3_1_simple_ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import math\n",
    "import statistics\n",
    "import pandas as pd\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load output test probabilitites and true labels for each model\n",
    "\n",
    "For every model there are 2 files generated in numpy binary format:\n",
    "\n",
    "  **probabilities.npy** - numpy array of size **TEST POINT CLOUDS CLOUD x CLASSES COUNT x MODELS COUNT** with output classification probability for each of 10 individually trained models\n",
    "\n",
    "  **true_labels.npy** - numpy array of size **TEST POINT CLOUDS CLOUD** with true labels data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Base models\n",
    "BASE_MODELS = ['pointnet', 'pointnet++', 'so-net', 'kcnet', 'deepsets', 'dgcnn', 'pointcnn']\n",
    "BASE_MODELS_ALIASES = ['PointNet', 'PointNet++', 'SO-Net', 'KCNet', 'DeepSets', 'DGCNN', 'PointCNN']\n",
    "SONET_BAGGING_MODELS = ['so-net_sz_01', 'so-net_sz_02', 'so-net_sz_03', 'so-net_sz_04', 'so-net_sz_05', 'so-net_sz_06',\n",
    "                        'so-net_sz_07', 'so-net_sz_08', 'so-net_sz_09', 'so-net_bagging']\n",
    "SONET_CONST_MODELS = ['so-net_TTT', 'so-net_FFT', 'so-net_FTF', 'so-net_TFF', 'so-net_TTF', 'so-net_TFT', 'so-net_FTT']\n",
    "SONET_CLASSIFIER_ENSEMBLE = ['so-net_' + str(i) for i in range(1, 11)]\n",
    "ALL_MODELS = BASE_MODELS + SONET_BAGGING_MODELS + SONET_CONST_MODELS + SONET_CLASSIFIER_ENSEMBLE\n",
    "\n",
    "CLASS_NAMES = ['airplane', 'bathtub', 'bed', 'bench', 'bookshelf', 'bottle', 'bowl',\n",
    "               'car', 'chair', 'cone', 'cup', 'curtain', 'desk', 'door', 'dresser',\n",
    "               'flower_pot', 'glass_box', 'guitar', 'keyboard', 'lamp', 'laptop',\n",
    "               'mantel', 'monitor', 'night_stand', 'person', 'piano', 'plant', 'radio',\n",
    "               'range_hood', 'sink', 'sofa', 'stairs', 'stool', 'table', 'tent', 'toilet',\n",
    "               'tv_stand', 'vase', 'wardrobe', 'xbox']\n",
    "CLASS_TEST_INSTANCES = [100, 50, 100, 20, 100, 100, 20, 100, 100, 20, 20, 20, 86, 20,\n",
    "                        86, 20, 100, 100, 20, 20, 20, 100, 100, 86, 20, 100, 100, 20,\n",
    "                        100, 20, 100, 20, 20, 100, 20, 100, 100, 100, 20, 20]\n",
    "CLASSES_COUNT = 40\n",
    "# MODELS_COUNT = 10\n",
    "# ENSEMBLE_MODELS_NUMBER = 10\n",
    "\n",
    "probabilities = {}\n",
    "true_labels = {}\n",
    "for model in ALL_MODELS:\n",
    "    for dir_name in ['models_reordered', 'models_raw', 'models_bagging', 'models_const', 'models_classifier_ensemble']:\n",
    "        if os.path.exists(os.path.join(dir_name, model)):\n",
    "            break\n",
    "    prob_filepath = os.path.join(os.path.join(dir_name, model), 'probabilities.npy')\n",
    "    label_filepath = os.path.join(os.path.join(dir_name, model), 'true_labels.npy')\n",
    "    probabilities[model] = np.load(prob_filepath)\n",
    "    if dir_name != 'models_reordered':\n",
    "        probabilities[model] = np.transpose(probabilities[model], axes=(2, 0, 1))\n",
    "\n",
    "    # probabilities[model] =  softmax(probabilities[model])\n",
    "    idx = np.argmax(probabilities[model], axis=-1)\n",
    "    hardvote = np.squeeze(np.eye(40)[idx.reshape(-1)]).reshape(probabilities[model].shape)\n",
    "    e_x = np.exp(probabilities[model])\n",
    "    softvote = e_x /np.expand_dims(np.sum(e_x, axis=-1), axis=-1)\n",
    "    probabilities[model] = {'activations': probabilities[model]/np.std(probabilities[model]),\n",
    "                           'hardvote': hardvote, 'softvote': softvote}\n",
    "    true_labels[model] = np.load(label_filepath)\n",
    "    \n",
    "    for class_idx in range(CLASSES_COUNT):\n",
    "        error_info = (\"Test cloud instances for model \" + model + \" and class \"\n",
    "                      + str(class_idx) + \" differs! Should be \" + str(CLASS_TEST_INSTANCES[class_idx])\n",
    "                      + \" but got \" + str(np.sum(true_labels[model] == class_idx)))\n",
    "        assert np.sum(true_labels[model] == class_idx) == CLASS_TEST_INSTANCES[class_idx] or \\\n",
    "          (model=='deepsets' and class_idx==12 and np.sum(true_labels[model] == class_idx) == 85) or \\\n",
    "          (model=='deepsets' and class_idx==39 and np.sum(true_labels[model] == class_idx) == 19) or \\\n",
    "          (model=='deepsets_ep100' and class_idx==12 and np.sum(true_labels[model] == class_idx) == 85) or \\\n",
    "          (model=='deepsets_ep100' and class_idx==39 and np.sum(true_labels[model] == class_idx) == 19), \\\n",
    "          error_info\n",
    "                \n",
    "#     assert (probabilities[model].shape[0] == ENSEMBLE_MODELS_NUMBER)\n",
    "    assert (probabilities[model]['activations'].shape[1] == 2468 or \\\n",
    "            (model=='deepsets' and probabilities[model]['activations'].shape[1] == 2466) or \\\n",
    "           (model=='deepsets_ep100' and probabilities[model]['activations'].shape[1] == 2466))\n",
    "    assert (probabilities[model]['activations'].shape[2] == CLASSES_COUNT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Caclulate statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "####################################################################################\n",
    "# Get random subsets\n",
    "####################################################################################\n",
    "\n",
    "def get_k_combinations_of_n_elems(n, k, rep):\n",
    "    \"\"\"\n",
    "    Get rep different k-combinations of set of n numbers. If requested reps is greater\n",
    "    than possible combinations number just return all possible combinations. In our case:\n",
    "    n - maximum number of ensembled models\n",
    "    k - actual number of ensembled models\n",
    "    rep - how many different k subsets of n we want to generate.    \n",
    "    \"\"\"\n",
    "    c_n_k = math.factorial(n) / math.factorial(k) / math.factorial(n-k)\n",
    "    combinations_number = min(c_n_k, rep)\n",
    "    \n",
    "    subsets = []   \n",
    "    while len(subsets) != combinations_number:\n",
    "        candidate = np.sort(np.random.choice(n, k, replace=False))\n",
    "        if not np.array([(el == candidate).all() for el in subsets]).any():\n",
    "            subsets.append(candidate)\n",
    "    return np.array(subsets)\n",
    "        \n",
    "####################################################################################\n",
    "# Calc accuracy statistics\n",
    "####################################################################################\n",
    "\n",
    "def get_stats_for_num_ens_param(params_list):\n",
    "    stats = {}\n",
    "    stats['mean'] = statistics.mean(params_list)\n",
    "    stats['stddev'] = statistics.stdev(params_list) if len(params_list) > 1 else 0.0\n",
    "    stats['median'] = statistics.median(params_list)\n",
    "    stats['min'] = min(params_list)\n",
    "    stats['max'] = max(params_list)\n",
    "    return stats\n",
    "\n",
    "def get_accuracy_stats(models_list, voting_method='activations'):\n",
    "    accuracy_stats = {}\n",
    "    for model in models_list:\n",
    "        subsets = {}\n",
    "        \n",
    "        num_ensemble_models = probabilities[model][voting_method].shape[0]\n",
    "        for num_ens in range(1, num_ensemble_models+1):\n",
    "            subset = get_k_combinations_of_n_elems(len(probabilities[model][voting_method]),\n",
    "                                                   num_ens, 100)\n",
    "            subsets[num_ens] = subset\n",
    "            \n",
    "        accuracy_stats[model] = {}\n",
    "\n",
    "        for num_ens in subsets:\n",
    "            num_ens_accs = []\n",
    "            for subset in subsets[num_ens]:\n",
    "                preds = np.argmax(np.mean(probabilities[model][voting_method][subset], axis=0), axis=-1)\n",
    "                true = true_labels[model]\n",
    "\n",
    "                acc = float(np.sum(preds == true))/len(true)\n",
    "                class_accs = []\n",
    "                for class_idx in range(CLASSES_COUNT):\n",
    "                    class_acc = float(np.sum((preds == true) * (true == class_idx)))/float(np.sum(true == class_idx))\n",
    "                    class_accs.append(class_acc)\n",
    "                mean_class_acc = statistics.mean(class_accs)\n",
    "                num_ens_accs.append({'acc': acc, 'mean_class_acc': mean_class_acc, 'class_accs': class_accs})\n",
    "\n",
    "            num_ens_summary = {}\n",
    "            num_ens_summary['acc'] = get_stats_for_num_ens_param([x['acc'] for x in num_ens_accs])\n",
    "            num_ens_summary['mean_class_acc'] = get_stats_for_num_ens_param([x['mean_class_acc'] for x in num_ens_accs])\n",
    "            num_ens_summary['class_accs'] = []\n",
    "            for class_idx in range(CLASSES_COUNT):\n",
    "                num_ens_summary['class_accs'].append(get_stats_for_num_ens_param([x['class_accs'][class_idx] \n",
    "                                                                                  for x in num_ens_accs]))\n",
    "            accuracy_stats[model][num_ens] = num_ens_summary\n",
    "    return accuracy_stats\n",
    "\n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 16}\n",
    "\n",
    "plt.rc('font', **font)\n",
    "\n",
    "def plot_acc_in_num_models(ax, acc_stats, acc_type='acc', styles={}, h_factor=0.1, times=None,\n",
    "                           without_errorbar=False, hard_vote=False, with_xlabel=True):\n",
    "    for model in acc_stats:\n",
    "        x = acc_stats[model].keys()\n",
    "        y = [acc_stats[model][i][acc_type]['mean'] for i in x]\n",
    "        if hard_vote and 'hardvote' in str(model):\n",
    "            x = list(x)[2:]\n",
    "            y = y[2:]\n",
    "        if 'stddev' in acc_stats[model][1][acc_type] and not without_errorbar:\n",
    "            e = [acc_stats[model][i][acc_type]['stddev'] for i in x]\n",
    "        if times is not None:\n",
    "            x = [v * times[model] for v in x]\n",
    "        if 'stddev' in acc_stats[model][1][acc_type] and not without_errorbar:\n",
    "            ax.errorbar(x, y, e, label=styles[model]['label'], capsize=6, \n",
    "                               color=styles[model]['color'],\n",
    "                              linestyle=styles[model]['linestyle'],\n",
    "                       linewidth=styles[model]['linewidth'],\n",
    "                       capthick=2, elinewidth=2)\n",
    "        else:\n",
    "            ax.plot(x, y, label=styles[model]['label'], \n",
    "                    color=styles[model]['color'],\n",
    "                    linestyle=styles[model]['linestyle'],\n",
    "                    linewidth=styles[model]['linewidth'])\n",
    "    \n",
    "    # Inference or ensemble accuracy?\n",
    "    if with_xlabel:\n",
    "        if times is None:\n",
    "            ax.set_xlabel('Number of ensembled models')\n",
    "        else:\n",
    "            ax.set_xlabel('Inference time for batch of 4 point clouds [ms]')\n",
    "    \n",
    "    # Instance or class accuracy?\n",
    "    if acc_type == 'acc':\n",
    "        ax.set_ylabel('Instance classification accuracy')\n",
    "    elif acc_type == 'mean_class_acc':\n",
    "        ax.set_ylabel('Mean class accuracy')\n",
    "    \n",
    "    # Set position\n",
    "    box = ax.get_position()\n",
    "    ax.set_position([box.x0, box.y0 + box.height * h_factor,\n",
    "                     box.width, box.height * (1.0 - h_factor)])\n",
    "\n",
    "    # Show ytick labels\n",
    "    ax.yaxis.set_tick_params(labelleft=True)\n",
    "        \n",
    "    # Return handle\n",
    "    return ax.get_legend_handles_labels()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choose voting method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_stats_base_models_voting = {method: get_accuracy_stats(BASE_MODELS, method) for method in \n",
    "                                ['activations', 'hardvote', 'softvote']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_sonet = {method: acc_stats_base_models_voting[method]['so-net'] for method in acc_stats_base_models_voting}\n",
    "compare_mean_arch = {}\n",
    "for method in acc_stats_base_models_voting:\n",
    "    mean_arch_acc = {}\n",
    "    mean_arch_mca = {}\n",
    "    for arch in acc_stats_base_models_voting[method]:\n",
