{
 "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. With graph.\n",
    "\n",
    "Conclusion: The trend observed in `13g_ratio` continues.\n",
    "\n",
    "Observations:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyper-parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter under test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Pname = 'test_size'\n",
    "Pvalues = [.90, .91, .92, .93, .94, .95, .96, .97, .98, .99]\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'] = 'cosine_sim'\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: 157.17 seconds\n",
      "All self-referenced in the first column: False\n",
      "dist in [0.644446253777, 1.0]\n",
      "w in [0.644446253777, 1.0]\n",
      "Ones on the diagonal: 148961 (over 149000)\n",
      "assert: False\n",
      "W in [0.0, 1.0]\n",
      "Datasets:\n",
      "  L_data    : (2392274,), float32\n",
      "  L_indices : (2392274,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2392274,), float32\n",
      "  W_indices : (2392274,), int32\n",
      "  W_indptr  : (149001,) , int32\n",
      "  W_shape   : (2,)      , int64\n",
      "Attributes:\n",
      "  K = 11\n",
      "  dm = cosine_sim\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Overall time: 166.15 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 = cosine_sim\n",
      "  Csigma = 1\n",
      "  diag = True\n",
      "  laplacian = normalized\n",
      "Datasets:\n",
      "  L_data    : (2392274,), float32\n",
      "  L_indices : (2392274,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2392274,), float32\n",
      "  W_indices : (2392274,), 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: 1915 seconds\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0687f64950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 901 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 7.605574e+04\n",
      "rdiff: 0.00149753958214\n",
      "i(Z) = ||Z||_1 = 5.637479e+04\n",
      "j(Z) = tr(Z^TLZ) = 9.808221e+03\n"
     ]
    },
    {
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       "REREREREREREREREREREREREREREREREREREpN/9fzVp2vQ/cLh+AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f067c095550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.422388e+05\n"
     ]
    },
    {
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       "w/J+v43L5xAK3RMJXQrdj02N+b5+h/8lNNc/RGiin9nHcXyNaU+ooFwfs11H+P0fBr5FKJjnxJ/v\n",
       "rnGc7v6XSoH/GuGV3r8h/O5vEQYxApwe894Xl5ePeQuwP5XBh8fG480B/gYc2cfvV+t3vYDwt3Qu\n",
       "cDVhsOWbfSzv7XoAbEn1v7WIiIjUsSqhsG9Xh9P7cx3OJFRaRMyoxUBE2s0rhDEXH7YOMkC1WnAg\n",
       "dCPsTLi1U0RERERERERERERERERERERERERERERERERERESkXf0fJAqUydqZ/FgAAAAASUVORK5C\n",
       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0645e2b150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 6 iterations\n",
      "\n",
      "Z in [-0.0576239787042, 0.727681100368]\n",
      "Sparsity of Z: 3,814,985 non-zero entries out of 19,072,000 entries, i.e. 20.0%.\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f06c993b490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.0280325673521, 0.897612154484]\n",
      "d in [0.999999642372, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0645e7d050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0645cd1c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "RK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0645ed79d0>"
      ]
     },
     "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: 1923 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 0x7f0639670050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 75.7 %\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: 63.1 %\n",
      "Clips accuracy: 72.5 %\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",
      "  65 (+/- 0.9) <- [64 65 64 63 66 65 65 65 65 65]\n",
      "  66 (+/- 0.9) <- [63 66 66 65 66 65 66 66 66 64]\n",
      "  66 (+/- 0.5) <- [65 65 65 64 66 66 66 64 64 65]\n",
      "  66 (+/- 0.7) <- [66 66 65 66 66 65 64 65 65 65]\n",
      "  66 (+/- 1.2) <- [65 64 66 67 67 66 65 65 64 63]\n",
      "  66 (+/- 0.9) <- [66 65 65 66 67 64 66 64 66 64]\n",
      "  66 (+/- 0.9) <- [65 64 66 66 66 66 65 66 63 66]\n",
      "  66 (+/- 1.0) <- [64 66 65 64 64 66 66 65 67 66]\n",
      "  66 (+/- 0.6) <- [65 65 66 65 65 65 66 66 67 66]\n",
      "  66 (+/- 0.7) <- [65 65 66 65 66 66 67 66 64 66]\n",
      "  66 (+/- 1.1) <- [67 67 66 65 64 65 64 65 66 66]\n",
      "  66 (+/- 0.6) <- [66 66 65 65 66 66 64 66 66 65]\n",
      "  65 (+/- 0.6) <- [64 64 66 65 66 65 64 65 65 64]\n",
      "  66 (+/- 0.5) <- [65 66 64 66 65 65 65 65 66 65]\n",
      "  66 (+/- 0.7) <- [65 64 65 66 66 66 66 65 65 67]\n",
      "  66 (+/- 1.1) <- [62 65 66 65 66 64 66 66 66 66]\n",
