retraining-eval.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "hp = {}\n",
    "hp['dataset'] = 'imagenet-full'\n",
    "hp['desired_num_clusters'] = 1000\n",
    "hp['num_merges_per_network_update'] = 3000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from torchvision import datasets\n",
    "from twosample import utils\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "from twosample import utils\n",
    "import os\n",
    "import itertools\n",
    "import sklearn\n",
    "from matplotlib.ticker import MaxNLocator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "matplotlib.rcParams.update({'font.size': 22})\n",
    "plt.rc('text', usetex=True)\n",
    "plt.rc('font', family='serif')\n",
    "plt.rc('text.latex', preamble=r'\\usepackage{amssymb}')\n",
    "figure_path = 'figures/real'\n",
    "utils.create_directory_if_not_exists(figure_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def run_evaluation(hp):\n",
    "    experiment_directory = 'results/real/{}'.format(hp['dataset'])\n",
    "    dataset = datasets.ImageFolder('./datasets/' + hp['dataset'])\n",
    "     \n",
    "    num_correct_merges = 0\n",
    "    num_correct_merges_list = []\n",
    "    for i in range(len(hp['checkpoints'])):\n",
    "        # Load latest clustering checkpoint\n",
    "        clustering_checkpoint = hp['checkpoints'][i]\n",
    "        clustering_checkpoint_directory = experiment_directory + '/{}'.format(clustering_checkpoint)\n",
    "        \n",
    "        # Load clustering\n",
    "        current_clustering_filename = clustering_checkpoint_directory + '/current_clustering.npy'\n",
    "        print('Load train-test split from', current_clustering_filename)\n",
    "        sample_indices_train, sample_indices_test = np.load(current_clustering_filename).tolist()\n",
    "        current_clustering = [a + b for a, b in zip(sample_indices_train, sample_indices_test)]\n",
    "        cluster_categories = [utils.get_majority_category(dataset, sample_indices)\n",
    "                              for sample_indices in current_clustering]\n",
    "        \n",
    "        # Load mean arithmetic means in order to find best epoch\n",
    "        mean_arithmetic_means_filename = clustering_checkpoint_directory + '/mean_arithmetic_means.csv'\n",
    "        print('Load mean arithmetic means from', mean_arithmetic_means_filename)\n",
    "        mean_arithmetic_means = np.loadtxt(mean_arithmetic_means_filename).tolist()\n",
    "        print('mean arithmetic means:\\n', mean_arithmetic_means)\n",
    "        \n",
    "        # Load affinities from epoch with highest mean arithmetic mean\n",
    "        best_epoch = np.argmax(mean_arithmetic_means)\n",
    "        print('Best epoch:', best_epoch)\n",
    "        affinities_directory = clustering_checkpoint_directory + '/affinities'\n",
    "        affinities_filename = affinities_directory + '/{}.npy'.format(best_epoch)\n",
    "        print('Load affinities from', affinities_filename)\n",
    "        affinities = np.load(affinities_filename)\n",
    "        \n",
    "        # Reproduce the merge process\n",
    "        desired_num_clusters = hp['checkpoints'][i + 1] if i + 1 < len(hp['checkpoints']) else hp['desired_num_clusters']\n",
    "        print('Merging from {} clusters to {} clusters.'.format(len(current_clustering),\n",
    "                                                                desired_num_clusters))\n",
    "        agglomerative_clustering = AgglomerativeClustering(linkage='average',\n",
    "                                                           affinity='precomputed',\n",
    "                                                           n_clusters=desired_num_clusters)\n",
    "        agglomerative_clustering.fit(affinities)\n",
    "        temp_clustering = {cluster_index: sample_indices for\n",
    "                           cluster_index, sample_indices in enumerate(current_clustering)}\n",
    "        counter = itertools.count(len(current_clustering))\n",
    "        merge_history = [{'new_cluster': next(counter), 'cluster_a': x[0], 'cluster_b': x[1]}\n",
    "                         for x in agglomerative_clustering.children_]\n",
    "\n",
    "        print('Clustering complete.')\n",
    "        for merge_step in merge_history:\n",
    "            temp_clustering[merge_step['new_cluster']] = temp_clustering[merge_step['cluster_a']] +\\\n",
    "                                                         temp_clustering[merge_step['cluster_b']]\n",