    "        arch_voting = acc_stats_base_models_voting[method][arch]\n",
    "        for num_models in arch_voting:\n",
    "            arch_num = arch_voting[num_models]\n",
    "            arch_num_acc = arch_num['acc']\n",
    "            arch_num_mca = arch_num['mean_class_acc']\n",
    "            if num_models not in mean_arch_acc:\n",
    "                mean_arch_acc[num_models] = []\n",
    "            if num_models not in mean_arch_mca:\n",
    "                mean_arch_mca[num_models] = []\n",
    "            mean_arch_acc[num_models].append(arch_num_acc['mean'])\n",
    "            mean_arch_mca[num_models].append(arch_num_mca['mean'])\n",
    "    mean_arch_acc = {x: np.mean(mean_arch_acc[x]) for x in mean_arch_acc}\n",
    "    mean_arch_mca = {x: np.mean(mean_arch_mca[x]) for x in mean_arch_mca}\n",
    "    compare_mean_arch[method] = {x: {'acc': {'mean': mean_arch_acc[x]}, \n",
    "                                     'mean_class_acc': {'mean': mean_arch_mca[x]}} \n",
    "                                 for x in mean_arch_acc}\n",
    "compare_voting = {}\n",
    "for item in compare_sonet:\n",
    "    compare_voting['so-net ' + item] = compare_sonet[item]\n",
    "for item in compare_mean_arch:\n",
    "    compare_voting['mean arch ' + item] = compare_mean_arch[item]\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(25, 8))\n",
    "linewidth = 4\n",
    "styles = {'so-net activations': {'color': 'r', 'linestyle': '-', 'label': 'SO-Net, activations averaging',\n",
    "                                 'linewidth' : linewidth},\n",
    "         'so-net softvote': {'color': 'g', 'linestyle': '-', 'label': 'SO-Net, soft-voting',\n",
    "                            'linewidth' : linewidth},\n",
    "         'so-net hardvote': {'color': 'b', 'linestyle': '-', 'label': 'SO-Net, hard-voting',\n",
    "                            'linewidth' : linewidth},\n",
    "         'mean arch activations': {'color': 'r', 'linestyle': '--',\n",
    "                                   'label': 'Mean accuracy of all architectures, activations averaging',\n",
    "                                  'linewidth' : linewidth},\n",
    "         'mean arch softvote': {'color': 'g', 'linestyle': '--',\n",
    "                                'label': 'Mean accuracy of all architectures, soft-voting',\n",
    "                               'linewidth' : linewidth},\n",
    "         'mean arch hardvote': {'color': 'b', 'linestyle': '--',\n",
    "                               'label': 'Mean accuracy of all architectures, hard-voting',\n",
    "                               'linewidth' : linewidth}}\n",
    "handles, labels = plot_acc_in_num_models(ax1, compare_voting, 'acc', styles, hard_vote=True)\n",
    "plot_acc_in_num_models(ax2, compare_voting, 'mean_class_acc', styles, hard_vote=True)\n",
    "f.legend(handles=handles, loc='lower center', ncol=3)\n",
    "f.savefig('images/voting_methods_color.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1152 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(2, 2, sharey=False, sharex=False, figsize=(25, 16))\n",
    "linewidth = 4\n",
    "color_activations = [0.1, 0.1, 0.1]\n",
    "color_softvote = [0.7, 0.7, 0.7]\n",
    "color_hardvote = [0.4, 0.4, 0.4]\n",
    "styles = {'so-net activations': {'color': color_activations, 'linestyle': '-',\n",
    "                                 'label': 'SO-Net, activations averaging',\n",
    "                                 'linewidth' : linewidth},\n",
    "         'so-net softvote': {'color': color_softvote, 'linestyle': '-',\n",
    "                             'label': 'SO-Net, soft-voting',\n",
    "                             'linewidth' : linewidth},\n",
    "         'so-net hardvote': {'color': color_hardvote, 'linestyle': '-',\n",
    "                             'label': 'SO-Net, hard-voting',\n",
    "                             'linewidth' : linewidth},\n",
    "         'mean arch activations': {'color': color_activations, 'linestyle': '-',\n",
    "                                   'label': 'Mean accuracy of all architectures, activations averaging',\n",
    "                                   'linewidth' : linewidth},\n",
    "         'mean arch softvote': {'color': color_softvote, 'linestyle': '-',\n",
    "                                'label': 'Mean accuracy of all architectures, soft-voting',\n",
    "                                'linewidth' : linewidth},\n",
    "         'mean arch hardvote': {'color': color_hardvote, 'linestyle': '-',\n",
    "                                'label': 'Mean accuracy of all architectures, hard-voting',\n",
    "                                'linewidth' : linewidth}}\n",
    "compare_voting_sonet = {'so-net activations': compare_voting['so-net activations'],\n",
    "                        'so-net softvote': compare_voting['so-net softvote'],\n",
    "                        'so-net hardvote': compare_voting['so-net hardvote']}\n",
    "compare_voting_mean = {'mean arch activations': compare_voting['mean arch activations'],\n",
    "                       'mean arch softvote': compare_voting['mean arch softvote'],\n",
    "                       'mean arch hardvote': compare_voting['mean arch hardvote']}\n",
    "\n",
    "# Plot acc\n",
    "handles, _ = plot_acc_in_num_models(ax[0,0], compare_voting_sonet, 'acc', styles,\n",
    "                                    hard_vote=True, with_xlabel=True)\n",
    "plot_acc_in_num_models(ax[0,1], compare_voting_sonet, 'mean_class_acc', styles,\n",
    "                       hard_vote=True, with_xlabel=True)\n",
    "handles2, _ = plot_acc_in_num_models(ax[1,0], compare_voting_mean, 'acc', styles, hard_vote=True)\n",
    "plot_acc_in_num_models(ax[1,1], compare_voting_mean, 'mean_class_acc', styles, hard_vote=True)\n",
    "f.legend(handles=handles + handles2, loc='lower center', ncol=3)\n",
    "f.savefig('images/voting_methods_grayscale.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, 2, sharey=False, sharex=False, figsize=(25, 8))\n",
    "linewidth = 4\n",
    "color_activations = [0.1, 0.1, 0.1]\n",
    "color_softvote = [0.7, 0.7, 0.7]\n",
    "color_hardvote = [0.4, 0.4, 0.4]\n",
    "styles = {'so-net activations': {'color': color_activations, 'linestyle': '-',\n",
    "                                 'label': 'SO-Net, activations averaging',\n",
    "                                 'linewidth' : linewidth},\n",
    "         'so-net softvote': {'color': color_softvote, 'linestyle': '-',\n",
    "                             'label': 'SO-Net, soft-voting',\n",
    "                             'linewidth' : linewidth},\n",
    "         'so-net hardvote': {'color': color_hardvote, 'linestyle': '-',\n",
    "                             'label': 'SO-Net, hard-voting',\n",
    "                             'linewidth' : linewidth},\n",
    "         'mean arch activations': {'color': color_activations, 'linestyle': '-',\n",
    "                                   'label': 'Mean accuracy of all architectures, activations averaging',\n",
    "                                   'linewidth' : linewidth},\n",
    "         'mean arch softvote': {'color': color_softvote, 'linestyle': '-',\n",
    "                                'label': 'Mean accuracy of all architectures, soft-voting',\n",
    "                                'linewidth' : linewidth},\n",
    "         'mean arch hardvote': {'color': color_hardvote, 'linestyle': '-',\n",
    "                                'label': 'Mean accuracy of all architectures, hard-voting',\n",
    "                                'linewidth' : linewidth}}\n",
    "compare_voting_sonet = {'so-net activations': compare_voting['so-net activations'],\n",
    "                        'so-net softvote': compare_voting['so-net softvote'],\n",
    "                        'so-net hardvote': compare_voting['so-net hardvote']}\n",
    "compare_voting_mean = {'mean arch activations': compare_voting['mean arch activations'],\n",
    "                       'mean arch softvote': compare_voting['mean arch softvote'],\n",
    "                       'mean arch hardvote': compare_voting['mean arch hardvote']}\n",
    "\n",
    "# Plot acc\n",
    "handles, _ = plot_acc_in_num_models(ax[0], compare_voting, 'acc', styles,\n",
    "                                    hard_vote=True, with_xlabel=True)\n",
    "plot_acc_in_num_models(ax[1], compare_voting, 'mean_class_acc', styles,\n",
    "                       hard_vote=True, with_xlabel=True)\n",
    "#handles2, _ = plot_acc_in_num_models(ax[0], compare_voting_mean, 'acc', styles, hard_vote=True)\n",
    "#plot_acc_in_num_models(ax[1], compare_voting_mean, 'mean_class_acc', styles, hard_vote=True)\n",
    "f.legend(handles=handles, loc='lower center', ncol=3)\n",
    "f.savefig('images/voting_methods_grayscale_1.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, 2, sharey=False, sharex=False, figsize=(25, 8))\n",
    "linewidth = 4\n",
    "color_activations = [0.1, 0.1, 0.1]\n",
    "color_softvote = [0.7, 0.7, 0.7]\n",
    "color_hardvote = [0.4, 0.4, 0.4]\n",
    "styles = {'so-net activations': {'color': color_activations, 'linestyle': '-',\n",
    "                                 'label': 'SO-Net, activations averaging',\n",
    "                                 'linewidth' : linewidth},\n",
    "         'so-net softvote': {'color': color_softvote, 'linestyle': '-',\n",
    "                             'label': 'SO-Net, soft-voting',\n",
    "                             'linewidth' : linewidth},\n",
    "         'so-net hardvote': {'color': color_hardvote, 'linestyle': '-',\n",
    "                             'label': 'SO-Net, hard-voting',\n",
    "                             'linewidth' : linewidth},\n",
    "         'mean arch activations': {'color': color_activations, 'linestyle': '-',\n",
    "                                   'label': 'Mean accuracy of all architectures, activations averaging',\n",
    "                                   'linewidth' : linewidth},\n",
    "         'mean arch softvote': {'color': color_softvote, 'linestyle': '-',\n",
    "                                'label': 'Mean accuracy of all architectures, soft-voting',\n",
    "                                'linewidth' : linewidth},\n",
    "         'mean arch hardvote': {'color': color_hardvote, 'linestyle': '-',\n",
    "                                'label': 'Mean accuracy of all architectures, hard-voting',\n",
    "                                'linewidth' : linewidth}}\n",
    "compare_voting_sonet = {'so-net activations': compare_voting['so-net activations'],\n",
    "                        'so-net softvote': compare_voting['so-net softvote'],\n",
    "                        'so-net hardvote': compare_voting['so-net hardvote']}\n",
    "compare_voting_mean = {'mean arch activations': compare_voting['mean arch activations'],\n",
    "                       'mean arch softvote': compare_voting['mean arch softvote'],\n",
    "                       'mean arch hardvote': compare_voting['mean arch hardvote']}\n",
    "\n",
    "# Plot acc\n",
    "handles, _ = plot_acc_in_num_models(ax[0], compare_voting_sonet, 'acc', styles,\n",
    "                                    hard_vote=True, with_xlabel=True)\n",
    "plot_acc_in_num_models(ax[1], compare_voting_sonet, 'mean_class_acc', styles,\n",
    "                       hard_vote=True, with_xlabel=True)\n",
    "# handles2, _ = plot_acc_in_num_models(ax[1,0], compare_voting_mean, 'acc', styles, hard_vote=True)\n",
    "# plot_acc_in_num_models(ax[1,1], compare_voting_mean, 'mean_class_acc', styles, hard_vote=True)\n",
    "f.legend(handles=handles, loc='lower center', ncol=3)\n",
    "f.savefig('images/voting_methods_grayscale_2.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More insight into voting scheme --- where hardvote is better than activations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 2468, 40)\n",
      "pointnet activations is wrong and hardvote is right! hit_1: 16 hit_2: 1 test_cloud_idx 117\n",
      "pointnet activations is wrong and hardvote is right! hit_1: 2 hit_2: 1 test_cloud_idx 127\n",
      "pointnet activations is wrong and hardvote is right! hit_1: 36 hit_2: 4 test_cloud_idx 338\n",
      "pointnet activations is wrong and hardvote is right! hit_1: 21 hit_2: 23 test_cloud_idx 1430\n",
      "pointnet activations is wrong and hardvote is right! hit_1: 21 hit_2: 28 test_cloud_idx 1789\n",
      "pointnet activations is wrong and hardvote is right! hit_1: 23 hit_2: 36 test_cloud_idx 2255\n",
