{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre recognition: experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: What if we remove the majority voting ? Rational: we may get more certain about the genre of a song but ignore it.\n",
    "\n",
    "Conclusion: It boosts the accuracy for the highest values of $\\lambda_g$, others are similar. The variance greatly decreases as there is many more examples. This is good for comparison and allows for less `Ncv` (faster experiments).\n",
    "\n",
    "Observations:\n",
    "* Accuracies have to be compared with experiment `13b_noise_lg`.\n",
    "* Other results are meaningless (features were reused).\n",
    "* Accuracy boosted for $\\lambda_g=100$ (3%) and $\\lambda_g=1000$ (2%).\n",
    "* Note that for $\\lambda_g=1000$ the sparsity was less enforced with $\\lambda_s= 0.1$. Still not enough though.\n",
    "* Ran for 2h50."
   ]
  },
  {
   "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, 0.1, 1, 10, 100, 1e3]\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\n",
    "p['majority_voting'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# HDF5 data stores.\n",
    "p['folder'] = 'data'\n",
    "p['filename_gtzan'] = 'gtzan.hdf5'\n",
    "p['filename_audio'] = 'audio.hdf5'\n",
    "p['filename_graph'] = 'graph.hdf5'\n",
    "p['filename_features'] = 'features.hdf5'\n",
    "\n",
    "# Dataset (10,100,644 | 5,100,149 | 2,10,644).\n",
    "p['Ngenres'] = 5\n",
    "p['Nclips'] = 100\n",
    "p['Nframes'] = 149\n",
    "\n",
    "# Added white noise.\n",
    "p['noise_std'] = 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Graph.\n",
    "p['tol'] = 1e-5\n",
    "\n",
    "# Feature extraction.\n",
    "p['rtol'] = 1e-5  # 1e-3, 1e-5, 1e-7\n",
    "p['N_inner'] = 500\n",
    "p['N_outer'] = 50\n",
    "\n",
    "# Classification.\n",
    "p['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",
      "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: 639 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 0x7f9d519d41d0>"
      ]
     },
     "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 0x7f9cff4f4fd0>"
      ]
     },
     "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 0x7f9cff06dd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 4 iterations\n",
      "\n",
      "Z in [-0.056636184454, 0.14264112711]\n",
      "Sparsity of Z: 8,786,613 non-zero entries out of 19,072,000 entries, i.e. 46.1%.\n"
     ]
    },
    {
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       "451Yftz2K1jeyz8by0HhFQA+BsB/Cv1daT/+D07nsfzVupfhApt+N/X5p1iqBXdiKcXfemh/yuH7\n",
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       "DAaDwWAwGAwGg8FgMBgMBoPBYDAY7Av/P9maY1PY7L9BAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d51a1fe90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.145910441875, 0.507630586624]\n",
      "d in [0.999999761581, 1.00000023842]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9cff0a84d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9cff2fecd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "RU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d51a1fe10>"
      ]
     },
     "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: 648 seconds\n",
      "\n",
      " -------------------- Features, lg = None -------------------- \n",
