13g_ratio.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre recognition: experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: Test if we have an edge with law training / testing ratios, i.e. when there is not much training data.\n",
    "\n",
    "Conclusion: As we cannot train the auto-encoder and extract features on a subset of the data yet (this is a limitation of the current implementation), only the ability of the classifier to generalize is actually tested. Maybe the extracted features are better for generalization, but there is no such indices in the results.\n",
    "\n",
    "Observations:\n",
    "* Classification accuracy drops with `test_size`.\n",
    "* Our margin over baseline too."
   ]
  },
  {
   "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 = 'test_size'\n",
    "Pvalues = [0.1, 0.3, 0.5, 0.7, 0.9]\n",
    "\n",
    "# Regenerate the graph or the features at each iteration.\n",
    "regen_graph = False\n",
    "regen_features = False\n",
    "regen_baseline = 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['data_scaling_graph'] = 'features'\n",
    "p['K'] = 10 + 1  # 5 to 10 + 1 for self-reference\n",
    "p['dm'] = 'euclidean'\n",
    "p['Csigma'] = 1\n",
    "p['diag'] = True\n",
    "p['laplacian'] = 'normalized'\n",
    "\n",
    "# Feature extraction.\n",
    "p['m'] = 128  # 64, 128, 512\n",
    "p['ls'] = 1\n",
    "p['ld'] = 10\n",
    "p['le'] = None\n",
    "p['lg'] = 100\n",
    "\n",
    "# Classification.\n",
    "p['scale'] = None\n",
    "p['Nvectors'] = 6\n",
    "p['svm_type'] = 'C'\n",
    "p['kernel'] = 'linear'\n",
    "p['C'] = 1\n",
    "p['nu'] = 0.5\n",
    "p['majority_voting'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# HDF5 data stores.\n",
    "p['folder'] = 'data'\n",
    "p['filename_gtzan'] = 'gtzan.hdf5'\n",
    "p['filename_audio'] = 'audio.hdf5'\n",
    "p['filename_graph'] = 'graph.hdf5'\n",
    "p['filename_features'] = 'features.hdf5'\n",
    "\n",
    "# Dataset (10,100,644 | 5,100,149 | 2,10,644).\n",
    "p['Ngenres'] = 5\n",
    "p['Nclips'] = 100\n",
    "p['Nframes'] = 149\n",
    "\n",
    "# Added white noise.\n",
    "p['noise_std'] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Graph.\n",
    "p['tol'] = 1e-5\n",
    "\n",
    "# Feature extraction.\n",
    "p['rtol'] = 1e-5  # 1e-3, 1e-5, 1e-7\n",
    "p['N_inner'] = 500\n",
    "p['N_outer'] = 50\n",
    "\n",
    "# Classification.\n",
    "p['test_size'] = 0.1\n",
    "p['Ncv'] = 20\n",
    "p['dataset_classification'] = 'Z'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "\n",
    "texperiment = time.time()\n",
    "\n",
    "# Result dictionary.\n",
    "res = ['accuracy', 'accuracy_std']\n",
    "res += ['sparsity', 'atoms']\n",
    "res += ['objective_g', 'objective_h', 'objective_i', 'objective_j']\n",
    "res += ['time_features', 'iterations_inner', 'iterations_outer']\n",
    "res = dict.fromkeys(res)\n",
    "for key in res.keys():\n",
    "    res[key] = []\n",
    "\n",
    "def separator(name, parameter=False):\n",
    "    if parameter:\n",
    "        name += ', {} = {}'.format(Pname, p[Pname])\n",
    "    dashes = 20 * '-'\n",
    "    print('\\n {} {} {} \\n'.format(dashes, name, dashes))\n",
    "    # Fair comparison when tuning parameters.\n",
    "    # Randomnesses: dictionary initialization, training and testing sets.\n",
    "    np.random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " -------------------- Graph -------------------- \n",