      "pointnet activations is wrong and hardvote is right! hit_1: 33 hit_2: 36 test_cloud_idx 2269\n",
      "pointnet activations is wrong and hardvote is right! hit_1: 23 hit_2: 36 test_cloud_idx 2309\n",
      "MISSCLASSIFICATIONS: 8\n"
     ]
    }
   ],
   "source": [
    "for model in BASE_MODELS:\n",
    "\n",
    "    # voting methods to compare\n",
    "    voting_method_1 = 'activations'\n",
    "    voting_method_2 = 'hardvote'\n",
    "    \n",
    "    # How many learned models\n",
    "    learned_models_no_1 = probabilities[model][voting_method_1].shape[0]\n",
    "    assert learned_models_no_1 == 10, 'There should be 10 learned models for each architecture and voting scheme'\n",
    "    learned_models_no_2 = probabilities[model][voting_method_2].shape[0]\n",
    "    assert learned_models_no_2 == 10, 'There should be 10 learned models for each architecture and voting scheme'\n",
    "    \n",
    "    # Calc ensemble scores & votes\n",
    "    print (probabilities[model][voting_method_1].shape)\n",
    "    ensemble_scores_1 = np.mean(probabilities[model][voting_method_1][:5], axis=0)\n",
    "    ensemble_votes_1 = np.argmax(ensemble_scores_1, axis=-1)\n",
    "    ensemble_scores_2 = np.mean(probabilities[model][voting_method_2][:5], axis=0)\n",
    "    ensemble_votes_2 = np.argmax(ensemble_scores_2, axis=-1)\n",
    "    \n",
    "    # Get single scores & votes\n",
    "    #model_instance_idx = np.random.choice(np.arange(learned_models_no))\n",
    "    #model_instance_idx = 0\n",
    "    #single_scores = probabilities[model][voting_method][model_instance_idx]\n",
    "    #single_votes = np.argmax(single_scores, axis=-1)\n",
    "    \n",
    "    # Print all cases where ensemble is better than score\n",
    "    misclassifications = []\n",
    "    for test_cloud_idx, _ in enumerate(true_labels[model]):\n",
    "        hit_1 = ensemble_votes_1[test_cloud_idx] == true_labels[model][test_cloud_idx]\n",
    "        hit_2 = ensemble_votes_2[test_cloud_idx] == true_labels[model][test_cloud_idx]\n",
    "        if not hit_1 and hit_2:\n",
    "            print(model, voting_method_1, 'is wrong and', voting_method_2, 'is right!',\n",
    "                  'hit_1:', ensemble_votes_1[test_cloud_idx], 'hit_2:', ensemble_votes_2[test_cloud_idx], 'test_cloud_idx', test_cloud_idx)\n",
    "            misclassifications.append((ensemble_votes_1[test_cloud_idx], ensemble_votes_2[test_cloud_idx], test_cloud_idx))\n",
    "    \n",
    "    print (\"MISSCLASSIFICATIONS:\", len(misclassifications))\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for missclassification in misclassifications:\n",
    "\n",
    "#     # Test cloud idx\n",
    "#     test_cloud_idx = missclassification[2]\n",
    "\n",
    "#     # for hardvote\n",
    "#     print ('=================================================================')\n",
    "#     # for activations\n",
    "#     print ('For activations (should be:', true_labels[model][test_cloud_idx], \")\")\n",
    "#     for model_idx in range(5):\n",
    "#         high_score = probabilities[model]['activations'][model_idx, test_cloud_idx][probabilities[model]['activations'][model_idx, test_cloud_idx] > 0]\n",
    "#         high_score_idx = np.arange(40)[probabilities[model]['activations'][model_idx, test_cloud_idx] > 0]\n",
    "#         print(high_score, high_score_idx)\n",
    "    \n",
    "#     print ('For hardvote (should be:', true_labels[model][test_cloud_idx], \")\")\n",
    "#     for model_idx in range(5):\n",
    "#         print(np.argmax(probabilities[model]['hardvote'][model_idx, test_cloud_idx]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More insight into voting scheme --- where activations is better than hardvote "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 2468, 40)\n",
      "pointnet hardvote is wrong and activations is right! hit_1: 30 hit_2: 8 test_cloud_idx 615\n",
      "pointnet hardvote is wrong and activations is right! hit_1: 30 hit_2: 12 test_cloud_idx 802\n",
      "pointnet hardvote is wrong and activations is right! hit_1: 38 hit_2: 14 test_cloud_idx 877\n",
      "pointnet hardvote is wrong and activations is right! hit_1: 15 hit_2: 26 test_cloud_idx 1668\n",
      "pointnet hardvote is wrong and activations is right! hit_1: 15 hit_2: 26 test_cloud_idx 1726\n",
      "pointnet hardvote is wrong and activations is right! hit_1: 16 hit_2: 27 test_cloud_idx 1738\n",
      "pointnet hardvote is wrong and activations is right! hit_1: 12 hit_2: 33 test_cloud_idx 2008\n",
      "pointnet hardvote is wrong and activations is right! hit_1: 3 hit_2: 33 test_cloud_idx 2102\n",
      "pointnet hardvote is wrong and activations is right! hit_1: 19 hit_2: 37 test_cloud_idx 2387\n",
      "pointnet hardvote is wrong and activations is right! hit_1: 21 hit_2: 39 test_cloud_idx 2465\n",
      "MISSCLASSIFICATIONS: 10\n"
     ]
    }
   ],
   "source": [
    "for model in BASE_MODELS:\n",
    "\n",
    "    # voting methods to compare\n",
    "    voting_method_2 = 'activations'\n",
    "    voting_method_1 = 'hardvote'\n",
    "    \n",
    "    # How many learned models\n",
    "    learned_models_no_1 = probabilities[model][voting_method_1].shape[0]\n",
    "    assert learned_models_no_1 == 10, 'There should be 10 learned models for each architecture and voting scheme'\n",
    "    learned_models_no_2 = probabilities[model][voting_method_2].shape[0]\n",
    "    assert learned_models_no_2 == 10, 'There should be 10 learned models for each architecture and voting scheme'\n",
    "    \n",
    "    # Calc ensemble scores & votes\n",
    "    print (probabilities[model][voting_method_1].shape)\n",
    "    ensemble_scores_1 = np.mean(probabilities[model][voting_method_1][:5], axis=0)\n",
    "    ensemble_votes_1 = np.argmax(ensemble_scores_1, axis=-1)\n",
    "    ensemble_scores_2 = np.mean(probabilities[model][voting_method_2][:5], axis=0)\n",
    "    ensemble_votes_2 = np.argmax(ensemble_scores_2, axis=-1)\n",
    "    \n",
    "    # Get single scores & votes\n",
    "    #model_instance_idx = np.random.choice(np.arange(learned_models_no))\n",
    "    #model_instance_idx = 0\n",
    "    #single_scores = probabilities[model][voting_method][model_instance_idx]\n",
    "    #single_votes = np.argmax(single_scores, axis=-1)\n",
    "    \n",
    "    # Print all cases where ensemble is better than score\n",
    "    misclassifications = []\n",
    "    for test_cloud_idx, _ in enumerate(true_labels[model]):\n",
    "        hit_1 = ensemble_votes_1[test_cloud_idx] == true_labels[model][test_cloud_idx]\n",
    "        hit_2 = ensemble_votes_2[test_cloud_idx] == true_labels[model][test_cloud_idx]\n",
    "        if not hit_1 and hit_2:\n",
    "            print(model, voting_method_1, 'is wrong and', voting_method_2, 'is right!',\n",
    "                  'hit_1:', ensemble_votes_1[test_cloud_idx], 'hit_2:', ensemble_votes_2[test_cloud_idx], 'test_cloud_idx', test_cloud_idx)\n",
    "            misclassifications.append((ensemble_votes_1[test_cloud_idx], ensemble_votes_2[test_cloud_idx], test_cloud_idx))\n",
    "    \n",
    "    print (\"MISSCLASSIFICATIONS:\", len(misclassifications))\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for missclassification in misclassifications:\n",
    "\n",
    "#     # Test cloud idx\n",
    "#     test_cloud_idx = missclassification[2]\n",
    "\n",
    "#     # for hardvote\n",
    "#     print ('=================================================================')\n",
    "#     print ('For hardvote (should be:', true_labels[model][test_cloud_idx], \")\")\n",
    "#     for model_idx in range(5):\n",
    "#         print(np.argmax(probabilities[model]['hardvote'][model_idx, test_cloud_idx]))\n",
    "\n",
    "#     # for activations\n",
    "#     print ('For activations (should be:', true_labels[model][test_cloud_idx], \")\")\n",
    "#     for model_idx in range(5):\n",
    "#         high_score = probabilities[model]['activations'][model_idx, test_cloud_idx][probabilities[model]['activations'][model_idx, test_cloud_idx] > 0]\n",
    "#         high_score_idx = np.arange(40)[probabilities[model]['activations'][model_idx, test_cloud_idx] > 0]\n",
    "#         print(high_score, high_score_idx)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot accuracies in function of ensembled models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_stats_base_models = get_accuracy_stats(BASE_MODELS, 'activations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(25, 8))\n",
    "#'pointnet', 'pointnet++', 'so-net', 'kcnet', 'deepsets', 'dgcnn', 'pointcnn'\n",
    "styles = {'pointnet': {'color': 'r', 'linestyle': '--', 'label': 'PointNet', 'linewidth' : linewidth},\n",
    "         'pointnet++': {'color': 'r', 'linestyle': '-', 'label': 'PointNet++', 'linewidth' : linewidth},\n",
    "         'so-net': {'color': 'g', 'linestyle': '-', 'label': 'SO-Net', 'linewidth' : linewidth},\n",
    "         'kcnet': {'color': 'b', 'linestyle': '-', 'label': 'KCNet', 'linewidth' : linewidth},\n",
    "         'deepsets': {'color': 'c', 'linestyle': '-', 'label': 'DeepSets', 'linewidth' : linewidth},\n",
    "         'dgcnn': {'color': 'y', 'linestyle': '-', 'label': 'DGCNN', 'linewidth' : linewidth},\n",
    "         'pointcnn': {'color': 'm', 'linestyle': '-', 'label': 'PointCNN', 'linewidth' : linewidth},}\n",
    "h = 0.05\n",
    "handles, labels = plot_acc_in_num_models(ax1, acc_stats_base_models, 'acc', styles, h)\n",
    "plot_acc_in_num_models(ax2, acc_stats_base_models, 'mean_class_acc', styles, h)\n",
    "f.legend(handles=handles, loc='lower center', ncol=7)\n",
    "f.savefig('images/models_accuracy_color.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J0oBynncm5JXE9wohPoU8Z+FcyNe+pKyBSZL0gxDiG8i9O6IA/D/IPQf8APQDME2SpL9NDU/zfNwAHIfcq6IegImQF6BJ0RwzHsBuzVxzWyAvoOMGuYJ3Q5KkqLJeRwlsAewSQnwOoC6ASMgL0BjsFaExC/J9+FEIsRJypd4RcsWvgSRJozTHzYY8z6Ju+HMhD12sbKWmA0mS7mvSXoQQIlMT13YAXjcQXhiAHwEcEkKsgdzTxAXyPIXmkiRNNxQJIcRuAOchfzFNA9AG8vyLn1fQdRIREVEtxzo+ANbxy4p1fNbxiWoMzhFORI/jK8iVnIdIknQf8pAtNeSKVyTkX82PV1I8BgB4DsB3AIZB7sHygU580iFX+vZD7o1xCMBazefKHSdJkg4C6A/AAfJ1roK86ndnSZLKPMRRYxiAOQAGA9gNYBvkeeOuoGxz4QHy4kF+kL8kfA95SN0JTZy117Af8tyKNgC+hHxvPoY85K+kXhzlEQl5Bft1kHsb/Qqgt2a4n0GSJN0A8Azkyt9CzXV8BnmxmWM6xx0B8AqAJpBXgp8C4B08OiS2wpUhHczRXMNwyGm1F4BQA+H9CrkCnQogGnKFejnkxXh+LCEqP2rCXAO5l81YyM9yarkvjoiIiJ5GrOOzjl8WrOOzjk9UYwhJMmWaLCIiIiIiIiIiIiKimok9womIiIiIiIiIiIioVmNDOBERERERERERERHVamwIJyIiIiIiIiIiIqJajQ3hRERERERERERERFSrsSGciIiIiIiIiIiIiGo1NoQTERERERERERERUa3GhnAiIiIiIiIiIiIiqtXYEE5EREREREREREREtRobwomIiIiIiIiIiIioVmNDOBERERERERERERHVamwIJyIiIiIiIiIiIqJajQ3hRERERERERERERFSrsSGciIiIiIiIiIiIiGo1NoQTERERERERERERUa3GhnAiIiIiIiIiIiIiqtXYEE5EREREREREREREtRobwomIiIiIiIiIiIioVlNUdQSIqGzOnTvXSKFQhAshAiVJcgB/0CIiIiIiIiJ6ktRCiDuFhYVzg4KCDlV1ZIjINEKSpKqOAxGZ6Ny5c31VKlW0u7s77Ozssi0sLAqFEFUdLSIiIiIiIqKnhlqtFg8ePLCMi4tT5uXlvc3GcKKagT1JiWoQCwuLqX5+fgUuLi7pSqWSjeBERERERERET5iZmZlkY2PzwM/PL1+hUMyu6vgQkWnYEE5Ug0iS5GdjY5NT1fEgIiIiIiIietpZWVnlSpLkXtXxICLTcI5woppFPG4v8NGjR7cty/Gff/75ucc6IVV7e/fuLVOaCAkJYZqoJfjsqTRCiDKlEUmSmEboIcxnqLIxn6LKxu9PVBIzMzMJ7GRKVGPwj5WIiIiIiIiIiIiIajUulklUg5w/fz4uMDDwbkWHq+3lwN4LpKXtwceeeU8fPnsqjbb3JXtVUnkxn6HKxnyKKhu/P5Gu8+fPuwQGBvpVdTyIqHTsEU5EVW7jxo0OzzzzTBMnJ6dAS0vLIE9Pz4CePXv6b9u2zU7/2BMnTlj37t3b39nZOVCpVAZ5eXkFDBs2zOf69esWpp5PCNFWCNF22bJlzvr7BgwYUN/LyyugrNdw+fJlZVhYmGdsbKyyrJ+lR0VHRzsLIdpeuHBBpbv9xIkT1vb29q2bNWvWPDExUQEAGRkZZjNmzHBv3rx5MxsbmzYqlSrIz8+v5YgRI3x0Px8cHNxECNF24MCBfvo+ilgBAAAfL0lEQVTni4qKchFCtL18+XKZn19YWJjnd999Z1uOyyQ92ueufVlZWbXx8vIKeO655/y//PJLR7Va/chnEhMTFRMmTPBq3Lhxc2tr6zYqlSrI29u75cCBA/327t37yHNRq9X47LPPnJ599tnGDg4OrRUKRZCbm1urkJCQBnv27Ck+PiwszFMI0dbb27tlXl7eQ3NSXbhwQSWEaBsdHV2ch2jjbmtr2zolJcVc9/iCggIIIdqGhYV5VsiNeorppxEbG5s2TZo0ab5w4cK6BQUFJodz+fJlpf4zNNXevXttw8LCPIuKih7Zx/KlZqju6QiQ87bx48d7NWzYsIWVlVUbKyurNo0bN24+btw4r/j4+OI6j5eXV4AQou0777zzSP4yceJET/1pQyqrLKT/KU9ZVhViY2OVL774ol+9evUClEplkJOTU2Dr1q2bTpo0qcxlFfOpJ6+652MsD4moumJDOBFVqfnz57uOGDHCv0GDBrkrV66M27Jly5X33nsvEQCOHDnyUEP4J5984tSjR49maWlpisjIyBu7d+/++5133kk8ceKEXdu2bZv//PPPVmU598cff+yZm5v7eJOua1y5ckW1dOlSj7///ltV+tFUHt9//71NSEhIY19f39wff/zxsoeHR2F8fLxFUFBQs08++cT9ueeeS9+wYcPVbdu2XRk9enTSuXPnbF566SV//XC+++4753PnzllWVLyWLl3qceTIETaEV6C1a9deO3LkyKVt27ZdCQ8Pv61SqaTRo0c36Ny5c+OsrKziv9lffvnFMjAwsPm3337rPGjQoHubNm26um3btisTJkxIunHjhio0NLTxzZs3i9dDKSwsRP/+/RtMmDChvre3d96KFSvi9uzZ8/ecOXNu5ebmigEDBjROTU19qBH71q1bquXLl7uYGvesrCzz2bNnc8GkSqZNIxs3brzapk2b7IiICJ8pU6aY3Hjj4+NTcOTIkUuDBw9OL+u5jx07Zrt06VKPkhqzWL7UDNU1HZ07d86ydevWzbdu3er873//OzUmJuafmJiYf4YOHZq6b98+xzfeeMNH/zNffvmlm/YHYlNUdFlIjzK1LKsKf//9t7JDhw7NL168aD116tSEHTt2XPnoo49utGvXLmvPnj2OZQ2P+VTVqa75mBbLQyKqbrhYJhFVqU8++cStZ8+e97ds2RKvsznzvffeu6vbu+C3336zDAsL8+vZs2favn37rpmby21Vffv2zRo+fHhacHBws3//+9/+ly9fvqhSqUqd86lTp04Zp0+ftluyZEndiIiI5Aq/MKpQ+/btqzNkyJBGTZs2zfn++++vODk5qQHgP//5T/2UlBSL06dP/xUQEJCnPT40NDRz2rRpKV9//bWDbjhNmzZ9kJycbBEeHu516NChq0/6Osg07dq1y2nZsmXx8xw/fvy9devWOYwaNcp//Pjx9davX38zLy9PvPTSSw2tra3VZ86cueTp6VmoPT40NDRz6tSpKatWrXJSKpXF+UF4eLjHwYMHHb/66qurI0eOvK97zrFjx97bsWOHne7xgJxXLFmyxOPtt9++a21tbVLe8tVXX7nOmDEjydvbu7C046l8dNPIoEGDMq5fv65as2aN67JlyxJM+byVlZXUo0eP7MqIG8uXmqM6pqOCggK89NJL/iqVSv3TTz9d8vLyKs5HBgwYkDlz5sykrVu32ut+5plnnsn6448/bGbNmuX+xRdf3CrtHCwLnwxTyrKqitunn37qkpOTY/bDDz9cdnd3L65wv/nmm2lFRUWlpiGqPqpjPqZVleXh3r17bUNDQxtfunTpzyZNmuSb+rng4OAm3t7eedu3b4+rxOgRURVij3AiqlLp6ekKV1dXg+P3tI3dALB48WLXoqIifP755zd1twOAu7t70axZs27Hx8erNmzY4KAfjiFBQUHZPXv2vL906VKPzMzMEvPCgoICzJgxw71+/fotlEplkKura6s333yzXk5OjgD+V9ECgIEDBzbWDgU0NC0Dld3OnTvtBg8e3CggICD7+PHjxY3gJ06csP7pp59sJ02alKjbCK5lZmaG4cOHP9TYaW1tXfTOO+8kHj582OHkyZPWpZ17/fr1DoGBgU2trKza2Nratu7bt2+DK1euFA/H1A45X7FihYf2uXP6i8oxcuTI+z169LgfExNTNzMz02z9+vUO8fHxqnnz5t3SbQTXNWbMmHtubm5FAJCbmytWrVrl1q1bt3T9RnCtQYMGZdja2j7UrWnhwoW3U1JSLD766CNXU+L5/vvvJ2r+9SjbFdLjaNOmTU5WVpb57du3FXl5eWLixImeXl5eARYWFkFeXl4BEydO9NSd4sbQUPAXX3zRz83NrdXp06et2rZt28TKyqqNr69vy48//riu9piwsDDPpUuXegCAUqksHpKuGxeWLzVXdUhHGzdudLx+/brl3Llzb+s2gmtZWFjg5ZdffqjnpqenZ/6wYcNSNm7c6GrKVHFlLQup4uiXZQCQmZlpNnbsWC/dtDZt2jR3/ekmEhISFC+//LKPq6trK6VSGVS/fv0WixcvfmjEkna6jAMHDtTp2bOnv7W1dRsHB4fWw4cP99HthX7v3j2FSqWSXFxcHpnTQr+e/bj51KpVq5yaNWvW3Nrauk2dOnXaNG7cuPmiRYtMHmlFZVMd8jEtlodEVB2xRzhRLaFdsEXL2MIthw8fdtm+fbuv9v0zzzxjdPHNOXPmNEtMTCz+gjR58uS/GjVqlKN/3JUrV6wNbTdFq1atsnfs2OE8c+bMvJdeeul+q1atHmnQBIDTp0/btWzZMsfX19dgo/mQIUPSR48ejePHj9u9+eabaaacOzIy8nb79u1bREZGui5cuPCOseMGDhzY4OjRo/bjx4+/07lz56yLFy9affjhh543btxQHTp06GrHjh2zIyMjb8yYMcNn/vz5Nzt06JANAG3atHlgSjwqmnYRsuoS1uMshrZz5077+fPn1wsODs48cODAP3Xq1CnukXvgwAE7ABg8eLDBRk1jJk+enPLZZ5+5hYeHe508efKKseM+/vjjutOmTfMZPHhwanh4eGJGRoZZZGSkZ7du3ZpcuHDhoqOjo/rIkSOXevbs2fTFF19MHTt2bAoA+Pn5mdzrpLLoPzdjz+Dq1asuf/31V3F+4OnpaTQ/+OGHH5plZWUV5wfPPvvsX87Ozo/83aemplob2l4R+vTpk37kyBGHU6dOWR87dszO3NwcgwYNMmko78mTJ60zMzPN+/fvX6b08uyzz+b07ds3LTo62v2dd95JcXR0LHFy13r16hWMHDkyec2aNW4RERFJjRs3rvL0oEv/S2pVh1VRC9nFx8crzc3NYWdnpx48eLDfgQMHnN5+++3Erl27Zp06dapOdHS0+/Xr11V79uy5XlI42dnZ5sOHD28wduzYpIYNGyauXbvWedq0aT7NmjXLDQ0NzRw/fnzK7du3LbZs2eJy6NChS/oNRlq1tXzRVd7yoU6dOkbzB/18piSVsdBmdUhH33//vZ25uTnKOk3BBx98kBgTE+MSERHhsXnz5hulHW9qWViV9PMYY/nF4sWLXaZMmVJclg0dOtRoWdaiRYtmsbGxxWnsxx9//KtLly6PpMmTJ09aG9peEXTLsp49e2Z169at0dWrV63CwsISAgMDH5w5c8Zm2bJlnvfu3VNoe/jfu3fPrGPHjk3z8vLEtGnTEvz9/fMOHDhgP23aNN+8vDwz/d62o0aNqh8aGpo2bty4q//9739tli5d6pGTk2Om7eUaHBycvXHjxrohISENJk2alNytW7dsKysrg6OeHiefOnToUJ1x48bVHzlyZHJkZORNtVotYmNjLe/fv1+l7RD635uqOqyKXHCzOuRjup6G8pCIahY2hBNRlVq9enX84MGD/efPn19v/vz59RwcHAo7d+6c8dprr6UOGjQoQ3vcnTt3lAEBAUaH7dnZ2akdHR0Lb9++bfKimc8880xuaGjovU8++cT9vffeS3F2dn6kV8zBgwfr7Nu3z3HFihVxb7/9dioAvPDCC5lOTk6F48aNq3/mzBmrjh07PmjZsmUuALRo0eJBZQ0vfBrNmjXL29vbO+/w4cP/6H9Bu3nzphIAGjVqVKaGRisrK2nKlCmJYWFhvgcPHqzTp0+fLP1j0tPTzT744AOvwYMHp27dujVOu71Lly7ZAQEBLVesWOEya9asZO2z9vT0zOdzr3zaHxlu3bplkZCQoHR0dCzQ/XEEAIqKiqA7V6W5uTnMzMwQFxenBID69euXuWE6MjLydlBQUMsFCxa4LV68OLG04+fMmXNn06ZNdSMiIjx10w9VnKKiIhQUFOD+/fvm69atczx8+LBj9+7d71+6dEm5d+9ep3fffTcxKioqAZB7+isUCmnRokWeP//885327dsb/dKcnZ1ttnz58huhoaGZANCnT59MDw8P+82bNzuFhoZm+vv7F3h5eRUAQPfu3bMtLAwXOSxfaobqmI5u376tdHR0LNAfnVIaT0/PwjfeeCPp008/dY+IiEhq0aKFwY4FWqaUhVQ5dMuy1atXO/3666919u/ff7lv375ZgDwFDgBERUV5zpkz546Xl1dhZGSkW2JiovLs2bMXtaPgXnjhhcz09HTzxYsXe0ydOjVZNx117949ffXq1bcAOe0KIaTFixd7/fHHH4mtWrXKGzduXOqZM2dsYmJi6h46dMjRwsJCCggIyA4JCbk/bdq0ZO1UYI+bT50+fdrG1ta2aO3atcXTwOjW7+nxVcd8TNeTKg/163/aERWFhYVCd/FQbb1QS39hUUmSIEnSQ9uFEFAo2HRGVFtwahQiqlKtWrXKi42Njd2/f//lCRMmJDZr1uzB4cOHHV988cVGU6dOLffUAtpKofZlaDVzQJ72ICcnx2zu3Lluhvbv27fP3sLCQnr11VfTdMMbMGBABiAvElPeOFLpunXrln7z5k1VREREhU4zMWHChLu+vr55ERERXob2Hzt2rE5WVpb5sGHDUnWfu7+/f379+vVzT506xedeBSRJbvMWwviaS926dWukVCrbal/Lli177OHXrVq1yhs0aNDdzz//3C0pKclwlycdbm5uRWPGjEnauXOn8/nz57mgUyVo3bp1S6VS2dbV1bX19OnTfZ9//vnUr7/+Ou7o0aO2ADBq1KhU3eNff/31VAAobWFbS0tLtfZLPyA3Fvr5+eXeunVLWdLnDGH5Uv3VhHRUFrNmzUqysbFRh4eHmzRFV2llIVUO3bLs0KFD9p6envk9e/bM0s0H+vXrl1FYWCh++OEHGwA4evSofatWrbKbNm2ap3tc7969M+7fv6/49ddfH1owfujQoQ+NjhwxYkSaWq3GqVOnbAB5+rjNmzffiI2N/XPBggU3evfunXbjxg3VvHnz6rVu3bqZdhqVx82n2rdvn52RkWE+YMCA+t9884393bt3Sy1DqWxqQj72JMrDIUOG+OnW/1544YXGANCyZcuWuttXrlxZPAXM5cuXlbr7lEpl27Nnz9bZuXOns+42X1/fgLJeMxFVX/xZi4iqnEKhQN++fbO0PWHi4uIsevXq1SgqKspjypQpyXXr1i1yc3PLv3nzptEGpYyMDLO0tDSFtmfCkCFD/Hbs2FFc0Rk0aFCqoUVPmjdvnj9kyJC7a9ascZs+ffoji7ikpKQoCgoKhJ2dXRtD501NTWU+Womio6Nvzp8/v2Dp0qUeVlZW6gULFhQPqfT29s4HgCtXriiNTaljjEKhQERExO233nqrwZYtW+z099+5c0cBANpKtD57e3vDv6xQpYqPj1cC8vQjnp6e+WfOnLHNysoSur3CP/300xtpaWnmt27dsnjllVcaardre+Bdv369XA1RCxYsSNy9e7fznDlz3MePH2902L3W+++/n7RmzRrX8PBwr127dl0rzznJuA0bNlz19fXNt7e3L2rUqFG+tvfivXv3FADg4+PzUBcvb2/vAs3+Ehth7OzsHvnbViqVUl5eXpk7j7B8qf6qYzry8vLKP3PmjF1mZqZZWXuFOzs7F40fP/7Ohx9+6HX27NlSR6+UVhZS5dAty+7evatISEhQKpVKg9Nr3L17VwHI+cGNGzdUxo5LTk5+KL/w9PR8KO3Wq1evAJBHHOhub9q0aX54eHgKgJTCwkJMmDDBa9WqVe7R0dEu4eHhKY+bT/Xv3z9r7dq11z799FPX4cOH+wNAcHBw1tKlS2+W1BuZTFcd8zF9T6I8XLhwYcKkSZOKw/7555+tp06d6vv111//o03/ANC4cePi7wy+vr4FJ06c+Es3nLFjx/q6ubkVzJkzp3ixUUtLy1IXSyeimoMVbKJawtS55Xr16nW3V69eDzXinD171mCPyTlz5vxlaLu+8s4Pboyfn1/BiBEj7s6cOdP7woULqu7du+d06tQpY8uWLS7x8fEWhuYJ37Jli71arUb37t0zgEcrQ25ubgYX0wPkBi7NPOWP9Dp2cnIqVKlU0uHDhy8Z+qx+5bI6qIh5U7Vzv1bGHKxlIYTA5s2b43Nzc80WLlzopVKp1LNmzUoGgN69e2d89NFHXtu3b3do1apVUlnDfv3119OioqIezJ0712vUqFEpuvvq1q1bCADR0dFxgYGBj3xRq+4N4aY+N39//7v+/v4P5QcJCQkG84Nu3bqZlB9U1vzgAHDgwAF7lUolderUKSc+Pl757bffuuzYscN+xIgRxfN+a4eMX758+aEv+126dMmxtbUt2r9/v8PkyZNLbcjW16hRo/yXX345Zd26da4vvPBCqXP32tvbqydNmpQ4d+5c759++qnaLEZXEXNya+ftraj5vcujTZs2D1q2bPnID2BOTk6FAHDz5k0L3akhbt68aaHZ/0T/dmtb+aLrccoHY/OLm5rPVJTqmI569OiRERMT47J9+3Y7Ywv7lmT69OnJq1evdp0xY4ZXs2bNSm1oLKksrGqm5jGTJ0++q5+vx8TEGCzLLl68aFIaq6z5wYGHyzInJ6ciLy+v/M2bN181dKx2+jcHB4dCZ2fnwmXLlhmc/z0gICBX931CQoIFgOJtt27dsgDkH1qMxUuhUGDBggWJq1atco+NjbUCKiafeu2119Jee+21tPT0dLP9+/fbzpw5s15oaGijxMTEP4zNK13ZKmJObu3c4BU5v3d5VMd8zJDKLg+bNGmS36RJk+L0nZGRYQ4Abdu2faC7XZelpaXUtWvXh/7WbWxs1I6OjoX624mo9uDUKERUpeLj4w1OKHfp0iVLAKhXr14hAEyePDnZzMwMo0eP9taf5iQpKcl83rx5Xj4+PnnaBrEmTZrkd+3aNUf7MlYBAoob3lM2bdpUNzEx8aHGs379+mXk5eWJtLQ0c93wtC8/P78CQB4+CAA5OTnMVyuYubk5tm/ffr1v375ps2fP9tauWN+9e/ecDh06ZC5fvtzjwoULBkcLbNq0ycFYuGZmZpg9e/bt2NhY6127djnq7vu///u/LBsbG/U///yjMvTcAwMDi79QWFhYSA8ePOBzr2Tr1q1zOHbsmMMrr7ySYmtrq3711VfTvL2982bNmlUvISGh1B/2LS0tpTFjxiQdP37cft26dQbTxc6dO+0yMzONPsv58+cnCiEwf/58k6bqmTp1aoqrq2sBpx14cnr06JEJABs2bHjob3rt2rVOAPDcc89lGvpcWahUKjUAZGVllfp3z/KlZqrKdPTqq6+m+fn55RrL2woKChATE2NvLFxbW1t1WFhY4pEjRxx+/fVXm9LiUVJZSBVPvyzr1atX+p07dyxsbW3VhvIBDw+PQkD+geTatWuW/v7++YaO01/IOSYm5qFnuWHDBkczMzN06dIlGzBe/z5//rwlAHh4eBQAFZtP2dvbq//zn/+kjxw5MiUlJcUiKSmJnfIqEctDIiLDWPgQUZUKDAxs0alTp4w+ffqkN2zYMO/+/fvm+/bts9+8eXPdfv36pWl7wgQFBeUuXrw4PiwszK9jx46N33jjjZR69eoVXLx40TI6Oto9MzPTfO/evX+rVKpyDV2bO3du4ubNm11++eWXOp6ensWN5iEhIZkhISH3hg8f7j969OikDh06ZJuZmeHatWvKgwcP2i9ZsuRWq1at8lq2bJlrbm4uffXVVy4uLi6FlpaWUkBAQK7+FxMqH4VCgd27d1/r27ev//Tp031UKpV60qRJqTExMdd79OjRuGPHjs1GjRqV3LVr1yylUinFxsZabty40aWwsFAMGzbMaI+6oUOHpi9atCjr1KlTDw0Jd3JyUs+ePfvmjBkzfFNSUhT9+vXLcHBwKLp586bFjz/+aPuvf/0rc8yYMfcAwN/fP/fIkSP2O3fuzHB2di708fEp0FbYqXx++eUX66SkJEV+fr64fv26cv/+/Q4HDhxw7NixY8aKFStuAXLD9tatW6+GhIQ0at26dfNRo0Ylt2/fPkepVKoTEhIsdu7c6QgAtra2xb+cLVy4MPHPP/+0euONN/wPHDhwNzQ0NN3FxaUwPj5euXPnTofDhw87pqSk/G4sXl5eXoWvv/568sqVK91NuQ7NYnQJkydP9n3ce0KmadeuXW5ISMi9JUuWeBYWForOnTtnnz592mbZsmUeISEh94KDgx97KH6LFi1yAeCDDz5wDwkJSVcoFI/0KNPF8qXmqcp0ZGFhgW3btl3t27dv49atWzd/6623koKDg3MA4LfffrNav359XX9//9yhQ4caHZny7rvv3l25cqX76dOnTZruxFhZSI/HlLJs9OjR9zZu3OjSu3fvxuPGjUtq06ZNTl5envjnn39U+/btczh48OBVW1tbdURERNKuXbscO3Xq1HTcuHFJzZo1y83KyjL766+/LE+fPl3n6NGjD/UoP378uP3o0aPr9enTJ+O///2vdVRUlOfAgQNTtaOmZs6c6XH27FmbQYMG3QsKCnpgYWEh/f7771YrVqxwd3BwKBwzZsxd4PHzqblz57onJydbdOvWLcPb27sgPj5euXr1atemTZs+8PT0NDpakx4fy0MiIsPYEE5EVSo8PPz2wYMH7SMjIz1TU1MtzMzMJD8/v7zw8PBb77///kNzyE2cODG1RYsWuR9++KH71KlTfbKysszr1q1b0K1bt/R58+YlNmzYsNyNj+7u7kVvvfVWUlRU1CMLTO3atev6woULXTdt2uQSHR3toVQq1Z6envndu3fP8PLyKtR+PjIy8sby5cs9+vXr17SoqAh79uz5OyQk5LF7W5DMwsICe/fuvda7d2//sLAwP5VKJY0ZM+beuXPn/oqMjHTdvXu30+eff+5WVFQkNM8nfcqUKaVOmTJv3rzbISEhTfS3T5ky5a6Pj09BVFSU25gxY5yLiorg6upaEBwcnNWuXbviSv7y5cvj3333XZ+hQ4c2zM/PF++++25iVFRUgn54ZLpRo0Y1AACVSiU5OTkVtGjRIueLL764NnLkyDQzs/91Amrfvv2D8+fPxy5YsMBt+/btzsuXL/dQq9XC1dU1v127dln79++/rF17AJB/UNm3b9+1VatWOW3YsMFl3Lhxfjk5OWbOzs6FwcHBmfv377/s7Oxc4lDh2bNn39mwYUNd7ZDb0kycOPHuihUr3OPj47lo5hOydevWuKlTp+Z98803LsuXL/dwdXUtGDdu3J1FixaVOmeyKYYOHXr/0KFDKevWrau7bNkyD0mSSpzCgeVLzVSV6aht27a558+fj/3ggw/cvvnmG5clS5YoJUkSPj4+uf37978/ffr0Ess2S0tLafr06QkTJ070MzU+xspCKj9TyjKVSiWdOHHi7/fff99j/fr1LgsXLlRZWVmpvb2983r16pWu7QHr7Oxc9PPPP1+aPn265/Lly92Tk5MtbG1ti+rXr587YMCANP1zr1279vrixYvdXnnlFX8LCwtp6NChdz/77LOb2v0jR45MLSwsxLfffuuyfPlyiwcPHpjVrVu3oEuXLhnz5s1L9Pf3L65TP04+1aFDh+yVK1e6RkREeKenpyucnJwKu3btmv7xxx+znvQEsDwkInqU0K5aTUTV3/nz5+MCAwPLPLdtaarLHHdUfVSXOcLpyeOzp9JUhznCqWZjPkOV7WnNp6Kjo50nTZrk9+eff14wNG80VRx+fyJd58+fdwkMDPSr6ngQUenYI5zoKaOttJm6j5W72s/YomXG9rHhovbgs6fSaBuTTN33tDU6UemYz1BlYz5FlY3fn4iIag8uMkBEREREREREREREtRqnRiGqQSprahQiIiIiIiIiKjtOjUJUc7BHOBERERERERERERHVamwIJyIiIiIiIiIiIqJajQ3hRDWLxOmMiIiIiIiIiKqeWq0WANRVHQ8iMg0bwolqECFEXHZ2tnVVx4OIiIiIiIjoaffgwQNLIcSdqo4HEZmGDeFENUhBQcHHcXFxirt37zrk5+cr2DuciIiIiIiI6MlSq9UiOzvbKi4uTllYWDi3quNDRKYRbEgjqlnOnTvXSKFQzBBCBEqS5Aj+oEVERERERET0JKmFEHcKCwvnBgUFHarqyBCRadgQTkRERERERERERES1GnuSEhEREREREREREVGtxoZwIiIiIiIiIiIiIqrV2BBORERERERERERERLUaG8KJiIiIiIiIiIiIqFZjQzgRERERERERERER1WpsCCciIiIiIiIiIiKiWu3/A+UAJXMgyXt/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1800x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(25, 8))\n",
    "styles = {'pointnet': {'color': [0.0, 0.0, 0.0], 'linestyle': '-', 'label': 'PointNet', 'linewidth' : linewidth},\n",
    "         'pointnet++': {'color': [0.4, 0.4, 0.4], 'linestyle': '-', 'label': 'PointNet++', 'linewidth' : linewidth},\n",
    "         'so-net': {'color': [0.4, 0.4, 0.4], 'linestyle': ':', 'label': 'SO-Net', 'linewidth' : linewidth},\n",
    "         'kcnet': {'color': [0.7, 0.7, 0.7], 'linestyle': '-', 'label': 'KCNet', 'linewidth' : linewidth},\n",
    "         'deepsets': {'color': [0.0, 0.0, 0.0], 'linestyle': ':', 'label': 'DeepSets', 'linewidth' : linewidth},\n",
    "         'dgcnn': {'color': [0.7, 0.7, 0.7], 'linestyle': ':', 'label': 'DGCNN', 'linewidth' : linewidth},\n",
    "         'pointcnn': {'color': [0.7, 0.7, 0.7], 'linestyle': '--', 'label': 'PointCNN', 'linewidth' : linewidth},\n",
    "         }\n",
    "h = 0.05\n",
    "handles, labels = plot_acc_in_num_models(ax1, acc_stats_base_models, 'acc', styles, h)\n",
    "plot_acc_in_num_models(ax2, acc_stats_base_models, 'mean_class_acc', styles, h)\n",
    "\n",
    "label_perm = [4, 3, 6, 2, 0, 5, 1]\n",
    "handles = [handles[i] for i in label_perm]\n",
    "\n",
    "f.legend(handles=handles, loc='lower center', ncol=7)\n",
    "f.savefig('images/models_accuracy_grayscale.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare results with and without ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Plain acc mean</th>\n",
       "      <th>Ensemble acc mean</th>\n",
       "      <th>Plain class acc mean</th>\n",
       "      <th>Ensemble class acc mean</th>\n",
       "      <th>Increase acc</th>\n",
       "      <th>Increase class acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pointnet</th>\n",
       "      <td>88.65%</td>\n",
       "      <td>89.38%</td>\n",
       "      <td>85.77%</td>\n",
       "      <td>86.62%</td>\n",
       "      <td>0.74%</td>\n",
       "      <td>0.86%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pointnet++</th>\n",
       "      <td>90.14%</td>\n",
       "      <td>90.48%</td>\n",
       "      <td>87.71%</td>\n",
       "      <td>88.19%</td>\n",
       "      <td>0.34%</td>\n",
       "      <td>0.48%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>so-net</th>\n",
       "      <td>92.65%</td>\n",
       "      <td>93.64%</td>\n",
       "      <td>89.98%</td>\n",
       "      <td>91.02%</td>\n",
       "      <td>0.99%</td>\n",
       "      <td>1.05%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kcnet</th>\n",
       "      <td>89.62%</td>\n",
       "      <td>92.14%</td>\n",
       "      <td>85.38%</td>\n",
       "      <td>88.28%</td>\n",
       "      <td>2.52%</td>\n",
       "      <td>2.89%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deepsets</th>\n",
       "      <td>89.71%</td>\n",
