12m_dm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre recognition: experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: Check the influence of the distance metric (euclidean or cosine).\n",
    "\n",
    "Conclusion: The cosine metric seems more appropriate.\n",
    "\n",
    "Observations:\n",
    "* Higher accuracy with the cosine distance.\n",
    "* Why are the atoms and the accuracy different from the experiment on $\\lambda_d$ ? With the initialized seed the results should really be the same. The baseline is however strictly the same.\n",
    "* Ran for 2h10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyper-parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter under test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Pname = 'dm'\n",
    "Pvalues = ['cosine', 'euclidean']\n",
    "\n",
    "# Regenerate the graph or the features at each iteration.\n",
    "regen_graph = True\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": [
    "### Numerical 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",
    "# 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": 4,
   "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": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " -------------------- Graph, dm = cosine -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 159.32 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 0.550418317318]\n",
      "w in [0.0235210377723, 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    : (2375982,), float32\n",
      "  L_indices : (2375982,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2375982,), float32\n",
      "  W_indices : (2375982,), 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: 168.58 seconds\n",
      "\n",
      " -------------------- Features, dm = cosine -------------------- \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    : (2375982,), float32\n",
      "  L_indices : (2375982,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2375982,), float32\n",
      "  W_indices : (2375982,), 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: 2421 seconds\n"
     ]
    },
    {
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       "74/ADlRveq/8/Gp5svOXYfsn3b/LsBPwKt9by9+xgvcK7O/5S8rnRNRavxw7GDoeWIodfE3A9st6\n",
       "/vXfwbpE8K9t9soHERGRvjUdO0kwVs1cPjgOOwhUJUl6Rk3rItJtz2A13T1CB2lRM/3d+wPnYecp\n",
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c1e79a250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1143 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.682912e+04\n",
      "rdiff: 5.1351784894e-05\n",
      "i(Z) = ||Z||_1 = 5.650498e+04\n",
      "j(Z) = tr(Z^TLZ) = 1.030553e+04\n"
     ]
    },
    {
     "data": {
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       "ERERERERERERERERERERERERERER6Xf/H40FYu3kJiyVAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c1becb790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.436396e+05\n"
     ]
    },
    {
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       "2+8Dvgf8M/fcr/AT2mqTD2vv+13Yfhe+0f06fkih775psD7Q73ATvrv+PnwX/e0D7McaLBu+QPlt\n",
       "yHYp/ve/H/gWvmG+K/xctcF++rqJeoP/Kv524Jfgf/c38ZMYAY4NeW8N22v7vBL4JPXJhweH/d0F\n",
       "3AscOMDv1+h3PRn/f+ndwIX4yZZvDLC9v+MBsAG9/6xFRESkiWXxjX2q9qX/6zocjy9aRKJRj4GI\n",
       "pOZF/JyLj8QOMkiNenDADyNsiT+1U0REREREREREREREREREREREREREREREREREREQkVf8fwbo2\n",
       "cHptAAoAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c1bb7a890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 7 iterations\n",
      "\n",
      "Z in [-0.109829813242, 0.799122154713]\n",
      "Sparsity of Z: 3,959,579 non-zero entries out of 19,072,000 entries, i.e. 20.8%.\n"
     ]
    },
    {
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       "CCGEEEIIWRf/H15i2+eOm3PVAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c1bc056d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.0257097557187, 0.876985788345]\n",