      "\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 0x7f9cf298a450>"
      ]
     },
     "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: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "  58 (+/- 1.8) <- [56 56 56 59 58 59 54 56 57 60]\n",
      "  57 (+/- 1.4) <- [57 58 59 55 57 55 57 55 56 55]\n",
      "  57 (+/- 1.8) <- [56 56 54 58 56 55 59 57 56 60]\n",
      "  57 (+/- 2.0) <- [54 60 56 57 57 56 59 53 58 56]\n",
      "  57 (+/- 1.4) <- [57 56 56 56 56 56 60 59 57 56]\n",
      "  57 (+/- 1.8) <- [56 54 57 60 56 55 56 59 56 54]\n",
      "  57 (+/- 1.4) <- [60 54 57 57 57 56 55 58 58 56]\n",
      "  57 (+/- 1.7) <- [58 58 56 57 56 56 56 60 59 54]\n",
      "  57 (+/- 2.2) <- [57 57 54 57 53 57 58 61 54 55]\n",
      "  57 (+/- 1.7) <- [58 56 57 58 56 60 57 56 56 53]\n",
      "  57 (+/- 2.1) <- [59 57 58 58 52 57 55 58 58 57]\n",
      "  56 (+/- 1.9) <- [55 56 57 55 55 60 57 57 55 53]\n",
      "  57 (+/- 1.5) <- [57 59 55 59 56 59 55 55 57 56]\n",
      "  57 (+/- 2.0) <- [55 53 58 59 55 54 55 56 59 57]\n",
      "  57 (+/- 2.6) <- [54 59 53 55 58 54 58 55 61 56]\n",
      "  57 (+/- 2.1) <- [56 58 53 60 54 58 57 57 54 58]\n",
      "  57 (+/- 2.0) <- [56 54 54 58 60 59 57 57 55 57]\n",
      "  57 (+/- 2.9) <- [56 52 61 58 58 61 54 59 55 57]\n",
      "  58 (+/- 2.2) <- [58 56 56 52 56 59 58 59 58 59]\n",
      "  57 (+/- 2.1) <- [58 61 54 58 56 55 55 56 55 58]\n",
      "  57 (+/- 1.9) <- [58 57 57 58 54 58 55 57 52 56]\n",
      "  57 (+/- 2.0) <- [56 57 55 55 57 61 56 58 53 56]\n",
      "  57 (+/- 2.1) <- [61 59 57 56 56 54 55 59 57 54]\n",
      "  57 (+/- 1.5) <- [56 54 58 56 57 60 56 57 57 55]\n",
      "  57 (+/- 1.9) <- [55 56 54 59 56 53 56 59 58 59]\n",
      "  57 (+/- 1.7) <- [58 54 54 59 58 56 58 57 55 56]\n",
      "  57 (+/- 1.8) <- [56 58 57 58 57 53 55 57 59 59]\n",
      "  57 (+/- 2.0) <- [58 54 56 55 60 56 59 56 54 58]\n",
      "  57 (+/- 1.8) <- [55 56 59 56 53 57 58 57 58 53]\n",
      "  57 (+/- 1.2) <- [56 57 55 57 58 56 58 56 59 55]\n",
      "  57 (+/- 1.8) <- [54 59 54 57 59 58 56 56 56 54]\n",
      "  57 (+/- 1.4) <- [57 54 58 58 55 56 56 56 57 56]\n",
      "  57 (+/- 1.7) <- [58 59 55 54 56 58 56 59 57 57]\n",
      "  57 (+/- 1.6) <- [54 56 57 56 57 56 59 57 59 54]\n",
      "  57 (+/- 1.8) <- [58 56 60 59 57 55 56 58 57 54]\n",
      "  57 (+/- 2.3) <- [56 58 56 56 60 58 56 59 51 54]\n",
      "  57 (+/- 1.8) <- [57 55 56 55 56 55 61 58 55 57]\n",
      "  57 (+/- 2.0) <- [54 57 56 56 55 61 59 57 55 58]\n",
      "  57 (+/- 1.7) <- [58 53 58 57 54 56 57 57 56 57]\n",
      "  57 (+/- 2.1) <- [55 56 58 57 58 59 55 58 55 52]\n",
      "Accuracy: 57.1 (+/- 1.92)\n",
      "Mean time (40 cv): 45.99 seconds\n",
      "Overall time: 1847.01 seconds\n",
      "\n",
      " -------------------- Features, lg = 0.1 -------------------- \n",
      "\n",
      "\n",
      " -------------------- Classification, lg = 0.1 -------------------- \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 0x7f9cf2510750>"
      ]
     },
     "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: 54.9 %\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: 48.8 %\n",
      "Clips accuracy: 57.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "  59 (+/- 1.4) <- [60 58 59 59 55 58 57 60 59 59]\n",
      "  59 (+/- 2.1) <- [56 61 61 56 57 55 60 61 58 57]\n",