      "  66 (+/- 0.7) <- [65 65 65 66 66 65 64 65 66 66]\n",
      "  65 (+/- 0.9) <- [64 65 65 63 65 66 66 64 65 66]\n",
      "  66 (+/- 0.7) <- [64 65 66 66 66 66 64 67 65 65]\n",
      "  66 (+/- 1.0) <- [64 65 65 65 63 66 67 67 66 65]\n",
      "Accuracy: 65.8 (+/- 0.88)\n",
      "Mean time (20 cv): 2.41 seconds\n",
      "Overall time: 52.19 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.91 -------------------- \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 0x7f06390dca50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 75.7 %\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: 63.1 %\n",
      "Clips accuracy: 72.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 540.0 training, 5460.0 testing\n",
      "  64 (+/- 0.9) <- [64 65 63 62 65 64 64 65 65 64]\n",
      "  65 (+/- 0.8) <- [63 65 65 64 65 65 65 64 65 63]\n",
      "  65 (+/- 0.6) <- [64 64 65 64 66 65 65 64 64 64]\n",
      "  65 (+/- 0.7) <- [66 65 65 65 65 65 64 64 63 64]\n",
      "  65 (+/- 0.9) <- [64 63 64 66 64 65 63 65 63 64]\n",
      "  65 (+/- 1.0) <- [65 63 64 65 66 63 66 64 65 64]\n",
      "  65 (+/- 0.9) <- [65 64 64 65 64 65 64 65 62 65]\n",
      "  65 (+/- 1.0) <- [64 65 64 62 64 65 65 64 66 65]\n",
      "  65 (+/- 0.7) <- [66 65 66 64 64 64 65 66 65 64]\n",
      "  65 (+/- 0.9) <- [64 64 65 65 64 65 66 66 63 66]\n",
      "  65 (+/- 1.2) <- [67 66 65 64 64 64 63 63 65 64]\n",
      "  65 (+/- 0.6) <- [65 64 65 63 66 65 64 65 65 64]\n",
      "  64 (+/- 0.9) <- [63 65 64 63 65 63 62 64 63 63]\n",
      "  65 (+/- 0.6) <- [63 65 64 65 65 65 65 65 64 64]\n",
      "  65 (+/- 1.0) <- [64 64 63 65 65 64 65 65 65 67]\n",
      "  65 (+/- 1.4) <- [61 65 65 64 65 64 64 65 66 65]\n",
      "  65 (+/- 0.9) <- [64 64 65 65 66 64 63 66 64 64]\n",
      "  65 (+/- 0.8) <- [64 65 64 64 63 64 66 63 64 66]\n",
      "  65 (+/- 0.8) <- [63 65 64 65 65 65 63 65 64 64]\n",
      "  65 (+/- 0.7) <- [64 64 65 64 63 65 65 65 64 65]\n",
      "Accuracy: 64.9 (+/- 0.92)\n",
      "Mean time (20 cv): 2.19 seconds\n",
      "Overall time: 47.92 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.92 -------------------- \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 0x7f0638cc87d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 75.7 %\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: 63.1 %\n",
      "Clips accuracy: 72.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 480.0 training, 5520.0 testing\n",
      "  64 (+/- 0.8) <- [63 65 63 62 65 63 62 64 64 63]\n",
      "  64 (+/- 0.9) <- [62 65 65 63 62 63 64 63 65 63]\n",
      "  64 (+/- 0.9) <- [65 63 63 63 65 64 64 64 62 63]\n",
      "  64 (+/- 0.7) <- [65 64 63 64 63 62 63 63 63 63]\n",
      "  64 (+/- 0.9) <- [62 62 64 64 64 65 62 65 63 64]\n",
      "  64 (+/- 0.9) <- [64 62 64 64 64 63 65 64 65 64]\n",
      "  64 (+/- 0.7) <- [63 63 64 64 64 64 64 64 62 64]\n",
      "  64 (+/- 1.1) <- [65 64 63 61 63 65 64 64 64 63]\n",
      "  65 (+/- 0.7) <- [65 64 65 64 63 63 64 64 64 64]\n",
      "  64 (+/- 1.1) <- [64 63 63 64 63 63 66 64 62 65]\n",
      "  64 (+/- 1.0) <- [65 64 64 63 62 64 62 62 63 63]\n",
      "  64 (+/- 0.8) <- [62 64 64 63 65 64 63 64 64 64]\n",
      "  64 (+/- 1.0) <- [62 65 64 63 65 62 63 63 62 63]\n",
      "  64 (+/- 0.7) <- [63 64 63 64 64 64 64 65 63 63]\n",
      "  64 (+/- 0.9) <- [64 64 62 64 64 64 63 64 64 66]\n",
      "  64 (+/- 1.4) <- [60 65 64 64 64 62 63 64 66 64]\n",
      "  64 (+/- 0.7) <- [63 63 64 64 65 62 63 64 63 64]\n",
      "  64 (+/- 0.7) <- [62 63 63 63 63 64 64 62 63 64]\n",
      "  64 (+/- 1.2) <- [62 64 64 63 63 65 61 65 63 63]\n",
      "  64 (+/- 1.3) <- [62 64 64 62 62 66 65 64 64 65]\n",
      "Accuracy: 64.0 (+/- 0.97)\n",
      "Mean time (20 cv): 1.98 seconds\n",
      "Overall time: 43.64 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.93 -------------------- \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 0x7f06395c5850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 75.7 %\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: 63.1 %\n",
      "Clips accuracy: 72.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 420.0 training, 5580.0 testing\n",
      "  63 (+/- 1.2) <- [62 63 62 60 64 63 61 63 63 61]\n",
      "  63 (+/- 1.1) <- [61 64 64 61 62 62 61 63 63 62]\n",
      "  63 (+/- 1.4) <- [65 60 63 62 64 62 63 62 61 62]\n",
      "  63 (+/- 0.8) <- [63 64 62 64 63 61 63 62 62 63]\n",
      "  63 (+/- 1.2) <- [61 62 64 65 62 64 61 63 64 63]\n",
      "  63 (+/- 0.8) <- [62 61 64 62 63 62 63 61 63 63]\n",
      "  63 (+/- 0.8) <- [62 62 63 63 63 64 63 63 61 64]\n",
      "  63 (+/- 1.1) <- [63 63 60 61 62 64 64 63 63 62]\n",
      "  63 (+/- 1.0) <- [65 64 63 62 63 62 62 61 63 62]\n",
