 (+/- 8.9) <- [52 68 68 66 68 48 68 52 46 54]\n",
      "  58 (+/- 6.9) <- [57 64 48 66 64 70 50 54 56 54]\n",
      "  59 (+/- 5.9) <- [66 62 52 66 60 62 57 62 52 48]\n",
      "Accuracy: 58.6 (+/- 6.74)\n",
      "Mean time (40 cv): 19.35 seconds\n",
      "Overall time: 778.74 seconds\n"
     ]
    }
   ],
   "source": [
    "#%run gtzan.ipynb\n",
    "#%run audio_preprocessing.ipynb\n",
    "if not regen_graph:\n",
    "    separator('Graph')\n",
    "    %run audio_graph.ipynb\n",
    "if not regen_features:\n",
    "    separator('Features')\n",
    "    %run audio_features.ipynb\n",
    "\n",
    "# Hyper-parameter under test.\n",
    "for p[Pname] in Pvalues:\n",
    "\n",
    "    if regen_graph:\n",
    "        separator('Graph', True)\n",
    "        %run audio_graph.ipynb\n",
    "    if regen_features:\n",
    "        separator('Features', True)\n",
    "        p['filename_features'] = 'features_{}_{}.hdf5'.format(Pname, p[Pname])\n",
    "        %run audio_features.ipynb\n",
    "    separator('Classification', True)\n",
    "    %run audio_classification.ipynb\n",
    "    \n",
    "    # Collect results.\n",
    "    for key in res:\n",
    "        res[key].append(globals()[key])\n",
    "\n",
    "# Baseline, i.e. classification with spectrograms.\n",
    "p['dataset_classification'] = 'X'\n",
    "p['scale'] = 'minmax'  # Todo: should be done in pre-processing.\n",
    "if not regen_graph and not regen_features:\n",
    "    # Classifier parameters are being tested.\n",
    "    for p[Pname] in Pvalues:\n",
    "        separator('Baseline', True)\n",
    "        %run audio_classification.ipynb\n",
    "else:\n",
    "    separator('Baseline')\n",
    "    %run audio_classification.ipynb\n",
    "res['baseline'] = len(Pvalues) * [accuracy]\n",
    "res['baseline_std'] = accuracy_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lg = [None, 100]\n",
      "res['accuracy_std'] = [6.6807110400016629, 6.5823703937107663]\n",
      "res['objective_j'] = [0, 20566.252136230469]\n",
      "res['objective_i'] = [333744.78125, 71275.6015625]\n",
      "res['objective_h'] = [0, 0]\n",
      "res['objective_g'] = [355671.5234375, 746237.734375]\n",
      "res['baseline'] = [58.574999999999989, 58.574999999999989]\n",
      "res['time_features'] = [387.99416518211365, 650.4659931659698]\n",
      "res['baseline_std'] = 6.7423567838\n",
      "res['sparsity'] = [28.681218540268457, 46.07074769295302]\n",
      "res['iterations_inner'] = [304, 246]\n",
      "res['iterations_outer'] = [13, 4]\n",
      "res['accuracy'] = [56.910000000000018, 61.820000000000022]\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f855faee6d0>"
      ]
     },
     "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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       "qvkTrKPkbOCLDY1URERERERERERERERERERERERERERERERERERERERExL+3fAcgIqVphkkRaUZa\n",
       "W0CkiSl5EJFm1gtb1fU5bFbAOylc4l1EREQEsJVcAQ7FEgaw1VmXUzj1uYh4oJoHEWlmu2ILsoGt\n",
       "ijjNYywi4ih5EJFmFlJ62XcR8UTJg4g0swexPg4ZrNki8BqNiADQx3cAIiIxcqMtbgHGA7OwpZWf\n",
       "BN70FZSIiIikwwD3c31gLvAxj7GICKp5EJHm9zdgMNAP+Dnwmt9wRERERERERERERERERERERERE\n",
       "RERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERE\n",
       "RERERERERET8+v+q+6+68e206wAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f855fad6e10>"
      ]
     },
     "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 0x7f855f6fe4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f855f687410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f855fb73250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8598242890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f854aee9fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary atoms for lg = None\n",
      "Dictionary atoms for lg = 100\n",
      "Experiment time: 5087 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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