      "d in [0.999999582767, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c1bbd3990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "zenMZQdWTFoAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c1bef39d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c1bb03e50>"
      ]
     },
     "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: 2429 seconds\n",
      "\n",
      " -------------------- Classification, dm = cosine -------------------- \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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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bd34c7a90>"
      ]
     },
     "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: 64.6 %\n",
      "Clips accuracy: 73.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (500, 1536), float64\n",
      "Labels: (500,), uint8\n",
      "  73 (+/- 6.3) <- [72 80 64 72 74 86 68 66 78 74]\n",
      "  73 (+/- 7.3) <- [70 74 86 64 82 78 68 68 62 74]\n",
      "  74 (+/- 5.6) <- [80 82 78 74 74 76 60 74 72 74]\n",
      "  73 (+/- 6.7) <- [57 80 72 70 64 76 80 74 78 74]\n",
      "  73 (+/- 5.1) <- [68 78 76 82 70 74 70 64 76 76]\n",
      "  72 (+/- 4.2) <- [68 78 74 74 74 78 70 70 74 64]\n",
      "  73 (+/- 7.3) <- [70 82 56 80 70 76 68 78 78 76]\n",
      "  74 (+/- 5.9) <- [70 66 72 74 86 66 80 76 78 74]\n",
      "  72 (+/- 7.2) <- [84 64 64 76 78 84 68 68 68 70]\n",
      "  74 (+/- 6.2) <- [76 72 86 74 78 74 72 70 60 74]\n",
      "  73 (+/- 6.1) <- [82 72 70 74 82 76 74 68 60 72]\n",
      "  72 (+/- 4.9) <- [74 66 74 84 68 70 68 74 74 70]\n",
      "  73 (+/- 8.8) <- [80 64 57 76 76 78 70 84 60 82]\n",
      "  74 (+/- 3.7) <- [76 72 80 70 74 72 68 72 78 78]\n",
      "  73 (+/- 7.3) <- [68 64 84 62 72 70 82 82 74 70]\n",
      "  75 (+/- 4.8) <- [72 84 82 78 70 74 74 74 72 68]\n",
      "  73 (+/- 2.2) <- [72 74 70 74 72 74 74 70 72 78]\n",
      "  75 (+/- 5.1) <- [78 76 80 64 78 72 76 68 78 80]\n",
      "  73 (+/- 6.6) <- [66 76 80 72 80 72 76 60 82 68]\n",
      "  73 (+/- 5.8) <- [66 78 74 76 74 68 76 60 80 74]\n",
      "  73 (+/- 6.6) <- [72 80 76 80 74 68 60 74 80 64]\n",
      "  73 (+/- 4.7) <- [72 70 80 78 80 68 70 66 70 72]\n",
      "  73 (+/- 8.5) <- [64 66 76 74 78 78 76 86 54 74]\n",
      "  73 (+/- 8.9) <- [68 74 72 76 84 64 88 68 56 76]\n",
      "  73 (+/- 5.9) <- [66 70 84 72 68 74 72 68 74 84]\n",
      "  73 (+/- 3.9) <- [76 72 74 70 80 72 74 72 64 72]\n",
      "  74 (+/- 3.8) <- [78 70 70 76 72 72 68 80 78 74]\n",
      "  73 (+/- 7.7) <- [72 82 70 76 66 68 60 68 86 80]\n",
      "  73 (+/- 5.8) <- [72 66 68 80 74 68 70 80 82 66]\n",
      "  72 (+/- 3.9) <- [74 74 68 68 68 74 80 68 72 76]\n",
      "  75 (+/- 7.1) <- [76 64 76 68 72 82 80 88 66 74]\n",
      "  72 (+/- 5.2) <- [66 74 68 84 72 68 78 68 70 74]\n",
      "  72 (+/- 4.6) <- [70 70 82 64 72 74 76 72 70 68]\n",
      "  73 (+/- 8.8) <- [88 80 82 64 78 62 62 78 66 72]\n",
      "  74 (+/- 7.5) <- [90 72 78 64 74 70 80 70 78 64]\n",
      "  74 (+/- 5.5) <- [68 76 66 74 74 74 76 68 86 78]\n",
      "  73 (+/- 4.9) <- [82 72 70 70 74 68 72 68 76 82]\n",
      "  74 (+/- 5.7) <- [72 84 72 78 66 68 82 70 70 76]\n",
      "  72 (+/- 5.4) <- [74 76 76 78 80 68 64 66 66 74]\n",
      "  74 (+/- 3.2) <- [74 72 66 78 76 76 74 76 72 74]\n",
      "Accuracy: 73.2 (+/- 6.13)\n",
      "Mean time (40 cv): 18.45 seconds\n",
      "Overall time: 742.22 seconds\n",
      "\n",
      " -------------------- Graph, dm = euclidean -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 215.49 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 1.54432785511]\n",
      "w in [0.00310685997829, 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    : (2358832,), float32\n",
      "  L_indices : (2358832,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2358832,), float32\n",
      "  W_indices : (2358832,), 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: 225.32 seconds\n",
      "\n",
      " -------------------- Features, dm = euclidean -------------------- \n",