      "  63 (+/- 0.8) <- [62 63 63 63 62 62 63 63 60 63]\n",
      "  63 (+/- 0.8) <- [64 62 63 62 61 63 61 61 62 62]\n",
      "  63 (+/- 0.9) <- [61 63 62 61 64 62 62 63 64 63]\n",
      "  62 (+/- 1.4) <- [62 64 63 61 64 60 62 61 60 62]\n",
      "  63 (+/- 0.9) <- [61 63 62 63 64 62 63 64 61 62]\n",
      "  63 (+/- 1.0) <- [63 63 62 62 61 63 62 64 63 65]\n",
      "  63 (+/- 1.3) <- [60 64 61 62 62 61 62 62 65 62]\n",
      "  63 (+/- 0.6) <- [62 61 62 63 62 61 62 62 62 63]\n",
      "  63 (+/- 0.8) <- [63 63 61 61 62 63 63 62 62 63]\n",
      "  63 (+/- 1.3) <- [62 62 63 62 63 64 59 64 62 63]\n",
      "  63 (+/- 1.0) <- [62 64 64 61 61 64 63 62 62 63]\n",
      "Accuracy: 62.9 (+/- 1.07)\n",
      "Mean time (20 cv): 1.76 seconds\n",
      "Overall time: 39.31 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.94 -------------------- \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 0x7f0638f422d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 75.7 %\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: 63.1 %\n",
      "Clips accuracy: 72.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 360.0 training, 5640.0 testing\n",
      "  61 (+/- 0.8) <- [61 61 60 59 60 62 60 62 62 61]\n",
      "  62 (+/- 1.2) <- [60 62 64 61 62 61 60 63 60 61]\n",
      "  61 (+/- 1.4) <- [62 59 60 61 61 61 62 63 60 62]\n",
      "  62 (+/- 1.1) <- [62 63 62 62 60 61 60 60 62 61]\n",
      "  62 (+/- 1.0) <- [60 61 62 63 60 63 61 60 62 61]\n",
      "  62 (+/- 1.0) <- [61 59 63 62 62 61 61 60 63 62]\n",
      "  62 (+/- 1.1) <- [60 61 62 62 62 63 62 63 60 62]\n",
      "  62 (+/- 1.2) <- [61 60 60 61 61 62 64 61 62 61]\n",
      "  62 (+/- 1.3) <- [64 62 63 60 61 61 61 61 64 62]\n",
      "  62 (+/- 0.6) <- [61 61 61 61 60 61 62 62 60 61]\n",
      "  61 (+/- 1.2) <- [62 60 62 61 58 63 61 61 61 60]\n",
      "  62 (+/- 1.1) <- [60 62 62 61 63 61 60 62 63 63]\n",
      "  61 (+/- 1.2) <- [61 61 62 59 63 59 60 60 60 60]\n",
      "  62 (+/- 1.0) <- [62 62 61 63 61 60 64 62 61 61]\n",
      "  62 (+/- 1.0) <- [62 62 60 62 61 61 60 62 62 63]\n",
      "  62 (+/- 1.5) <- [58 63 60 61 62 60 62 62 63 61]\n",
      "  61 (+/- 0.7) <- [61 59 61 60 61 60 61 61 61 62]\n",
      "  61 (+/- 0.7) <- [60 61 60 60 62 60 61 60 61 62]\n",
      "  61 (+/- 1.9) <- [60 63 60 58 63 63 58 62 62 61]\n",
      "  62 (+/- 0.9) <- [61 62 62 60 61 63 62 61 60 62]\n",
      "Accuracy: 61.7 (+/- 1.18)\n",
      "Mean time (20 cv): 1.54 seconds\n",
      "Overall time: 34.94 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.95 -------------------- \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 0x7f0639231fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 75.7 %\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: 63.1 %\n",
      "Clips accuracy: 72.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 300.0 training, 5700.0 testing\n",
      "  60 (+/- 1.4) <- [58 61 60 57 61 60 60 60 62 60]\n",
      "  60 (+/- 1.6) <- [61 62 63 59 59 58 59 61 57 59]\n",
      "  60 (+/- 1.6) <- [62 57 57 59 60 59 60 61 59 62]\n",
      "  60 (+/- 1.2) <- [59 61 61 62 58 60 59 58 61 60]\n",
      "  61 (+/- 1.1) <- [59 60 59 60 60 63 61 60 60 60]\n",
      "  61 (+/- 1.1) <- [60 58 61 62 61 59 60 60 61 62]\n",
      "  61 (+/- 1.0) <- [60 60 60 61 60 62 59 61 59 61]\n",
      "  61 (+/- 1.2) <- [61 60 59 59 58 61 62 60 62 59]\n",
      "  61 (+/- 1.0) <- [62 59 60 59 60 59 61 59 61 60]\n",
      "  60 (+/- 0.8) <- [59 61 60 59 61 60 61 60 59 59]\n",
      "  60 (+/- 1.0) <- [60 58 60 60 58 61 59 59 58 58]\n",
      "  61 (+/- 1.4) <- [58 61 61 59 62 60 57 61 62 61]\n",
      "  60 (+/- 1.3) <- [59 60 61 58 61 56 59 59 60 60]\n",
      "  60 (+/- 1.4) <- [59 60 60 61 59 58 61 61 57 57]\n",
      "  61 (+/- 1.0) <- [60 61 59 60 62 60 59 61 60 62]\n",
      "  60 (+/- 1.5) <- [56 61 58 60 60 59 60 61 61 59]\n",
      "  60 (+/- 1.1) <- [59 58 58 61 58 59 60 60 61 59]\n",
      "  60 (+/- 0.9) <- [59 58 60 59 61 59 61 58 59 61]\n",
      "  60 (+/- 1.5) <- [58 61 58 58 62 60 56 60 59 59]\n",
      "  60 (+/- 1.1) <- [60 59 60 58 60 62 60 58 60 60]\n",
      "Accuracy: 60.3 (+/- 1.30)\n",
      "Mean time (20 cv): 1.32 seconds\n",
      "Overall time: 30.46 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.96 -------------------- \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 0x7f0638a87690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 75.7 %\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: 63.1 %\n",
      "Clips accuracy: 72.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 240.0 training, 5760.0 testing\n",
      "  59 (+/- 1.2) <- [57 58 59 56 57 58 60 59 60 57]\n",
      "  58 (+/- 1.3) <- [57 58 61 58 59 55 57 58 57 57]\n",
