--- a
+++ b/notebooks/AUC_stats.ipynb
@@ -0,0 +1,378 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "columns: Index(['dataset', 'algorithm', 'parameters', 'seed', 'accuracy', 'f1_macro',\n",
+      "       'bal_accuracy', 'roc_auc', 'time', 'disease', 'rare', 'cutoff'],\n",
+      "      dtype='object')\n",
+      "datasets: ['geis_250.00' 'geis_250.00_cutoff182' 'geis_250.00_cutoff365'\n",
+      " 'geis_250.40' 'geis_250.40_cutoff182' 'geis_250.40_cutoff365'\n",
+      " 'geis_327.23' 'geis_327.23_cutoff182' 'geis_327.23_cutoff365'\n",
+      " 'geis_331.0' 'geis_331.0_cutoff182' 'geis_331.0_cutoff365' 'geis_530.81'\n",
+      " 'geis_530.81_cutoff182' 'geis_530.81_cutoff365' 'geis_571.8'\n",
+      " 'geis_571.8_cutoff182' 'geis_571.8_cutoff365' 'geis_585.9'\n",
+      " 'geis_585.9_cutoff182' 'geis_585.9_cutoff365']\n",
+      "diseases: ['Diabetes' 'Diabetes with renal manifestations' 'Sleep apnea'\n",
+      " \"Alzheimer's disease\" 'Esophageal reflux' 'Liver disease'\n",
+      " 'Kidney disease']\n",
+      "algorithms: ['RF' 'LR' 'XGB']\n",
+      "seeds: [27164  9168 11085  3674  1188  4180  7016 16612 30993 14899]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dataset</th>\n",
+       "      <th>algorithm</th>\n",
+       "      <th>parameters</th>\n",
+       "      <th>seed</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>f1_macro</th>\n",
+       "      <th>bal_accuracy</th>\n",
+       "      <th>roc_auc</th>\n",
+       "      <th>time</th>\n",
+       "      <th>disease</th>\n",
+       "      <th>rare</th>\n",
+       "      <th>cutoff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>geis_250.00</td>\n",
+       "      <td>RF</td>\n",
+       "      <td>bootstrap=True,class_weight=balanced,criterion...</td>\n",
+       "      <td>27164</td>\n",
+       "      <td>0.851710</td>\n",
+       "      <td>0.851672</td>\n",
+       "      <td>0.851709</td>\n",
+       "      <td>0.924868</td>\n",
+       "      <td>11859.727018</td>\n",
+       "      <td>Diabetes</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1 visit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>geis_250.00</td>\n",
+       "      <td>RF</td>\n",
+       "      <td>bootstrap=True,class_weight=balanced,criterion...</td>\n",
+       "      <td>9168</td>\n",
+       "      <td>0.855070</td>\n",
+       "      <td>0.855014</td>\n",
+       "      <td>0.854969</td>\n",
+       "      <td>0.927769</td>\n",
+       "      <td>12042.685165</td>\n",
+       "      <td>Diabetes</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1 visit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>geis_250.00</td>\n",
+       "      <td>RF</td>\n",
+       "      <td>bootstrap=True,class_weight=balanced,criterion...</td>\n",
+       "      <td>11085</td>\n",
+       "      <td>0.855924</td>\n",
+       "      <td>0.855885</td>\n",
+       "      <td>0.855898</td>\n",
+       "      <td>0.928139</td>\n",
+       "      <td>12209.464033</td>\n",
+       "      <td>Diabetes</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1 visit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>geis_250.00</td>\n",
+       "      <td>RF</td>\n",
+       "      <td>bootstrap=True,class_weight=balanced,criterion...</td>\n",
+       "      <td>3674</td>\n",
+       "      <td>0.851656</td>\n",
+       "      <td>0.851633</td>\n",
+       "      <td>0.851681</td>\n",
+       "      <td>0.926606</td>\n",
+       "      <td>12156.885094</td>\n",
+       "      <td>Diabetes</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1 visit</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>geis_250.00</td>\n",
+       "      <td>RF</td>\n",
+       "      <td>bootstrap=True,class_weight=balanced,criterion...</td>\n",
+       "      <td>1188</td>\n",
+       "      <td>0.852990</td>\n",
+       "      <td>0.852942</td>\n",
+       "      <td>0.853044</td>\n",
+       "      <td>0.926543</td>\n",
+       "      <td>12240.883428</td>\n",
+       "      <td>Diabetes</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1 visit</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       dataset algorithm                                         parameters  \\\n",
+       "0  geis_250.00        RF  bootstrap=True,class_weight=balanced,criterion...   \n",
