read_results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as sstt\n",
    "\n",
    "from incremental_hyser.hyser import hyser as hy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results = pickle.load(open('./results/baseline_feb11.pkl', 'rb'))['results']\n",
    "\n",
    "# RETEST:\n",
    "# results = pickle.load(open('./results/retest_mar19_baseline.pkl', 'rb'))['results']\n",
    "results = pickle.load(open('./results/retest_mar19_online.pkl', 'rb'))['results']\n",
    "\n",
    "\n",
    "mae_dict = {\n",
    "    'stage': {\n",
    "        0: {'onedof': {'day': {}}, 'ndof': {'day': {}}, 'random': {'day': {}}},\n",
    "        1: {'onedof': {'day': {}}, 'ndof': {'day': {}}, 'random': {'day': {}}},\n",
    "        2: {'onedof': {'day': {}}, 'ndof': {'day': {}}, 'random': {'day': {}}},\n",
    "    }\n",
    "}\n",
    "\n",
    "for idx_stage in [0, 1, 2]:\n",
    "    for idx_day in range(hy.NUM_SESSIONS):\n",
    "        mae_dict['stage'][idx_stage]['onedof']['day'][idx_day] = np.zeros((hy.NUM_SUBJECTS, hy.NUM_FINGERS, hy.NUM_TRIALS_ONEDOF, hy.NUM_FINGERS), dtype=np.float32)\n",
    "        mae_dict['stage'][idx_stage]['ndof']['day'][idx_day] = np.zeros((hy.NUM_SUBJECTS, hy.NUM_COMBINATIONS_NDOF, hy.NUM_TRIALS_NDOF, hy.NUM_FINGERS), dtype=np.float32)\n",
    "        mae_dict['stage'][idx_stage]['random']['day'][idx_day] = np.zeros((hy.NUM_SUBJECTS, hy.NUM_TRIALS_RANDOM, hy.NUM_FINGERS), dtype=np.float32)\n",
    "\n",
    "        for idx_subj in range(hy.NUM_SUBJECTS):\n",
    "\n",
    "            # onedof\n",
    "            for idx_finger in range(hy.NUM_FINGERS):\n",
    "                for idx_trial in range(hy.NUM_TRIALS_ONEDOF):\n",
    "                    mae_dict['stage'][idx_stage]['onedof']['day'][idx_day][idx_subj, idx_finger, idx_trial] = \\\n",
    "                        results['subject'][idx_subj]['stage'][idx_stage]['onedof']['day'][idx_day]['finger'][idx_finger]['trial'][idx_trial]['mae']\n",
    "\n",
    "            # ndof\n",
    "            for idx_comb in range(hy.NUM_COMBINATIONS_NDOF):\n",
    "                for idx_trial in range(hy.NUM_TRIALS_NDOF):\n",
    "                    mae_dict['stage'][idx_stage]['ndof']['day'][idx_day][idx_subj, idx_comb, idx_trial] = \\\n",
    "                        results['subject'][idx_subj]['stage'][idx_stage]['ndof']['day'][idx_day]['combination'][idx_comb]['trial'][idx_trial]['mae']\n",
    "\n",
    "            # random\n",
    "            for idx_trial in range(hy.NUM_TRIALS_RANDOM):\n",
    "                mae_dict['stage'][idx_stage]['random']['day'][idx_day][idx_subj, idx_trial] = \\\n",
    "                    results['subject'][idx_subj]['stage'][idx_stage]['random']['day'][idx_day]['trial'][idx_trial]['mae']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [\n",
    "    \"Stage 0\", \"Stage 0\",\n",
    "    \"Stage 0\", \"Stage 1\",     \"Stage 0\", \"Stage 1\",\n",