      "\n",
      "Data: (149000, 96), float32\n",
      "Elapsed time: 172.55 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.0, 1.52838051319]\n",
      "w in [0.00524357147515, 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    : (2417284,), float32\n",
      "  L_indices : (2417284,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2417284,), float32\n",
      "  W_indices : (2417284,), 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: 181.80 seconds\n",
      "\n",
      " -------------------- Features -------------------- \n",
      "\n",
      "Attributes:\n",
      "  sr = 22050\n",
      "  labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'\n",
      " 'reggae' 'rock']\n",
      "Datasets:\n",
      "  Xa: (10, 100, 644, 2, 1024) , float32\n",
      "  Xs: (10, 100, 644, 2, 96)   , float32\n",
      "Full dataset:\n",
      "  size: N=1,288,000 x n=96 -> 123,648,000 floats\n",
      "  dim: 123,648 features per clip\n",
      "  shape: (10, 100, 644, 2, 96)\n",
      "<class 'h5py._hl.dataset.Dataset'>\n",
      "Reduced dataset:\n",
      "  size: N=149,000 x n=96 -> 14,304,000 floats\n",
      "  dim: 28,608 features per clip\n",
      "  shape: (5, 100, 149, 2, 96)\n",
      "<type 'numpy.ndarray'>\n",
      "Data: (149000, 96), float32\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = euclidean\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2417284,), float32\n",
      "  L_indices : (2417284,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2417284,), float32\n",
      "  W_indices : (2417284,), 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: 2816 seconds\n"
     ]
    },
    {
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       "a+Upzl+CHZ/8+C7FbsCrfm8992MF72XY7/N8yvdE1Fv/FHYy9FlgCXbyNQ07LmvE7b+BXRIhbtvq\n",
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       "IiIiIiIiIiIiIiIiIiIiIiIi4uX/AzWqwuq7ft4sAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a38104810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 1314 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.192160e+04\n",
      "rdiff: 5.52630858948e-05\n",
      "i(Z) = ||Z||_1 = 5.660139e+04\n",
      "j(Z) = tr(Z^TLZ) = 8.681122e+03\n"
     ]
    },
    {
     "data": {
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       "REREREREREREREREREREREREREREREREREREpN/9fxPI87RpWC0RAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f62dc450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.372041e+05\n"
     ]
    },
    {
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       "AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f6076850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 7 iterations\n",
      "\n",
      "Z in [-0.125608801842, 1.03517985344]\n",
      "Sparsity of Z: 3,793,973 non-zero entries out of 19,072,000 entries, i.e. 19.9%.\n"
     ]
    },
    {
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       "IYQQQghZFv8fVi1ZkoVWh9AAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a79ac7b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.0457091145217, 0.892917752266]\n",
      "d in [0.999999642372, 1.00000023842]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f613fbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19f6092d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "RK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a79ac7990>"
      ]
     },