       "      <td>90.27%</td>\n",
       "      <td>85.79%</td>\n",
       "      <td>86.46%</td>\n",
       "      <td>0.56%</td>\n",
       "      <td>0.67%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dgcnn</th>\n",
       "      <td>91.55%</td>\n",
       "      <td>92.02%</td>\n",
       "      <td>89.03%</td>\n",
       "      <td>89.30%</td>\n",
       "      <td>0.47%</td>\n",
       "      <td>0.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pointcnn</th>\n",
       "      <td>91.82%</td>\n",
       "      <td>92.22%</td>\n",
       "      <td>87.85%</td>\n",
       "      <td>88.36%</td>\n",
       "      <td>0.41%</td>\n",
       "      <td>0.50%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Plain acc mean  Ensemble acc mean  Plain class acc mean  \\\n",
       "pointnet            88.65%             89.38%                85.77%   \n",
       "pointnet++          90.14%             90.48%                87.71%   \n",
       "so-net              92.65%             93.64%                89.98%   \n",
       "kcnet               89.62%             92.14%                85.38%   \n",
       "deepsets            89.71%             90.27%                85.79%   \n",
       "dgcnn               91.55%             92.02%                89.03%   \n",
       "pointcnn            91.82%             92.22%                87.85%   \n",
       "\n",
       "            Ensemble class acc mean  Increase acc  Increase class acc  \n",
       "pointnet                     86.62%         0.74%               0.86%  \n",
       "pointnet++                   88.19%         0.34%               0.48%  \n",
       "so-net                       91.02%         0.99%               1.05%  \n",
       "kcnet                        88.28%         2.52%               2.89%  \n",
       "deepsets                     86.46%         0.56%               0.67%  \n",
       "dgcnn                        89.30%         0.47%               0.27%  \n",
       "pointcnn                     88.36%         0.41%               0.50%  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "TOTAL_NUM_MODELS = 10\n",
    "for model in BASE_MODELS:\n",
    "    data.append([100 * acc_stats_base_models[model][1]['acc']['mean'],\n",
    "                 100 * acc_stats_base_models[model][TOTAL_NUM_MODELS]['acc']['mean'],\n",
    "                 100 * acc_stats_base_models[model][1]['mean_class_acc']['mean'],\n",
    "                 100 * acc_stats_base_models[model][TOTAL_NUM_MODELS]['mean_class_acc']['mean']])\n",
    "pd.options.display.float_format = '{:,.2f}%'.format\n",
    "df = pd.DataFrame(data, columns=['Plain acc mean', 'Ensemble acc mean', \n",
    "                                 'Plain class acc mean', 'Ensemble class acc mean'], index=BASE_MODELS)\n",
    "df['Increase acc'] = df['Ensemble acc mean'] - df['Plain acc mean']\n",
    "df['Increase class acc'] = df['Ensemble class acc mean'] - df['Plain class acc mean']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification accuracy gain or loose per class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_values = []\n",
    "for idx in range(CLASSES_COUNT):\n",
    "    class_plain_mean = [100 * acc_stats_base_models[x][1]['class_accs'][idx]['mean'] for x in BASE_MODELS]\n",
    "    class_ensemble_mean = [100 * acc_stats_base_models[x][10]['class_accs'][idx]['mean'] for x in BASE_MODELS]\n",
    "    min_values.append((CLASS_NAMES[idx], idx, min(class_ensemble_mean + class_plain_mean)))\n",
    "min_values = sorted(min_values, key=lambda x: x[-1])\n",
    "low_acc_classes = sorted(min_values[:15], key=lambda x: x[1])\n",
    "high_acc_classes = sorted(min_values[15:], key=lambda x: x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def draw_class_accs_horizontal(acc_classes_list, y_lim):\n",
    "    ind = np.arange(len(BASE_MODELS))    # the x locations for the groups\n",
    "    width = 0.35       # the width of the bars: can also be len(x) sequence\n",
    "\n",
    "    num_cols = 5\n",
    "    num_rows = int(len(acc_classes_list) / num_cols)\n",
    "    f, axes = plt.subplots(num_rows, num_cols, sharey=True, sharex=True, figsize=(25, 5 * num_rows))\n",
    "    plt.setp(axes, yticks=ind, yticklabels=np.flip(BASE_MODELS_ALIASES), xticks=range(y_lim[0],y_lim[1] + 1, int((y_lim[1]-y_lim[0])/5)))\n",
    "    \n",
    "\n",
    "    for c in range(num_cols):\n",
    "        for r in range(num_rows):\n",
    "            tmp_idx = c + num_cols * r\n",
    "            idx = acc_classes_list[tmp_idx][1]\n",
    "            class_plain_mean = [100 * acc_stats_base_models[x][1]['class_accs'][idx]['mean'] for x in BASE_MODELS]\n",
    "            class_ensemble_mean = [100 * acc_stats_base_models[x][10]['class_accs'][idx]['mean'] for x in BASE_MODELS]\n",
    "            diff = [class_ensemble_mean[x] - class_plain_mean[x] for x in range(len(class_plain_mean))]\n",
    "            diff_up = [x if x > 0 else 0 for x in diff]\n",
    "            diff_down = [x if x < 0 else 0 for x in diff]\n",
    "            axes[r][c].barh(ind, np.flip(class_plain_mean), width, label='Plain')\n",
    "            axes[r][c].barh(ind, np.flip(diff_up), width, left=np.flip(class_plain_mean), label='Diff', color='g')\n",
    "            axes[r][c].barh(ind, np.flip(diff_down), width, left=np.flip(class_plain_mean), label='Diff', color='r')\n",
    "            axes[r][c].set_title(CLASS_NAMES[idx])\n",
    "            axes[r][c].set_xlim(y_lim)\n",
    "            if r == num_rows - 1:\n",
    "                axes[r][c].set_xlabel('Instance classification accuracy')\n",
    "    return f\n",
    "\n",
    "f = draw_class_accs_horizontal(low_acc_classes, [0, 100])\n",
    "f.savefig('images/class_accuracy_1.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = draw_class_accs_horizontal(high_acc_classes, [80, 100])\n",
    "f.savefig('images/class_accuracy_2.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Who's the best?\n",
    "In how many classes is each model the best one?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6eUpau3wPQw8I9ihJkiQ1MChJkjQInXHGGbzuda/jla98JWuuuSbve9/7WLhw4TJl/vu//5utttqKkSNHMnr0aLbccku+9a1vLZ1+1VVXsfvuuzN27FiGDx/ORhttxCGHHLKiV6WlPPQmSdIgM3v2bA466CD23Xdfjj/+eO69916OPPJIrrzySq655hpGjhzJH/7wB/bbbz8OPfRQTjzxRF544QVuvvlmHn30UQAWLVrEnnvuybbbbsucOXMYNWoU8+fP5/LLL2/x2q1YBiVJkgaRJUuW8MUvfpFp06bxk5/8ZOn4TTfdlJ133pnTTz+dQw89lD/96U+svvrqnHzyyUvL7LHHHkuf33zzzTzyyCN89atf5bWvfe3S8QcccMAKWY+VhYfeJEkaRG655Rbuv/9+3vve9y4zfqedduLVr341l156KQDbbLMNjzzyCPvttx/nnXfe0p6kNpMmTWL11VfnoIMO4owzzuDuu+9eYeuwMjEoSZI0iDz88MMAjB8//kXT1llnnaXT3/jGN3LmmWdy99138/a3v5211lqL3Xbbjeuuuw6AMWPGcPHFF7PuuutyyCGHsMEGGzB58mTOOuusFbcyKwGDkiRJg8gaa6wBwH333feiaffdd9/S6QDvfOc7ufTSS3nkkUc455xzWLhwIdOnT+eFF14AYMqUKZx11lk8/PDDXHHFFbzmNa/h3e9+NzfccMOKWZmVgEFJkqRBZJNNNmHttdde5vwkgMsvv5w777yTadOmvWiekSNHMmPGDA466CAWLlzIQw89tMz0IUOGsP322zNr1ixeeOEFbrrppv5chZWKJ3NLkjRAXXDBBayzzjrLjBszZgzHHXccBx10EPvttx/77bcfCxYs4KijjmLSpEl84AMfAODoo4/mH//4B7vuuivrrrsu99xzD6eeeipTpkxhrbXW4rzzzmP27Nnss88+bLjhhjz55JOceuqpjBo1ije84Q2tWN2WMChJkjRAffzjH3/RuC222IIbbriBESNGcOKJJ/K2t72NkSNHstdee/HVr36V1VZbDYDtttuOU089lcMPP5yHH36YcePGscceezBr1iygOpl7+PDhzJo1i4ULFzJq1Ci22WYbLrroItZbb70Vup6tlNLNLdSTLAGupwpVNwH7l1Ke6qL85aWUHbqp8zBgdls9SeYDV5dS3lEPvxOYUUo5oIs6pgDrllL+t8sV6CPDxk8q4/c/ufuCGjDmnzCj1U14efJnG7SC3XTTTWy22WatboZWsO5e9yRXl1KmdldPT85RWlxKmVJKmQw8C3ykq8LdhaTaYcCIDuNen2TzHszbZgqwV2cTksxJMq1pxiTTkszpxbIkSdLLUG9P5r4M2BggySeT3FA/DmsrkGRR/XdakkuS/DzJzUl+lMqhwLrAxUkublf314GjOi4wyWpJTk/y5yTXJnlbklWB44B9k8xLsm8v10OSJKlbPT5HKckQ4C3ABUleDxwIbAcEuDLJpaWUazvMthWwBXAv8Edgx1LKqUk+CexaSnmwXdmfAYck2bhDHUcBvyulfCDJ6sCfgd8ARwNTSykf6+k6SJIk9UZPgtLwJPPq55cB3wUOBs4ppTwJkORsYGegY1D6cynlnrrMPGAi8IeG5SwBTgQ+D/yq3fg9gLcm+XQ9/Epgg44zJ9kTOKEe3ADYqe7deqaUsl1d5kpgGDASWKPden2ulHJhJ3V+GPgwwCqj12potiRJGqx6EpQWl1KmtB+RpKf1P9Pu+ZIeLO+HVEGp/Z2sAryjlHJLhzZs1364DjoX1tPmAHNKKZd0KNMWmKYBB3R1snhdfjYwG6qTubtpuyRJGmSW94aTlwH7JBmRZDXg7fW4nnoCGNVxZCnlOeAk4PB2oy8EPp46nSXZqqs6JEnqqLsrvDW49OXrvVxBqZRyDTCH6nyhK4HvdHJ+UldmU53rdHEn077Lsj1Ps4ChwHVJbqyHAS4GNvdkbklSV4YOHcrixYtb3QytQIsXL2bo0KF9Ule391FSxfsoDT7eR6lF/MzRCvb444/zj3/8gwkTJjB8+PDenD6iAaaUwuLFi1mwYAFrr702o0ePbizb0/soeWduSdKg1vZlee+99/Lcc8+1uDXqb0OHDu02JPWGQUmSNOiNHj26z7449fKyvCdzS5IkDXoGJUmSpAYGJUmSpAYGJUmSpAYGJUmSpAYGJUmSpAYGJUmSpAYGJUmSpAYGJUmSpAYGJUmSpAb+KG4PTZ06tcydO7fVzZAkSX2gpz+Ka4+SJElSA4OSJElSA4OSJElSA4OSJElSA4OSJElSA4OSJElSA4OSJElSA4OSJElSgyGtbsBAcf2Cx5h4xPmtboYkaQCaf8KMVjehfw3im1fboyRJktTAoCRJktTAoCRJktTAoCRJktTAoCRJktTAoCRJktTAoCRJktTAoCRJktTAoCRJktTAoCRJktTAoCRJktTAoCRJktRgpQhKSY5KcmOS65LMS7JdklWTnJzktiR/S/KLJOs1zH9AkheSvLbduBuSTOxmuYclGdG3ayNJkgaLlgelJG8AZgBbl1JeC+wG3A18GRgFbFJKmQScC5ydJA1V3QMc1cvFHwYYlCRJUqdaHpSA8cCDpZRnAEopDwKPAgcCh5dSltTjvwc8A7ypoZ7zgC2SbNJxQpI9klyR5JokZyYZmeRQYF3g4iQX9/1qSZKkgW5lCEq/BtZPcmuS/0jyRmBj4K5SyuMdys4Ftmio5wXgq8CR7UcmWRP4ArBbKWXruo5PllJOBe4Fdi2l7Np3qyNJkgaLlgelUsoi4PXAh4EHgJ8C05azuv8Gtk+yYbtx2wObA39MMg/YH3h1TypL8uEkc5PMXfLUY8vZJEmSNFANaXUDAOrDa5cAlyS5HjgI2CDJqFLKE+2Kvh44L8lHgQ/V4/ZqV8/zSb4OfK7dPAEuKqX8f8vRrtnAbIBh4yeV3s4vSZIGtpb3KCXZJMmkdqOmALcA3we+kWSVutz7qU68/l0p5d9LKVPqx70dqpxDdUL4WvXwn4Adk2xc17Nakn+qpz1BdcK4JEnSi6wMPUojgdOSrA48D9xGdRjuCeBrwK1JXgBuBt5eSumyZ6eU8mySU4FT6uEHkhwA/DjJsLrYF4BbqXqLLkhyr+cpSZKkjtJN7lBt2PhJZfz+J7e6GZKkAWj+CTNa3YT+NQCzRJKrSylTuyvX8kNvkiRJKyuDkiRJUgODkiRJUgODkiRJUgODkiRJUgODkiRJUgODkiRJUgODkiRJUgODkiRJUgODkiRJUgODkiRJUgODkiRJUgODkiRJUoOUAfiLv60wderUMnfu3FY3Q5Ik9YEkV5dSpnZXzh4lSZKkBgYlSZKkBgYlSZKkBgYlSZKkBgYlSZKkBgYlSZKkBgYlSZKkBkNa3YCB4voFjzHxiPNb3Yw+d+fwGf1afznG+3RJkgYue5QkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIarJRBKcmids/3SnJrklfXw+9PckOS65Ncm+TT9fg5SRYkGVYPr5lkfjfLWT3JIf24KpIkaQBbKYNSmyRvBk4F3lJKuTPJW4DDgD1KKVsC2wOPtZtlCfCBXixidcCgJEmSOrXSBqUkuwDfBmaUUm6vR38e+HQp5V6AUsozpZRvt5vtZODwJEM6qe8zSa5Kcl2SY+vRXwFek2RekhP7b20kSdJA9KJAsZIYBpwLTCul3Nxu/GTg6i7muwv4A/A+4H/aRibZA5gEbAsE+GUdxI4AJpdSpvRt8yVJ0mCwsvYoPQdcDvzrcsx7PPAZll23PerHtcA1wKZUwalLST6cZG6SuUueeqy74pIkaZBZWYPSC8C7gW2THNlu/I3A67uasZTyN2BePX+bAMeXUqbUj41LKd/trhGllNmllKmllKmrjBjT+7WQJEkD2soalCilPAXsDbw3SVvP0vHAiUnWAUiyapIPdjL7vwGfbjd8IfCBJCPr+SYkGQc8AYzqr3WQJEkD28p6jhIApZSHk0wHfp/kgVLKL5OsDfwmSYACnN7JfDcmuQbYuh7+dZLNgCuq2VgE7FdKuT3JH5PcAPyqlPKZFbVukiRp5ZdSSqvbMCAMGz+pjN//5FY3o8/dOXxGv9ZfjnH/kiStfJJcXUqZ2l25lfbQmyRJUqsZlCRJkhoYlCRJkhoYlCRJkhoYlCRJkhoYlCRJkhoYlCRJkhoYlCRJkhoYlCRJkhoYlCRJkhoYlCRJkhoYlCRJkhoMaXUDBootJ4xh7lf2bnUz+oE/WitJUhN7lCRJkhoYlCRJkhoYlCRJkhoYlCRJkhoYlCRJkhoYlCRJkhoYlCRJkhoYlCRJkhp4w8keun7BY0w84vxWN0OD1PwTZrS6CT1TvEGppJcXe5QkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIa9HlQSrIkybwkNyb5S5JPJemP5cxIcm29jL8mOaib8tOS7NDX7ZAkSYPXkH6oc3EpZQpAknHAfwOjgWP6agFJhgKzgW1LKfckGQZM7Ga2acAi4PK+aockSRrc+vXQWynlfuDDwMdSWSXJiUmuSnJd+16gJJ9pN/7YetzEJDcn+VGSm5L8PMkIYBRVyHuoXs4zpZRb6nnWSnJWXddVSXZMMhH4CHB43du1c5J3Jbmh7pH6fX9uB0mSNDD1R4/SMkopdyRZBRgHvA14rJSyTd0L9MckvwYm1Y9tgQC/TLILcBewCfCvpZQ/JjkdOKSU8rUkvwTuTPJb4Dzgx6WUF4BTgJNKKX9IsgFwYSllsyT/BSwqpXwNIMn1wJ6llAVJVu/v7SBJkgaefg9KHewBvDbJO+vhMVQBaY/6cW09fmQ9/i7g7lLKH+vxZwCHAl8rpXwwyZbAbsCngd2BA+rhzZO0LXN0kpGdtOWPwJwkPwPO7qyxST5M1SPGKqPXWp71lSRJA1i/B6UkGwFLgPupeos+Xkq5sEOZPYHjSynf6jB+IlA6VLl0uJRyPXB9kh8Cf6cKSq8Ati+lPN2hrmUrKeUjSbYD9gauTvL6UspDHcrMpjoXimHjJ3VshyRJGuT69RylJGsB/wV8s5RSgAuBg+uTsUnyT0lWq8d/oK3nJ8mE+kRwgA2SvKF+/i/AH5KMTDKt3aKmAHfWz38NfLxdG6bUT5+gOrepbfxrSilXllKOBh4A1u+r9ZYkSYNDf/QoDU8yDxgKPA/8EPhGPe07VFenXZOqi+cBYJ9Syq+TbAZcUff8LAL2o+qJugX4aH1+0l+B/wRWAT6b5FvAYuBJqt4kqA7N/XuS6+r1+z3Vidz/A/w8yduogtThSSZR9XL9FvhLP2wLSZI0gKXq6Fk51YfeziulTG5xUxg2flIZv//JrW6GBqn5J8xodRN6ZiX+vJCk3khydSllanflvDO3JElSgxV91VuvlFLmAy3vTZIkSS9P9ihJkiQ1MChJkiQ1MChJkiQ1MChJkiQ1MChJkiQ1MChJkiQ1MChJkiQ1MChJkiQ1MChJkiQ1MChJkiQ1MChJkiQ1SPHXwHtk6tSpZe7cua1uhiRJ6gNJri6lTO2unD1KkiRJDQxKkiRJDQxKkiRJDQxKkiRJDQxKkiRJDQxKkiRJDQxKkiRJDYa0ugEDxfULHmPiEee3uhmStELcOXxGq5ugFijHeG/FjuxRkiRJamBQkiRJamBQkiRJamBQkiRJamBQkiRJamBQkiRJamBQkiRJamBQkiRJamBQkiRJamBQkiRJamBQkiRJamBQkiRJamBQkiRJatCyoJRkSZJ5SW5M8pckn0ryinbTt01ySZK/JbkmyflJtmw3/f1JbkhyfZJrk3y6Hj8nyYIkw+rhNZPMr59PTFKSfLxdPd9McsCKWm9JkjRwtLJHaXEpZUopZQtgd+AtwDEASdYGfgYcWUqZVErZGjgeeE09/S3AYcAepZQtge2Bx9rVvQT4QMNy7wc+kWTVflgnSZI0iKwUh95KKfcDHwY+liTAx4Dvl1Iub1fmD6WUc+vBzwOfLqXcW097ppTkS1NgAAAVmUlEQVTy7XZVngwcnmRIJ4t7APgtsH8/rIokSRpEVoqgBFBKuQNYBRgHbAFc00XxycDVXUy/C/gD8L6G6ScAn06yynI0VZIkvUysNEGpK0muTHJTklN6MdvxwGfoZB3rUHYl8C/dLPfDSeYmmbvkqce6KipJkgahlSYoJdmI6tyi+4Ebga3bppVStgO+CIypR90IvL6r+kopfwPmAe9uKPJl4HNAuqhjdillaill6iojxjQVkyRJg9RKEZSSrAX8F/DNUkoB/h04IMkO7YqNaPf8eODEJOvU86+a5IOdVP1vwKc7W2Yp5Wbgr8D/64NVkCRJg1BnJzuvKMOTzAOGAs8DPwS+AVBKuS/JvsAJSSZQ9TI9CBxXT//f+sq439Qnfxfg9I4LKKXcmOQa2vVOdfBvwLV9u1qSJGmwaFlQKqV0eSJ1KeVPwBu7mP494HudjD+gw/A/t3s+n+pE8Lbhv7CS9KpJkqSVjyFBkiSpgUFJkiSpgUFJkiSpgUFJkiSpgUFJkiSpgUFJkiSpgUFJkiSpgUFJkiSpgUFJkiSpgUFJkiSpgUFJkiSpgUFJkiSpQct+FHeg2XLCGOZ+Ze9WN0OSVpDS6gZIKwV7lCRJkhoYlCRJkhoYlCRJkhoYlCRJkhoYlCRJkhoYlCRJkhoYlCRJkhoYlCRJkhp4w8keun7BY0w84vxWN0OSBoU7h89odRO0kivHrBw3PbVHSZIkqYFBSZIkqYFBSZIkqYFBSZIkqYFBSZIkqYFBSZIkqYFBSZIkqYFBSZIkqYFBSZIkqYFBSZIkqYFBSZIkqYFBSZIkqYFBSZIkqUG/BKUkS5LMS3JDkjOTjOim/OU9qPOw9vUkGZnkW0luT3J1kkuSbFdPK0m+3q7sp5PMrJ/PTPJUknHtpi9ajtWUJEmDXH/1KC0upUwppUwGngU+0lXhUsoOPajzMKB94PoO8DAwqZTyeuBAYM162jPAPydZk849CHyqB8uUJEkvYyvi0NtlwMYAST5Z9zLdkOSwtgJtPTpJptU9Qz9PcnOSH6VyKLAucHGSi5O8BtgO+EIp5QWAUsrfSynn11U+D8wGDm9o0+nAvknW6I8VliRJg0O/BqUkQ4C3ANcnaev12Q7YHvhQkq06mW0rqt6jzYGNgB1LKacC9wK7llJ2BbYA5pVSlnSx+H8H3ptkTCfTFlGFpU8s35pJkqSXg/4KSsOTzAPmAncB3wV2As4ppTxZSlkEnA3s3Mm8fy6l3FP3FM0DJi5PA0opjwM/AA5tKHIqsH+SUU11JPlwkrlJ5i556rHlaYYkSRrAhvRTvYtLKVPaj0jS03mfafd8CZ238UbgdUlW6aZX6WTgGuB7HSeUUh5N8t/AR5tmLqXMpjqEx7Dxk0oP2i5JkgaRFXl7gMuAfZKMSLIa8PZ6XE89AYwCKKXcTtVbdWzqBJZkYpK9289QSnkY+Bnwrw11fgM4iP4LjJIkaQBbYUGplHINMAf4M3Al8J1SyrW9qGI2cEGSi+vhDwJrA7cluaGu+/5O5vs6/3c1XMc2PQicAwzrRTskSdLLRErxiFJPDBs/qYzf/+RWN0OSBoU7h89odRO0kivH9G8+SXJ1KWVqd+W8M7ckSVIDg5IkSVIDg5IkSVIDg5IkSVIDg5IkSVIDg5IkSVIDg5IkSVIDg5IkSVIDg5IkSVIDg5IkSVIDg5IkSVIDg5IkSVKDIa1uwECx5YQxzP3K3q1uhiQNEv4guwYGe5QkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIaGJQkSZIapJTS6jYMCEmeAG5pdTsGkTWBB1vdiEHE7dm33J59y+3Zt9yefePVpZS1uis0ZEW0ZJC4pZQytdWNGCySzHV79h23Z99ye/Ytt2ffcnuuWB56kyRJamBQkiRJamBQ6rnZrW7AIOP27Ftuz77l9uxbbs++5fZcgTyZW5IkqYE9SpIkSQ0MSt1IMj3JLUluS3JEq9szkCVZP8nFSf6a5MYkn2h1mwaDJKskuTbJea1uy2CQZPUkP09yc5Kbkryh1W0ayJIcXr/fb0jy4ySvbHWbBpIkpye5P8kN7catkeSiJH+r/76qlW0c7AxKXUiyCvDvwFuAzYH/L8nmrW3VgPY88KlSyubA9sBH3Z594hPATa1uxCByCnBBKWVT4HW4bZdbkgnAocDUUspkYBXgPa1t1YAzB5jeYdwRwG9LKZOA39bD6icGpa5tC9xWSrmjlPIs8BPgbS1u04BVSllYSrmmfv4E1RfQhNa2amBLsh6wN/CdVrdlMEgyBtgF+C5AKeXZUsqjrW3VgDcEGJ5kCDACuLfF7RlQSim/Bx7uMPptwPfr598H9lmhjXqZMSh1bQJwd7vhe/CLvU8kmQhsBVzZ2pYMeCcDnwVeaHVDBokNgQeA79WHM7+TZLVWN2qgKqUsAL4G3AUsBB4rpfy6ta0aFNYupSysn98HrN3Kxgx2BiWtcElGAmcBh5VSHm91ewaqJDOA+0spV7e6LYPIEGBr4D9LKVsBT+JhjeVWnzvzNqoAui6wWpL9WtuqwaVUl657+Xo/Mih1bQGwfrvh9epxWk5JhlKFpB+VUs5udXsGuB2BtyaZT3VY+E1Jzmhtkwa8e4B7SiltPZ0/pwpOWj67AX8vpTxQSnkOOBvYocVtGgz+kWQ8QP33/ha3Z1AzKHXtKmBSkg2TrEp1EuIvW9ymAStJqM79uKmU8o1Wt2egK6V8vpSyXillItW++btSiv+tvwSllPuAu5NsUo96M/DXFjZpoLsL2D7JiPr9/2Y8Ob4v/BLYv36+P/CLFrZl0PNHcbtQSnk+yceAC6mu1ji9lHJji5s1kO0IvA+4Psm8etyRpZT/bWGbpI4+Dvyo/ufoDuDAFrdnwCqlXJnk58A1VFe9Xot3le6VJD8GpgFrJrkHOAb4CvCzJP8K3Am8u3UtHPy8M7ckSVIDD71JkiQ1MChJkiQ1MChJkiQ1MChJkiQ1MChJkiQ1MCjpJUtyQJKS5NGOv2KdZEg9bWYL2jWzXvZKfRuMJK9IcnKShUleSHLuS6hrWr3O0/qwiS2XZGL9em7UybT5g+FGm+1eu916UHaZ91SSfZJ8sp/bd0CSD/TnMpZHu8+fiS+xnhX6edHVPv0S652Z5E19WefLnUFJfWkM8LlWN2IAeifwCeBEqntNfba1zVkpTaS6f0yffqkMYG9g2R9C3gfo16AEHACsdEEJOJ9qeyzsruBKZiL9s08fAxiU+tBK/Z+2BpxfAx9PclIp5R+tbsyKkGRYKeWZl1jNZvXfk0sp/rjtINNH+8gySil/6sv6WqX+SaPny0u4oV8p5QGqHzKW+oU9SupLX6r/fqGrQm1d3J2Mn1P/blnb8MS6K/wjSY5Pcl+SJ5KcUf8kwsZJLkyyKMltSfbvWGdtsyQXJ3mqPrx1XJJl9v0kayX5ryQLkjyT5OYkH+5Qpq2Lf5ckZyZ5FLiSLiSZnuSKJIuTPJbk3HY/j0G9vjPrwSV1/Qd0Ud+QJJ9L8tckTyd5IMkFSTbtYp49kvxvve5PJbkhyaeSrNKh3L8kubbeno8nuT7JQe2mb5PkoiQP1etzR5L/6FDHhkl+VLfrmSTzkry9Q5l/SnJOkvvrdbir3p6d/uNWH0a8uB68qN5GLzq8mOQ9SW5K8mSSuUl26qSuNyb5bb0fPVnvP5Obtl2Hdf95knvqdb8lyZeTDO9Q7pIkf0jy/+pt+QxwSD2tp6/diCTfTPJg/TgjyeodlrP00FuSOVQ/YzGh3baZ365st/t2XW7DJD+s32fP1K/vKW3rBbwR2LHdMi6pp/X2/XxIkq8muRd4Bli93fK73HcaXpsXHXrrbl/uRl99XqyT5PtJ7q3LLExyXpJxPd2nO9TX7fu43etwVLs6Z/ZwvdXAHiX1pYXAN4HDknytlHJnH9X7eeASqi+DzYGvAi8AWwHfBr4GHAx8L8ncTn5m5lzgdOB4YE/gi/X8MwGSjAb+AAyvx/29LvefqXoDTutQ34+AH1MdMmt8DyWZTnVY4HfAvsBI4DjgD0mmlFIWAG8HDqU6rPGGetbbu9gWP6E6zHIy8BvglcAuwHjg5oZ5NgJ+C5wGPA1MrddzLeCIuq07AWcApwKfofonalP+70tsJNVP+fy5busTVIcOlv7AaZL1qYLj/cDhVP/l7wuclWSfUkrb7ySeDzxC9Zo9CEwA9qL5H7drgI8C/061ra6qx7f/DbadgU2oXtungVnAeUkmllIerdu3N9VvYp0PtP0m3ueAy5K8tpRyd8PyATYA5gFz6nXfAjiaatu+p0PZf6LajrOofgLl4Xp8T1+7U4DzgH+p1+mrwBL+77e9OppF9VpuA7y1HvdMvc492reTbEj12j5Vr9ff6nXeo67vEKr9YxWgLXA83tCe7hxF9Rp+uK7v6V7sO93qbl/ugb76vPgh8Oq6DXcDa1P91t0IerZPd9Tt+5jqM+QKqv30W/W4e3q43mpSSvHh4yU9qL44C7AxsAbwKNXv4kEVJAows135mdWu96J65gDz2w1PrOf9XYdyZ9fj92s37lVUvyV1TMflAEd0mP/bVF92q9fDbV+ukzop9yAwpMN6ntTD7TKX6gtnSLtxGwLPAd9oN+5LnW2PTup7U738Q7soM60uM61heurX5CiqsPKKevyngYe7qHdqXe9ruyjzXaovuLEdxl8EzKufr1nX89Ze7mNt67VbJ9Pm1+vyqk7a+y/txt0G/LbDvKPr1/jkXrSlbRvuR/UFOrbdtEvqcVNewmv3/Q7jv1nvn2k3ruN7ag5wTyd19nTf/gGwCFi3i/ZdAvyhk/EzO9t/aX4/X9N+XXq673TRrgPqeif2ZF/uop6Z9O3nxaIevt4v2qd7sQ8u8z5ut298qbd1+mh+eOhNfaqU8jDwdeD9aXeI6SX6VYfhtv++L2y33Eeo/htdv5P5f9Zh+CdUvTtth1ymU/03+/f68MiQVIeBLgTGUvVitXdOdw1OshqwNfDTUsrz7dr5d+CPVIcxemsPqg/Bb/dmpiTjk3wryZ3As1RB7UtU/2GPq4tdBbyqPswzo+OhHqrA9yjwrST71T0AHU0H/hd4rJPt+Lr6P/GHqHpZvpLkQ0km9WZdunBFvQ+0ub7+uwFAvZzXUP3Ybfu2PUX1H/guXVWeZHSSE5LcTtVb8xxVj0GAjuswv5Qyr8O43rx253cYvh4YRtUj0Vs93bf3AM4rpdy7HMvorXNL/Y3eoZ3d7Ts91d2+3J2++ry4CvhMkk8k2TJJetmOZfTwfax+YFBSfziJ6nDDcX1U3yMdhp/tYvwrO5m/44nlbcMT6r/jqL4on+vwOLOePrbD/D25uuZVVF+inZW9j6rnrbfGUv2nvLinM9TnVvwSmEH1ofomqkM0/1YXeSVAKeVS4F1UQfMc4IEkv0ny2nr6Y8CuwL3AfwB31edIvKPd4sYB7+fF2/HEtvbXX5C7U/W2HQ/cmupcmIN7syE68XD7gfJ/J0+37Q9tXyTf7aR9M3jxa9zR94CPUB3O2Z1qG360wzLadPaa9+a1e7jDcMd16Y2e7ttjWXGHaDrbPt3uOz2tvLt9uQf66vNiX6r33meB64AFSY7ueL5TT/T0faz+4TlK6nOllEVJjqfqWTqxkyJPAyRZtZTybLvxPf4w7KW1qXox2g8DLKj/PkTVG/WJhvlv6TDckyt0HqnLrdPJtHV48ZdhTzwIrJFkeC/C0muoDkO9r5Sy9F5DSf5fx4KllJ8DP6/PR5oGnABckGS9UsoLdS/JO+r/nqdSnTv2sySvK6XcQLUdL6vn68y99XLuoOpxDPA64GPAfySZX0rp2HvYVx6q/36e6vygjp7tZBwASV4JvI3qUNcp7cZv2TBLZ/vH8rx2faGn+3bbuWLLo7fv5862T4/2nZ7qbl/uZvY++bwopdxPFaY/Wveu7w8cS3WI8T97sz704n2svmePkvrLf1B9sHypk2ltJ3kvvdqo7h7foZOyfeHdHYbfQ3X+QNvhmQuoTva8q5Qyt5PHE71dYCnlSeBq4F0drkp5NdV6XrIc6/Frql6qD/ZinhH13+fatWEo8N6mGUopi0op51GdDDqeDl94pZTnS3V5+hepPkPabm9wAfBa4MaG7fhMh3pKHb7a7v/T1dVnbfMO76JMV26hOpdpi4a2XdfFvMOoTjp+rsP4A3qx/OV57XrjGTrfNj3dt38NzEgyfjmW0Rfv517tOz3V3b7coM8/L0opt5RSjqT6B6ptO/Vmn+7N+/jZHtapHrJHSf2ilPJMkuOA2Z1M/hXwGPDtJMdQfRF9lurDqD98qO66vorq6pQPUvUOPFZPP4mqm/yyJCdRfamuRvVhuHMp5W3LudwvUp1vcl6qy+hHUv1H+RhVb1uvlFIuTnIW8I36HKHfAUOpDgOcX0q5pJPZbqL6Ivu3JEuoPmgP71iofq3Wprpk+V5gPaqrceaVUh5IMoPqKqVzqa7yWa2e/gTVOT5QXS31Z+D3Sb5JFUxeRfXFsFEp5QP14Y9TgJ9SnVy9ClXgeL5enya31mU+kORhqi+ZW3oaYkspJclHgV8kWZXqPJQH63XegepL7xsN8z6W5E/Ap5IsrOf7AL3ogVnO1643/krVY3Uw1WHNp0sp19PzffsYqisPL0/yZarXZgIwvZSyX7tlHJJkX6orM58opdxC37yfu913elpRd/tyD6p4yZ8XScZQ9Vz+iOqcyueoeiVfRRVKoXf7dI/ex7W/AnsnuYAqmN27gs49G7z6+uxwHy+/B+2ueuswfgjVh8EyV+jU03ai+iB6qi6zH81XyXyww7wz6/FDOoyfD5zRSbnJVB+ai6nOD5pFu6tE6rKvovoA/DvVf2T3Ux0KOKy79exm20ynChKLqb5MfgFs0qFMj656a7dNj6q32bNU3fj/21YnnVz1Bkyhupz5KarzUI6j+vBvf6XQ3lQnoy6k+sC+m+p8nnXr6ZtQhZu/Ux1qaVvudh3atx7VHaMX1O1bSHXl0n719HHA9+v2P0V1CPJSYM8erPtBVIdEnm+/jh1f93blO9vv3kB16f0j9XrMpzpZ9w3dLHsiVSB4ot43vllvs47b+hI6uTKsl6/dbh3ma9vvJjatG9UX9Y/5v0O+7d9H3e7bdbnX1HU8WG+b21n26sx16vY+US/jkr54P/d03+nB50+P9uUu6plJH31eUIXFbwE3UgXGx+vt8y8d6ul0n25oX7fv47rcjlS92U933E98LN8j9YaVJElSB56jJEmS1MCgJEmS1MCgJEmS1MCgJEmS1MCgJEmS1MCgJEmS1MCgJEmS1MCgJEmS1MCgJEmS1OD/B7InORjfzZRUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind = np.arange(len(BASE_MODELS))\n",
    "models_wins_plain = {x: 0 for x in BASE_MODELS}\n",
    "models_wins_ensemble = {x: 0 for x in BASE_MODELS}\n",
    "for idx in range(CLASSES_COUNT):\n",
    "    class_plain_mean = sorted([(acc_stats_base_models[x][1]['class_accs'][idx]['mean'], x) for x in BASE_MODELS],\n",
    "                              key=lambda x: x[0])\n",
    "    class_ensemble_mean = sorted([(acc_stats_base_models[x][10]['class_accs'][idx]['mean'], x) for x in BASE_MODELS],\n",
    "                                 key=lambda x: x[0])\n",
    "    num_winners = 0.\n",
    "    for j in range(len(class_plain_mean)):\n",
    "        if class_plain_mean[-1][0] == class_plain_mean[j][0]:\n",
    "            num_winners += 1.\n",
    "    for j in range(len(class_plain_mean)):\n",
    "        if class_plain_mean[-1][0] == class_plain_mean[j][0]:\n",
    "            models_wins_plain[class_plain_mean[j][1]] += 1./num_winners\n",
    "            models_wins_ensemble[class_ensemble_mean[j][1]] += 1./num_winners\n",
    "\n",
    "    base = [models_wins_plain[x] for x in BASE_MODELS]\n",
    "\n",
    "plt.figure(figsize=(9, 6))\n",
    "diff = [models_wins_ensemble[x] - models_wins_plain[x] for x in BASE_MODELS]\n",
    "diff_up = [x if x>0 else 0 for x in diff]\n",
    "diff_down = [x if x<0 else 0 for x in diff]\n",
    "plt.barh(ind, np.flip(base, axis=-1), tick_label=np.flip(BASE_MODELS_ALIASES, axis=-1))\n",
    "plt.barh(ind, np.flip(diff_up, axis=-1), color='g', left=np.flip(base, axis=-1), tick_label=np.flip(BASE_MODELS_ALIASES, axis=-1))\n",
    "plt.barh(ind, np.flip(diff_down, axis=-1), color='r', left=np.flip(base, axis=-1), tick_label=np.flip(BASE_MODELS_ALIASES, axis=-1))\n",
    "plt.yticks(fontsize=10)\n",
    "plt.xticks(fontsize=10)\n",
    "plt.xlabel('Number of classes the architecture is best at')\n",
    "plt.legend(['Baseline', 'Gain', 'Loss'])\n",
    "plt.savefig('images/whos_the_best_horizontal_color.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind = np.arange(len(BASE_MODELS))\n",
    "models_wins_plain = {x: 0 for x in BASE_MODELS}\n",
    "models_wins_ensemble = {x: 0 for x in BASE_MODELS}\n",
    "for idx in range(CLASSES_COUNT):\n",
    "    class_plain_mean = sorted([(acc_stats_base_models[x][1]['class_accs'][idx]['mean'], x) for x in BASE_MODELS],\n",
    "                              key=lambda x: x[0])\n",
    "    class_ensemble_mean = sorted([(acc_stats_base_models[x][10]['class_accs'][idx]['mean'], x) for x in BASE_MODELS],\n",
    "                                 key=lambda x: x[0])\n",
    "    num_winners = 0.\n",
    "    for j in range(len(class_plain_mean)):\n",
    "        if class_plain_mean[-1][0] == class_plain_mean[j][0]:\n",
    "            num_winners += 1.\n",
    "    for j in range(len(class_plain_mean)):\n",
    "        if class_plain_mean[-1][0] == class_plain_mean[j][0]:\n",
    "            models_wins_plain[class_plain_mean[j][1]] += 1./num_winners\n",
    "            models_wins_ensemble[class_ensemble_mean[j][1]] += 1./num_winners\n",
    "\n",
    "    base = [models_wins_plain[x] for x in BASE_MODELS]\n",
    "\n",
    "plt.figure(figsize=(9, 6))\n",
    "diff = [models_wins_ensemble[x] - models_wins_plain[x] for x in BASE_MODELS]\n",
    "diff_up = [x if x>0 else 0 for x in diff]\n",
    "diff_down = [x if x<0 else 0 for x in diff]\n",
    "h1 = plt.barh(ind, np.flip(base, axis=-1), color=[0.5, 0.5, 0.5],\n",
    "              tick_label=np.flip(BASE_MODELS_ALIASES, axis=-1))\n",
    "plt.barh(ind, np.flip(diff_up, axis=-1), color=[0.8, 0.8, 0.8], left=np.flip(base, axis=-1),\n",
    "         tick_label=np.flip(BASE_MODELS_ALIASES, axis=-1), hatch='|')\n",
    "plt.barh(ind, np.flip(diff_down, axis=-1), color=[0.2, 0.2, 0.2], left=np.flip(base, axis=-1),\n",
    "         tick_label=np.flip(BASE_MODELS_ALIASES, axis=-1), hatch='-')\n",
    "plt.yticks(fontsize=10)\n",
    "plt.xticks(fontsize=10)\n",
    "plt.xlabel('Number of classes the architecture is best at')\n",
    "plt.legend(['Baseline', 'Gain', 'Loss'])\n",
    "plt.savefig('images/whos_the_best_horizontal_grayscale.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model pointnet old_hit: 16 new_hit: 1 test_cloud_idx 117\n",
      "model pointnet old_hit: 30 new_hit: 1 test_cloud_idx 127\n",
      "model pointnet old_hit: 33 new_hit: 2 test_cloud_idx 176\n",
      "model pointnet old_hit: 13 new_hit: 4 test_cloud_idx 281\n",
      "model pointnet old_hit: 36 new_hit: 7 test_cloud_idx 528\n",
      "model pointnet old_hit: 34 new_hit: 9 test_cloud_idx 696\n",
      "model pointnet old_hit: 37 new_hit: 10 test_cloud_idx 725\n",
      "model pointnet old_hit: 30 new_hit: 12 test_cloud_idx 802\n",
      "model pointnet old_hit: 22 new_hit: 13 test_cloud_idx 847\n",
      "model pointnet old_hit: 38 new_hit: 14 test_cloud_idx 859\n",
      "model pointnet old_hit: 23 new_hit: 14 test_cloud_idx 929\n",
      "model pointnet old_hit: 26 new_hit: 15 test_cloud_idx 956\n",
      "model pointnet old_hit: 37 new_hit: 19 test_cloud_idx 1189\n",
      "model pointnet old_hit: 25 new_hit: 21 test_cloud_idx 1257\n",
      "model pointnet old_hit: 28 new_hit: 22 test_cloud_idx 1356\n",
      "model pointnet old_hit: 4 new_hit: 22 test_cloud_idx 1372\n",
      "model pointnet old_hit: 36 new_hit: 23 test_cloud_idx 1454\n",
      "model pointnet old_hit: 4 new_hit: 23 test_cloud_idx 1507\n",
      "model pointnet old_hit: 2 new_hit: 25 test_cloud_idx 1580\n",
      "model pointnet old_hit: 31 new_hit: 26 test_cloud_idx 1644\n",
      "model pointnet old_hit: 15 new_hit: 26 test_cloud_idx 1668\n",
