{
 "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. Without graph.\n",
    "\n",
    "Conclusion: The trend observed in `13g_ratio_nograph` 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'] = None\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: 156.61 seconds\n",
      "All self-referenced in the first column: True\n",
      "dist in [0.61036324501, 1.0]\n",
      "w in [0.61036324501, 1.0]\n",
      "Ones on the diagonal: 148962 (over 149000)\n",
      "assert: False\n",
      "W in [0.0, 1.0]\n",
      "Datasets:\n",
      "  L_data    : (2421546,), float32\n",
      "  L_indices : (2421546,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2421546,), float32\n",
      "  W_indices : (2421546,), 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: 165.71 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    : (2421546,), float32\n",
      "  L_indices : (2421546,), int32\n",
      "  L_indptr  : (149001,) , int32\n",
      "  L_shape   : (2,)      , int64\n",
      "  W_data    : (2421546,), float32\n",
      "  W_indices : (2421546,), 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: 1182 seconds\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd01f2e950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner loop: 898 iterations\n",
      "g(Z) = ||X-DZ||_2^2 = 4.231717e+04\n",
      "rdiff: 0.00242506942781\n",
      "i(Z) = ||Z||_1 = 6.887042e+04\n"
     ]
    },
    {
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       "rkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcec7f1150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global objective: 1.111876e+05\n"
     ]
    },
    {
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       "XzgfeQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcec67e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outer loop: 14 iterations\n",
      "\n",
      "Z in [-1.15960109234, 1.81123566628]\n",
      "Sparsity of Z: 905,112 non-zero entries out of 19,072,000 entries, i.e. 4.7%.\n"
     ]
    },
    {
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       "ERERERERERERERERERERERERERERERERESnL/wfWSjzRO45PqgAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd43927490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D in [-0.760848701, 0.938695788383]\n",
      "d in [0.999999701977, 1.00000035763]\n",
      "Constraints on D: True\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcec77e7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcec7d9750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcec6f8110>"
      ]
     },
     "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: 1189 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"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcc7de2050>"
      ]
     },
     "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: 74.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 61.4 %\n",
      "Clips accuracy: 71.0 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Data: (6000, 128), float64\n",
      "Labels: (6000,), uint8\n",
      "Ratio: 600.0 training, 5400.0 testing\n",
      "  64 (+/- 0.7) <- [63 63 63 64 63 63 63 64 64 61]\n",
      "  64 (+/- 0.8) <- [61 64 64 63 64 64 63 62 64 63]\n",
      "  64 (+/- 0.9) <- [64 63 63 63 64 62 64 62 62 63]\n",
      "  64 (+/- 0.8) <- [63 64 62 64 62 64 64 63 63 63]\n",
