VSP_vs_semantic_without_outliers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from visiprog.data import *\n",
    "from visiprog.metriclearning import *\n",
    "from visiprog.evaluate import *\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from visiprog.metric_learn.fda_visiprog import LFDA_VISIPROG\n",
    "from metric_learn import LFDA\n",
    "from sklearn.metrics import pairwise_distances_argmin_min\n",
    "\n",
    "from IPython.display import Image, display\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "import logging\n",
    "import sys\n",
    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_list = range(10,150,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5245\n"
     ]
    }
   ],
   "source": [
    "raw_feature = read_raw_feature()\n",
    "label_material = read_material_label()\n",
    "groups_VSP, group_material = read_VSP_group(pappas_only=False)\n",
    "label_VSP = get_VSP_label(groups_VSP, raw_feature.shape[0])\n",
    "\n",
    "print(raw_feature.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove outlier from both dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1537\n"
     ]
    }
   ],
   "source": [
    "idx_outlier = np.where(label_VSP == 0)[0]\n",
    "X = raw_feature[~idx_outlier,:]\n",
    "Y_material = label_material[~idx_outlier]\n",
    "Y_VSP = label_VSP[~idx_outlier]\n",
    "\n",
    "print(X.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:visiprog.evaluate:Nearest neighbor on same data\n",
      "0.971372804164\n"
     ]
    }
   ],
   "source": [
    "model = LFDA()\n",
    "model.fit(X, Y_material)\n",
    "X_fisher = model.transform(X)\n",
    "res_fda = leave_one_sample_out(X_fisher, Y_material)\n",
    "print(res_fda['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "30\n",
      "50\n",
      "70\n",
      "90\n",
      "110\n",
      "130\n"
     ]
    }
   ],
   "source": [
    "representative = []\n",
    "\n",
    "for n in n_list:\n",
    "    print(n)\n",
    "    km = KMeans(n_clusters = n, init='k-means++')\n",
    "    km.fit(X_fisher)\n",
    "\n",
    "    closest, _ = pairwise_distances_argmin_min(km.cluster_centers_, X_fisher)\n",
    "\n",
    "    representative_label = [Y_material[closest[i]] for i in km.labels_]\n",
    "    representative_label = np.array(representative_label)\n",
    "\n",
    "    representative.append(np.mean(representative_label == Y_material))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:visiprog.evaluate:Nearest neighbor on same data\n",
      "0.957709824333\n"
     ]
    }
   ],
   "source": [
    "model = LFDA_VISIPROG()\n",
    "model.fit(X, Y_VSP)\n",
    "X_vsp = model.transform(X)\n",
    "res_vsp = leave_one_sample_out(X_vsp, Y_material)\n",
    "print(res_vsp['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "30\n",
      "50\n",
      "70\n",
      "90\n",
      "110\n",
      "130\n"
     ]
    }
   ],
   "source": [
    "representative_VSP = []\n",
    "\n",
    "for n in n_list:\n",
    "    print(n)\n",
    "    km = KMeans(n_clusters = n, init='k-means++')\n",
    "    km.fit(X_fisher)\n",
    "\n",
    "    closest, _ = pairwise_distances_argmin_min(km.cluster_centers_, X_fisher)\n",
    "\n",
    "    representative_label = [Y_material[closest[i]] for i in km.labels_]\n",
    "    representative_label = np.array(representative_label)\n",
    "\n",
    "    representative_VSP.append(np.mean(representative_label == Y_material))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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KViaYpjml9E3DMF4FHgP+Adx/phsYhhEJ/AlIB7qYpnmkwrGBwAqslTEKV0REREREpN7J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\nSZIkSZKkKvw/F25DNdVPgM8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113420978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.gcf()\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 20})\n",
    "\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "plt.plot(np.array(n_list)/61,representative, label='semantic')\n",
    "plt.plot(np.array(n_list)/61,representative_VSP, 'r', label='VSP')\n",
    "\n",
    "plt.ylabel('Precision @1')\n",
    "plt.xlabel('Number of average exemplars per class')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "fig.savefig('thrasos/exemplars_without_outlier.pdf')\n",
    "fig.savefig('thrasos/exemplars_without_outlier.eps', dpi=1200)"
   ]
  },
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   "source": []
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