     "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: 2824 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 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"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e97ce050>"
      ]
     },
     "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: 76.9 %\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: 63.3 %\n",
      "Clips accuracy: 72.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 5400.0 training, 600.0 testing\n",
      "  77 (+/- 2.3) <- [77 74 77 77 70 77 76 79 79 76]\n",
      "  78 (+/- 1.3) <- [77 77 79 75 80 77 78 78 77 78]\n",
      "  79 (+/- 1.6) <- [79 76 78 78 80 82 78 76 79 77]\n",
      "  78 (+/- 1.7) <- [79 73 77 77 78 77 78 79 78 78]\n",
      "  78 (+/- 0.8) <- [78 78 78 77 77 77 78 77 77 76]\n",
      "  79 (+/- 1.1) <- [78 77 78 80 79 79 77 80 78 80]\n",
      "  77 (+/- 0.9) <- [76 76 77 77 77 76 77 76 78 78]\n",
      "  77 (+/- 1.2) <- [75 78 78 76 77 78 77 77 75 79]\n",
      "  78 (+/- 1.3) <- [77 78 81 80 77 77 78 78 79 77]\n",
      "  78 (+/- 1.4) <- [78 78 78 77 77 76 77 78 78 82]\n",
      "  79 (+/- 1.7) <- [79 80 79 75 81 77 80 76 79 77]\n",
      "  78 (+/- 1.4) <- [77 79 79 78 76 75 76 78 79 77]\n",
      "  77 (+/- 1.4) <- [79 76 78 76 76 77 79 74 77 76]\n",
      "  77 (+/- 1.9) <- [80 76 80 76 75 74 75 76 78 77]\n",
      "  78 (+/- 1.1) <- [76 78 77 78 78 80 78 79 77 78]\n",
      "  79 (+/- 2.1) <- [80 77 79 81 76 76 78 77 82 77]\n",
      "  78 (+/- 1.4) <- [76 80 79 77 78 75 77 78 79 78]\n",
      "  78 (+/- 1.2) <- [77 80 77 79 77 79 77 76 79 78]\n",
      "  78 (+/- 0.9) <- [77 78 77 78 77 79 77 78 76 77]\n",
      "  77 (+/- 1.1) <- [77 78 78 75 76 76 78 77 75 78]\n",
      "Accuracy: 77.9 (+/- 1.57)\n",
      "Mean time (20 cv): 20.59 seconds\n",
      "Overall time: 415.82 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.3 -------------------- \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 0x7f19e9288c10>"
      ]
     },
     "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: 76.9 %\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: 63.3 %\n",
      "Clips accuracy: 72.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 4200.0 training, 1800.0 testing\n",
      "  77 (+/- 1.2) <- [77 74 77 76 74 76 75 78 76 77]\n",
      "  77 (+/- 0.8) <- [77 77 76 75 77 78 77 77 76 78]\n",
      "  78 (+/- 0.8) <- [78 76 76 77 77 78 77 77 78 76]\n",
      "  77 (+/- 0.9) <- [77 74 77 77 76 78 76 77 76 77]\n",
      "  77 (+/- 0.7) <- [77 77 78 76 76 77 76 76 77 78]\n",
      "  78 (+/- 0.8) <- [77 78 78 79 78 76 76 77 78 77]\n",
      "  78 (+/- 0.8) <- [77 78 78 79 78 76 78 77 78 76]\n",
      "  77 (+/- 0.8) <- [75 78 77 77 77 78 78 76 76 77]\n",
      "  78 (+/- 0.9) <- [76 76 79 78 78 77 79 77 78 77]\n",
      "  78 (+/- 0.9) <- [78 76 77 76 77 76 77 77 78 79]\n",
      "  77 (+/- 1.1) <- [79 77 77 76 78 75 76 76 77 77]\n",
      "  78 (+/- 0.9) <- [77 79 78 77 76 77 77 77 78 77]\n",
      "  77 (+/- 0.8) <- [78 77 77 77 75 75 77 76 76 75]\n",
      "  77 (+/- 1.0) <- [78 76 77 74 77 77 77 77 77 76]\n",
      "  78 (+/- 0.9) <- [77 78 77 77 78 79 77 77 78 76]\n",
      "  77 (+/- 0.5) <- [77 76 77 77 77 76 77 77 77 77]\n",
      "  77 (+/- 0.6) <- [76 78 77 77 78 77 78 76 76 76]\n",
      "  78 (+/- 0.8) <- [76 78 76 77 78 77 78 76 76 78]\n",
      "  77 (+/- 0.8) <- [77 77 76 77 77 76 78 77 75 77]\n",
      "  77 (+/- 1.0) <- [78 76 76 76 76 75 78 78 77 78]\n",
      "Accuracy: 77.4 (+/- 0.94)\n",
      "Mean time (20 cv): 15.51 seconds\n",
