stat_plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.rc('xtick', labelsize=12) \n",
    "matplotlib.rc('ytick', labelsize=12) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_plot_pourcentage_perfmax(u, label_methods, our_bins, save_bool, namesave):\n",
    "    u_normal = u / np.max(u, 1, keepdims=1)\n",
    "\n",
    "    u_rank = [st.rankdata(-u[i], method='min') for i in range(np.shape(u)[0])]\n",
    "    \n",
    "    fig, axes = plt.subplots(figsize=(18, 7.5), nrows=1, ncols=2)\n",
    "    ax0, ax1 = axes.flatten()\n",
    "\n",
    "    ax0.hist(np.array(u_rank), our_bins, density=1, histtype='bar', label=label_methods)\n",
    "    ax0.legend(prop={'size': 12})\n",
    "    ax0.set_title('Rank histogram')\n",
    "    \n",
    "    ax1.boxplot(u_normal, labels=label_methods)\n",
    "    ax1.set_title('Boxplot of the ratio to the max performance')\n",
    "    \n",
    "    plt.show()\n",
    "    if save_bool:\n",
    "        fig.savefig(namesave, format='eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applied to results on UCR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "with open('results_ucr.csv', 'r') as csv_file:\n",
    "    csv_reader = csv.reader(csv_file) \n",
    "    \n",
    "    i=0\n",
    "    score=[]\n",
    "    labels_datasets = []\n",
    "    for line in csv_reader:\n",
    "        if i == 0:\n",
    "            labels_methods = np.delete(line[2:-3], 6)\n",
    "        else:\n",
    "            labels_datasets.append(line[0])\n",
    "            score.append([float(x) for x in np.delete(line[2:-3], 6)])\n",
    "        i += 1\n",
    "        if i == 85:\n",
    "            break\n",
    "    \n",
    "score = np.array(score)\n",
    "\n",
    "# Define submatrix with our_scores, and scores_for_comparison\n",
    "score_ours = score[:, 0:6]\n",
    "labels_our_methods = labels_methods[0:6]\n",
    "\n",
    "score_comparison  = score[:, [4, 6, 7, 8, 9, 10]]\n",
    "labels_comparison = [labels_methods[4]] + list(labels_methods[6:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x540 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_methods = labels_our_methods\n",
    "our_bins = [k+0.5 for k in range(len(label_methods)+1)] \n",
    "\n",
    "bar_plot_pourcentage_perfmax(score_ours, label_methods, our_bins, 1, 'Our_methods_plot.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x540 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_methods = labels_comparison\n",
    "our_bins = [k+0.5 for k in range(len(labels_comparison)+1)]\n",
    "\n",
    "bar_plot_pourcentage_perfmax(score_comparison[:, [0, 5, 2, 3, 1, 4]], [label_methods[i] for i in [0, 5, 2, 3, 1, 4]], our_bins, 1, 'Comparison_to_other_methods.eps') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretation:\n",
    "  - right plot : our method has the second-to-best median when we look at perf / max_perf.\n",
    "  - left plot : our method is the best ranked in 26% of the cases (22/85) (COTE: 54%, ST: 25%)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}