      "  63 (+/- 0.9) <- [62 62 63 63 63 65 64 63 62 63]\n",
      "  63 (+/- 0.9) <- [64 63 62 63 63 61 62 61 63 63]\n",
      "  64 (+/- 1.1) <- [64 63 64 64 63 64 63 63 61 62]\n",
      "  64 (+/- 0.8) <- [63 61 62 63 64 63 64 63 63 64]\n",
      "  64 (+/- 0.4) <- [63 64 63 63 64 64 64 63 63 63]\n",
      "  64 (+/- 0.5) <- [63 64 63 64 64 64 63 64 63 63]\n",
      "  63 (+/- 0.6) <- [63 63 62 63 63 63 61 62 63 63]\n",
      "  64 (+/- 0.7) <- [61 63 64 63 64 63 63 63 64 63]\n",
      "  63 (+/- 1.0) <- [64 63 64 64 64 61 65 62 63 61]\n",
      "  64 (+/- 0.6) <- [63 63 64 63 63 64 64 64 62 63]\n",
      "  64 (+/- 1.2) <- [64 62 64 63 65 62 63 65 63 65]\n",
      "  64 (+/- 0.9) <- [63 64 63 63 63 63 65 65 63 63]\n",
      "  64 (+/- 0.7) <- [63 63 63 65 63 63 62 64 64 63]\n",
      "  63 (+/- 0.9) <- [62 64 62 63 62 64 64 62 62 64]\n",
      "  64 (+/- 0.9) <- [63 65 62 64 64 64 62 64 63 64]\n",
      "  64 (+/- 0.9) <- [63 64 64 62 62 64 64 63 64 63]\n",
      "Accuracy: 63.6 (+/- 0.87)\n",
      "Mean time (20 cv): 2.38 seconds\n",
      "Overall time: 52.64 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 0x7fdcc785cb90>"
      ]
     },
     "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: 74.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 61.4 %\n",
      "Clips accuracy: 71.0 %\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",
      "  63 (+/- 1.0) <- [62 61 62 63 63 63 62 62 64 60]\n",
      "  63 (+/- 1.0) <- [61 63 64 63 63 63 62 61 63 63]\n",
      "  63 (+/- 1.3) <- [65 62 62 62 64 60 64 61 61 62]\n",
      "  63 (+/- 0.7) <- [62 63 62 63 62 62 62 61 63 62]\n",
      "  63 (+/- 0.6) <- [61 62 62 62 63 63 62 62 62 63]\n",
      "  63 (+/- 0.8) <- [64 62 62 62 63 62 63 61 62 62]\n",
      "  63 (+/- 1.1) <- [63 61 63 63 61 63 62 63 60 61]\n",
      "  63 (+/- 1.1) <- [61 61 60 62 63 63 64 62 62 62]\n",
      "  63 (+/- 0.6) <- [62 63 63 61 63 62 63 62 62 63]\n",
      "  63 (+/- 0.6) <- [62 63 63 64 62 63 63 62 62 63]\n",
      "  62 (+/- 0.7) <- [62 62 62 62 62 63 60 62 63 61]\n",
      "  63 (+/- 0.7) <- [61 62 63 63 63 62 62 62 63 61]\n",
      "  63 (+/- 0.9) <- [63 63 63 62 62 61 64 62 62 60]\n",
      "  63 (+/- 1.0) <- [62 61 64 64 63 63 61 64 63 62]\n",
      "  63 (+/- 1.6) <- [62 60 61 62 65 61 63 64 62 65]\n",
      "  63 (+/- 0.7) <- [62 63 62 63 63 62 63 64 63 62]\n",
      "  63 (+/- 0.9) <- [62 61 63 64 63 62 61 61 63 62]\n",
      "  63 (+/- 0.8) <- [62 63 61 62 61 63 63 61 61 63]\n",
      "  63 (+/- 1.0) <- [61 64 61 63 62 63 61 63 63 62]\n",
      "  63 (+/- 0.9) <- [63 64 64 61 62 62 63 62 64 62]\n",
      "Accuracy: 62.9 (+/- 0.98)\n",
      "Mean time (20 cv): 2.15 seconds\n",
      "Overall time: 48.17 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 0x7fdcc72708d0>"
      ]
     },
     "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: 74.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 61.4 %\n",
      "Clips accuracy: 71.0 %\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",
      "  62 (+/- 0.8) <- [60 60 62 61 61 62 60 61 63 61]\n",
      "  62 (+/- 0.8) <- [61 62 63 61 61 61 61 61 63 62]\n",
      "  62 (+/- 1.5) <- [64 62 61 61 63 59 63 61 60 62]\n",
      "  62 (+/- 0.9) <- [62 63 62 61 62 61 62 60 63 61]\n",
      "  62 (+/- 0.8) <- [60 61 60 61 63 63 62 61 61 61]\n",