      "Overall time: 314.26 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.5 -------------------- \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 0x7f19e8d30c10>"
      ]
     },
     "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: 76.9 %\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: 63.3 %\n",
      "Clips accuracy: 72.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 3000.0 training, 3000.0 testing\n",
      "  76 (+/- 0.9) <- [76 75 75 75 74 75 76 77 74 76]\n",
      "  76 (+/- 0.5) <- [75 75 75 75 75 75 76 75 76 76]\n",
      "  76 (+/- 0.6) <- [77 75 76 75 75 76 76 75 76 76]\n",
      "  76 (+/- 0.9) <- [76 75 76 75 75 78 75 75 75 75]\n",
      "  76 (+/- 0.8) <- [77 75 78 76 76 76 75 75 77 76]\n",
      "  76 (+/- 0.6) <- [76 77 76 76 77 75 75 75 75 76]\n",
      "  76 (+/- 0.9) <- [76 76 76 76 77 75 76 75 77 75]\n",
      "  76 (+/- 0.6) <- [75 76 76 75 75 76 76 76 75 76]\n",
      "  76 (+/- 0.8) <- [74 75 75 76 77 76 76 76 77 76]\n",
      "  76 (+/- 0.7) <- [76 76 75 75 76 77 75 76 77 77]\n",
      "  76 (+/- 1.0) <- [76 77 76 75 76 75 76 75 75 77]\n",
      "  77 (+/- 0.7) <- [75 76 77 76 76 76 76 75 77 76]\n",
      "  76 (+/- 0.6) <- [76 76 75 75 74 75 76 77 75 75]\n",
      "  76 (+/- 0.7) <- [76 76 76 74 76 76 76 75 77 76]\n",
      "  76 (+/- 0.8) <- [75 76 75 74 76 77 75 75 76 76]\n",
      "  76 (+/- 0.4) <- [76 75 76 76 76 75 75 77 75 76]\n",
      "  76 (+/- 0.6) <- [75 76 75 76 76 76 76 75 75 76]\n",
      "  76 (+/- 0.9) <- [74 76 75 76 77 76 77 75 75 77]\n",
      "  76 (+/- 0.6) <- [76 76 75 77 75 75 76 76 75 76]\n",
      "  76 (+/- 0.5) <- [76 77 76 75 75 76 75 76 76 77]\n",
      "Accuracy: 76.2 (+/- 0.74)\n",
      "Mean time (20 cv): 10.86 seconds\n",
      "Overall time: 221.24 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.7 -------------------- \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 0x7f19e9737fd0>"
      ]
     },
     "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: 76.9 %\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: 63.3 %\n",
      "Clips accuracy: 72.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 1800.0 training, 4200.0 testing\n",
      "  74 (+/- 0.7) <- [75 73 73 73 73 74 73 73 73 74]\n",
      "  74 (+/- 0.6) <- [73 73 74 74 72 74 73 73 72 74]\n",
      "  74 (+/- 0.5) <- [74 74 74 72 74 73 74 74 73 73]\n",
      "  74 (+/- 0.6) <- [74 73 74 73 74 74 73 72 73 73]\n",
      "  74 (+/- 0.8) <- [74 73 74 73 73 73 71 74 74 73]\n",
      "  74 (+/- 0.9) <- [73 74 74 73 75 72 74 73 74 74]\n",
      "  74 (+/- 0.4) <- [73 73 73 73 73 74 74 74 74 73]\n",
      "  74 (+/- 0.9) <- [73 73 75 74 72 73 75 73 72 74]\n",
      "  74 (+/- 0.7) <- [72 72 72 74 72 73 74 73 73 74]\n",
      "  74 (+/- 0.3) <- [73 73 73 73 74 73 73 74 73 73]\n",
      "  74 (+/- 0.5) <- [73 74 73 73 74 74 73 73 73 75]\n",
      "  74 (+/- 0.6) <- [74 74 74 73 74 74 74 73 74 73]\n",
      "  73 (+/- 0.8) <- [72 73 73 74 72 74 72 74 72 73]\n",
      "  74 (+/- 0.5) <- [73 73 73 73 73 73 74 73 74 74]\n",
      "  74 (+/- 0.8) <- [72 73 74 74 74 73 74 73 74 74]\n",
      "  74 (+/- 0.6) <- [74 75 73 74 74 74 73 73 74 73]\n",
      "  74 (+/- 0.9) <- [73 74 72 75 73 74 72 74 72 73]\n",
      "  74 (+/- 0.6) <- [74 73 72 73 74 74 74 73 73 73]\n",
      "  74 (+/- 0.5) <- [74 73 73 73 73 74 73 74 74 74]\n",
      "  74 (+/- 0.6) <- [74 74 73 73 73 74 74 73 73 73]\n",
      "Accuracy: 73.9 (+/- 0.69)\n",
      "Mean time (20 cv): 6.49 seconds\n",