    "            num_correct_merges += utils.get_majority_category(dataset, temp_clustering[merge_step['cluster_a']]) ==\\\n",
    "                                  utils.get_majority_category(dataset, temp_clustering[merge_step['cluster_b']])\n",
    "            num_correct_merges_list.append(num_correct_merges)\n",
    "            del temp_clustering[merge_step['cluster_a']]\n",
    "            del temp_clustering[merge_step['cluster_b']]\n",
    "            if len(temp_clustering) == desired_num_clusters:\n",
    "                break\n",
    "        print(len(num_correct_merges_list))\n",
    "    return num_correct_merges_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8793]\n",
      "Load train-test split from results/real/imagenet-full/8793/current_clustering.npy\n",
      "Load mean arithmetic means from results/real/imagenet-full/8793/mean_arithmetic_means.csv\n",
      "mean arithmetic means:\n",
      " [0.8437413573265076, 0.9576355218887329, 0.9778926372528076, 0.9857324957847595, 0.9835563898086548]\n",
      "Best epoch: 3\n",
      "Load affinities from results/real/imagenet-full/8793/affinities/3.npy\n",
      "Merging from 8793 clusters to 1000 clusters.\n",
      "Clustering complete.\n",
      "7793\n",
      "7793\n",
      "[8793, 5793]\n",
      "Load train-test split from results/real/imagenet-full/8793/current_clustering.npy\n",
      "Load mean arithmetic means from results/real/imagenet-full/8793/mean_arithmetic_means.csv\n",
      "mean arithmetic means:\n",
      " [0.8437413573265076, 0.9576355218887329, 0.9778926372528076, 0.9857324957847595, 0.9835563898086548]\n",
      "Best epoch: 3\n",
      "Load affinities from results/real/imagenet-full/8793/affinities/3.npy\n",
      "Merging from 8793 clusters to 5793 clusters.\n",
      "Clustering complete.\n",
      "3000\n",
      "Load train-test split from results/real/imagenet-full/5793/current_clustering.npy\n",
      "Load mean arithmetic means from results/real/imagenet-full/5793/mean_arithmetic_means.csv\n",
      "mean arithmetic means:\n",
      " [0.9111455678939819, 0.9720435738563538, 0.9797861576080322, 0.9851221442222595, 0.9907147288322449, 0.9926402568817139, 0.9934490919113159, 0.993990957736969, 0.9941162467002869, 0.9945335984230042, 0.9949347376823425, 0.9951536059379578, 0.9950887560844421]\n",
      "Best epoch: 11\n",
      "Load affinities from results/real/imagenet-full/5793/affinities/11.npy\n",
      "Merging from 5793 clusters to 1000 clusters.\n",
      "Clustering complete.\n",
      "7793\n",
      "7793\n"
     ]
    }
   ],
   "source": [
    "hp['checkpoints'] = []\n",
    "experiment_directory = 'results/real/{}'.format(hp['dataset'])\n",
    "clustering_checkpoints = [int(clustering_checkpoint_directory)\n",
    "                          for clustering_checkpoint_directory in os.listdir(experiment_directory)]\n",
    "num_correct_merges_list_list = []\n",
    "for clustering_checkpoint in sorted(clustering_checkpoints, reverse=True)[:-2]:\n",
    "    hp['checkpoints'].append(clustering_checkpoint)\n",
    "    print(hp['checkpoints'])\n",
    "    num_correct_merges_list_list.append(run_evaluation(hp))\n",
    "    print(len(num_correct_merges_list_list[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1608 1832 1.1393034825870647\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(num_correct_merges_list_list[0][-1], num_correct_merges_list_list[1][-1],\n",
    "      num_correct_merges_list_list[1][-1] / num_correct_merges_list_list[0][-1])\n",
    "\n",
    "labels = ['computing $\\mathbb{D}$ only initially',\n",
    "          'recomputing $\\mathbb{D}$ after 3000 merges']\n",
    "for num_correct_merges_list in num_correct_merges_list_list:\n",
    "    plt.plot(np.arange(hp['num_merges_per_network_update'], len(num_correct_merges_list)),\n",
    "             num_correct_merges_list[hp['num_merges_per_network_update']:])\n",
    "xposition = [hp['num_merges_per_network_update'],\n",
    "             len(num_correct_merges_list)]\n",
    "for xc in xposition:\n",
    "    plt.axvline(x=xc, color='k', linestyle='--')\n",
    "plt.legend(labels, fontsize=16, loc=8)\n",
    "plt.xlabel('\\#merges $k$')\n",
    "plt.ylabel('\\#correct merges $CM(k)$')\n",
    "plt.xticks(np.arange(3000, 9000, 1000))\n",
    "plt.yticks(np.arange(800, 2000, 200))\n",
    "plt.grid(True)\n",
    "plt.savefig(figure_path + '/' +'real.pdf',\n",
    "                bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (pytorch1.0)",
   "language": "python",
   "name": "pytorch1.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}