      "  62 (+/- 0.6) <- [62 60 61 61 61 61 62 60 62 61]\n",
      "  62 (+/- 1.3) <- [63 61 62 61 61 64 62 63 59 60]\n",
      "  61 (+/- 1.2) <- [60 59 58 61 62 61 61 62 62 61]\n",
      "  62 (+/- 0.6) <- [62 63 63 61 62 62 63 62 61 62]\n",
      "  62 (+/- 0.7) <- [62 62 62 63 61 61 62 62 61 63]\n",
      "  61 (+/- 0.7) <- [61 61 61 60 61 62 59 61 61 61]\n",
      "  61 (+/- 1.0) <- [60 60 63 60 61 61 61 60 62 60]\n",
      "  62 (+/- 0.8) <- [62 61 62 61 61 61 62 62 61 60]\n",
      "  62 (+/- 0.8) <- [61 62 62 63 62 61 61 63 61 61]\n",
      "  62 (+/- 1.5) <- [61 60 60 61 62 61 61 63 61 65]\n",
      "  62 (+/- 0.6) <- [61 62 61 62 62 61 61 63 62 61]\n",
      "  62 (+/- 1.2) <- [62 59 62 63 63 61 60 62 62 60]\n",
      "  62 (+/- 1.1) <- [60 61 60 60 61 62 63 60 61 62]\n",
      "  62 (+/- 1.1) <- [60 63 61 62 61 63 60 63 62 61]\n",
      "  62 (+/- 1.0) <- [61 63 63 61 61 61 63 60 63 61]\n",
      "Accuracy: 61.9 (+/- 1.07)\n",
      "Mean time (20 cv): 1.95 seconds\n",
      "Overall time: 44.07 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 0x7fdcc79e3050>"
      ]
     },
     "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: 74.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 61.4 %\n",
      "Clips accuracy: 71.0 %\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",
      "  61 (+/- 0.7) <- [59 60 62 60 61 61 60 60 61 61]\n",
      "  61 (+/- 1.7) <- [58 61 64 61 61 59 59 59 62 62]\n",
      "  61 (+/- 1.1) <- [62 60 60 60 61 59 59 60 60 62]\n",
      "  61 (+/- 1.3) <- [61 62 62 62 60 60 61 59 63 59]\n",
      "  61 (+/- 0.9) <- [59 60 61 62 62 62 62 61 60 61]\n",
      "  61 (+/- 0.8) <- [61 59 61 59 60 59 60 60 61 61]\n",
      "  61 (+/- 1.2) <- [62 58 60 62 60 62 60 61 59 60]\n",
      "  60 (+/- 1.1) <- [59 58 58 60 62 60 60 60 61 60]\n",
      "  61 (+/- 0.8) <- [61 62 62 60 61 60 60 60 61 61]\n",
      "  61 (+/- 1.0) <- [61 62 60 62 60 61 60 61 58 61]\n",
      "  60 (+/- 0.9) <- [60 59 59 61 60 61 58 61 60 59]\n",
      "  61 (+/- 1.0) <- [59 60 62 60 60 60 60 59 62 59]\n",
      "  61 (+/- 1.1) <- [61 60 62 60 61 58 59 60 60 59]\n",
      "  61 (+/- 1.1) <- [60 60 61 61 61 58 59 62 60 59]\n",
      "  61 (+/- 1.5) <- [60 59 59 59 61 60 61 61 59 64]\n",
      "  61 (+/- 0.9) <- [61 60 59 62 60 60 61 62 61 60]\n",
      "  61 (+/- 0.9) <- [61 59 61 61 62 60 60 60 61 59]\n",
      "  61 (+/- 1.7) <- [62 61 59 59 60 61 62 59 58 63]\n",
      "  61 (+/- 1.2) <- [59 62 60 60 61 62 59 62 61 60]\n",
      "  61 (+/- 0.9) <- [60 63 61 60 61 61 61 61 61 60]\n",
      "Accuracy: 60.9 (+/- 1.17)\n",
      "Mean time (20 cv): 1.74 seconds\n",
      "Overall time: 39.90 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 0x7fdcc75b3450>"
      ]
     },
     "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: 74.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 61.4 %\n",
      "Clips accuracy: 71.0 %\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",
      "  60 (+/- 1.1) <- [58 57 59 60 58 60 59 59 61 60]\n",
      "  60 (+/- 1.4) <- [57 60 61 62 59 60 60 59 62 60]\n",
      "  60 (+/- 1.1) <- [62 58 59 59 60 59 60 60 58 61]\n",
      "  60 (+/- 1.7) <- [59 62 62 60 58 58 60 57 61 57]\n",
      "  61 (+/- 1.0) <- [59 60 59 62 61 61 60 59 58 60]\n",
      "  60 (+/- 1.1) <- [61 59 59 60 59 57 58 59 60 59]\n",