      "Overall time: 133.88 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.9 -------------------- \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 0x7f19e8c54b50>"
      ]
     },
     "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: 76.9 %\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: 63.3 %\n",
      "Clips accuracy: 72.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 600.0 training, 5400.0 testing\n",
      "  66 (+/- 0.7) <- [66 67 65 65 66 65 66 66 67 65]\n",
      "  67 (+/- 0.8) <- [65 67 67 66 66 66 65 66 66 67]\n",
      "  66 (+/- 0.8) <- [67 64 66 65 66 66 67 66 65 67]\n",
      "  67 (+/- 0.7) <- [67 68 67 67 67 67 66 66 66 66]\n",
      "  67 (+/- 0.7) <- [66 66 68 67 67 67 66 66 66 67]\n",
      "  66 (+/- 0.7) <- [66 65 66 65 67 66 66 65 67 67]\n",
      "  67 (+/- 0.6) <- [67 66 67 66 66 67 66 67 65 66]\n",
      "  66 (+/- 0.9) <- [64 64 66 66 66 65 67 65 66 65]\n",
      "  67 (+/- 0.5) <- [66 66 66 66 65 67 66 67 66 67]\n",
      "  67 (+/- 1.1) <- [66 66 67 66 67 66 68 66 64 67]\n",
      "  67 (+/- 1.0) <- [68 69 68 67 67 67 66 65 67 67]\n",
      "  67 (+/- 0.8) <- [65 67 67 66 66 68 66 66 67 67]\n",
      "  66 (+/- 1.0) <- [65 66 68 66 67 66 64 66 65 64]\n",
      "  66 (+/- 1.0) <- [66 67 65 66 66 64 66 68 66 65]\n",
      "  67 (+/- 0.8) <- [67 67 66 67 67 66 66 65 66 67]\n",
      "  67 (+/- 1.2) <- [64 66 67 66 68 64 67 68 67 66]\n",
      "  67 (+/- 1.1) <- [67 65 66 68 66 65 64 66 67 66]\n",
      "  66 (+/- 0.6) <- [66 66 66 65 67 66 67 66 65 66]\n",
      "  66 (+/- 0.9) <- [66 66 65 67 66 67 65 65 65 67]\n",
      "  67 (+/- 0.6) <- [67 66 66 65 65 66 66 67 67 66]\n",
      "Accuracy: 66.7 (+/- 0.91)\n",
      "Mean time (20 cv): 2.36 seconds\n",
      "Overall time: 51.12 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 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=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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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e4ea4990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %\n",
      "Clips accuracy: 68.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 5400.0 training, 600.0 testing\n",
      "  69 (+/- 2.2) <- [70 68 71 67 64 67 70 68 72 66]\n",
      "  69 (+/- 1.4) <- [71 68 67 67 72 69 67 70 69 70]\n",
      "  70 (+/- 2.0) <- [71 69 73 68 68 71 71 68 71 66]\n",
      "  69 (+/- 1.4) <- [70 67 66 67 67 70 68 70 70 68]\n",
      "  70 (+/- 2.4) <- [71 69 72 68 70 75 71 68 68 66]\n",
      "  71 (+/- 0.7) <- [71 71 70 70 71 71 70 69 70 70]\n",
      "  70 (+/- 1.4) <- [70 68 72 69 69 67 71 69 70 69]\n",
      "  70 (+/- 1.5) <- [69 70 68 71 65 70 70 69 70 69]\n",
      "  69 (+/- 1.7) <- [68 69 68 69 68 65 70 70 69 72]\n",
      "  69 (+/- 1.8) <- [72 69 66 68 69 66 69 69 70 71]\n",
      "  70 (+/- 1.4) <- [71 70 69 69 69 69 73 70 71 68]\n",
      "  70 (+/- 1.6) <- [71 70 71 68 69 69 73 68 69 71]\n",
      "  69 (+/- 1.3) <- [69 70 66 68 69 70 68 71 68 68]\n",
      "  69 (+/- 2.0) <- [71 66 68 66 67 69 66 69 69 72]\n",
      "  70 (+/- 1.1) <- [69 70 72 68 69 70 69 71 71 69]\n",
      "  70 (+/- 2.0) <- [72 71 69 71 67 68 71 72 69 67]\n",
      "  70 (+/- 1.6) <- [67 71 69 69 68 70 71 71 72 69]\n",
      "  70 (+/- 0.9) <- [70 68 71 70 68 70 69 69 71 69]\n",
      "  70 (+/- 1.2) <- [72 71 69 69 71 69 72 70 68 69]\n",
      "  69 (+/- 1.7) <- [67 70 71 67 67 72 67 70 70 69]\n",
      "Accuracy: 69.7 (+/- 1.71)\n",
