victor charts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                    Sum\n",
      "patient student                        \n",
      "0       asklyarova@stonybrook.edu   0.0\n",
      "        chaotsai@stonybrook.edu     0.0\n",
      "        pstdenis@stonybrook.edu     0.0\n",
      "        shuwyang@stonybrook.edu     0.0\n",
      "1       chaotsai@stonybrook.edu     0.0\n",
      "        vlgarcia@stonybrook.edu    12.0\n",
      "2       chaotsai@stonybrook.edu     0.0\n",
      "        ikleiman@stonybrook.edu     0.0\n",
      "        vlgarcia@stonybrook.edu     0.0\n",
      "3       chaotsai@stonybrook.edu     6.0\n",
      "        ikleiman@stonybrook.edu     6.0\n",
      "4       chaotsai@stonybrook.edu     0.0\n",
      "        vlgarcia@stonybrook.edu     0.0\n",
      "class accuracy: 23.08%\n",
      "\n",
      "\n",
      "scenario performance\n",
      "patient 0: 0.00%\n",
      "patient 1: 50.00%\n",
      "patient 2: 0.00%\n",
      "patient 3: 100.00%\n",
      "patient 4: 0.00%\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "performance by medication group within each scenario\n",
      "pt case:  0\n",
      "medication group_4: 25.00%\n",
      "medication group_6: 100.00%\n",
      "medication group_2: 50.00%\n",
      "medication group_5: 25.00%\n",
      "medication group_1: 25.00%\n",
      "medication group_7: 100.00%\n",
      "medication group_3: 50.00%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pt case:  1\n",
      "medication group_4: 50.00%\n",
      "medication group_6: 100.00%\n",
      "medication group_2: 100.00%\n",
      "medication group_5: 100.00%\n",
      "medication group_1: 100.00%\n",
      "medication group_7: 100.00%\n",
      "medication group_3: 100.00%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pt case:  2\n",
      "medication group_4: 0.00%\n",
      "medication group_2: 33.33%\n",
      "medication group_5: 100.00%\n",
      "medication group_1: 33.33%\n",
      "medication group_3: 66.67%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pt case:  3\n",
      "medication group_1: 100.00%\n",
      "medication group_4: 100.00%\n",
      "medication group_3: 100.00%\n",
      "medication group_2: 100.00%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pt case:  4\n",
      "medication group_1: 50.00%\n",
      "medication group_4: 50.00%\n",
      "medication group_3: 100.00%\n",
      "medication group_2: 100.00%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "student's overall performance\n",
      "pstdenis@stonybrook.edu: 0.00% of patients\n",
      "ikleiman@stonybrook.edu: 50.00% of patients\n",
      "asklyarova@stonybrook.edu: 0.00% of patients\n",
      "chaotsai@stonybrook.edu: 20.00% of patients\n",
      "vlgarcia@stonybrook.edu: 33.33% of patients\n",
      "shuwyang@stonybrook.edu: 0.00% of patients\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from io import StringIO\n",
    "import requests\n",
    "\n",
    "r = requests.get(url = \"https://apps.tlt.stonybrook.edu/reconciliation/api/public2/generate_master_doc\", params = {\"download\": \"true\"} ) \n",
    "master_data = r.text\n",
    "\n",
    "# class accuracy\n",
    "df = pd.read_csv(StringIO(master_data))\n",
    "df = df.groupby(by=['patient', 'student'])['trial correct'].agg(Sum = 'sum')\n",
    "a = df[df['Sum'] > 0.0]\n",
    "aa = a.shape[0]\n",
    "b= df[df['Sum'] == 0.0]\n",
    "bb = b.shape[0]\n",