      "  60 (+/- 1.6) <- [60 57 59 60 59 61 57 61 58 61]\n",
      "  59 (+/- 1.1) <- [58 57 57 59 59 58 60 60 60 59]\n",
      "  60 (+/- 0.8) <- [61 60 60 58 59 59 59 58 59 60]\n",
      "  60 (+/- 1.2) <- [60 60 57 60 58 59 59 60 58 60]\n",
      "  59 (+/- 1.2) <- [59 59 57 59 57 60 59 60 59 57]\n",
      "  59 (+/- 1.1) <- [58 59 61 59 58 58 59 59 60 58]\n",
      "  60 (+/- 0.9) <- [60 58 60 59 60 58 59 60 58 59]\n",
      "  60 (+/- 1.1) <- [60 58 59 61 59 57 59 61 59 58]\n",
      "  60 (+/- 1.5) <- [59 59 57 58 59 59 59 61 58 63]\n",
      "  60 (+/- 1.0) <- [58 59 57 60 60 58 60 60 60 60]\n",
      "  60 (+/- 1.1) <- [59 59 61 60 62 60 58 60 60 59]\n",
      "  59 (+/- 1.5) <- [60 60 58 58 59 57 61 58 57 61]\n",
      "  60 (+/- 1.3) <- [59 61 58 57 60 61 57 60 59 59]\n",
      "  60 (+/- 0.8) <- [59 61 59 59 60 59 59 59 60 59]\n",
      "Accuracy: 59.7 (+/- 1.25)\n",
      "Mean time (20 cv): 1.53 seconds\n",
      "Overall time: 35.77 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"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcc78c10d0>"
      ]
     },
     "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: 74.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 61.4 %\n",
      "Clips accuracy: 71.0 %\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",
      "  59 (+/- 0.7) <- [57 58 57 58 58 58 58 57 59 60]\n",
      "  59 (+/- 1.2) <- [56 59 61 60 58 57 58 59 60 59]\n",
      "  59 (+/- 1.1) <- [60 57 57 57 59 57 60 58 59 58]\n",
      "  59 (+/- 1.5) <- [57 60 59 58 57 58 60 56 60 56]\n",
      "  59 (+/- 1.1) <- [58 59 58 60 59 59 59 59 56 59]\n",
      "  58 (+/- 1.4) <- [59 58 57 58 56 56 56 58 59 59]\n",
      "  58 (+/- 1.8) <- [59 54 56 59 57 59 57 60 57 60]\n",
      "  58 (+/- 1.1) <- [57 57 56 58 55 59 58 58 58 58]\n",
      "  59 (+/- 0.9) <- [60 58 60 58 58 58 59 57 57 59]\n",
      "  59 (+/- 1.6) <- [61 60 55 59 58 58 58 59 56 59]\n",
      "  57 (+/- 1.3) <- [57 55 55 56 56 57 56 58 57 54]\n",
      "  58 (+/- 1.3) <- [57 58 59 58 56 55 56 58 59 57]\n",
      "  59 (+/- 1.1) <- [59 58 60 57 59 57 57 59 59 58]\n",
      "  58 (+/- 1.3) <- [59 57 59 59 58 55 59 57 57 56]\n",
      "  59 (+/- 1.5) <- [56 59 57 57 58 57 58 60 59 61]\n",
      "  59 (+/- 1.1) <- [58 59 56 60 59 58 59 58 59 57]\n",
      "  59 (+/- 0.9) <- [60 57 58 57 59 59 57 58 59 58]\n",
      "  58 (+/- 1.3) <- [58 58 58 58 57 56 60 57 56 59]\n",
      "  58 (+/- 1.4) <- [57 60 55 57 58 59 56 58 58 57]\n",
      "  58 (+/- 0.9) <- [58 59 58 58 59 57 59 57 59 58]\n",
      "Accuracy: 58.4 (+/- 1.37)\n",
      "Mean time (20 cv): 1.32 seconds\n",
      "Overall time: 31.49 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 0x7fdcc7601b50>"
      ]
     },
     "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: 74.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 61.4 %\n",
      "Clips accuracy: 71.0 %\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",
      "  57 (+/- 1.2) <- [53 57 56 56 57 59 57 57 56 57]\n",
      "  58 (+/- 1.1) <- [57 57 57 58 59 55 58 58 58 56]\n",
      "  57 (+/- 1.4) <- [58 54 55 56 55 56 58 55 57 58]\n",
      "  57 (+/- 1.8) <- [56 57 58 57 56 55 60 54 57 53]\n",
      "  57 (+/- 1.1) <- [57 56 57 59 58 56 55 57 55 58]\n",
      "  56 (+/- 1.3) <- [56 55 56 55 55 53 57 57 57 57]\n",
      "  57 (+/- 2.0) <- [55 53 54 58 55 58 54 58 57 59]\n",