      "  58 (+/- 1.5) <- [58 56 56 58 57 57 60 60 56 59]\n",
      "  59 (+/- 1.8) <- [55 60 58 60 57 58 61 56 60 57]\n",
      "  59 (+/- 1.5) <- [60 56 56 58 57 57 59 59 58 61]\n",
      "  58 (+/- 2.4) <- [55 56 57 62 60 56 58 60 59 54]\n",
      "  59 (+/- 1.7) <- [61 55 58 58 60 57 56 59 60 58]\n",
      "  59 (+/- 2.6) <- [61 56 55 58 59 56 55 61 63 58]\n",
      "  58 (+/- 3.5) <- [62 57 55 56 51 57 63 62 57 54]\n",
      "  59 (+/- 2.0) <- [58 57 57 60 60 62 59 58 56 54]\n",
      "  58 (+/- 1.4) <- [58 59 59 56 56 60 57 58 59 56]\n",
      "  58 (+/- 1.8) <- [57 59 58 56 56 61 58 59 55 56]\n",
      "  58 (+/- 1.7) <- [55 60 58 60 55 57 59 56 56 56]\n",
      "  58 (+/- 2.9) <- [55 53 59 61 56 59 56 56 63 61]\n",
      "  59 (+/- 2.1) <- [58 60 53 58 59 56 61 58 60 59]\n",
      "  59 (+/- 2.0) <- [60 56 55 59 55 59 58 61 59 60]\n",
      "  58 (+/- 1.1) <- [58 57 56 58 56 57 56 59 58 58]\n",
      "  58 (+/- 2.6) <- [59 54 60 56 58 61 53 58 55 59]\n",
      "  58 (+/- 2.2) <- [58 57 57 53 58 56 61 59 58 61]\n",
      "  58 (+/- 1.3) <- [58 58 59 59 61 56 57 56 56 58]\n",
      "  58 (+/- 1.4) <- [60 58 59 56 58 59 57 56 56 56]\n",
      "  58 (+/- 2.2) <- [57 57 56 55 59 61 57 60 54 59]\n",
      "  58 (+/- 1.2) <- [60 59 57 57 57 56 57 59 58 57]\n",
      "  58 (+/- 1.1) <- [57 56 58 57 59 60 56 59 57 57]\n",
      "  58 (+/- 1.4) <- [56 59 57 59 59 57 56 60 56 59]\n",
      "  58 (+/- 2.3) <- [63 54 56 59 59 58 56 60 58 57]\n",
      "  59 (+/- 2.2) <- [58 60 59 59 57 54 58 60 63 59]\n",
      "  58 (+/- 1.9) <- [57 56 55 56 61 60 58 59 55 60]\n",
      "  58 (+/- 2.5) <- [56 59 59 63 53 60 59 57 58 56]\n",
      "  58 (+/- 2.2) <- [57 59 56 56 60 54 61 58 60 56]\n",
      "  59 (+/- 1.9) <- [56 59 55 60 59 61 61 58 58 56]\n",
      "  59 (+/- 1.8) <- [58 55 58 62 58 60 60 59 58 57]\n",
      "  58 (+/- 2.4) <- [59 61 55 52 57 58 57 57 59 58]\n",
      "  59 (+/- 2.1) <- [61 57 58 57 59 56 62 62 59 55]\n",
      "  58 (+/- 1.3) <- [60 57 58 58 59 56 56 59 58 57]\n",
      "  58 (+/- 1.7) <- [56 58 59 60 59 59 55 60 57 54]\n",
      "  58 (+/- 1.4) <- [60 57 58 55 58 57 60 58 57 57]\n",
      "  58 (+/- 1.8) <- [56 58 55 55 57 58 60 57 60 59]\n",
      "  58 (+/- 1.8) <- [59 55 59 59 54 59 57 59 60 58]\n",
      "  58 (+/- 2.1) <- [60 59 58 60 60 59 55 58 56 54]\n",
      "Accuracy: 58.3 (+/- 2.02)\n",
      "Mean time (40 cv): 47.32 seconds\n",
      "Overall time: 1900.22 seconds\n",
      "\n",
      " -------------------- Features, lg = 1 -------------------- \n",
      "\n",
      "\n",
      " -------------------- Classification, lg = 1 -------------------- \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 0x7f9cf2977d10>"
      ]
     },
     "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: 55.6 %\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: 48.5 %\n",
      "Clips accuracy: 59.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "  58 (+/- 1.6) <- [56 56 57 60 59 58 54 57 58 59]\n",
      "  58 (+/- 1.7) <- [56 59 61 57 58 56 60 55 57 57]\n",
      "  58 (+/- 1.6) <- [57 56 54 59 56 57 58 58 57 61]\n",
      "  57 (+/- 2.3) <- [53 60 57 58 58 55 59 54 59 55]\n",
      "  58 (+/- 1.4) <- [57 55 56 56 56 57 59 60 57 57]\n",
      "  58 (+/- 1.8) <- [56 56 57 60 57 54 57 61 56 56]\n",
      "  58 (+/- 1.8) <- [61 55 58 56 59 57 56 57 60 58]\n",