      "  58 (+/- 1.3) <- [58 57 55 58 58 57 60 58 57 59]\n",
      "  59 (+/- 1.1) <- [58 58 59 58 58 58 60 57 60 56]\n",
      "  59 (+/- 1.5) <- [58 58 57 60 58 61 58 57 56 59]\n",
      "  59 (+/- 1.4) <- [58 55 58 58 58 57 59 60 58 60]\n",
      "  59 (+/- 1.3) <- [58 57 57 59 57 61 56 59 59 60]\n",
      "  59 (+/- 0.9) <- [60 58 58 57 59 59 60 58 59 58]\n",
      "  58 (+/- 1.0) <- [59 58 57 59 59 58 59 56 57 57]\n",
      "  59 (+/- 1.2) <- [57 56 59 59 58 59 60 59 57 59]\n",
      "  58 (+/- 1.3) <- [57 56 59 56 54 59 58 58 57 59]\n",
      "  59 (+/- 1.4) <- [57 61 58 58 60 59 56 59 60 60]\n",
      "  58 (+/- 1.4) <- [55 58 60 56 59 55 58 57 58 57]\n",
      "  58 (+/- 1.9) <- [58 59 58 61 57 58 59 58 54 54]\n",
      "  59 (+/- 1.1) <- [57 59 60 58 58 58 57 59 58 61]\n",
      "  58 (+/- 1.6) <- [54 59 57 60 59 56 59 59 57 58]\n",
      "  58 (+/- 1.3) <- [58 57 57 60 58 56 56 59 59 58]\n",
      "  58 (+/- 1.0) <- [55 57 58 59 59 58 59 57 57 58]\n",
      "  58 (+/- 1.4) <- [57 59 55 58 60 59 56 58 58 58]\n",
      "  59 (+/- 1.4) <- [57 58 59 55 58 60 60 58 58 58]\n",
      "Accuracy: 58.5 (+/- 1.37)\n",
      "Mean time (20 cv): 1.09 seconds\n",
      "Overall time: 25.99 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.97 -------------------- \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 0x7f063912a790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 75.7 %\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: 63.1 %\n",
      "Clips accuracy: 72.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 180.0 training, 5820.0 testing\n",
      "  56 (+/- 1.6) <- [54 53 55 54 55 56 57 58 56 53]\n",
      "  56 (+/- 1.3) <- [55 56 58 58 55 54 55 55 54 56]\n",
      "  56 (+/- 1.8) <- [58 54 51 57 55 56 57 54 56 57]\n",
      "  56 (+/- 1.3) <- [56 58 56 58 56 55 55 55 56 53]\n",
      "  56 (+/- 0.9) <- [55 56 55 55 55 57 56 56 54 54]\n",
      "  56 (+/- 1.3) <- [55 53 55 56 56 54 56 57 54 58]\n",
      "  57 (+/- 1.2) <- [56 55 56 56 56 58 55 56 56 59]\n",
      "  57 (+/- 1.4) <- [55 56 56 56 54 55 59 56 58 57]\n",
      "  57 (+/- 1.2) <- [58 55 55 57 56 56 58 55 56 54]\n",
      "  56 (+/- 1.1) <- [56 54 55 56 55 58 56 56 56 57]\n",
      "  55 (+/- 1.6) <- [56 54 54 52 51 55 56 55 54 57]\n",
      "  57 (+/- 1.3) <- [55 59 57 56 57 57 54 57 56 57]\n",
      "  56 (+/- 1.9) <- [56 56 57 54 57 51 56 54 56 58]\n",
      "  56 (+/- 1.0) <- [56 56 54 57 54 55 57 57 55 55]\n",
      "  56 (+/- 1.9) <- [55 57 56 56 56 53 54 55 58 59]\n",
      "  56 (+/- 1.5) <- [52 55 56 57 57 55 55 55 57 56]\n",
      "  56 (+/- 1.4) <- [56 52 54 55 56 55 56 57 57 55]\n",
      "  56 (+/- 1.5) <- [53 55 57 54 55 58 58 56 55 56]\n",
      "  55 (+/- 1.0) <- [55 56 53 56 56 55 54 56 54 55]\n",
      "  57 (+/- 1.2) <- [55 56 55 54 59 57 57 55 56 57]\n",
      "Accuracy: 56.1 (+/- 1.49)\n",
      "Mean time (20 cv): 0.86 seconds\n",
      "Overall time: 21.36 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.98 -------------------- \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 0x7f0638d23b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 75.7 %\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: 63.1 %\n",
      "Clips accuracy: 72.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 120.0 training, 5880.0 testing\n",
      "  52 (+/- 1.1) <- [51 52 51 50 52 53 53 51 53 52]\n",
      "  53 (+/- 1.5) <- [54 55 53 52 54 55 52 51 50 52]\n",
      "  52 (+/- 1.6) <- [53 51 48 54 51 52 51 52 52 53]\n",
      "  53 (+/- 1.2) <- [53 55 52 54 54 53 52 53 54 51]\n",
      "  52 (+/- 0.9) <- [50 52 52 52 52 53 51 51 50 52]\n",
      "  53 (+/- 1.5) <- [51 51 53 52 54 52 51 53 50 55]\n",
      "  53 (+/- 1.2) <- [52 51 55 51 51 54 53 52 53 53]\n",
      "  53 (+/- 2.3) <- [52 51 53 52 51 47 56 53 54 55]\n",
      "  53 (+/- 1.2) <- [53 51 54 53 53 52 54 51 53 51]\n",
      "  53 (+/- 1.4) <- [50 51 52 52 52 55 52 54 54 54]\n",
      "  52 (+/- 0.8) <- [51 51 53 52 51 53 52 52 53 53]\n",
      "  53 (+/- 2.0) <- [52 56 56 50 55 54 52 52 52 50]\n",
      "  53 (+/- 1.7) <- [55 54 52 52 53 49 54 50 52 54]\n",
      "  53 (+/- 1.0) <- [55 53 52 54 52 54 53 52 53 52]\n",
      "  53 (+/- 2.1) <- [53 52 54 52 52 47 50 52 55 55]\n",
      "  53 (+/- 1.6) <- [51 54 53 55 55 53 50 54 51 53]\n",
      "  53 (+/- 1.7) <- [52 48 52 53 50 52 53 54 54 52]\n",
      "  53 (+/- 2.2) <- [51 53 54 52 53 54 54 54 47 51]\n",
      "  53 (+/- 1.8) <- [53 53 52 55 52 52 51 55 51 48]\n",
      "  53 (+/- 1.5) <- [53 54 52 52 55 54 54 49 53 54]\n",
      "Accuracy: 52.9 (+/- 1.63)\n",
      "Mean time (20 cv): 0.63 seconds\n",
      "Overall time: 16.66 seconds\n",
      "\n",