      "  56 (+/- 1.2) <- [56 58 54 56 54 55 56 56 56 58]\n",
      "  57 (+/- 1.1) <- [57 55 58 55 57 56 58 57 56 57]\n",
      "  57 (+/- 1.5) <- [58 58 54 59 56 57 57 57 56 58]\n",
      "  55 (+/- 1.2) <- [56 53 53 56 55 56 55 57 56 55]\n",
      "  56 (+/- 1.5) <- [55 59 57 56 54 55 55 55 58 55]\n",
      "  57 (+/- 1.0) <- [57 56 57 54 57 55 57 56 57 57]\n",
      "  56 (+/- 1.1) <- [56 55 56 58 56 55 58 56 55 54]\n",
      "  57 (+/- 1.8) <- [56 57 58 56 54 53 55 57 57 59]\n",
      "  57 (+/- 1.2) <- [57 56 55 59 57 55 55 58 56 56]\n",
      "  57 (+/- 1.0) <- [56 57 56 56 58 55 55 56 55 56]\n",
      "  57 (+/- 1.2) <- [57 57 57 58 56 56 59 55 55 58]\n",
      "  56 (+/- 1.8) <- [56 57 53 56 57 56 54 57 58 53]\n",
      "  57 (+/- 0.9) <- [56 58 56 57 57 56 58 55 56 57]\n",
      "Accuracy: 56.7 (+/- 1.44)\n",
      "Mean time (20 cv): 1.10 seconds\n",
      "Overall time: 27.22 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 0x7fdcc79db9d0>"
      ]
     },
     "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: 74.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 61.4 %\n",
      "Clips accuracy: 71.0 %\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",
      "  55 (+/- 1.4) <- [51 53 54 54 55 56 56 55 55 52]\n",
      "  55 (+/- 1.0) <- [55 55 55 57 55 54 55 56 54 53]\n",
      "  54 (+/- 1.8) <- [56 50 53 54 53 55 55 53 55 54]\n",
      "  55 (+/- 1.5) <- [55 56 54 55 54 53 57 52 54 51]\n",
      "  55 (+/- 1.4) <- [55 55 54 57 54 53 53 55 51 54]\n",
      "  54 (+/- 1.1) <- [55 53 53 53 56 52 54 54 53 56]\n",
      "  55 (+/- 1.6) <- [55 51 53 55 52 56 55 54 55 56]\n",
      "  54 (+/- 1.4) <- [55 54 51 53 52 52 54 53 55 55]\n",
      "  55 (+/- 1.4) <- [56 54 56 52 55 54 57 55 54 54]\n",
      "  55 (+/- 1.6) <- [55 55 52 57 52 55 54 54 55 57]\n",
      "  54 (+/- 1.9) <- [55 51 50 54 51 52 54 57 54 54]\n",
      "  55 (+/- 0.8) <- [53 55 54 53 54 54 55 55 54 54]\n",
      "  55 (+/- 1.2) <- [55 55 56 53 54 52 54 55 54 56]\n",
      "  54 (+/- 1.2) <- [55 54 53 56 53 53 55 55 53 52]\n",
      "  54 (+/- 2.3) <- [53 54 56 53 52 49 52 54 55 58]\n",
      "  54 (+/- 1.4) <- [53 55 54 56 56 52 53 53 54 53]\n",
      "  54 (+/- 1.4) <- [55 53 53 51 57 54 53 53 53 53]\n",
      "  55 (+/- 1.1) <- [54 55 54 55 52 55 57 55 53 54]\n",
      "  54 (+/- 1.4) <- [55 54 52 54 54 51 54 54 53 51]\n",
      "  55 (+/- 1.5) <- [52 54 54 54 57 55 56 51 54 55]\n",
      "Accuracy: 54.5 (+/- 1.52)\n",
      "Mean time (20 cv): 0.88 seconds\n",
      "Overall time: 22.72 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 0x7fdcc71e9090>"
      ]
     },
     "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: 74.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 61.4 %\n",
      "Clips accuracy: 71.0 %\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",
      "  51 (+/- 1.9) <- [48 51 49 49 53 54 51 52 51 50]\n",
      "  52 (+/- 1.4) <- [52 53 53 50 54 54 52 51 50 51]\n",
      "  52 (+/- 2.2) <- [54 47 47 51 51 53 52 51 52 53]\n",
      "  51 (+/- 1.4) <- [49 53 51 50 50 51 53 50 51 49]\n",
      "  51 (+/- 1.9) <- [48 51 52 53 53 50 50 52 47 52]\n",
      "  51 (+/- 1.6) <- [50 50 49 49 52 52 50 50 48 54]\n",
      "  52 (+/- 1.9) <- [50 49 51 51 48 53 53 49 51 55]\n",
      "  51 (+/- 2.3) <- [48 49 50 51 47 48 51 50 54 54]\n",