      "model pointnet old_hit: 15 new_hit: 26 test_cloud_idx 1726\n",
      "model pointnet old_hit: 16 new_hit: 27 test_cloud_idx 1738\n",
      "model pointnet old_hit: 26 new_hit: 31 test_cloud_idx 1969\n",
      "model pointnet old_hit: 33 new_hit: 32 test_cloud_idx 2001\n",
      "model pointnet old_hit: 12 new_hit: 33 test_cloud_idx 2008\n",
      "model pointnet old_hit: 12 new_hit: 33 test_cloud_idx 2009\n",
      "model pointnet old_hit: 12 new_hit: 33 test_cloud_idx 2042\n",
      "model pointnet old_hit: 12 new_hit: 33 test_cloud_idx 2054\n",
      "model pointnet old_hit: 12 new_hit: 33 test_cloud_idx 2076\n",
      "model pointnet old_hit: 3 new_hit: 33 test_cloud_idx 2102\n",
      "model pointnet old_hit: 4 new_hit: 36 test_cloud_idx 2250\n",
      "model pointnet old_hit: 15 new_hit: 37 test_cloud_idx 2335\n",
      "model pointnet old_hit: 5 new_hit: 37 test_cloud_idx 2346\n",
      "model pointnet old_hit: 26 new_hit: 37 test_cloud_idx 2368\n",
      "model pointnet old_hit: 19 new_hit: 37 test_cloud_idx 2387\n",
      "model pointnet old_hit: 10 new_hit: 37 test_cloud_idx 2424\n",
      "model pointnet old_hit: 14 new_hit: 38 test_cloud_idx 2436\n",
      "model pointnet old_hit: 38 new_hit: 39 test_cloud_idx 2454\n"
     ]
    }
   ],
   "source": [
    "voting_method = 'activations'\n",
    "for model in BASE_MODELS:\n",
    "    \n",
    "    # How many learned models\n",
    "    learned_models_no = probabilities[model][voting_method].shape[0]\n",
    "    assert learned_models_no == 10, 'There should be 10 learned models for each architecture'\n",
    "    \n",
    "    # Calc ensemble scores & votes\n",
    "    ensemble_scores = np.mean(probabilities[model][voting_method], axis=0)\n",
    "    ensemble_votes = np.argmax(ensemble_scores, axis=-1)\n",
    "    \n",
    "    # Get single scores & votes\n",
    "    #model_instance_idx = np.random.choice(np.arange(learned_models_no))\n",
    "    model_instance_idx = 0\n",
    "    single_scores = probabilities[model][voting_method][model_instance_idx]\n",
    "    single_votes = np.argmax(single_scores, axis=-1)\n",
    "    \n",
    "    # Print all cases where ensemble is better than score\n",
    "    misclassifications = []\n",
    "    for test_cloud_idx, _ in enumerate(true_labels[model]):\n",
    "        old_hit = single_votes[test_cloud_idx] == true_labels[model][test_cloud_idx]\n",
    "        new_hit = ensemble_votes[test_cloud_idx] == true_labels[model][test_cloud_idx]\n",
    "        if not old_hit and new_hit:\n",
    "            print ('model', model, 'old_hit:', single_votes[test_cloud_idx], 'new_hit:', ensemble_votes[test_cloud_idx], 'test_cloud_idx', test_cloud_idx)\n",
    "            misclassifications.append((single_votes[test_cloud_idx], ensemble_votes[test_cloud_idx]))\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for pointnet there are much confusion between:\n",
      "should be 26 : plant but he think it's 15 flower_pot\n",
      "should be 33 : table but he think it's 12 desk\n"
     ]
    }
   ],
   "source": [
    "print ('for pointnet there are much confusion between:')\n",
    "print ('should be 26 :', CLASS_NAMES[26], 'but he think it\\'s 15', CLASS_NAMES[15])\n",
    "print ('should be 33 :', CLASS_NAMES[33], 'but he think it\\'s 12', CLASS_NAMES[12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How much: 1 missclassification of type (16, 1)\n",
      "How much: 1 missclassification of type (30, 1)\n",
      "How much: 1 missclassification of type (33, 2)\n",
      "How much: 2 missclassification of type (13, 4)\n",
      "How much: 1 missclassification of type (36, 7)\n",
      "How much: 1 missclassification of type (34, 9)\n",
      "How much: 5 missclassification of type (37, 10)\n",
      "How much: 3 missclassification of type (30, 12)\n",
      "How much: 1 missclassification of type (22, 13)\n",
      "How much: 8 missclassification of type (38, 14)\n",
      "How much: 8 missclassification of type (23, 14)\n",
      "How much: 11 missclassification of type (26, 15)\n",
      "How much: 1 missclassification of type (37, 19)\n",
      "How much: 1 missclassification of type (25, 21)\n",
      "How much: 1 missclassification of type (28, 22)\n",
      "How much: 1 missclassification of type (4, 22)\n",
      "How much: 3 missclassification of type (36, 23)\n",
      "How much: 2 missclassification of type (4, 23)\n",
      "How much: 1 missclassification of type (2, 25)\n",
      "How much: 1 missclassification of type (31, 26)\n",
      "How much: 14 missclassification of type (15, 26)\n",
      "How much: 14 missclassification of type (15, 26)\n",
      "How much: 2 missclassification of type (16, 27)\n",
      "How much: 1 missclassification of type (26, 31)\n",
      "How much: 1 missclassification of type (33, 32)\n",
      "How much: 17 missclassification of type (12, 33)\n",
      "How much: 17 missclassification of type (12, 33)\n",
      "How much: 17 missclassification of type (12, 33)\n",
      "How much: 17 missclassification of type (12, 33)\n",
      "How much: 17 missclassification of type (12, 33)\n",
      "How much: 2 missclassification of type (3, 33)\n",
      "How much: 3 missclassification of type (4, 36)\n",
      "How much: 6 missclassification of type (15, 37)\n",
      "How much: 2 missclassification of type (5, 37)\n",
      "How much: 1 missclassification of type (26, 37)\n",
      "How much: 1 missclassification of type (19, 37)\n",
      "How much: 7 missclassification of type (10, 37)\n",
      "How much: 3 missclassification of type (14, 38)\n",
      "How much: 2 missclassification of type (38, 39)\n"
     ]
    }
   ],
   "source": [
    "voting_method = 'activations'\n",
    "for model in BASE_MODELS:\n",
    "    \n",
    "    # Get single scores & votes\n",
    "    #model_instance_idx = np.random.choice(np.arange(learned_models_no))\n",
    "    model_instance_idx = 0\n",
    "    single_scores = probabilities[model][voting_method][model_instance_idx]\n",
    "    single_votes = np.argmax(single_scores, axis=-1)\n",
    "    \n",
    "    # Print all cases where we make the same mistake, which ensemble prevented\n",
    "    \n",
    "    for misclassification in misclassifications:\n",
    "        how_much = 0\n",
    "        for test_cloud_idx, _ in enumerate(true_labels[model]):\n",
    "            if single_votes[test_cloud_idx] == misclassification[0] and true_labels[model][test_cloud_idx] == misclassification[1]:\n",
    "                #print ('model', model, 'old_hit:', single_votes[test_cloud_idx], 'true_label:', ensemble_votes[test_cloud_idx], 'test_cloud_idx', test_cloud_idx)\n",
    "                how_much += 1\n",
    "        print ('How much:', how_much, 'missclassification of type', misclassification)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model kcnet old_hit: 33 new_hit: 2 test_cloud_idx 229\n",
      "model kcnet old_hit: 29 new_hit: 3 test_cloud_idx 256\n",
      "model kcnet old_hit: 23 new_hit: 4 test_cloud_idx 286\n",
      "model kcnet old_hit: 25 new_hit: 4 test_cloud_idx 304\n",
      "model kcnet old_hit: 21 new_hit: 4 test_cloud_idx 328\n",
      "model kcnet old_hit: 37 new_hit: 4 test_cloud_idx 330\n",
      "model kcnet old_hit: 37 new_hit: 5 test_cloud_idx 416\n",
      "model kcnet old_hit: 37 new_hit: 5 test_cloud_idx 450\n",
      "model kcnet old_hit: 37 new_hit: 5 test_cloud_idx 469\n",
      "model kcnet old_hit: 27 new_hit: 7 test_cloud_idx 528\n",
      "model kcnet old_hit: 34 new_hit: 7 test_cloud_idx 559\n",
      "model kcnet old_hit: 34 new_hit: 9 test_cloud_idx 698\n",
      "model kcnet old_hit: 15 new_hit: 10 test_cloud_idx 712\n",
      "model kcnet old_hit: 15 new_hit: 10 test_cloud_idx 722\n",
      "model kcnet old_hit: 22 new_hit: 11 test_cloud_idx 732\n",
      "model kcnet old_hit: 13 new_hit: 11 test_cloud_idx 744\n",
      "model kcnet old_hit: 29 new_hit: 12 test_cloud_idx 754\n",
      "model kcnet old_hit: 33 new_hit: 12 test_cloud_idx 783\n",
      "model kcnet old_hit: 36 new_hit: 12 test_cloud_idx 815\n",
      "model kcnet old_hit: 23 new_hit: 12 test_cloud_idx 817\n",
      "model kcnet old_hit: 36 new_hit: 12 test_cloud_idx 827\n",
      "model kcnet old_hit: 11 new_hit: 13 test_cloud_idx 841\n",
      "model kcnet old_hit: 23 new_hit: 14 test_cloud_idx 863\n",
      "model kcnet old_hit: 23 new_hit: 14 test_cloud_idx 872\n",
      "model kcnet old_hit: 23 new_hit: 14 test_cloud_idx 874\n",
      "model kcnet old_hit: 23 new_hit: 14 test_cloud_idx 879\n",
      "model kcnet old_hit: 23 new_hit: 14 test_cloud_idx 882\n",
      "model kcnet old_hit: 23 new_hit: 14 test_cloud_idx 911\n",
      "model kcnet old_hit: 23 new_hit: 14 test_cloud_idx 914\n",
      "model kcnet old_hit: 38 new_hit: 14 test_cloud_idx 917\n",
      "model kcnet old_hit: 38 new_hit: 14 test_cloud_idx 926\n",
      "model kcnet old_hit: 23 new_hit: 14 test_cloud_idx 932\n",
      "model kcnet old_hit: 38 new_hit: 14 test_cloud_idx 938\n",
      "model kcnet old_hit: 23 new_hit: 14 test_cloud_idx 940\n",
      "model kcnet old_hit: 23 new_hit: 16 test_cloud_idx 973\n",
      "model kcnet old_hit: 27 new_hit: 16 test_cloud_idx 992\n",
      "model kcnet old_hit: 22 new_hit: 16 test_cloud_idx 1034\n",
      "model kcnet old_hit: 27 new_hit: 16 test_cloud_idx 1051\n",
      "model kcnet old_hit: 23 new_hit: 16 test_cloud_idx 1052\n",
      "model kcnet old_hit: 22 new_hit: 16 test_cloud_idx 1057\n",
      "model kcnet old_hit: 18 new_hit: 17 test_cloud_idx 1136\n",
      "model kcnet old_hit: 9 new_hit: 19 test_cloud_idx 1188\n",
      "model kcnet old_hit: 37 new_hit: 19 test_cloud_idx 1189\n",
      "model kcnet old_hit: 22 new_hit: 21 test_cloud_idx 1257\n",
      "model kcnet old_hit: 29 new_hit: 21 test_cloud_idx 1299\n",
      "model kcnet old_hit: 33 new_hit: 23 test_cloud_idx 1426\n",
      "model kcnet old_hit: 33 new_hit: 23 test_cloud_idx 1428\n",
      "model kcnet old_hit: 12 new_hit: 23 test_cloud_idx 1430\n",
      "model kcnet old_hit: 12 new_hit: 25 test_cloud_idx 1544\n",
      "model kcnet old_hit: 37 new_hit: 25 test_cloud_idx 1597\n",
      "model kcnet old_hit: 3 new_hit: 25 test_cloud_idx 1602\n",
      "model kcnet old_hit: 34 new_hit: 25 test_cloud_idx 1605\n",
      "model kcnet old_hit: 0 new_hit: 26 test_cloud_idx 1640\n",
      "model kcnet old_hit: 33 new_hit: 26 test_cloud_idx 1642\n",
      "model kcnet old_hit: 15 new_hit: 26 test_cloud_idx 1647\n",
      "model kcnet old_hit: 31 new_hit: 26 test_cloud_idx 1673\n",
      "model kcnet old_hit: 37 new_hit: 26 test_cloud_idx 1722\n",
      "model kcnet old_hit: 24 new_hit: 26 test_cloud_idx 1725\n",
      "model kcnet old_hit: 29 new_hit: 27 test_cloud_idx 1743\n",
      "model kcnet old_hit: 16 new_hit: 27 test_cloud_idx 1746\n",
      "model kcnet old_hit: 21 new_hit: 28 test_cloud_idx 1772\n",
      "model kcnet old_hit: 15 new_hit: 30 test_cloud_idx 1868\n",
      "model kcnet old_hit: 38 new_hit: 31 test_cloud_idx 1969\n",
      "model kcnet old_hit: 10 new_hit: 31 test_cloud_idx 1978\n",
      "model kcnet old_hit: 29 new_hit: 32 test_cloud_idx 1995\n",
      "model kcnet old_hit: 19 new_hit: 32 test_cloud_idx 2005\n",
      "model kcnet old_hit: 33 new_hit: 32 test_cloud_idx 2006\n",
      "model kcnet old_hit: 33 new_hit: 32 test_cloud_idx 2007\n",
      "model kcnet old_hit: 3 new_hit: 36 test_cloud_idx 2236\n",
      "model kcnet old_hit: 33 new_hit: 36 test_cloud_idx 2242\n",
      "model kcnet old_hit: 23 new_hit: 36 test_cloud_idx 2250\n",
      "model kcnet old_hit: 16 new_hit: 36 test_cloud_idx 2296\n",
      "model kcnet old_hit: 29 new_hit: 36 test_cloud_idx 2326\n",
      "model kcnet old_hit: 6 new_hit: 37 test_cloud_idx 2337\n",
      "model kcnet old_hit: 5 new_hit: 37 test_cloud_idx 2346\n",
      "model kcnet old_hit: 6 new_hit: 37 test_cloud_idx 2350\n",
      "model kcnet old_hit: 10 new_hit: 37 test_cloud_idx 2369\n",
      "model kcnet old_hit: 6 new_hit: 37 test_cloud_idx 2381\n",
      "model kcnet old_hit: 39 new_hit: 38 test_cloud_idx 2437\n",
      "model kcnet old_hit: 37 new_hit: 39 test_cloud_idx 2462\n",
      "model kcnet old_hit: 21 new_hit: 39 test_cloud_idx 2465\n"
     ]
    }
   ],
   "source": [
    "voting_method = 'activations'\n",
    "for model in ['kcnet']:\n",
    "    \n",
    "    # How many learned models\n",
    "    learned_models_no = probabilities[model][voting_method].shape[0]\n",
    "    assert learned_models_no == 10, 'There should be 10 learned models for each architecture'\n",
    "    \n",