      " -------------------- Classification, test_size = 0.99 -------------------- \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 0x7f063907b250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (3600, 128), float64\n",
      "Testing data: (2400, 128), float64\n",
      "Training labels: (3600,), uint8\n",
      "Testing labels: (2400,), uint8\n",
      "Accuracy: 75.7 %\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: 63.1 %\n",
      "Clips accuracy: 72.5 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 60.0 training, 5940.0 testing\n",
      "  47 (+/- 2.2) <- [47 50 44 43 47 49 45 45 49 49]\n",
      "  48 (+/- 1.9) <- [48 51 49 44 47 49 49 47 46 47]\n",
      "  48 (+/- 2.6) <- [45 45 49 51 49 45 51 48 44 50]\n",
      "  49 (+/- 1.3) <- [47 49 47 47 50 49 48 46 47 49]\n",
      "  47 (+/- 2.5) <- [42 48 46 46 47 48 45 48 44 51]\n",
      "  47 (+/- 2.0) <- [45 47 48 47 49 48 44 44 46 51]\n",
      "  48 (+/- 2.1) <- [46 48 52 48 44 49 46 46 46 48]\n",
      "  48 (+/- 1.3) <- [47 46 47 48 48 48 50 47 50 49]\n",
      "  47 (+/- 2.3) <- [49 42 48 46 46 46 51 45 47 47]\n",
      "  49 (+/- 1.9) <- [48 44 48 49 48 48 48 48 50 52]\n",
      "  47 (+/- 1.4) <- [47 49 49 46 45 45 49 48 46 46]\n",
      "  48 (+/- 1.2) <- [46 50 48 46 49 47 48 46 48 46]\n",
      "  48 (+/- 1.8) <- [49 49 51 47 47 46 48 48 44 48]\n",
      "  49 (+/- 1.6) <- [50 48 46 45 48 50 49 50 47 48]\n",
      "  48 (+/- 2.7) <- [48 49 51 48 44 41 49 48 49 50]\n",
      "  48 (+/- 1.1) <- [48 47 47 48 49 48 47 48 46 50]\n",
      "  47 (+/- 2.2) <- [50 43 45 48 44 48 47 48 48 47]\n",
      "  48 (+/- 1.5) <- [46 48 49 47 47 49 47 48 47 51]\n",
      "  48 (+/- 1.6) <- [51 48 47 46 47 49 46 47 46 48]\n",
      "  48 (+/- 2.4) <- [48 47 52 45 44 48 47 45 51 49]\n",
      "Accuracy: 48.0 (+/- 1.99)\n",
      "Mean time (20 cv): 0.38 seconds\n",
      "Overall time: 11.62 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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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0638ca6390>"
      ]
     },
     "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): 2.08 seconds\n",
      "Overall time: 45.36 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.91 -------------------- \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 0x7f0639007550>"
      ]
     },
     "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: 540.0 training, 5460.0 testing\n",
      "  60 (+/- 1.1) <- [58 61 59 58 61 59 62 60 60 61]\n",
      "  60 (+/- 0.9) <- [58 60 59 60 61 61 59 60 59 59]\n",
      "  60 (+/- 0.7) <- [60 59 59 59 61 60 60 59 59 61]\n",
      "  61 (+/- 0.9) <- [61 60 59 60 60 61 62 59 61 59]\n",
      "  60 (+/- 1.0) <- [59 59 60 61 62 60 59 61 59 60]\n",
      "  60 (+/- 0.5) <- [60 61 60 59 60 60 60 60 60 60]\n",
      "  60 (+/- 1.2) <- [60 60 58 60 58 60 61 60 58 62]\n",
      "  60 (+/- 0.9) <- [61 61 60 60 58 58 60 60 61 60]\n",
      "  60 (+/- 0.8) <- [60 58 60 60 60 60 61 59 60 60]\n",
      "  61 (+/- 0.8) <- [59 61 60 61 61 61 60 60 59 59]\n",
      "  60 (+/- 0.9) <- [60 61 60 60 59 58 60 59 61 61]\n",
      "  60 (+/- 0.6) <- [60 61 60 59 59 60 60 60 60 61]\n",
      "  60 (+/- 0.8) <- [60 60 59 60 60 59 59 58 59 58]\n",
      "  60 (+/- 0.8) <- [59 60 61 60 59 58 60 60 60 60]\n",
      "  60 (+/- 0.7) <- [61 60 60 59 60 59 59 60 60 61]\n",
      "  61 (+/- 0.7) <- [60 61 60 61 60 62 60 60 59 61]\n",
      "  61 (+/- 0.6) <- [61 60 61 59 61 59 60 61 61 59]\n",
      "  60 (+/- 0.7) <- [59 60 59 59 60 60 61 59 60 60]\n",
      "  60 (+/- 0.8) <- [58 59 59 61 60 61 58 60 59 60]\n",
      "  61 (+/- 0.6) <- [61 60 60 60 60 61 60 61 61 61]\n",
      "Accuracy: 60.4 (+/- 0.87)\n",
      "Mean time (20 cv): 1.89 seconds\n",
      "Overall time: 41.53 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.92 -------------------- \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 0x7f0638f42bd0>"
      ]
     },
     "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: 480.0 training, 5520.0 testing\n",
      "  60 (+/- 1.0) <- [58 59 59 57 58 60 60 59 59 60]\n",
      "  60 (+/- 0.9) <- [58 59 59 59 61 60 59 60 59 59]\n",
      "  60 (+/- 0.9) <- [60 58 59 58 60 58 60 59 59 60]\n",
      "  60 (+/- 0.9) <- [61 60 58 59 59 60 59 58 61 58]\n",
      "  60 (+/- 1.3) <- [58 58 60 61 60 59 58 62 59 60]\n",
      "  60 (+/- 0.5) <- [59 60 60 59 58 60 59 60 60 60]\n",
      "  60 (+/- 1.0) <- [59 59 59 59 58 59 60 59 57 61]\n",
      "  60 (+/- 0.9) <- [60 61 59 59 58 58 60 61 60 59]\n",
      "  60 (+/- 0.9) <- [60 57 59 59 59 60 60 59 59 58]\n",
      "  60 (+/- 0.8) <- [59 60 59 60 61 61 59 59 58 59]\n",
      "  60 (+/- 1.0) <- [60 60 60 59 58 57 59 59 60 60]\n",
      "  60 (+/- 0.9) <- [60 60 59 58 57 59 60 59 60 60]\n",
      "  59 (+/- 0.8) <- [60 59 59 60 59 57 59 59 59 58]\n",