      "  58 (+/- 1.6) <- [57 60 57 56 56 56 55 59 59 56]\n",
      "  57 (+/- 2.0) <- [57 56 55 57 53 57 59 60 56 55]\n",
      "  58 (+/- 2.0) <- [59 57 57 58 57 61 57 57 56 53]\n",
      "  58 (+/- 1.9) <- [60 58 57 59 53 57 55 59 58 58]\n",
      "  57 (+/- 1.7) <- [56 56 59 56 55 61 57 57 56 56]\n",
      "  58 (+/- 1.2) <- [57 59 57 59 56 59 56 55 57 57]\n",
      "  57 (+/- 2.3) <- [55 54 59 61 56 54 56 57 60 58]\n",
      "  58 (+/- 3.0) <- [55 61 53 55 59 54 57 56 63 58]\n",
      "  57 (+/- 1.8) <- [56 58 52 59 56 58 59 57 56 58]\n",
      "  58 (+/- 1.6) <- [57 55 55 59 60 59 57 56 55 57]\n",
      "  58 (+/- 2.7) <- [57 52 61 56 58 62 54 58 56 57]\n",
      "  58 (+/- 2.3) <- [59 55 57 52 56 60 59 59 58 59]\n",
      "  58 (+/- 1.4) <- [58 61 56 59 59 57 57 58 56 58]\n",
      "  58 (+/- 1.4) <- [59 58 58 59 56 57 56 59 54 57]\n",
      "  57 (+/- 1.9) <- [57 57 56 55 59 61 57 58 53 56]\n",
      "  57 (+/- 2.3) <- [61 59 58 58 57 54 55 56 57 53]\n",
      "  58 (+/- 1.5) <- [57 55 59 56 57 61 57 57 56 56]\n",
      "  57 (+/- 2.0) <- [54 57 54 60 57 54 54 59 56 57]\n",
      "  58 (+/- 2.0) <- [60 56 54 62 58 56 57 57 57 56]\n",
      "  58 (+/- 2.0) <- [57 60 57 60 56 54 56 58 60 60]\n",
      "  58 (+/- 2.7) <- [58 52 57 55 62 57 59 57 54 60]\n",
      "  57 (+/- 1.4) <- [56 57 59 56 54 57 56 57 57 54]\n",
      "  58 (+/- 1.5) <- [57 57 56 57 60 56 59 55 60 58]\n",
      "  57 (+/- 1.7) <- [54 59 55 57 59 59 56 56 56 55]\n",
      "  58 (+/- 1.5) <- [58 55 59 59 56 59 57 56 58 55]\n",
      "  58 (+/- 1.8) <- [57 60 55 54 55 58 59 59 57 59]\n",
      "  57 (+/- 1.7) <- [55 56 58 57 56 57 59 57 59 54]\n",
      "  58 (+/- 1.6) <- [59 56 59 60 57 55 56 57 58 55]\n",
      "  57 (+/- 2.3) <- [56 59 56 58 61 58 57 59 52 55]\n",
      "  58 (+/- 1.9) <- [57 54 57 56 56 55 61 59 57 58]\n",
      "  57 (+/- 1.5) <- [55 58 55 57 56 60 58 56 57 58]\n",
      "  57 (+/- 1.6) <- [58 53 59 57 55 56 58 56 57 57]\n",
      "  57 (+/- 2.0) <- [57 56 58 59 57 59 56 58 55 52]\n",
      "Accuracy: 57.6 (+/- 1.92)\n",
      "Mean time (40 cv): 42.40 seconds\n",
      "Overall time: 1702.70 seconds\n",
      "\n",
      " -------------------- Features, lg = 10 -------------------- \n",
      "\n",
      "\n",
      " -------------------- Classification, lg = 10 -------------------- \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 0x7f9cf287a110>"
      ]
     },
     "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: 59.2 %\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: 50.6 %\n",
      "Clips accuracy: 61.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "  61 (+/- 1.0) <- [61 61 59 61 62 61 59 60 62 62]\n",
      "  61 (+/- 1.6) <- [58 63 63 60 59 60 62 60 59 60]\n",
      "  61 (+/- 1.8) <- [60 60 59 62 60 60 63 59 59 64]\n",
      "  61 (+/- 2.2) <- [57 62 59 63 61 60 63 57 63 60]\n",
      "  60 (+/- 1.7) <- [61 57 60 60 58 60 62 63 59 60]\n",
      "  61 (+/- 2.1) <- [61 60 61 63 62 56 59 64 59 59]\n",
      "  61 (+/- 1.2) <- [63 60 61 60 61 59 59 61 61 61]\n",
      "  61 (+/- 1.7) <- [60 61 60 62 61 60 60 64 63 58]\n",
      "  60 (+/- 1.8) <- [60 59 60 59 57 58 64 62 61 59]\n",
      "  61 (+/- 1.9) <- [62 60 60 63 61 63 60 59 62 56]\n",
      "  60 (+/- 1.2) <- [60 61 60 61 59 63 58 58 59 60]\n",
      "  60 (+/- 1.4) <- [60 59 62 60 60 62 59 61 57 60]\n",