    "    \"Stage 0\", \"Stage 1\", \"Stage 2\",     \"Stage 0\", \"Stage 1\", \"Stage 2\",\n",
    "]\n",
    "\n",
    "WHITE = (1.00, 1.00, 1.00)\n",
    "LIGHTGRAY = (0.67, 0.67, 0.67)\n",
    "DARKGRAY = (0.33, 0.33, 0.33)\n",
    "color_sequence = [\n",
    "    WHITE, WHITE,\n",
    "    WHITE, LIGHTGRAY, WHITE, LIGHTGRAY,\n",
    "    WHITE, LIGHTGRAY, DARKGRAY, WHITE, LIGHTGRAY, DARKGRAY,\n",
    "]\n",
    "medianprops = {'color': 'black'}\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(10.0, 5.0))\n",
    "\n",
    "ax.set_ylabel(\"Mean absolute error [% MVC]\")\n",
    "\n",
    "'''\n",
    "data = [\n",
    "    # stage 0\n",
    "    mae_dict['stage'][0]['onedof']['day'][0],\n",
    "    mae_dict['stage'][0]['onedof']['day'][1],\n",
    "    mae_dict['stage'][0]['ndof']['day'][0],\n",
    "    mae_dict['stage'][0]['ndof']['day'][1],\n",
    "    mae_dict['stage'][0]['random']['day'][0],\n",
    "    mae_dict['stage'][0]['random']['day'][1],\n",
    "    # stage 1\n",
    "    mae_dict['stage'][1]['ndof']['day'][0],\n",
    "    mae_dict['stage'][1]['ndof']['day'][1],\n",
    "    mae_dict['stage'][1]['random']['day'][0],\n",
    "    mae_dict['stage'][1]['random']['day'][1],\n",
    "    # stage 2\n",
    "    mae_dict['stage'][2]['random']['day'][0],\n",
    "    mae_dict['stage'][2]['random']['day'][1],\n",
    "]\n",
    "'''\n",
    "data = [\n",
    "    mae_dict['stage'][0]['onedof']['day'][0],\n",
    "    mae_dict['stage'][0]['onedof']['day'][1],\n",
    "\n",
    "    mae_dict['stage'][0]['ndof']['day'][0],\n",
    "    mae_dict['stage'][1]['ndof']['day'][0],\n",
    "    mae_dict['stage'][0]['ndof']['day'][1],\n",
    "    mae_dict['stage'][1]['ndof']['day'][1],\n",
    "    \n",
    "    mae_dict['stage'][0]['random']['day'][0],\n",
    "    mae_dict['stage'][1]['random']['day'][0],\n",
    "    mae_dict['stage'][2]['random']['day'][0],\n",
    "    mae_dict['stage'][0]['random']['day'][1],\n",
    "    mae_dict['stage'][1]['random']['day'][1],\n",
    "    mae_dict['stage'][2]['random']['day'][1],\n",
    "]\n",
    "\n",
    "def aggr_flat(d: np.array[np.float32]) -> np.array[np.float32]:\n",
    "    return np.median(d, axis=(-2, -1)).flatten() * 100.0  # to % for reviewer\n",
    "\n",
    "positions = [\n",
    "     0.5,  2.5,\n",
    "     5.5,  7.0,  9.0, 10.5, \n",
    "    13.5, 15.0, 16.5, 18.5, 20.0, 21.5,\n",
    "]\n",
    "\n",
    "bp_dict = ax.boxplot(\n",
    "    x=[aggr_flat(dt) for dt in data],  # [aggr_flat(data[i]) for i in [0, 1, 2, 6, 3, 7, 4, 8, 10, 5, 9, 11]],\n",
    "    positions=positions,\n",
    "    labels= [\"\"] * 12,\n",
    "    widths=1.0,\n",
    "    medianprops=medianprops,\n",
    "    patch_artist=True,\n",
    ")\n",
    "\n",
    "# COLORS?\n",
    "idx = 0\n",
    "for b in bp_dict['boxes']:\n",
    "   lbl = ax.get_xticklabels()[idx].get_text()\n",
    "   b.set_facecolor(color_sequence[idx])\n",