      "  60 (+/- 0.8) <- [58 60 59 59 60 58 59 60 60 59]\n",
      "  60 (+/- 0.8) <- [60 60 59 59 59 59 58 60 61 61]\n",
      "  60 (+/- 0.7) <- [60 61 59 60 59 61 60 60 59 60]\n",
      "  60 (+/- 0.6) <- [60 59 60 59 61 59 58 60 60 60]\n",
      "  60 (+/- 0.7) <- [59 59 58 59 60 59 61 58 60 59]\n",
      "  60 (+/- 0.9) <- [57 60 59 60 59 61 58 59 59 59]\n",
      "  60 (+/- 0.7) <- [59 59 59 59 58 60 60 59 59 60]\n",
      "Accuracy: 59.8 (+/- 0.90)\n",
      "Mean time (20 cv): 1.71 seconds\n",
      "Overall time: 37.91 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.93 -------------------- \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 0x7f0638bc0590>"
      ]
     },
     "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: 420.0 training, 5580.0 testing\n",
      "  59 (+/- 1.0) <- [58 59 60 56 58 60 58 58 58 60]\n",
      "  59 (+/- 1.2) <- [56 57 59 58 59 60 58 59 57 59]\n",
      "  59 (+/- 0.9) <- [59 57 57 58 60 58 58 58 58 59]\n",
      "  59 (+/- 1.1) <- [59 59 57 58 58 60 60 57 60 58]\n",
      "  59 (+/- 1.4) <- [57 57 59 61 60 58 57 60 59 58]\n",
      "  59 (+/- 0.6) <- [58 59 59 58 58 58 58 58 59 59]\n",
      "  59 (+/- 1.1) <- [59 58 58 58 56 60 58 58 56 60]\n",
      "  59 (+/- 0.6) <- [59 59 58 59 57 58 60 60 59 59]\n",
      "  59 (+/- 0.9) <- [60 57 59 57 58 59 58 57 59 57]\n",
      "  59 (+/- 1.1) <- [57 60 59 60 59 60 58 57 58 58]\n",
      "  59 (+/- 0.8) <- [59 60 58 58 58 57 58 58 59 58]\n",
      "  59 (+/- 0.8) <- [60 59 58 58 57 58 58 59 59 59]\n",
      "  58 (+/- 1.0) <- [59 59 57 58 59 56 57 58 58 58]\n",
      "  59 (+/- 1.3) <- [56 59 59 58 59 56 60 59 59 59]\n",
      "  59 (+/- 0.8) <- [59 58 58 58 59 57 57 59 60 59]\n",
      "  59 (+/- 0.6) <- [59 60 58 59 59 59 58 60 58 60]\n",
      "  59 (+/- 0.9) <- [59 57 59 58 60 58 58 59 59 59]\n",
      "  59 (+/- 0.8) <- [58 58 58 58 60 58 60 58 59 59]\n",
      "  59 (+/- 0.8) <- [57 59 58 59 58 60 57 59 58 58]\n",
      "  59 (+/- 0.6) <- [58 59 59 58 58 60 58 58 59 58]\n",
      "Accuracy: 58.9 (+/- 0.97)\n",
      "Mean time (20 cv): 1.50 seconds\n",
      "Overall time: 33.77 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.94 -------------------- \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 0x7f0639082f90>"
      ]
     },
     "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: 360.0 training, 5640.0 testing\n",
      "  58 (+/- 1.1) <- [57 58 58 55 56 59 57 57 58 58]\n",
      "  58 (+/- 1.4) <- [55 58 58 58 58 59 56 59 55 58]\n",
      "  58 (+/- 1.0) <- [58 56 57 57 58 56 58 58 57 60]\n",
      "  58 (+/- 0.7) <- [59 58 57 57 57 58 58 57 59 57]\n",
      "  58 (+/- 1.1) <- [56 56 57 59 57 58 56 58 57 57]\n",
      "  58 (+/- 1.0) <- [57 57 57 55 57 57 57 58 59 59]\n",
      "  58 (+/- 1.3) <- [59 56 57 56 56 58 58 58 56 59]\n",
      "  58 (+/- 0.8) <- [57 59 57 58 58 57 59 59 57 58]\n",
      "  58 (+/- 0.8) <- [59 56 59 57 58 58 58 57 57 58]\n",
      "  58 (+/- 1.0) <- [57 59 56 59 59 59 57 56 57 58]\n",
      "  58 (+/- 0.8) <- [58 57 57 57 57 56 59 57 58 59]\n",
      "  58 (+/- 0.8) <- [59 57 58 57 56 57 58 57 59 59]\n",
      "  58 (+/- 1.2) <- [59 58 57 57 59 56 56 56 58 56]\n",
      "  58 (+/- 1.5) <- [55 59 58 58 58 54 59 58 57 59]\n",
      "  58 (+/- 0.9) <- [58 57 58 58 59 55 57 58 57 58]\n",
      "  59 (+/- 1.0) <- [58 59 57 56 59 59 59 58 58 59]\n",
      "  58 (+/- 1.3) <- [59 55 58 56 59 57 56 59 57 59]\n",
      "  58 (+/- 1.1) <- [58 57 56 58 59 57 59 58 58 60]\n",
      "  58 (+/- 1.1) <- [56 59 57 59 58 59 56 58 58 58]\n",
      "  58 (+/- 0.6) <- [58 58 58 57 57 59 57 58 58 57]\n",
      "Accuracy: 58.1 (+/- 1.08)\n",
      "Mean time (20 cv): 1.31 seconds\n",
      "Overall time: 29.91 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.95 -------------------- \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 0x7f06390543d0>"
      ]
     },
     "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: 300.0 training, 5700.0 testing\n",
      "  57 (+/- 1.2) <- [56 57 56 54 56 59 56 56 55 56]\n",
      "  57 (+/- 1.4) <- [54 57 56 57 57 57 55 58 53 56]\n",
      "  57 (+/- 1.2) <- [55 55 56 56 56 54 56 58 56 59]\n",
      "  57 (+/- 0.8) <- [57 57 56 57 56 57 58 55 58 57]\n",
      "  56 (+/- 1.2) <- [54 54 56 57 55 57 54 57 56 57]\n",
      "  57 (+/- 0.9) <- [57 57 56 56 57 56 56 56 57 59]\n",
      "  57 (+/- 1.2) <- [57 56 56 56 55 58 56 56 54 57]\n",
      "  58 (+/- 0.9) <- [57 58 55 58 56 57 58 58 56 57]\n",
      "  57 (+/- 0.8) <- [57 55 57 57 57 57 56 56 55 56]\n",
      "  57 (+/- 1.0) <- [55 57 56 58 56 57 56 55 55 57]\n",
      "  57 (+/- 0.7) <- [56 55 56 56 56 56 57 57 57 56]\n",
      "  57 (+/- 1.2) <- [58 56 57 56 55 56 56 55 59 57]\n",
      "  57 (+/- 1.2) <- [58 58 56 56 57 54 56 56 56 55]\n",
