result_viewer3-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# snakeviz is a good tool for measuring time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-02T16:05:56.873866Z",
     "start_time": "2022-06-02T16:05:56.157766Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3f6ea221db3e4488a1a1b2c6f78ea3dc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='file', options=(('meta_dataset 220602_15-19-15-A4H', 'meta_dataset…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib widget\n",
    "%pylab inline\n",
    "# pylab.rcParams['figure.figsize'] = (8, 8)\n",
    "import  general.utils as utils\n",
    "import result_analyse.resultloader\n",
    "import result_analyse.visualisation as vs\n",
    "\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "\n",
    "@interact\n",
    "def result_selector(file=result_analyse.resultloader.get_runs()):\n",
    "    if(file==None):return\n",
    "    print('Analysing ',file)\n",
    "    run_info,dataset,evalres=utils.loadState(file)\n",
    "    for i in range(len(evalres)):\n",
    "        evaldata=evalres[i]['test']\n",
    "        quality=evaldata.quality\n",
    "        print('Evalution quality fold=%d is %s' % (i, quality))\n",
    "        print('func%s' % (evaldata.functions))\n",
    "        \n",
    "    #vs.visualize(dataset)\n",
    "    print(len(dataset.sensor_events))\n",
    "#     vs.plot_per_act(dataset,evalres)\n",
    "    vs.plot_CM(dataset,evalres)\n",
    "    for i in range(len(evalres)):\n",
    "        evaldata=evalres[i]['test']\n",
    "        # vs.my_result_analyse(evalres[i].real_events,evalres[i].pred_events)\n",
    "#         vs.plotJoinAct(dataset,evaldata.real_events,evaldata.pred_events)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T08:53:57.380535Z",
     "start_time": "2020-08-27T08:51:04.275725Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning:\n",
      "\n",
      "pylab import has clobbered these variables: ['interactive']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize'] = (8, 3)\n",
    "import  general.utils as utils\n",
    "import result_analyse.resultloader\n",
    "import result_analyse.visualisation as vs\n",
    "\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "\n",
    "def get_my_eval(data_dscr='Home2',reqinfo=[],hide=['SimpleKeras','SimpleFeatureExtraction']):\n",
    "    import os\n",
    "    import pandas as pd\n",
    "    list=os.listdir('save_data/')\n",
    "    list.sort(key=lambda f:os.path.getmtime('save_data/'+f),reverse=True)\n",
    "    result={}\n",
    "    dataset=None\n",
    "    for item in list:\n",
    "#         print(item)\n",
    "        if not (data_dscr in item):continue\n",
    "        try:\n",
    "            res=utils.loadState(item)\n",
    "            if(len(res)!=3):\n",
    "                #raise Error\n",
    "                logger.warn('File %s can not import'%item)\n",
    "                continue\n",
    "            [run_info,datasetdscr,evalres]=res\n",
    "            name=evalres[0]['test'].shortrunname.split('_')\n",
    "#             print(name)\n",
    "            name=name[1]\n",
    "            if not(datasetdscr.data_dscr==data_dscr):continue\n",
    "            \n",
    "            for r in reqinfo:\n",
    "                if not(r in evalres[0]['test'].shortrunname):\n",
    "                    #print(r)\n",
    "                    continue\n",
    "#             tbl=getRunTable(run_info,datasetdscr,evalres)\n",
    "            \n",
    "            \n",
    "            result[name]=evalres\n",
    "            dataset=datasetdscr\n",
    "        except Exception as e:\n",
    "            print('File %s can not import error '%item)\n",
    "            import sys\n",
    "            import traceback\n",
    "            print(e, file=sys.stderr)\n",
    "            traceback.print_exc()\n",
    "\n",
    "    return dataset,result\n",
    "\n",
    "\n",
    "dataset,myevalres=get_my_eval()\n",
    "vs.plot_per_act(dataset,myevalres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
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   },
   "types_to_exclude": [
    "module",
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