      "  52 (+/- 1.1) <- [52 50 51 50 51 51 54 52 50 50]\n",
      "  52 (+/- 1.9) <- [48 50 52 52 50 51 52 53 54 55]\n",
      "  51 (+/- 2.2) <- [50 45 48 52 49 53 49 52 52 51]\n",
      "  52 (+/- 1.4) <- [52 54 51 50 54 50 53 52 51 50]\n",
      "  51 (+/- 1.6) <- [54 52 51 51 51 47 50 50 49 53]\n",
      "  52 (+/- 1.0) <- [52 50 50 51 52 53 51 50 50 51]\n",
      "  51 (+/- 2.1) <- [52 50 51 52 50 45 49 53 53 52]\n",
      "  52 (+/- 1.4) <- [50 53 49 54 52 52 50 52 51 51]\n",
      "  51 (+/- 1.1) <- [52 50 50 48 50 51 49 52 51 51]\n",
      "  52 (+/- 1.5) <- [51 52 53 51 50 52 52 52 48 51]\n",
      "  51 (+/- 2.3) <- [56 50 51 51 52 51 50 51 50 46]\n",
      "  52 (+/- 1.6) <- [51 52 50 53 54 52 53 49 53 51]\n",
      "Accuracy: 51.5 (+/- 1.81)\n",
      "Mean time (20 cv): 0.64 seconds\n",
      "Overall time: 17.85 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 0x7fdcc7ace2d0>"
      ]
     },
     "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: 74.8 %\n",
      "5 genres: blues, classical, country, disco, hiphop\n",
      "Training data: (300, 1536), float64\n",
      "Testing data: (200, 1536), float64\n",
      "Training labels: (300,), uint8\n",
      "Testing labels: (200,), uint8\n",
      "Feature vectors accuracy: 61.4 %\n",
      "Clips accuracy: 71.0 %\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",
      "  46 (+/- 2.3) <- [45 47 45 40 47 46 44 46 49 47]\n",
      "  47 (+/- 1.9) <- [45 46 47 43 48 46 49 47 44 47]\n",
      "  47 (+/- 2.1) <- [47 43 46 48 46 47 49 46 43 49]\n",
      "  46 (+/- 2.8) <- [42 49 47 43 47 47 51 42 45 46]\n",
      "  47 (+/- 2.6) <- [40 47 47 47 47 45 45 49 44 51]\n",
      "  46 (+/- 2.2) <- [45 47 46 45 48 49 42 42 46 48]\n",
      "  47 (+/- 1.8) <- [45 45 51 46 45 49 46 45 46 47]\n",
      "  46 (+/- 1.9) <- [46 45 43 47 44 47 47 44 49 48]\n",
      "  47 (+/- 2.8) <- [50 40 47 45 47 47 50 46 44 47]\n",
      "  47 (+/- 2.1) <- [45 44 47 49 45 46 48 45 49 51]\n",
      "  47 (+/- 1.0) <- [46 47 44 47 46 45 47 48 46 46]\n",
      "  47 (+/- 1.6) <- [45 49 47 45 50 46 46 46 45 47]\n",
      "  47 (+/- 1.8) <- [47 48 47 49 47 43 47 47 44 48]\n",
      "  46 (+/- 1.4) <- [47 44 45 44 47 48 45 46 46 47]\n",
      "  47 (+/- 2.2) <- [46 44 49 48 47 41 46 47 47 46]\n",
      "  47 (+/- 1.6) <- [48 45 44 47 46 47 47 49 44 49]\n",
      "  46 (+/- 2.7) <- [48 42 42 44 42 47 47 50 46 47]\n",
      "  47 (+/- 2.0) <- [43 45 48 46 43 46 48 46 45 49]\n",
      "  47 (+/- 1.9) <- [51 46 44 44 46 47 45 47 47 45]\n",
      "  47 (+/- 2.9) <- [47 45 49 46 43 49 46 40 49 47]\n",
      "Accuracy: 46.6 (+/- 2.16)\n",
      "Mean time (20 cv): 0.38 seconds\n",
      "Overall time: 12.77 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 0x7fdcc74fed90>"
      ]
     },
     "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: 45.51 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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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd43979c10>"
      ]
     },
     "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: 41.69 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 0x7fdcc7920390>"
      ]
     },
     "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): 1.69 seconds\n",