      "Mean time (20 cv): 17.17 seconds\n",
      "Overall time: 347.24 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.3 -------------------- \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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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e8f17450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %\n",
      "Clips accuracy: 68.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 4200.0 training, 1800.0 testing\n",
      "  68 (+/- 1.3) <- [69 68 69 66 66 68 68 70 68 68]\n",
      "  69 (+/- 0.7) <- [69 70 67 69 68 69 69 69 68 69]\n",
      "  69 (+/- 0.9) <- [70 69 69 68 69 70 69 68 70 67]\n",
      "  69 (+/- 0.9) <- [70 68 68 68 68 69 68 68 69 70]\n",
      "  70 (+/- 0.4) <- [69 69 70 69 69 69 69 69 69 68]\n",
      "  69 (+/- 1.2) <- [70 71 68 71 70 68 67 67 68 69]\n",
      "  69 (+/- 0.9) <- [69 68 70 68 68 69 71 67 70 68]\n",
      "  69 (+/- 0.8) <- [68 69 67 69 68 70 69 69 69 69]\n",
      "  69 (+/- 0.7) <- [69 67 70 70 68 69 69 68 69 70]\n",
      "  69 (+/- 0.8) <- [70 68 68 68 68 68 69 69 69 69]\n",
      "  69 (+/- 1.3) <- [72 69 69 67 69 68 69 67 68 68]\n",
      "  69 (+/- 0.9) <- [69 69 70 67 67 69 70 69 69 68]\n",
      "  68 (+/- 0.8) <- [68 69 67 68 67 68 67 70 69 67]\n",
      "  69 (+/- 1.2) <- [69 68 68 65 68 69 68 68 69 70]\n",
      "  70 (+/- 0.6) <- [69 70 69 68 69 70 69 69 70 69]\n",
      "  69 (+/- 0.9) <- [69 68 70 69 67 67 69 70 68 68]\n",
      "  70 (+/- 0.8) <- [68 69 69 69 70 70 69 70 70 67]\n",
      "  69 (+/- 0.7) <- [67 68 69 67 68 68 69 69 68 69]\n",
      "  70 (+/- 0.7) <- [70 69 68 69 70 69 69 69 70 70]\n",
      "  69 (+/- 0.8) <- [68 68 69 69 66 68 68 68 69 68]\n",
      "Accuracy: 69.1 (+/- 0.96)\n",
      "Mean time (20 cv): 13.23 seconds\n",
      "Overall time: 268.38 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.5 -------------------- \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"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e9219690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %\n",
      "Clips accuracy: 68.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 3000.0 training, 3000.0 testing\n",
      "  68 (+/- 0.7) <- [68 67 68 66 66 67 67 68 68 68]\n",
      "  68 (+/- 0.7) <- [67 68 69 68 66 68 68 67 67 68]\n",
      "  68 (+/- 0.6) <- [68 68 68 67 67 68 69 67 68 67]\n",
      "  68 (+/- 0.7) <- [68 68 67 67 68 68 67 66 68 67]\n",
      "  68 (+/- 0.6) <- [68 67 69 68 69 69 68 68 68 67]\n",
      "  68 (+/- 0.7) <- [68 68 67 68 70 68 68 67 67 68]\n",
      "  68 (+/- 0.7) <- [69 67 68 67 67 67 67 67 66 67]\n",
      "  68 (+/- 0.6) <- [67 68 67 67 69 68 67 67 67 66]\n",
      "  68 (+/- 0.8) <- [66 67 67 68 69 68 67 67 68 68]\n",
      "  68 (+/- 0.7) <- [68 66 67 66 67 67 67 67 68 68]\n",
      "  68 (+/- 0.7) <- [68 69 67 67 68 67 68 67 67 69]\n",
      "  68 (+/- 0.5) <- [68 67 69 67 68 68 67 68 69 68]\n",
      "  68 (+/- 0.6) <- [68 67 66 67 67 68 67 68 67 67]\n",
      "  68 (+/- 0.7) <- [68 67 68 67 67 68 68 67 69 68]\n",
      "  68 (+/- 0.7) <- [68 67 66 66 67 68 68 67 68 68]\n",
      "  68 (+/- 0.5) <- [67 67 68 67 67 66 68 67 67 68]\n",
      "  68 (+/- 0.8) <- [68 68 68 67 68 69 67 68 68 67]\n",
      "  68 (+/- 0.6) <- [67 67 66 67 67 68 68 66 68 68]\n",
      "  68 (+/- 0.5) <- [68 69 68 68 67 67 68 67 68 68]\n",
      "  68 (+/- 0.4) <- [68 68 68 68 67 68 68 67 67 68]\n",
      "Accuracy: 68.1 (+/- 0.70)\n",
      "Mean time (20 cv): 9.52 seconds\n",
