result_viewer-actplot.ipynb

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     "text": [
      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
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     "text": [
      "File meta_dataset 220806_13-01-55-Home1-s=0 4 can not import!\n",
      "File meta_dataset 220806_13-01-55-Home1-s=0 3 can not import!\n",
      "File meta_dataset 220806_13-01-55-Home1-s=0 2 can not import!\n",
      "File meta_dataset 220806_13-01-55-Home1-s=0 1 can not import!\n",
      "File meta_dataset 220806_13-01-55-Home1-s=0 0 can not import!\n",
      "File meta_dataset 220806_12-12-50-Home1-s=0 0 can not import!\n"
     ]
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       "interactive(children=(Dropdown(description='file', options=((\"220809_17-13-51-Home1-s=2:f1=0.42==SimplePreproc…"
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   "source": [
    "#%matplotlib widget\n",
    "%pylab inline\n",
    "# pylab.rcParams['figure.figsize'] = (8, 6)\n",
    "import  general.utils as utils\n",
    "import  datatool.testdata as testdata\n",
    "import result_analyse.visualisation as vs\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import  general.utils as utils\n",
    "import result_analyse.resultloader\n",
    "import result_analyse.visualisation as vs\n",
    "import pandas as pd\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "from metric.CMbasedMetric import CMbasedMetric\n",
    "from metric.event_confusion_matrix import event_confusion_matrix\n",
    "\n",
    "import combiner.SimpleCombiner\n",
    "comb=combiner.SimpleCombiner.EmptyCombiner()\n",
    "\n",
    "def showres(dataset,real_events,pred_events,acts,weight,duration=None):\n",
    "    real_events=vs.filterTime(real_events,duration)\n",
    "    pred_events=vs.filterTime(pred_events,duration)\n",
    "#     print(real_events)\n",
    "#     print(pred_events)\n",
    "#     utils.saveState((real_events,pred_events),'ali')\n",
    "    event_cm     =event_confusion_matrix(real_events,pred_events,acts)\n",
    "    quality      =CMbasedMetric(event_cm,'macro',None)\n",
    "    quality_w    =CMbasedMetric(event_cm,'macro',weight)\n",
    "    quality2     =CMbasedMetric(event_cm)\n",
    "    print(f'quality={quality[\"f1\"]} wq={quality_w[\"f1\"]}')\n",
    "    \n",
    "#     print([f'{acts[i]}=>f={quality2[\"f1\"][i]} p={quality2[\"precision\"][i]} r={quality2[\"recall\"][i]}' for i in range(len(acts))])\n",
    "    \n",
    "    # visualize(dataset)\n",
    "    #vs.remove_gaps(real_events,pred_events)\n",
    "    print('visualizing real and pred')\n",
    "    vs.plotJoinAct(dataset,real_events,pred_events)\n",
    "    from matplotlib.pylab import plt\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "def groupize(datasetdscr,acts):\n",
    "    #gacts=[[a] for a in datasetdscr.activities_map]\n",
    "    #gacts.append([a for a in datasetdscr.activities_map])\n",
    "    gacts=[[a] for a in acts[1:]]\n",
    "    gacts.append([a for a in acts])\n",
    "    return gacts\n",
    "\n",
    "@interact\n",
    "def result_selector(file=result_analyse.resultloader.get_runs_summary()):\n",
    "    if(file==None):return\n",
    "    print('Analysing ',file)\n",
    "#     info=utils.loadState(file,'info')\n",
    "#     for i in info:\n",
    "#         print(i,info[i])\n",
    "    @interact_manual\n",
    "    def continue_select():\n",
    "        savedfile=file\n",
    "        #result='A4Hr1'\n",
    "        run_info,dataset,evalres=utils.loadState(savedfile)\n",
    "        global a\n",
    "        a=run_info,dataset,evalres\n",
    "        print(run_info)\n",
    "        #print(dataset)\n",
    "        acts=[a for a in dataset.activities_map]\n",
    "        gacts=groupize(dataset,acts)\n",
    "        for fold in range(len(evalres)):\n",
    "                global foldres\n",
    "                foldres=evalres[fold]['test']\n",
    "                print(f'fold----------{fold}')\n",
    "         #       print(foldres)\n",
    "                \n",
    "                real_events=foldres.real_events\n",
    "                pred_events=foldres.pred_events\n",
    "                quality=foldres.quality\n",
    "                s=real_events.iloc[0]['StartTime']\n",
    "                e=s+pd.Timedelta('2 day')\n",
    "\n",
    "    #             pred_events2=comb.combine2(foldres.times, foldres.predicted)\n",
    "                showres(dataset,real_events,pred_events,acts,None,duration=[s,e])\n",
    "        \n",
    "                return\n",
    "    #             showres(dataset,real_events,pred_events2,acts,None,duration=[s,e])\n",
    "                print('Evalution quality fold=%d is %s' % (fold, quality))\n",
    "\n",
    "\n",
    "#                 for gindx,tacts in enumerate(gacts):\n",
    "#                 for gindx in foldres.functions:\n",
    "#                     weight=np.ones(len(acts))\n",
    "#                     for a in tacts:weight[a]=20\n",
    "#                 print('seg %s'% (foldres.functions['segmentor']))\n",
    "\n",
    "                print(foldres.quality)\n",
    "    #             print(foldres.results[gindx].event_cm)\n",
    "    #             print(foldres.results[gindx].predicted_classes)\n",
    "    #             print(foldres.results[gindx].Sdata.label)\n",
    "#                 if(gindx+1 in dataset.activities_map):\n",
    "#                     print(f'--------------{dataset.activities_map[gindx+1]}----------------')\n",
    "\n",
    "                real_events=foldres.real_events\n",
    "                pred_events=foldres.pred_events\n",
    "\n",
    "                times=[]\n",
    "                act_data=np.zeros((len(pred_events),12))\n",
    "                for i in range(len(pred_events)):\n",
    "                    p=pred_events.iloc[i]\n",
    "                    times.append({'begin':p['StartTime'],'end':p['EndTime']})\n",
    "                    act_data[i,p['Activity']]=1\n",
    "                \n",
    "#                 if hasattr(foldres.results[gindx],'Sdata'):\n",
    "#                     Sdata=foldres.results[gindx].Sdata\n",
    "#                     pred_events2=comb.combine(Sdata.s_event_list,Sdata.set_window, foldres.results[gindx].predicted)\n",
    "                weight=None\n",
    "                showres(dataset,real_events,pred_events,acts,weight,duration=[s,e])            \n",
    "                pred_events2=comb.combine2(times,act_data)\n",
    "                showres(dataset,real_events,pred_events2,acts,weight,duration=[s,e])\n",
    "\n",
    "\n",
    "\n",
    "        #             print(confusion_matrix(evalres[i].results[f].Sdata.label,evalres[i].results[f].predicted_classes))\n",
    "\n",
    "            \n",
    "\n",
    "\n",
    "\n"
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