      "  56 (+/- 1.8) <- [55 57 57 57 57 52 56 56 53 58]\n",
      "  57 (+/- 1.2) <- [57 56 56 57 58 54 56 57 57 57]\n",
      "  58 (+/- 1.1) <- [58 59 55 55 58 57 58 58 57 58]\n",
      "  57 (+/- 1.2) <- [57 54 56 56 58 56 55 58 57 57]\n",
      "  58 (+/- 1.0) <- [56 57 57 57 58 55 58 57 57 59]\n",
      "  56 (+/- 0.9) <- [55 57 54 56 56 57 56 57 56 55]\n",
      "  57 (+/- 1.0) <- [57 55 57 55 57 57 55 56 57 56]\n",
      "Accuracy: 56.9 (+/- 1.20)\n",
      "Mean time (20 cv): 1.10 seconds\n",
      "Overall time: 25.87 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.96 -------------------- \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 0x7f0638c3b2d0>"
      ]
     },
     "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: 240.0 training, 5760.0 testing\n",
      "  55 (+/- 1.4) <- [56 56 52 53 53 57 55 53 54 55]\n",
      "  55 (+/- 1.5) <- [54 56 55 56 55 55 53 56 51 56]\n",
      "  56 (+/- 1.1) <- [54 53 55 55 56 55 55 56 55 57]\n",
      "  56 (+/- 1.0) <- [56 55 55 55 57 57 57 54 57 55]\n",
      "  54 (+/- 1.4) <- [53 52 54 56 53 55 53 55 52 54]\n",
      "  56 (+/- 1.5) <- [56 55 53 53 57 53 56 56 55 57]\n",
      "  55 (+/- 1.2) <- [55 53 55 56 53 57 55 55 54 56]\n",
      "  56 (+/- 1.2) <- [56 55 54 58 55 54 57 56 55 58]\n",
      "  55 (+/- 1.0) <- [56 53 55 56 56 56 55 54 55 54]\n",
      "  56 (+/- 1.1) <- [55 56 54 57 54 57 54 56 54 56]\n",
      "  55 (+/- 1.0) <- [56 52 55 55 55 55 55 55 56 55]\n",
      "  56 (+/- 1.1) <- [57 56 55 54 54 55 55 54 57 55]\n",
      "  55 (+/- 1.2) <- [54 57 55 54 56 52 54 53 56 54]\n",
      "  56 (+/- 1.9) <- [55 56 56 57 56 52 55 56 51 56]\n",
      "  56 (+/- 1.3) <- [54 56 56 57 56 53 54 54 54 56]\n",
      "  56 (+/- 1.2) <- [57 58 54 55 56 55 56 57 56 56]\n",
      "  56 (+/- 1.5) <- [55 52 55 55 56 53 55 57 56 57]\n",
      "  56 (+/- 1.1) <- [54 56 54 56 57 54 54 54 55 56]\n",
      "  55 (+/- 1.2) <- [53 56 52 55 55 56 54 55 54 54]\n",
      "  55 (+/- 0.8) <- [55 54 55 54 56 56 55 54 56 55]\n",
      "Accuracy: 55.5 (+/- 1.37)\n",
      "Mean time (20 cv): 0.91 seconds\n",
      "Overall time: 22.01 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.97 -------------------- \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 0x7f0638a2a5d0>"
      ]
     },
     "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: 180.0 training, 5820.0 testing\n",
      "  53 (+/- 2.0) <- [53 52 50 49 52 54 56 53 54 52]\n",
      "  54 (+/- 2.2) <- [50 54 54 56 56 53 53 54 49 55]\n",
      "  54 (+/- 1.3) <- [53 50 53 53 54 53 52 54 54 54]\n",
      "  55 (+/- 0.8) <- [54 55 53 55 54 53 54 53 55 53]\n",
      "  52 (+/- 1.6) <- [51 51 52 55 49 52 52 54 51 52]\n",
      "  54 (+/- 1.0) <- [54 52 52 53 55 52 54 54 53 54]\n",
      "  54 (+/- 1.4) <- [54 50 54 54 52 54 54 54 53 55]\n",
      "  54 (+/- 1.3) <- [54 54 51 55 54 53 54 54 52 55]\n",
      "  54 (+/- 1.2) <- [55 51 54 53 55 55 54 53 53 52]\n",
      "  53 (+/- 1.4) <- [52 54 53 54 50 55 51 53 53 53]\n",
      "  53 (+/- 1.2) <- [53 50 53 54 51 53 53 52 54 53]\n",
      "  54 (+/- 1.8) <- [56 56 54 54 53 53 52 50 56 53]\n",
      "  54 (+/- 1.5) <- [52 55 54 54 54 52 51 51 55 53]\n",
      "  54 (+/- 1.1) <- [52 54 53 55 54 52 54 54 52 54]\n",
      "  54 (+/- 1.5) <- [53 55 54 54 54 50 52 53 55 54]\n",
      "  54 (+/- 1.0) <- [54 56 54 54 55 52 53 54 54 54]\n",
      "  54 (+/- 1.8) <- [52 50 52 53 54 53 53 57 54 55]\n",
      "  55 (+/- 1.3) <- [53 54 55 54 56 53 53 54 52 55]\n",
      "  53 (+/- 1.7) <- [51 54 52 53 52 51 52 56 51 51]\n",
      "  54 (+/- 1.0) <- [54 53 55 53 56 54 54 53 54 53]\n",
      "Accuracy: 53.7 (+/- 1.55)\n",
      "Mean time (20 cv): 0.71 seconds\n",
      "Overall time: 17.98 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.98 -------------------- \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 0x7f064289c310>"
      ]
     },
     "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: 120.0 training, 5880.0 testing\n",
      "  49 (+/- 4.5) <- [50 52 50 47 49 51 53 36 52 50]\n",
      "  51 (+/- 2.3) <- [49 51 49 52 53 52 52 51 46 52]\n",
      "  50 (+/- 2.1) <- [52 45 50 50 50 49 47 51 52 51]\n",
      "  51 (+/- 1.6) <- [51 53 49 52 50 49 50 49 53 52]\n",
      "  50 (+/- 1.8) <- [47 51 48 52 48 47 51 51 49 52]\n",
      "  52 (+/- 1.5) <- [51 50 49 53 54 52 51 49 52 52]\n",
      "  51 (+/- 1.8) <- [51 47 52 52 51 53 52 50 49 52]\n",
      "  51 (+/- 1.5) <- [49 51 47 51 50 49 52 52 52 49]\n",
      "  51 (+/- 1.5) <- [53 47 49 51 52 50 50 51 52 49]\n",
      "  51 (+/- 1.2) <- [50 50 52 51 48 51 49 52 52 52]\n",
      "  51 (+/- 1.8) <- [53 47 51 52 48 51 52 51 51 50]\n",
      "  51 (+/- 2.5) <- [54 55 52 51 51 51 47 47 51 47]\n",
      "  51 (+/- 1.5) <- [49 54 50 52 51 50 50 50 50 52]\n",