      "Overall time: 194.26 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.7 -------------------- \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 0x7f19e9105710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %\n",
      "Clips accuracy: 68.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 1800.0 training, 4200.0 testing\n",
      "  66 (+/- 0.6) <- [66 66 65 65 65 66 66 67 66 66]\n",
      "  66 (+/- 0.4) <- [65 66 66 66 65 65 66 66 65 66]\n",
      "  66 (+/- 0.7) <- [66 67 65 65 66 66 67 65 66 65]\n",
      "  67 (+/- 0.2) <- [66 66 66 66 66 66 67 66 66 66]\n",
      "  67 (+/- 0.6) <- [67 66 66 66 66 66 65 66 66 66]\n",
      "  66 (+/- 0.6) <- [66 66 66 67 67 66 66 66 64 66]\n",
      "  66 (+/- 0.5) <- [66 65 66 67 65 66 66 66 65 66]\n",
      "  66 (+/- 0.5) <- [65 66 66 66 66 66 66 65 65 65]\n",
      "  66 (+/- 0.7) <- [65 65 65 66 65 66 65 67 66 67]\n",
      "  66 (+/- 0.7) <- [66 66 64 65 66 65 65 66 65 65]\n",
      "  66 (+/- 0.6) <- [65 66 65 65 67 65 66 66 66 66]\n",
      "  67 (+/- 0.5) <- [66 66 67 65 67 66 66 67 66 66]\n",
      "  66 (+/- 0.7) <- [66 65 66 66 65 66 64 66 66 65]\n",
      "  66 (+/- 0.5) <- [66 66 67 66 65 65 66 66 67 66]\n",
      "  66 (+/- 0.5) <- [66 65 67 65 66 66 66 66 66 66]\n",
      "  66 (+/- 0.5) <- [66 66 66 66 67 65 66 66 66 65]\n",
      "  66 (+/- 0.6) <- [66 66 65 66 66 65 65 66 65 65]\n",
      "  66 (+/- 0.5) <- [66 66 66 66 66 67 66 65 67 66]\n",
      "  66 (+/- 0.5) <- [65 65 66 67 65 65 66 65 66 66]\n",
      "  66 (+/- 0.5) <- [65 65 66 66 65 66 66 67 66 66]\n",
      "Accuracy: 66.3 (+/- 0.59)\n",
      "Mean time (20 cv): 5.86 seconds\n",
      "Overall time: 120.95 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.9 -------------------- \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 0x7f19e4ec98d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 96), float64\n",
      "Testing data: (2400, 96), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 68.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1152), float64\n",
      "Testing data: (200, 1152), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 62.1 %\n",
      "Clips accuracy: 68.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 96), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 600.0 training, 5400.0 testing\n",
      "  61 (+/- 1.2) <- [60 61 60 58 62 61 62 60 61 62]\n",
      "  61 (+/- 0.8) <- [59 60 59 60 61 61 61 61 59 60]\n",
      "  61 (+/- 0.6) <- [61 59 60 60 61 60 61 60 60 61]\n",
      "  61 (+/- 0.8) <- [62 61 60 61 60 62 61 60 62 60]\n",
      "  61 (+/- 1.0) <- [60 60 61 62 62 60 59 63 60 60]\n",
      "  61 (+/- 0.7) <- [60 61 59 59 61 61 60 61 61 61]\n",
      "  61 (+/- 1.0) <- [61 61 59 60 60 60 61 61 59 62]\n",
      "  61 (+/- 1.0) <- [60 62 60 61 59 59 62 62 62 60]\n",
      "  61 (+/- 0.9) <- [61 58 60 60 60 62 61 60 60 59]\n",
      "  61 (+/- 1.0) <- [61 62 60 62 62 61 61 60 59 59]\n",
      "  61 (+/- 0.7) <- [61 61 61 60 59 59 61 60 61 61]\n",
      "  61 (+/- 0.7) <- [60 61 61 60 60 60 60 60 61 62]\n",
      "  61 (+/- 0.6) <- [61 60 60 60 61 61 60 60 61 59]\n",
      "  61 (+/- 0.8) <- [60 61 61 60 60 59 61 61 60 60]\n",
      "  61 (+/- 0.9) <- [62 60 61 60 61 59 60 60 61 61]\n",
      "  62 (+/- 0.6) <- [61 61 60 62 61 62 62 61 60 62]\n",
      "  61 (+/- 0.6) <- [61 60 61 61 62 60 60 61 61 60]\n",
      "  61 (+/- 0.6) <- [60 60 61 59 59 60 61 60 61 60]\n",
      "  61 (+/- 0.7) <- [60 60 60 61 60 62 59 60 60 61]\n",