    "   idx += 1\n",
    "\n",
    "ax.legend(\n",
    "    [bp_dict[\"boxes\"][0], bp_dict[\"boxes\"][3], bp_dict[\"boxes\"][8]],\n",
    "    [\"Stage 0\", \"Stage 1\", \"Stage 2\"],\n",
    "    loc='upper left',\n",
    ")\n",
    "\n",
    "ax.grid(axis='y')\n",
    "ax.set_xlim([-0.5, +22.5])\n",
    "ax.set_ylim([0.0, 20.0])  # as % for revewer\n",
    "\n",
    "\n",
    "# https://stackoverflow.com/questions/20532614/multiple-lines-of-x-tick-labels\n",
    "\n",
    "# upper line\n",
    "# ax.set_xticks([1.0, 2.0, 4.0, 5.0, 7.0, 8.0])\n",
    "# ax.set_xticklabels([\"Day 1\", \"Day 2\", \"Day 1\", \"Day 2\", \"Day 1\", \"Day 2\"], weight='bold')\n",
    "# lower line\n",
    "ax.set_xticks(\n",
    "    [\n",
    "        0.5, 2.5, 6.25, 9.75, 15.0, 20.0,  # day\n",
    "        1.5, 8.0, 17.5,  # dataset\n",
    "    ],\n",
    ")\n",
    "ax.set_xticklabels(\n",
    "    [\n",
    "        \"day 1\", \"day 2\", \"day 1\", \"day 2\", \"day 1\", \"day 2\",  # day\n",
    "        \"1-DOF DATASET\", \"5-DOF DATASET\", \"RANDOM DATASET\", # dataset\n",
    "    ],\n",
    "    weight='bold',\n",
    ")\n",
    "\n",
    "# vertical alignment of xtick labels\n",
    "# https://stackoverflow.com/questions/20532614/multiple-lines-of-x-tick-labels\n",
    "va = [\n",
    "     0.00,  0.00,  0.00, 0.00, 0.00, 0.00,  # day\n",
    "    -0.05, -0.05, -0.05,  # dataset\n",
    "]\n",
    "for t, y in zip(ax.get_xticklabels(), va):\n",
    "    t.set_y(y)\n",
    "\n",
    "ax.tick_params(axis='x', which='both', bottom=False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./plot_mae_stages.pdf\", dpi=600, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09453207\n",
      "0.0652550458908081\n",
      "9.581373\n",
      "3.8949995040893555\n"
     ]
    }
   ],
   "source": [
    "# not pre-aggregated\n",
    "print(np.median(data[-1]))\n",
    "print(sstt.iqr(data[-1]))\n",
    "\n",
    "# pre-aggregated\n",
    "print(np.median(aggr_flat(data[-1])))\n",
    "print(sstt.iqr(aggr_flat(data[-1])))\n",
    "\n",
    "# 0.08646361\n",
    "# 0.05782429035753012\n",
    "# 0.0910473\n",
    "# 0.04024534858763218\n",
    "\n",
    "# retested:\n",
    "# 0.084675804\n",
    "# 0.05751451198011637\n",
    "# 0.089981385\n",
    "# 0.03471312113106251"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(\n",
    "    [0, 1, 2, 3, 4, 5],\n",
    "    [np.median(data[i]) for i in [0, 1, 2, 4, 6, 9]],\n",
    "    'k.-',\n",
    ")\n",
    "plt.plot(\n",
    "    [2, 3, 4, 5],\n",
    "    [np.median(data[i]) for i in [3, 5, 7, 10]],\n",
    "    'b.-',\n",
    ")\n",
    "plt.plot(\n",
    "    [4, 5],\n",
    "    [np.median(data[i]) for i in [8, 11]],\n",
    "    'r.-',\n",
    ")\n",
    "plt.grid()\n",
    "plt.axis([-0.5, +5.5, 0.0, None])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.8.17"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}