      "\n",
      "The memory_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext memory_profiler\n",
      "The line_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext line_profiler\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  Xa: (10, 100, 644, 2, 1024) , float32\n",
      "  Xs: (10, 100, 644, 2, 96)   , float32\n",
      "Full dataset:\n",
      "  size: N=1,288,000 x n=96 -> 123,648,000 floats\n",
      "  dim: 123,648 features per clip\n",
      "  shape: (10, 100, 644, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Data: (149000, 96), float32\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2358832,), float32\n",
      "  L_indices : (2358832,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2358832,), float32\n",
      "  W_indices : (2358832,), 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: 2869 seconds\n"
     ]
    },
    {
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       "EREREREREREREREREfHy/wEGtsf3Mb6TqwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bd2f43d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1357 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 6.993616e+04\n",
      "rdiff: 4.51025160641e-05\n",
      "i(Z) = ||Z||_1 = 5.727794e+04\n",
      "j(Z) = tr(Z^TLZ) = 8.521706e+03\n"
     ]
    },
    {
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       "slmks0kAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bd3191710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.357358e+05\n"
     ]
    },
    {
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       "UfR/XYevEYoWETMaMRCR1PyNMOfi7dZBBqnZCA6Ewwh7EE7tFBERERERERERERERERERERERERER\n",
       "ERERERERERFJ1f8H/TDnD4uH0csAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c1a5b7c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 7 iterations\n",
      "\n",
      "Z in [-0.193726345897, 1.06732833385]\n",
      "Sparsity of Z: 3,632,050 non-zero entries out of 19,072,000 entries, i.e. 19.0%.\n"
     ]
    },
    {
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       "hBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQghZF/8fmfz+FgxTCLsAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bd2f5c590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.0318740010262, 0.893661141396]\n",
      "d in [0.999999582767, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c616da050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c1bbd3590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c1bcae850>"
      ]
     },
     "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: 2877 seconds\n",
      "\n",
      " -------------------- Classification, dm = euclidean -------------------- \n",
      "\n",
      "Software versions:\n",
      "  numpy: 1.8.2\n",
      "  sklearn: 0.14.1\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  D : (128, 96)               , float32\n",
      "  X : (5, 100, 149, 2, 96)    , float32\n",
      "  Z : (5, 100, 149, 2, 128)   , float32\n",
      "Full dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 149, 2, 128)\n",
      "<type 'numpy.ndarray'>\n",
      "Flattened frames:\n",
      "  size: N=149,000 x n=128 -> 19,072,000 floats\n",
      "  dim: 38,144 features per clip\n",
      "  shape: (5, 100, 298, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Truncated and grouped:\n",
      "  size: N=135,000 x n=128 -> 17,280,000 floats\n",
      "  dim: 34,560 features per clip\n",
      "  shape: (5, 100, 6, 45, 128)\n",
      "Feature vectors:\n",
      "  size: N=6,000 x n=128 -> 768,000 floats\n",
      "  dim: 1,536 features per clip\n",
      "  shape: (5, 100, 6, 2, 128)\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bdcd2b2d0>"
      ]
     },
     "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: 77.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.7 %\n",
      "Clips accuracy: 70.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (500, 1536), float64\n",