      "  51 (+/- 2.0) <- [51 51 51 54 53 50 48 49 48 53]\n",
      "  51 (+/- 2.0) <- [50 51 52 51 50 45 49 51 53 50]\n",
      "  52 (+/- 1.6) <- [51 53 49 52 53 50 51 55 50 50]\n",
      "  51 (+/- 2.2) <- [49 46 48 54 51 49 51 53 52 52]\n",
      "  52 (+/- 1.7) <- [50 53 53 53 53 52 51 53 48 50]\n",
      "  51 (+/- 1.9) <- [50 52 50 53 50 46 51 51 48 48]\n",
      "  52 (+/- 1.6) <- [51 53 53 52 54 51 53 49 53 49]\n",
      "Accuracy: 51.1 (+/- 2.15)\n",
      "Mean time (20 cv): 0.51 seconds\n",
      "Overall time: 13.90 seconds\n",
      "\n",
      " -------------------- Baseline, test_size = 0.99 -------------------- \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 0x7f0638ae4c10>"
      ]
     },
     "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: 60.0 training, 5940.0 testing\n",
      "  46 (+/- 2.6) <- [46 49 46 41 46 47 45 40 47 45]\n",
      "  46 (+/- 2.0) <- [46 47 43 42 49 47 48 47 44 46]\n",
      "  46 (+/- 2.4) <- [44 40 46 49 48 44 45 46 47 46]\n",
      "  47 (+/- 2.5) <- [45 49 42 46 51 45 45 43 48 46]\n",
      "  46 (+/- 2.9) <- [41 46 44 48 44 41 46 50 44 48]\n",
      "  47 (+/- 1.4) <- [48 45 44 47 48 47 46 45 47 48]\n",
      "  47 (+/- 2.8) <- [48 39 50 45 44 50 46 46 46 46]\n",
      "  45 (+/- 3.7) <- [49 44 35 44 48 44 48 46 44 47]\n",
      "  46 (+/- 1.6) <- [44 42 48 45 45 46 48 45 46 46]\n",
      "  45 (+/- 4.8) <- [47 42 46 46 45 46 46 46 47 30]\n",
      "  46 (+/- 2.0) <- [49 42 46 47 47 42 46 45 45 47]\n",
      "  45 (+/- 4.6) <- [49 48 48 48 35 49 39 43 48 43]\n",
      "  47 (+/- 2.4) <- [44 50 48 48 49 47 50 45 42 47]\n",
      "  49 (+/- 1.5) <- [48 48 46 47 51 48 51 48 46 49]\n",
      "  46 (+/- 3.8) <- [42 47 47 49 42 37 45 48 49 48]\n",
      "  48 (+/- 2.5) <- [48 48 44 44 50 46 50 51 44 48]\n",
      "  47 (+/- 2.9) <- [51 41 44 49 45 44 46 49 45 50]\n",
      "  48 (+/- 1.8) <- [44 47 48 47 49 49 46 50 45 49]\n",
      "  46 (+/- 2.4) <- [47 47 42 45 45 41 48 49 45 44]\n",
      "  47 (+/- 2.1) <- [46 49 48 46 46 47 46 42 50 47]\n",
      "Accuracy: 46.5 (+/- 2.95)\n",
      "Mean time (20 cv): 0.30 seconds\n",
      "Overall time: 9.67 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 = [0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99]\n",
      "res['accuracy_std'] = [0.88165583398636616, 0.92316651332780619, 0.96804698356522423, 1.0687817911193573, 1.1820511759846726, 1.2953417024834313, 1.3741130258576952, 1.4914311421494204, 1.6346640729106237, 1.9857480601694719]\n",
      "res['objective_j'] = [9808.2206726074219, 9808.2206726074219, 9808.2206726074219, 9808.2206726074219, 9808.2206726074219, 9808.2206726074219, 9808.2206726074219, 9808.2206726074219, 9808.2206726074219, 9808.2206726074219]\n",
      "res['objective_i'] = [56374.7890625, 56374.7890625, 56374.7890625, 56374.7890625, 56374.7890625, 56374.7890625, 56374.7890625, 56374.7890625, 56374.7890625, 56374.7890625]\n",
      "res['objective_h'] = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "res['objective_g'] = [76055.7421875, 76055.7421875, 76055.7421875, 76055.7421875, 76055.7421875, 76055.7421875, 76055.7421875, 76055.7421875, 76055.7421875, 76055.7421875]\n",
      "res['baseline'] = [60.973703703703706, 60.376190476190459, 59.755344202898506, 58.929480286738354, 58.062677304964552, 56.86350877192983, 55.507986111111116, 53.744501718213066, 51.096088435374142, 46.473737373737357]\n",
      "res['time_features'] = [1915.18248295784, 1915.18248295784, 1915.18248295784, 1915.18248295784, 1915.18248295784, 1915.18248295784, 1915.18248295784, 1915.18248295784, 1915.18248295784, 1915.18248295784]\n",
      "res['baseline_std'] = [0.85954329596629586, 0.86612385814216353, 0.89706844615576686, 0.97087716860715956, 1.0826780027089868, 1.1987016675987279, 1.3656555460323629, 1.5531957132032486, 2.1520828759669808, 2.9536838364939091]\n",
      "res['sparsity'] = [20.003067323825505, 20.003067323825505, 20.003067323825505, 20.003067323825505, 20.003067323825505, 20.003067323825505, 20.003067323825505, 20.003067323825505, 20.003067323825505, 20.003067323825505]\n",
      "res['iterations_inner'] = [901, 901, 901, 901, 901, 901, 901, 901, 901, 901]\n",
      "res['iterations_outer'] = [6, 6, 6, 6, 6, 6, 6, 6, 6, 6]\n",
      "res['accuracy'] = [65.751111111111101, 64.906043956043959, 64.01476449275367, 62.926702508960645, 61.735106382978714, 60.28596491228069, 58.475694444444436, 56.110395189003462, 52.886904761904717, 47.953114478114486]\n"
     ]
    },
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f06394658d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment time: 2697 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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