      "  60 (+/- 1.3) <- [61 61 61 59 60 60 60 58 57 61]\n",
      "  61 (+/- 2.4) <- [55 58 62 63 61 59 59 58 63 63]\n",
      "  61 (+/- 2.6) <- [59 64 59 59 63 56 61 59 65 61]\n",
      "  61 (+/- 1.6) <- [60 61 58 64 60 62 60 60 59 61]\n",
      "  61 (+/- 1.7) <- [59 57 60 64 61 62 58 60 59 61]\n",
      "  61 (+/- 2.5) <- [60 56 65 59 61 64 60 62 57 62]\n",
      "  61 (+/- 1.9) <- [60 58 60 56 61 61 63 61 60 62]\n",
      "  61 (+/- 1.5) <- [59 62 58 63 63 61 59 61 59 62]\n",
      "  61 (+/- 0.5) <- [61 60 61 60 60 61 61 60 60 61]\n",
      "  61 (+/- 2.5) <- [61 59 57 59 62 63 61 64 56 60]\n",
      "  60 (+/- 1.7) <- [63 61 61 61 61 57 57 60 59 59]\n",
      "  61 (+/- 1.5) <- [61 59 62 60 61 62 59 63 59 60]\n",
      "  61 (+/- 1.4) <- [60 61 58 61 61 58 59 63 61 61]\n",
      "  61 (+/- 1.7) <- [64 60 58 62 60 59 59 62 60 60]\n",
      "  61 (+/- 2.2) <- [60 61 60 62 58 56 57 62 63 62]\n",
      "  60 (+/- 1.8) <- [58 59 60 56 64 60 61 61 60 61]\n",
      "  60 (+/- 1.6) <- [60 61 61 61 56 61 59 61 60 58]\n",
      "  61 (+/- 2.0) <- [60 62 60 61 61 56 63 59 64 61]\n",
      "  61 (+/- 1.7) <- [59 61 60 62 62 64 60 59 61 59]\n",
      "  61 (+/- 1.4) <- [62 58 61 62 59 61 61 62 62 59]\n",
      "  61 (+/- 1.8) <- [60 62 60 57 60 60 64 60 61 59]\n",
      "  61 (+/- 1.5) <- [58 59 61 61 60 61 62 61 61 57]\n",
      "  61 (+/- 2.1) <- [60 60 65 62 61 58 60 60 61 57]\n",
      "  61 (+/- 2.1) <- [59 59 61 63 63 63 58 62 57 60]\n",
      "  60 (+/- 1.2) <- [61 59 60 60 59 60 61 62 59 59]\n",
      "  61 (+/- 2.0) <- [59 61 58 58 60 64 64 60 62 62]\n",
      "  60 (+/- 1.5) <- [60 58 59 61 58 62 59 59 62 61]\n",
      "  61 (+/- 1.9) <- [63 61 60 60 61 62 58 62 61 56]\n",
      "Accuracy: 60.8 (+/- 1.81)\n",
      "Mean time (40 cv): 31.37 seconds\n",
      "Overall time: 1260.58 seconds\n",
      "\n",
      " -------------------- Features, lg = 100 -------------------- \n",
      "\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 0x7f9cf06756d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 63.2 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 57.2 %\n",
      "Clips accuracy: 62.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "  64 (+/- 1.9) <- [63 64 59 66 66 65 65 63 65 64]\n",
      "  64 (+/- 2.7) <- [59 68 65 65 61 64 67 62 62 66]\n",
      "  64 (+/- 1.5) <- [64 64 60 65 65 62 65 63 63 65]\n",
      "  64 (+/- 1.8) <- [62 62 61 66 61 64 66 62 64 65]\n",
      "  64 (+/- 2.3) <- [60 61 65 64 62 63 65 69 63 66]\n",
      "  64 (+/- 2.6) <- [62 62 64 68 66 63 64 68 61 61]\n",
      "  64 (+/- 1.5) <- [63 62 66 64 64 64 61 61 63 65]\n",
      "  64 (+/- 1.3) <- [64 63 63 63 63 63 62 65 66 62]\n",
      "  64 (+/- 1.7) <- [66 64 65 63 61 60 65 65 64 65]\n",
      "  64 (+/- 2.2) <- [67 64 63 67 63 65 63 62 65 59]\n",
      "  64 (+/- 2.4) <- [65 65 66 64 65 67 62 62 63 58]\n",
      "  64 (+/- 2.1) <- [60 63 63 66 62 67 62 65 61 64]\n",
      "  64 (+/- 1.5) <- [65 65 65 63 64 62 64 61 62 65]\n",
      "  64 (+/- 2.1) <- [62 62 63 64 64 61 61 62 66 69]\n",
      "  64 (+/- 2.8) <- [63 66 60 62 67 59 65 67 67 61]\n",
      "  64 (+/- 1.9) <- [63 65 60 67 63 65 61 64 62 61]\n",
      "  64 (+/- 1.7) <- [63 61 65 63 65 67 62 63 64 65]\n",
      "  64 (+/- 2.6) <- [63 59 64 65 65 67 61 63 61 67]\n",
      "  64 (+/- 1.7) <- [61 63 66 61 64 65 64 66 66 64]\n",