      "Labels: (500,), uint8\n",
      "  71 (+/- 6.1) <- [72 80 68 68 70 84 70 62 72 66]\n",
      "  71 (+/- 8.0) <- [68 78 78 56 82 78 70 62 66 76]\n",
      "  72 (+/- 5.4) <- [74 76 82 68 72 72 60 70 74 70]\n",
      "  70 (+/- 5.8) <- [57 76 64 72 68 68 76 72 78 72]\n",
      "  71 (+/- 2.7) <- [70 70 76 74 66 74 72 70 70 70]\n",
      "  72 (+/- 4.6) <- [74 72 76 74 70 68 80 74 70 62]\n",
      "  68 (+/- 5.9) <- [64 76 56 76 66 72 64 70 72 68]\n",
      "  70 (+/- 7.8) <- [70 68 62 70 86 57 78 64 76 70]\n",
      "  70 (+/- 6.8) <- [76 62 74 64 80 80 64 64 72 64]\n",
      "  69 (+/- 7.0) <- [80 68 78 74 68 70 68 64 57 57]\n",
      "  70 (+/- 7.8) <- [78 64 68 72 78 80 68 68 52 70]\n",
      "  71 (+/- 7.3) <- [68 64 76 82 68 74 56 72 80 70]\n",
      "  70 (+/- 7.5) <- [78 66 56 72 72 68 62 82 64 76]\n",
      "  71 (+/- 3.9) <- [70 76 74 72 64 66 70 74 68 76]\n",
      "  70 (+/- 6.9) <- [68 60 80 60 72 68 78 78 74 66]\n",
      "  74 (+/- 6.7) <- [62 84 76 78 64 76 74 68 80 76]\n",
      "  70 (+/- 4.2) <- [68 68 66 74 64 76 74 64 74 70]\n",
      "  72 (+/- 6.2) <- [68 72 82 62 72 76 66 76 78 64]\n",
      "  69 (+/- 5.5) <- [64 76 72 72 68 64 72 60 78 66]\n",
      "  70 (+/- 5.8) <- [72 76 60 74 78 68 72 62 72 64]\n",
      "  71 (+/- 8.5) <- [74 74 78 74 72 64 50 80 78 66]\n",
      "  70 (+/- 4.4) <- [68 70 74 78 68 74 62 68 74 68]\n",
      "  70 (+/- 2.9) <- [68 70 72 68 72 72 66 74 66 74]\n",
      "  69 (+/- 6.4) <- [64 76 70 72 76 64 80 62 60 68]\n",
      "  70 (+/- 5.2) <- [74 70 74 68 64 64 66 64 74 80]\n",
      "  72 (+/- 5.1) <- [66 68 68 66 80 70 78 74 68 78]\n",
      "  71 (+/- 5.2) <- [74 64 72 74 64 80 64 76 72 72]\n",
      "  70 (+/- 6.4) <- [78 74 74 72 60 64 62 68 80 72]\n",
      "  70 (+/- 6.8) <- [72 66 64 76 66 57 64 80 76 76]\n",
      "  72 (+/- 4.5) <- [68 74 74 72 66 64 78 70 74 78]\n",
      "  72 (+/- 7.4) <- [76 68 74 57 74 84 76 74 60 72]\n",
      "  70 (+/- 4.7) <- [64 72 70 74 78 66 72 66 62 72]\n",
      "  70 (+/- 3.7) <- [72 74 72 62 68 68 76 70 72 70]\n",
      "  72 (+/- 9.0) <- [90 76 80 60 68 66 62 80 66 68]\n",
      "  71 (+/- 9.3) <- [88 68 74 57 72 66 82 66 78 57]\n",
      "  70 (+/- 5.6) <- [64 66 66 68 82 72 72 64 76 66]\n",
      "  69 (+/- 8.1) <- [82 68 54 66 80 66 72 60 74 72]\n",
      "  71 (+/- 7.1) <- [64 80 72 80 68 60 78 64 68 78]\n",
      "  70 (+/- 4.5) <- [66 66 68 78 76 70 62 68 70 72]\n",
      "  71 (+/- 5.6) <- [72 72 64 80 76 62 64 76 72 70]\n",
      "Accuracy: 70.5 (+/- 6.34)\n",
      "Mean time (40 cv): 18.70 seconds\n",
      "Overall time: 752.01 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 0x7f6bdc0633d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %\n",
      "Clips accuracy: 68.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (500, 1152), float64\n",
      "Labels: (500,), uint8\n",
      "  67 (+/- 8.0) <- [72 74 60 48 62 74 72 66 66 74]\n",
      "  66 (+/- 6.5) <- [70 60 62 52 64 70 72 68 66 76]\n",
      "  68 (+/- 6.7) <- [74 68 84 62 68 70 62 62 70 62]\n",
      "  68 (+/- 5.5) <- [60 72 62 66 74 74 72 64 76 64]\n",
      "  67 (+/- 5.1) <- [57 74 66 70 68 64 72 62 74 64]\n",
      "  68 (+/- 6.1) <- [68 78 74 64 72 70 68 57 57 66]\n",
      "  66 (+/- 3.7) <- [62 70 60 68 68 66 60 68 70 68]\n",
      "  67 (+/- 5.9) <- [68 60 57 68 80 64 64 68 64 72]\n",
      "  65 (+/- 6.0) <- [68 54 68 66 76 62 68 70 62 57]\n",
      "  66 (+/- 6.3) <- [72 62 78 64 74 64 62 57 60 70]\n",
      "  65 (+/- 6.6) <- [72 64 54 66 74 64 72 56 57 66]\n",
      "  67 (+/- 6.1) <- [66 56 60 76 66 70 66 70 76 62]\n",
      "  68 (+/- 7.3) <- [78 66 57 76 70 68 57 76 57 70]\n",
      "  68 (+/- 5.1) <- [68 64 74 66 74 72 66 70 56 68]\n",
      "  68 (+/- 5.1) <- [76 57 76 68 66 68 64 66 66 70]\n",
      "  68 (+/- 7.4) <- [66 82 72 76 60 56 66 62 72 70]\n",
      "  68 (+/- 7.5) <- [70 62 60 64 60 82 80 68 64 72]\n",
      "  67 (+/- 4.6) <- [74 66 76 66 66 62 70 64 62 64]\n",
      "  68 (+/- 7.2) <- [56 70 76 70 72 60 72 70 76 56]\n",