    "c = aa/(aa+bb) * 100\n",
    "print('class accuracy: {:0.2f}%'.format(c))\n",
    "\n",
    "print('\\n')\n",
    "\n",
    "#accuracy by patient scenario, aka Scenario Performance by Class\n",
    "df = pd.read_csv(StringIO(master_data))\n",
    "all_pts = set(df['patient'])\n",
    "print('scenario performance')\n",
    "scenario_xcoord = list(range(1,len(all_pts)+1))\n",
    "scenario_y = []\n",
    "scenario_ticks = []\n",
    "for pt in all_pts:\n",
    "    mini = df[df['patient'] == pt]\n",
    "    mini = mini.groupby(by=['student'])['trial correct'].agg(Sum = 'sum')\n",
    "    a = mini[mini['Sum'] > 0.0]\n",
    "    aa = a.shape[0]\n",
    "    b = mini[mini['Sum'] == 0.0]\n",
    "    bb = b.shape[0]\n",
    "    c = aa/(aa+bb) * 100\n",
    "    scenario_y.append(c)\n",
    "    scenario_ticks.append(pt)\n",
    "    print('patient {}: {:0.2f}%'.format(pt,c))\n",
    "\n",
    "\n",
    "print('\\n')\n",
    "\n",
    "plt.bar(scenario_xcoord, scenario_y, tick_label = scenario_ticks, \n",
    "        width = 0.7, color = ['green']) \n",
    "plt.title('Scenario Performance by Class')   \n",
    "plt.ylabel('Percent Correct') \n",
    "plt.xlabel('Patient Scenario') \n",
    "plt.show() \n",
    "\n",
    "print('\\n')\n",
    "\n",
    "#accuracy by medication group within each scenario\n",
    "df = pd.read_csv(StringIO(master_data))\n",
    "all_pts = set(df['patient'])\n",
    "print('performance by medication group within each scenario')\n",
    "group_y = []\n",
    "group_ticks = []\n",
    "for pt in all_pts:\n",
    "    print('pt case: ',pt)\n",
    "    mini = df[df['patient'] == pt]\n",
    "    all_groups = set(mini['group'])\n",
    "    group_xcoord = list(range(1,len(all_groups)+1))\n",
    "    for group in all_groups:\n",
    "        group_ticks.append(group)\n",
    "        mini_2 = mini[mini['group'] == group]\n",
    "        mini_2 = mini_2.groupby(by=['group','student'])['group correct'].agg(Sum = 'sum', Count='count')  \n",
    "        a = mini_2[mini_2['Sum'] > 0.0]  \n",
    "        aa = a.shape[0]\n",
    "        b = mini_2[mini_2['Sum'] == 0.0]\n",
    "        bb = b.shape[0]\n",
    "        c = aa/(aa+bb) * 100\n",
    "        group_y.append(c)\n",
    "        print('medication {}: {:0.2f}%'.format(group,c))\n",
    "    plt.title('Medication Group Performance for Scenario {}'.format(pt))   \n",
    "    plt.ylabel('Percent Correct') \n",
    "    plt.xlabel('Medication Groups') \n",
    "    plt.bar(group_xcoord, group_y, tick_label = group_ticks, \n",
    "        width = 0.7, color = ['green']) \n",
    "    plt.show()\n",
    "    group_y = []\n",
    "    group_ticks = []\n",
    "    group_xcoord = []\n",
    "\n",
    "print('\\n')\n",
    "\n",
    "#accuracy by student\n",
    "df = pd.read_csv(StringIO(master_data))\n",
    "all_stdns = set(df['student'])\n",
    "print(\"student's overall performance\")\n",
    "for stdn in all_stdns:\n",
    "    mini_3 = df[df['student'] == stdn]\n",
    "    mini_3 = mini_3.groupby(by=['patient'])['trial correct'].agg(Sum = 'sum')\n",
    "    a = mini_3[mini_3['Sum'] > 0.0]\n",
    "    aa = a.shape[0]\n",
    "    b = mini_3[mini_3['Sum'] == 0.0]\n",
    "    bb = b.shape[0]\n",
    "    c = aa/(aa+bb) * 100\n",
    "    print('{}: {:0.2f}% of patients'.format(stdn,c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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