      "  61 (+/- 0.6) <- [60 60 61 60 61 61 61 61 62 61]\n",
      "Accuracy: 61.0 (+/- 0.86)\n",
      "Mean time (20 cv): 2.10 seconds\n",
      "Overall time: 45.69 seconds\n"
     ]
    }
   ],
   "source": [
    "#%run gtzan.ipynb\n",
    "#%run audio_preprocessing.ipynb\n",
    "if not regen_graph:\n",
    "    separator('Graph')\n",
    "    %run audio_graph.ipynb\n",
    "if not regen_features:\n",
    "    separator('Features')\n",
    "    %run audio_features.ipynb\n",
    "\n",
    "# Hyper-parameter under test.\n",
    "for p[Pname] in Pvalues:\n",
    "\n",
    "    if regen_graph:\n",
    "        separator('Graph', True)\n",
    "        %run audio_graph.ipynb\n",
    "    if regen_features:\n",
    "        separator('Features', True)\n",
    "        p['filename_features'] = 'features_{}_{}.hdf5'.format(Pname, p[Pname])\n",
    "        %run audio_features.ipynb\n",
    "    separator('Classification', True)\n",
    "    %run audio_classification.ipynb\n",
    "    \n",
    "    # Collect results.\n",
    "    for key in res:\n",
    "        res[key].append(globals()[key])\n",
    "\n",
    "# Baseline, i.e. classification with spectrograms.\n",
    "p['dataset_classification'] = 'X'\n",
    "p['scale'] = 'minmax'  # Todo: should be done in pre-processing.\n",
    "if regen_baseline:\n",
    "    res['baseline'] = []\n",
    "    res['baseline_std'] = []\n",
    "    for p[Pname] in Pvalues:\n",
    "        separator('Baseline', True)\n",
    "        %run audio_classification.ipynb\n",
    "        res['baseline'].append(accuracy)\n",
    "        res['baseline_std'].append(accuracy_std)\n",
    "else:\n",
    "    separator('Baseline')\n",
    "    %run audio_classification.ipynb\n",
    "    res['baseline'] = len(Pvalues) * [accuracy]\n",
    "    res['baseline_std'] = accuracy_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_size = [0.1, 0.3, 0.5, 0.7, 0.9]\n",
      "res['accuracy_std'] = [1.5715966315254757, 0.9443928090459911, 0.74437736845411029, 0.68784711913117158, 0.91044216164357028]\n",
      "res['objective_j'] = [8681.121826171875, 8681.121826171875, 8681.121826171875, 8681.121826171875, 8681.121826171875]\n",
      "res['objective_i'] = [56601.39453125, 56601.39453125, 56601.39453125, 56601.39453125, 56601.39453125]\n",
      "res['objective_h'] = [0, 0, 0, 0, 0]\n",
      "res['objective_g'] = [71921.5966796875, 71921.5966796875, 71921.5966796875, 71921.5966796875, 71921.5966796875]\n",
      "res['baseline'] = [69.729166666666742, 69.101388888888863, 68.060166666666689, 66.250476190476164, 60.973703703703706]\n",
      "res['time_features'] = [2816.1098749637604, 2816.1098749637604, 2816.1098749637604, 2816.1098749637604, 2816.1098749637604]\n",
      "res['baseline_std'] = [1.7102986662503645, 0.95690211162614125, 0.69960065990058462, 0.58584473963407446, 0.85954329596629586]\n",
      "res['sparsity'] = [19.892895343959733, 19.892895343959733, 19.892895343959733, 19.892895343959733, 19.892895343959733]\n",
      "res['iterations_inner'] = [1314, 1314, 1314, 1314, 1314]\n",
      "res['iterations_outer'] = [7, 7, 7, 7, 7]\n",
      "res['accuracy'] = [77.90749999999997, 77.363333333333344, 76.187000000000054, 73.853214285714259, 66.678055555555545]\n"
     ]
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e8efee90>"
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment time: 5122 seconds\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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