      "  67 (+/- 5.7) <- [70 78 62 74 64 70 68 57 68 62]\n",
      "  66 (+/- 7.4) <- [62 62 60 78 72 68 56 74 72 56]\n",
      "  67 (+/- 4.7) <- [66 68 66 57 64 68 72 66 76 62]\n",
      "  65 (+/- 5.5) <- [68 60 60 68 72 56 72 68 70 60]\n",
      "  67 (+/- 8.2) <- [62 64 57 74 72 56 82 66 60 76]\n",
      "  66 (+/- 4.0) <- [66 66 68 62 62 60 68 64 70 74]\n",
      "  68 (+/- 8.0) <- [66 56 66 76 66 76 74 68 52 76]\n",
      "  69 (+/- 5.3) <- [76 70 60 70 62 74 66 76 66 66]\n",
      "  67 (+/- 7.5) <- [68 64 78 68 57 60 57 66 80 74]\n",
      "  67 (+/- 4.7) <- [62 62 64 74 66 66 64 76 70 70]\n",
      "  66 (+/- 6.2) <- [52 66 62 70 70 66 72 66 64 76]\n",
      "  65 (+/- 4.6) <- [70 56 68 62 64 70 66 64 57 68]\n",
      "  68 (+/- 5.5) <- [62 72 64 78 64 64 68 66 62 76]\n",
      "  66 (+/- 6.9) <- [66 68 74 57 76 66 76 57 62 57]\n",
      "  68 (+/- 9.7) <- [90 70 72 56 56 62 62 70 64 76]\n",
      "  68 (+/- 6.5) <- [84 66 74 64 64 66 64 70 66 60]\n",
      "  68 (+/- 7.0) <- [74 68 52 76 66 57 72 70 72 68]\n",
      "  67 (+/- 5.6) <- [66 70 57 74 74 66 72 60 62 72]\n",
      "  69 (+/- 6.6) <- [60 78 72 74 70 68 78 62 60 64]\n",
      "  67 (+/- 6.1) <- [74 68 62 74 66 80 62 62 64 62]\n",
      "  67 (+/- 6.7) <- [64 72 56 76 68 66 64 80 62 64]\n",
      "Accuracy: 67.1 (+/- 6.43)\n",
      "Mean time (40 cv): 14.97 seconds\n",
      "Overall time: 602.81 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": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dm = ['cosine', 'euclidean']\n",
      "res['accuracy_std'] = [6.1280319026584698, 6.3406446833109902]\n",
      "res['objective_j'] = [10305.532073974609, 8521.7063903808594]\n",
      "res['objective_i'] = [56504.98046875, 57277.94140625]\n",
      "res['objective_h'] = [0, 0]\n",
      "res['objective_g'] = [76829.12109375, 69936.162109375]\n",
      "res['baseline'] = [67.059999999999974, 67.059999999999974]\n",
      "res['time_features'] = [2421.2433190345764, 2868.63295006752]\n",
      "res['baseline_std'] = 6.42778344377\n",
      "res['sparsity'] = [20.761215394295302, 19.043886325503355]\n",
      "res['iterations_inner'] = [1143, 1357]\n",
      "res['iterations_outer'] = [7, 7]\n",
      "res['accuracy'] = [73.165000000000035, 70.535000000000082]\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bdc792a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "yYCSuYhUcxdwIEE1+/6hx+qZp1pEeoGmQBWRamYD1wBzsSl273Prw1PoRk2nKyIiIiIiIiIiIiIi\n",
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bdc792610>"
      ]
     },
     "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 0x7f6bdc5d1f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bdc048c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bdbfb2350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bdc8d5b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bdbff5c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary atoms for dm = cosine\n",
      "Dictionary atoms for dm = euclidean\n",
      "Experiment time: 7804 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 / (len(Pvalues)-1)\n",
    "    params['markersize'] = 10\n",
    "    plt.xlim(-margin, len(Pvalues)-1+margin)\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",
    "# 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)\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",
    "    div = np.array(Pvalues if Pname is 'ld' else p['ld'])\n",
    "    res['objective_g_un'] = res['objective_g'] / div\n",
    "    div = np.array(Pvalues if Pname is 'ls' else p['ls'])\n",
    "    res['objective_i_un'] = res['objective_i'] / div\n",
    "    div = np.array(Pvalues if Pname is 'lg' else p['lg'])\n",
    "    res['objective_j_un'] = res['objective_j'] / div\n",
    "    plot('objective_g_un', 'objective_i_un', 'objective_j_un', log=True)\n",
    "    plot('sparsity')\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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