result_eval.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-03T21:38:31.384393Z",
     "start_time": "2020-11-03T21:38:31.358436Z"
    }
   },
   "outputs": [],
   "source": [
    "# matplotlib.use('WebAgg')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-06-01T15:09:13.093111Z",
     "start_time": "2022-06-01T15:09:12.972591Z"
    },
    "scrolled": false
   },
   "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/python3.8/site-packages/IPython/core/magics/pylab.py:159: UserWarning: pylab import has clobbered these variables: ['interactive']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  warn(\"pylab import has clobbered these variables: %s\"  % clobbered +\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d64675a33457454ca5e7d50e6f3ffaa8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='file', options=(('meta_dataset 220519_12-44-58-Home2', 'meta_datas…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib widget\n",
    "%pylab inline\n",
    "# %pylab widget\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,widgets\n",
    "import numpy as np\n",
    "import pandas as pd\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",
    "    stime=dataset.activity_events.iloc[0].StartTime\n",
    "    #etime=stime+np.timedelta64(1,'D')\n",
    "    etime=dataset.activity_events.iloc[-1].EndTime\n",
    "\n",
    "    for i in range(len(evalres)):\n",
    "            quality=evalres[i]['test'].quality\n",
    "            print('Evalution quality fold=%d is %s' % (i, quality))\n",
    "    print(len(dataset.sensor_events))\n",
    "    \n",
    "#     vs.plot_CM(dataset,evalres)\n",
    "    \n",
    "    @interact\n",
    "    def viewFold(fold= range(len(evalres))):\n",
    "        @interact_manual\n",
    "        def view(start_date=widgets.DatePicker(value=pd.to_datetime(stime)),end_date=widgets.DatePicker(value=pd.to_datetime(etime)),debug=widgets.Checkbox(value=False)):\n",
    "            duration=(pd.to_datetime(start_date),pd.to_datetime(end_date))\n",
    "            duration2=(pd.to_datetime(start_date),pd.to_datetime(start_date)+pd.DateOffset(days=7))\n",
    "            real_events=vs.filterTime(evalres[fold]['test'].real_events,duration)\n",
    "            pred_events=vs.filterTime(evalres[fold]['test'].pred_events,duration)\n",
    "            #vs.plotJoinAct(dataset,real_events,pred_events)\n",
    "            acts=[p for p in dataset.activities_map]\n",
    "            labels=[dataset.activities_map[p] for p in acts]\n",
    "            print(acts)\n",
    "            print(labels)\n",
    "            vs.plotJoinAct2(real_events,pred_events,acts,labels,duration=duration2)\n",
    "            #vs.plot_per_act(dataset,{'test':evalres})\n",
    "            \n",
    "            from matplotlib import pyplot as plt\n",
    "            plt.rc_context(rc={'figure.max_open_warning': 0})\n",
    "            import result_analyse.SpiderChart\n",
    "            result_analyse.SpiderChart.radar_factory(5, frame='polygon')\n",
    "            acount=len(dataset.activities_map)\n",
    "            a_fig,a_ax=plt.subplots(acount-1,1,figsize=(10, acount*.25),)\n",
    "#             a_fig.tight_layout(pad=3.0)\n",
    "            col=4        \n",
    "            row=int(np.ceil((acount-1.0)/float(col)))\n",
    "            m_fig,m_ax=plt.subplots(row,col,figsize=(col*3, row*3),subplot_kw=dict(projection='radar'))\n",
    "            if type(a_ax)!=np.ndarray:\n",
    "                print('dddd',a_ax)\n",
    "                print(type(a_ax))\n",
    "                a_ax=np.array([a_ax])\n",
    "            else:\n",
    "                m_ax=m_ax.flatten()\n",
    "            for i in range(acount-1,len(m_ax)):\n",
    "                m_ax[i].set_visible(False)\n",
    "\n",
    "            for i in range(1,len(dataset.activities_map)):\n",
    "#                 real_events2,pred_events2=vs.remove_gaps(real_events,pred_events,i)\n",
    "                #real_events2,pred_events2=vs.remove_gaps(real_events,pred_events,i,max_events=10)\n",
    "                real_events2,pred_events2=real_events,pred_events\n",
    "#                 vs.plotJoinAct(dataset,real_events,pred_events,onlyAct=i,ax=a_ax[i-1])\n",
    "                try:\n",
    "#                     vs.plotJoinAct(dataset,real_events2,pred_events2,onlyAct=i,ax=a_ax[i-1])\n",
    "                    vs.plotMyMetric2(dataset,real_events2,pred_events2,onlyAct=i,ax=m_ax[i-1],debug=debug,calcne=0)\n",
    "#                     vs.plotWardMetric(dataset,real_events,pred_events,onlyAct=i)\n",
    "                except:\n",
    "                    import sys\n",
    "                    import traceback\n",
    "                    print(e, file=sys.stderr)\n",
    "                    traceback.print_exc()\n",
    "\n",
    "                #    print('error in ',i)\n",
    "                #vs.plotWardMetric(dataset,real_events,pred_events,onlyAct=i)\n",
    "#             vs.plotJoinMyMetric(dataset,real_events2,pred_events2,calcne=0)\n",
    "            a_fig.show()\n",
    "            m_fig.tight_layout(pad=0,h_pad=-20.0, w_pad=3.0)\n",
    "            m_fig.show()\n",
    "                \n",
    "#             @interact\n",
    "#             def view2(onlyAct=[(str(i)+\" : \"+dataset.activities[i],i)for i in dataset.activities_map]):\n",
    "#                 print(onlyAct)\n",
    "#                 #vs.visualize(dataset)\n",
    "#                 # vs.my_result_analyse(evalres[i].real_events,evalres[i].pred_events)               \n",
    "#                 #vs.plotJoinAct(dataset,real_events,pred_events,onlyAct=onlyAct)\n",
    "#                 real_events2,pred_events2=vs.remove_gaps(real_events,pred_events,onlyAct)\n",
    "#                 vs.plotJoinAct(dataset,real_events2,pred_events2,onlyAct=onlyAct)\n",
    "#                 vs.plotMyMetric(dataset,real_events,pred_events,onlyAct=onlyAct)\n",
    "#                 vs.plotWardMetric(dataset,real_events,pred_events,onlyAct=onlyAct)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-03T20:52:29.616518Z",
     "start_time": "2020-11-03T20:52:29.471782Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3497572390>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,ax=plt.subplots()\n",
    "alpha=1\n",
    "x=numpy.arange(0,1,.01)\n",
    "\n",
    "for alpha in [1, 2, 4, 8 ,16]:\n",
    "    y=1-np.exp(-alpha *x)\n",
    "    ax.plot(x,y, label='\\u03B2 ='+str(alpha))\n",
    "ax.legend()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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