      "  64 (+/- 1.6) <- [62 64 62 66 64 65 63 61 62 65]\n",
      "  64 (+/- 1.5) <- [64 66 63 62 64 66 63 63 64 61]\n",
      "  64 (+/- 2.3) <- [66 64 59 65 63 67 63 66 61 64]\n",
      "  64 (+/- 1.9) <- [65 62 63 67 65 60 62 65 64 63]\n",
      "  64 (+/- 1.7) <- [66 62 64 64 63 66 61 66 63 62]\n",
      "  64 (+/- 2.0) <- [63 64 62 65 67 61 61 63 64 67]\n",
      "  64 (+/- 1.8) <- [67 64 63 64 65 63 61 65 64 61]\n",
      "  64 (+/- 1.3) <- [65 64 64 62 63 62 66 64 65 64]\n",
      "  64 (+/- 1.2) <- [62 63 65 63 63 65 63 63 62 66]\n",
      "  64 (+/- 1.5) <- [63 62 64 64 61 65 62 64 66 63]\n",
      "  64 (+/- 2.0) <- [62 65 64 64 64 60 67 65 65 60]\n",
      "  64 (+/- 1.6) <- [63 64 62 64 66 65 65 63 61 63]\n",
      "  64 (+/- 1.5) <- [64 62 64 65 66 63 62 65 65 61]\n",
      "  64 (+/- 1.7) <- [65 64 62 61 60 63 66 65 64 64]\n",
      "  64 (+/- 1.9) <- [63 59 63 64 66 65 65 66 64 61]\n",
      "  64 (+/- 1.0) <- [64 63 63 62 65 62 63 63 64 62]\n",
      "  64 (+/- 2.6) <- [62 66 62 67 69 64 62 63 59 63]\n",
      "  64 (+/- 1.0) <- [64 62 63 63 64 63 65 65 63 64]\n",
      "  64 (+/- 1.9) <- [61 65 63 60 62 67 64 63 64 65]\n",
      "  64 (+/- 1.4) <- [65 63 62 62 64 65 63 63 67 65]\n",
      "  64 (+/- 2.4) <- [69 63 63 61 66 65 61 64 62 62]\n",
      "Accuracy: 64.0 (+/- 1.92)\n",
      "Mean time (40 cv): 22.69 seconds\n",
      "Overall time: 912.58 seconds\n",
      "\n",
      " -------------------- Features, lg = 1000.0 -------------------- \n",
      "\n",
      "\n",
      " -------------------- Classification, lg = 1000.0 -------------------- \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 0x7f9cf24986d0>"
      ]
     },
     "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: 57.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 53.2 %\n",
      "Clips accuracy: 56.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "  58 (+/- 1.8) <- [58 58 54 59 56 60 57 55 57 57]\n",
      "  58 (+/- 2.1) <- [55 59 59 59 55 55 60 58 55 59]\n",
      "  58 (+/- 2.6) <- [59 54 52 60 59 58 56 56 56 61]\n",
      "  57 (+/- 1.6) <- [56 56 54 56 55 58 59 58 59 58]\n",
      "  58 (+/- 2.3) <- [54 58 55 55 55 56 60 62 56 59]\n",
      "  58 (+/- 2.1) <- [57 57 56 60 60 56 59 60 55 53]\n",
      "  57 (+/- 1.5) <- [58 55 59 60 58 56 56 55 57 57]\n",
      "  57 (+/- 2.0) <- [61 55 56 56 57 56 58 59 58 53]\n",
      "  57 (+/- 1.4) <- [58 55 59 56 55 57 58 59 57 56]\n",
      "  57 (+/- 1.6) <- [59 57 57 59 56 60 56 56 56 54]\n",
      "  58 (+/- 2.0) <- [56 59 57 57 58 62 57 55 55 55]\n",
      "  58 (+/- 1.5) <- [56 55 60 57 58 58 55 56 58 57]\n",
      "  58 (+/- 2.2) <- [58 63 57 58 56 55 57 54 56 56]\n",
      "  58 (+/- 1.7) <- [56 56 55 58 57 55 57 57 58 61]\n",
      "  57 (+/- 2.2) <- [56 56 53 57 59 54 55 59 60 58]\n",
      "  57 (+/- 2.0) <- [58 57 56 58 54 59 54 58 60 55]\n",
      "  58 (+/- 1.6) <- [58 56 55 58 60 60 55 57 56 58]\n",
      "  57 (+/- 1.3) <- [59 55 58 55 57 58 56 57 57 58]\n",
      "  57 (+/- 1.4) <- [57 56 57 56 57 59 54 56 58 59]\n",
      "  58 (+/- 1.5) <- [57 58 55 60 58 57 56 57 55 60]\n",
      "  57 (+/- 1.8) <- [55 59 54 59 59 57 57 54 57 57]\n",
      "  57 (+/- 2.1) <- [60 58 54 57 57 61 59 56 56 54]\n",
      "  57 (+/- 1.7) <- [58 56 57 57 57 54 59 56 61 56]\n",
      "  57 (+/- 1.7) <- [56 56 56 57 57 60 57 59 55 55]\n",