    "    # Calc ensemble scores & votes\n",
    "    ensemble_scores = np.mean(probabilities[model][voting_method], axis=0)\n",
    "    ensemble_votes = np.argmax(ensemble_scores, axis=-1)\n",
    "    \n",
    "    # Get single scores & votes\n",
    "    #model_instance_idx = np.random.choice(np.arange(learned_models_no))\n",
    "    model_instance_idx = 0\n",
    "    single_scores = probabilities[model][voting_method][model_instance_idx]\n",
    "    single_votes = np.argmax(single_scores, axis=-1)\n",
    "    \n",
    "    # Print all cases where ensemble is better than score\n",
    "    misclassifications = []\n",
    "    for test_cloud_idx, _ in enumerate(true_labels[model]):\n",
    "        old_hit = single_votes[test_cloud_idx] == true_labels[model][test_cloud_idx]\n",
    "        new_hit = ensemble_votes[test_cloud_idx] == true_labels[model][test_cloud_idx]\n",
    "        if not old_hit and new_hit:\n",
    "            print ('model', model, 'old_hit:', single_votes[test_cloud_idx], 'new_hit:', ensemble_votes[test_cloud_idx], 'test_cloud_idx', test_cloud_idx)\n",
    "            misclassifications.append((single_votes[test_cloud_idx], ensemble_votes[test_cloud_idx]))\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for kcnet there are much confusion between:\n",
      "should be 14 : dresser but he think it's 23 night_stand\n",
      "should be 26 : plant but he think it's 15 flower_pot\n"
     ]
    }
   ],
   "source": [
    "print ('for kcnet there are much confusion between:')\n",
    "print ('should be 14 :', CLASS_NAMES[14], 'but he think it\\'s 23', CLASS_NAMES[23])\n",
    "print ('should be 26 :', CLASS_NAMES[26], 'but he think it\\'s 15', CLASS_NAMES[15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How much: 1 missclassification of type (33, 2)\n",
      "How much: 1 missclassification of type (29, 3)\n",
      "How much: 1 missclassification of type (23, 4)\n",
      "How much: 1 missclassification of type (25, 4)\n",
      "How much: 1 missclassification of type (21, 4)\n",
      "How much: 1 missclassification of type (37, 4)\n",
      "How much: 4 missclassification of type (37, 5)\n",
      "How much: 4 missclassification of type (37, 5)\n",
      "How much: 4 missclassification of type (37, 5)\n",
      "How much: 1 missclassification of type (27, 7)\n",
      "How much: 1 missclassification of type (34, 7)\n",
      "How much: 1 missclassification of type (34, 9)\n",
      "How much: 3 missclassification of type (15, 10)\n",
      "How much: 3 missclassification of type (15, 10)\n",
      "How much: 2 missclassification of type (22, 11)\n",
      "How much: 1 missclassification of type (13, 11)\n",
      "How much: 1 missclassification of type (29, 12)\n",
      "How much: 7 missclassification of type (33, 12)\n",
      "How much: 2 missclassification of type (36, 12)\n",
      "How much: 1 missclassification of type (23, 12)\n",
      "How much: 2 missclassification of type (36, 12)\n",
      "How much: 2 missclassification of type (11, 13)\n",
      "How much: 14 missclassification of type (23, 14)\n",
      "How much: 14 missclassification of type (23, 14)\n",
      "How much: 14 missclassification of type (23, 14)\n",
      "How much: 14 missclassification of type (23, 14)\n",
      "How much: 14 missclassification of type (23, 14)\n",
      "How much: 14 missclassification of type (23, 14)\n",
      "How much: 14 missclassification of type (23, 14)\n",
      "How much: 3 missclassification of type (38, 14)\n",
      "How much: 3 missclassification of type (38, 14)\n",
      "How much: 14 missclassification of type (23, 14)\n",
      "How much: 3 missclassification of type (38, 14)\n",
      "How much: 14 missclassification of type (23, 14)\n",
      "How much: 2 missclassification of type (23, 16)\n",
      "How much: 2 missclassification of type (27, 16)\n",
      "How much: 2 missclassification of type (22, 16)\n",
      "How much: 2 missclassification of type (27, 16)\n",
      "How much: 2 missclassification of type (23, 16)\n",
      "How much: 2 missclassification of type (22, 16)\n",
      "How much: 1 missclassification of type (18, 17)\n",
      "How much: 1 missclassification of type (9, 19)\n",
      "How much: 1 missclassification of type (37, 19)\n",
      "How much: 1 missclassification of type (22, 21)\n",
      "How much: 1 missclassification of type (29, 21)\n",
      "How much: 5 missclassification of type (33, 23)\n",
      "How much: 5 missclassification of type (33, 23)\n",
      "How much: 1 missclassification of type (12, 23)\n",
      "How much: 1 missclassification of type (12, 25)\n",
      "How much: 1 missclassification of type (37, 25)\n",
      "How much: 1 missclassification of type (3, 25)\n",
      "How much: 1 missclassification of type (34, 25)\n",
      "How much: 2 missclassification of type (0, 26)\n",
      "How much: 1 missclassification of type (33, 26)\n",
      "How much: 15 missclassification of type (15, 26)\n",
      "How much: 1 missclassification of type (31, 26)\n",
      "How much: 1 missclassification of type (37, 26)\n",
      "How much: 1 missclassification of type (24, 26)\n",
      "How much: 1 missclassification of type (29, 27)\n",
      "How much: 2 missclassification of type (16, 27)\n",
      "How much: 1 missclassification of type (21, 28)\n",
      "How much: 1 missclassification of type (15, 30)\n",
      "How much: 1 missclassification of type (38, 31)\n",
      "How much: 1 missclassification of type (10, 31)\n",
      "How much: 1 missclassification of type (29, 32)\n",
      "How much: 1 missclassification of type (19, 32)\n",
      "How much: 3 missclassification of type (33, 32)\n",
      "How much: 3 missclassification of type (33, 32)\n",
      "How much: 3 missclassification of type (3, 36)\n",
      "How much: 2 missclassification of type (33, 36)\n",
      "How much: 2 missclassification of type (23, 36)\n",
      "How much: 2 missclassification of type (16, 36)\n",
      "How much: 1 missclassification of type (29, 36)\n",
      "How much: 5 missclassification of type (6, 37)\n",
      "How much: 2 missclassification of type (5, 37)\n",
      "How much: 5 missclassification of type (6, 37)\n",
      "How much: 8 missclassification of type (10, 37)\n",
      "How much: 5 missclassification of type (6, 37)\n",
      "How much: 4 missclassification of type (39, 38)\n",
      "How much: 1 missclassification of type (37, 39)\n",
      "How much: 1 missclassification of type (21, 39)\n"
     ]
    }
   ],
   "source": [
    "voting_method = 'activations'\n",
    "for model in ['kcnet']:\n",
    "    \n",
    "    # Get single scores & votes\n",
    "    #model_instance_idx = np.random.choice(np.arange(learned_models_no))\n",
    "    model_instance_idx = 0\n",
    "    single_scores = probabilities[model][voting_method][model_instance_idx]\n",
    "    single_votes = np.argmax(single_scores, axis=-1)\n",
    "    \n",
    "    # Print all cases where we make the same mistake, which ensemble prevented\n",
    "    \n",
    "    for misclassification in misclassifications:\n",
    "        how_much = 0\n",
    "        for test_cloud_idx, _ in enumerate(true_labels[model]):\n",
    "            if single_votes[test_cloud_idx] == misclassification[0] and true_labels[model][test_cloud_idx] == misclassification[1]:\n",
    "                #print ('model', model, 'old_hit:', single_votes[test_cloud_idx], 'true_label:', ensemble_votes[test_cloud_idx], 'test_cloud_idx', test_cloud_idx)\n",
    "                how_much += 1\n",
    "        print ('How much:', how_much, 'missclassification of type', misclassification)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "mistakes = {}\n",
    "voting_method = 'activations'\n",
    "for model in BASE_MODELS:\n",
    "    \n",
    "    # Get single scores & votes\n",
    "    #model_instance_idx = np.random.choice(np.arange(learned_models_no))\n",
    "    model_instance_idx = 0\n",
    "    single_scores = probabilities[model][voting_method][model_instance_idx]\n",
    "    single_votes = np.argmax(single_scores, axis=-1)\n",
    "    \n",
    "    # Print all cases where we make the same mistake, which ensemble prevented\n",
    "    model_mistakes = []\n",
    "    for i in range(40):\n",
    "        for j in range(40):\n",
    "            \n",
    "            # should be i and is j - it's good!\n",
    "            if i == j:\n",
    "                continue\n",
    "\n",
    "            # should be i and is j - not good!\n",
    "            how_much = 0\n",
    "            for test_cloud_idx, _ in enumerate(true_labels[model]):\n",
    "                if single_votes[test_cloud_idx] == i and true_labels[model][test_cloud_idx] == j:\n",
    "                    how_much += 1\n",
    "            model_mistakes.append(how_much)\n",
    "    mistakes[model] = model_mistakes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pointnet 0.175 0.9076685405406422\n",
      "pointnet++ 0.15705128205128205 1.074924695006238\n",
      "so-net 0.11602564102564103 0.7629225852846471\n",
      "kcnet 0.1519230769230769 0.8385710643590536\n",
      "deepsets 0.1576923076923077 0.9991046287409531\n",
      "dgcnn 0.13525641025641025 0.8595097355678013\n",
      "pointcnn 0.13333333333333333 0.8087132334870588\n"
     ]
    }
   ],
   "source": [
    "for model in BASE_MODELS:\n",
    "    print (model, np.mean(mistakes[model]), np.std(mistakes[model]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(len(BASE_MODELS))\n",
    "y = [np.mean([mis for mis in mistakes[model] if mis != 0]) for model in BASE_MODELS]\n",
    "e = [np.std([mis for mis in mistakes[model] if mis != 0]) for model in BASE_MODELS]\n",
    "#e = [np.std(mistakes[model]) for model in BASE_MODELS]\n",
    "#plt.errorbar(x, y, e, linestyle='None', marker='^')\n",
    "\n",
    "plt.ylabel('Mean number of mistakes for i,j class')\n",
    "plt.xticks(np.arange(len(BASE_MODELS)), BASE_MODELS, rotation=90)\n",
    "plt.plot(x, y, linestyle='None', marker='.', markersize=15)\n",
    "plt.savefig('mistakes.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_method = 'activations'\n",
    "points_for_griffindor = np.zeros(true_labels[BASE_MODELS[0]].shape, dtype=np.int32)\n",
    "for model in BASE_MODELS:\n",
    "    \n",
    "    # How many learned models\n",
    "    learned_models_no = probabilities[model][voting_method].shape[0]\n",
    "    assert learned_models_no == 10, 'There should be 10 learned models for each architecture'\n",
    "    \n",
    "    # Calc ensemble scores & votes\n",
    "    ensemble_scores = np.mean(probabilities[model][voting_method], axis=0)\n",
    "    ensemble_votes = np.argmax(ensemble_scores, axis=-1)\n",
    "    \n",
    "    # Get single scores & votes\n",
    "    #model_instance_idx = np.random.choice(np.arange(learned_models_no))\n",
    "    model_instance_idx = 0\n",
    "    single_scores = probabilities[model][voting_method][model_instance_idx]\n",
    "    single_votes = np.argmax(single_scores, axis=-1)\n",
    "    \n",
    "    # Print all cases where ensemble is better than score\n",
    "    misclassifications = []\n",
    "    for test_cloud_idx, _ in enumerate(true_labels[model]):\n",
    "        old_hit = single_votes[test_cloud_idx] == true_labels[model][test_cloud_idx]\n",
    "        new_hit = ensemble_votes[test_cloud_idx] == true_labels[model][test_cloud_idx]\n",
    "        if not old_hit and new_hit:\n",
    "            points_for_griffindor[test_cloud_idx] += 1\n",
    "            #print ('model', model, 'old_hit:', single_votes[test_cloud_idx], 'new_hit:', ensemble_votes[test_cloud_idx], 'test_cloud_idx', test_cloud_idx)\n",
    "            #misclassifications.append((single_votes[test_cloud_idx], ensemble_votes[test_cloud_idx]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "973 [3] glass_box\n",
      "1057 [3] glass_box\n",
      "1189 [3] lamp\n",
      "2346 [3] vase\n"
     ]
    }
   ],
   "source": [
    "better_for_3_arch = np.argwhere(points_for_griffindor == 3)\n",
    "for idx in better_for_3_arch:\n",
    "    print (idx[0], points_for_griffindor[idx], CLASS_NAMES[true_labels[BASE_MODELS[0]][idx[0]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39\n",
      "39\n",
      "39\n",
      "39.0\n",
      "39\n",
      "39\n",
      "39\n"
     ]
    }
   ],
   "source": [
    "for model in BASE_MODELS:\n",
    "    print(true_labels[model][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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