      "Overall time: 37.66 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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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcc7bfca90>"
      ]
     },
     "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: 33.80 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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       "rwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcc7ca9710>"
      ]
     },
     "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): 1.30 seconds\n",
      "Overall time: 29.88 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 0x7fdcc71e9310>"
      ]
     },
     "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: 25.90 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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       "rwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcc7cc0650>"
      ]
     },
     "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): 0.92 seconds\n",
      "Overall time: 22.19 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 0x7fdcc75ac850>"
      ]
     },
     "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: 18.00 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 0x7fdcc719ccd0>"
      ]
     },
     "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: 13.91 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 0x7fdcc7263350>"
      ]
     },
     "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: 9.71 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'] = [0.87450428835022609, 0.9762856160127158, 1.066634160239367, 1.1691844174692472, 1.2452060882856728, 1.3728464093894361, 1.4367703441224047, 1.5227625400425093, 1.8131093299496335, 2.1603462116332328]\n",
      "res['objective_j'] = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "res['objective_i'] = [68870.421875, 68870.421875, 68870.421875, 68870.421875, 68870.421875, 68870.421875, 68870.421875, 68870.421875, 68870.421875, 68870.421875]\n",
      "res['objective_h'] = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "res['objective_g'] = [42317.16796875, 42317.16796875, 42317.16796875, 42317.16796875, 42317.16796875, 42317.16796875, 42317.16796875, 42317.16796875, 42317.16796875, 42317.16796875]\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'] = [1181.790431022644, 1181.790431022644, 1181.790431022644, 1181.790431022644, 1181.790431022644, 1181.790431022644, 1181.790431022644, 1181.790431022644, 1181.790431022644, 1181.790431022644]\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'] = [4.745763422818792, 4.745763422818792, 4.745763422818792, 4.745763422818792, 4.745763422818792, 4.745763422818792, 4.745763422818792, 4.745763422818792, 4.745763422818792, 4.745763422818792]\n",
      "res['iterations_inner'] = [898, 898, 898, 898, 898, 898, 898, 898, 898, 898]\n",
      "res['iterations_outer'] = [14, 14, 14, 14, 14, 14, 14, 14, 14, 14]\n",
      "res['accuracy'] = [63.649629629629622, 62.856501831501845, 61.899818840579677, 60.899999999999984, 59.742907801418397, 58.417719298245608, 56.743229166666673, 54.506529209622009, 51.492942176870734, 46.648063973063977]\n"
     ]
    },
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcc7d25d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment time: 1972 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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