      "  58 (+/- 2.3) <- [56 58 56 60 62 56 54 58 55 59]\n",
      "  57 (+/- 1.6) <- [57 56 56 57 58 60 55 57 57 54]\n",
      "  58 (+/- 1.3) <- [58 56 60 58 57 57 55 55 58 57]\n",
      "  58 (+/- 1.6) <- [57 56 56 56 59 57 59 57 55 59]\n",
      "  57 (+/- 1.9) <- [58 57 57 58 55 60 54 56 59 55]\n",
      "  57 (+/- 2.0) <- [56 56 54 58 58 55 61 59 58 55]\n",
      "  57 (+/- 2.0) <- [57 57 55 56 61 60 56 57 54 56]\n",
      "  57 (+/- 1.5) <- [56 57 58 60 55 56 56 58 57 55]\n",
      "  58 (+/- 2.6) <- [60 60 56 52 56 55 58 60 58 58]\n",
      "  58 (+/- 1.5) <- [55 55 56 57 58 58 60 58 55 58]\n",
      "  58 (+/- 1.6) <- [59 55 59 57 58 54 58 57 58 55]\n",
      "  57 (+/- 3.2) <- [56 62 52 61 61 58 57 55 54 56]\n",
      "  58 (+/- 1.8) <- [58 57 60 54 57 56 57 56 56 60]\n",
      "  58 (+/- 2.1) <- [54 60 56 54 57 61 58 57 58 58]\n",
      "  58 (+/- 2.5) <- [59 53 57 60 56 57 53 58 61 58]\n",
      "  58 (+/- 1.9) <- [59 58 56 60 59 56 54 56 58 55]\n",
      "Accuracy: 57.5 (+/- 1.93)\n",
      "Mean time (40 cv): 26.15 seconds\n",
      "Overall time: 1051.47 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 0x7f9cf22bdcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 59.3 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 54.8 %\n",
      "Clips accuracy: 62.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "  58 (+/- 1.5) <- [57 59 55 59 59 59 57 55 59 59]\n",
      "  59 (+/- 2.7) <- [52 61 62 58 59 58 63 58 57 58]\n",
      "  59 (+/- 2.4) <- [60 57 53 61 59 58 59 58 56 62]\n",
      "  59 (+/- 1.8) <- [57 60 56 61 56 59 60 60 59 56]\n",
      "  59 (+/- 2.2) <- [55 57 56 58 57 59 60 62 58 61]\n",
      "  59 (+/- 1.7) <- [56 59 57 61 60 56 59 59 58 55]\n",
      "  59 (+/- 1.0) <- [59 57 58 61 58 58 59 57 59 59]\n",
      "  59 (+/- 2.1) <- [57 57 57 57 57 58 59 61 63 55]\n",
      "  59 (+/- 2.3) <- [59 56 57 57 55 55 59 61 59 62]\n",
      "  59 (+/- 1.9) <- [59 58 60 62 56 62 58 59 59 56]\n",
      "  59 (+/- 2.5) <- [57 61 58 60 55 64 56 56 58 55]\n",
      "  59 (+/- 1.5) <- [60 59 61 59 56 60 59 59 57 56]\n",
      "  59 (+/- 2.8) <- [59 62 57 58 52 62 60 55 59 58]\n",
      "  59 (+/- 2.0) <- [59 55 58 58 56 59 57 57 62 60]\n",
      "  58 (+/- 2.6) <- [57 60 54 59 60 54 58 57 62 58]\n",
      "  59 (+/- 2.1) <- [56 61 55 61 56 61 57 59 57 58]\n",
      "  59 (+/- 1.8) <- [58 57 59 61 60 61 55 59 60 56]\n",
      "  59 (+/- 1.4) <- [59 58 60 58 58 57 58 57 55 60]\n",
      "  58 (+/- 1.3) <- [57 57 56 57 58 59 59 60 59 59]\n",
      "  59 (+/- 2.4) <- [58 60 57 61 62 61 56 54 58 59]\n",
      "  58 (+/- 1.3) <- [57 60 57 58 57 60 59 57 57 56]\n",
      "  58 (+/- 2.4) <- [60 59 53 56 58 60 61 58 54 58]\n",
      "  58 (+/- 1.2) <- [59 57 57 58 60 56 59 57 58 56]\n",
      "  59 (+/- 2.0) <- [58 57 57 59 57 63 58 62 57 59]\n",
      "  59 (+/- 2.4) <- [59 59 60 59 60 55 54 62 56 60]\n",
      "  59 (+/- 1.9) <- [58 57 59 60 59 57 57 58 62 55]\n",
      "  59 (+/- 1.5) <- [57 59 61 59 61 59 56 60 56 58]\n",
      "  59 (+/- 2.5) <- [55 56 60 57 57 62 61 61 54 59]\n",
      "  59 (+/- 2.2) <- [59 61 60 59 54 57 57 58 62 55]\n",
      "  58 (+/- 2.1) <- [57 58 58 61 57 56 59 59 59 53]\n",
      "  59 (+/- 1.5) <- [59 58 60 57 60 61 59 58 56 56]\n",
      "  59 (+/- 1.6) <- [60 55 57 58 60 57 58 60 59 59]\n",
      "  59 (+/- 2.9) <- [62 62 56 52 58 58 61 61 57 57]\n",
      "  59 (+/- 1.3) <- [56 58 58 58 58 60 60 59 58 56]\n",
      "  59 (+/- 1.5) <- [59 58 59 60 60 58 56 60 56 56]\n",
      "  59 (+/- 1.9) <- [58 61 58 61 62 58 57 57 56 56]\n",
      "  59 (+/- 1.7) <- [58 57 56 56 58 59 58 58 58 62]\n",
      "  58 (+/- 1.9) <- [56 61 57 57 57 59 56 57 60 60]\n",
      "  59 (+/- 2.1) <- [60 58 57 60 54 56 58 58 62 58]\n",
      "  59 (+/- 1.7) <- [60 59 59 58 61 59 54 59 58 58]\n",
      "Accuracy: 58.7 (+/- 2.01)\n",
      "Mean time (40 cv): 19.82 seconds\n",
      "Overall time: 797.31 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, 0.1, 1, 10, 100, 1000.0]\n",
      "res['accuracy_std'] = [1.9160245301143717, 2.0150868036858589, 1.9187624411467814, 1.8084254488827196, 1.9215625423510598, 1.9294211495218514]\n",
      "res['objective_j'] = [20566.252136230469, 20566.252136230469, 20566.252136230469, 20566.252136230469, 20566.252136230469, 20566.252136230469]\n",
      "res['objective_i'] = [71275.6015625, 71275.6015625, 71275.6015625, 71275.6015625, 71275.6015625, 71275.6015625]\n",
      "res['objective_h'] = [0, 0, 0, 0, 0, 0]\n",
      "res['objective_g'] = [746237.734375, 746237.734375, 746237.734375, 746237.734375, 746237.734375, 746237.734375]\n",
      "res['baseline'] = [58.700000000000053, 58.700000000000053, 58.700000000000053, 58.700000000000053, 58.700000000000053, 58.700000000000053]\n",
      "res['time_features'] = [639.2638561725616, 639.2638561725616, 639.2638561725616, 639.2638561725616, 639.2638561725616, 639.2638561725616]\n",
      "res['baseline_std'] = 2.01042422278\n",
      "res['sparsity'] = [46.07074769295302, 46.07074769295302, 46.07074769295302, 46.07074769295302, 46.07074769295302, 46.07074769295302]\n",
      "res['iterations_inner'] = [246, 246, 246, 246, 246, 246]\n",
      "res['iterations_outer'] = [4, 4, 4, 4, 4, 4]\n",
      "res['accuracy'] = [57.073333333333331, 58.34041666666667, 57.587499999999956, 60.767083333333375, 64.012916666666641, 57.515833333333354]\n"
     ]
    },
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9cf24aa610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "h1UqwGYafpHqW+uLiPyDWjBEJK3tsIkDwWaVnBcwFhHpcapgiEhaJTRDroikpAqGiKR1KzbmYgDr\n",
       "InFBoxGRnjYmdAAi0vPKV5H8AtgZmI9NX30XsDhUUCIiIhKP8f7nasAC4B0BYxGRHqYWDBFpxy+B\n",
       "VYHlgG8DfwkbjoiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiETk/wASkSDfkBwZNQAAAABJ\n",
       "RU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9cff06df10>"
      ]
     },
     "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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       "gg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9cf2498590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9cf0565210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9cf263eb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9cfc0c9810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9cf0983150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary atoms for lg = None\n",
      "Dictionary atoms for lg = 0.1\n",
      "Dictionary atoms for lg = 1\n",
      "Dictionary atoms for lg = 10\n",
      "Dictionary atoms for lg = 100\n",
      "Dictionary atoms for lg = 1000.0\n",
      "Experiment time: 10125 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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