+       "1  geis_250.00        RF  bootstrap=True,class_weight=balanced,criterion...   \n",
+       "2  geis_250.00        RF  bootstrap=True,class_weight=balanced,criterion...   \n",
+       "3  geis_250.00        RF  bootstrap=True,class_weight=balanced,criterion...   \n",
+       "4  geis_250.00        RF  bootstrap=True,class_weight=balanced,criterion...   \n",
+       "\n",
+       "    seed  accuracy  f1_macro  bal_accuracy   roc_auc          time   disease  \\\n",
+       "0  27164  0.851710  0.851672      0.851709  0.924868  11859.727018  Diabetes   \n",
+       "1   9168  0.855070  0.855014      0.854969  0.927769  12042.685165  Diabetes   \n",
+       "2  11085  0.855924  0.855885      0.855898  0.928139  12209.464033  Diabetes   \n",
+       "3   3674  0.851656  0.851633      0.851681  0.926606  12156.885094  Diabetes   \n",
+       "4   1188  0.852990  0.852942      0.853044  0.926543  12240.883428  Diabetes   \n",
+       "\n",
+       "   rare   cutoff  \n",
+       "0  True  1 visit  \n",
+       "1  True  1 visit  \n",
+       "2  True  1 visit  \n",
+       "3  True  1 visit  \n",
+       "4  True  1 visit  "
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "##################### rename diseases based on icd9 codes\n",
+    "df_icd = pd.read_csv('icd9_to_english.txt',sep=',',index_col=False,dtype={'Disease': str, 'Code': str})\n",
+    "dis = df_icd['Disease']\n",
+    "code = df_icd['Code']\n",
+    "icd_to_dis = {}\n",
+    "for i,d in zip(code,dis):\n",
+    "    icd_to_dis[i] = d\n",
+    "#####################  \n",
+    "\n",
+    "from glob import glob\n",
+    "import numpy as np\n",
+    "from collections import defaultdict\n",
+    "\n",
+    "resdir = '../results'\n",
+    "\n",
+    "frames = []\n",
+    "\n",
+    "for f in glob(resdir+'/icd*/*.csv'):\n",
+    "#     print(f)\n",
+    "    df = pd.read_csv(f,sep='\\t')\n",
+    "#     print(df.columns)\n",
+    "    if (len(df) > 0):\n",
+    "        df['disease'] = icd_to_dis[f.split('icd9_')[1].split('_')[0].replace('/','')]\n",
+    "        cutoff = f.split('cutoff')[-1].split('_')[0]\n",
+    "        df['rare'] = False if 'noRare' in f else True\n",
+    "#         print(cutoff)\n",
+    "        df['cutoff'] = defaultdict(lambda: '1 visit',{'182':'6 months','365':'1 year'})[cutoff]\n",
+    "#         print(f,'','','','','','',df['algorithm'].iloc[0])\n",
+    "#         not in ['FeatLR0', 'LR', 'RF', 'FeatLR']:\n",
+    "#             print(f)\n",
+    "    #     print(df['dataset'])\n",
+    "        frames.append(df)\n",
+    "    \n",
+    "dfa = pd.concat(frames)\n",
+    "dfa = dfa.dropna(axis=0,how='any')\n",
+    "print('columns:',dfa.columns)\n",
+    "print('datasets:',dfa['dataset'].unique())\n",
+    "print('diseases:',dfa['disease'].unique())\n",
+    "dfa['algorithm'] = dfa['algorithm'].apply(lambda x: 'LR' if x == 'ScaleLR' else x)\n",
+    "print('algorithms:',dfa['algorithm'].unique())\n",
+    "print('seeds:',dfa['seed'].unique())\n",
+    "# print(dfa.loc[dfa['algorithm'].isna()])\n",
+    "dfa.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_ranks = pd.DataFrame(dtype=float)\n",
+    "# df_ranks['algorithm'] = dfa.groupby(['dataset','cutoff'])['algorithm']\n",
+    "dfa['rank_auc'] = dfa.groupby(['dataset','cutoff','seed'])['roc_auc'].rank(ascending=False)\n",
+    "df_ranks = dfa[['dataset','cutoff','algorithm','seed','rank_auc']].drop_duplicates()\n",
+    "df_ranks.head()\n",
+    "df_ranks.to_csv('auc_rankings.csv',index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x540 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# AUC violin plots\n",
+    "# plot boxplots\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_style('whitegrid')\n",
+    "%matplotlib inline\n",
+    "\n",
+    "Order=['LR','RF','XGB']\n",
+    "h = plt.figure(figsize=(15,7.5))\n",
+    "# dfs = dfa.loc[dfa.cutoff=='365'] \n",
+    "dfs = dfa.loc[dfa.rare==True]\n",
+    "n_disease = len(dfs.disease.unique())\n",
+    "# for j, (cutoff, dfc) in enumerate(dfs.groupby('cutoff')):\n",
+    "for i, (disease, dfad) in enumerate(dfs.groupby('disease')):\n",
+    "    ax = h.add_subplot(2,4,i+1)\n",
+    "    leg = i == n_disease - 1\n",
+    "    ax = sns.violinplot(ax = ax, x=\"algorithm\", y=\"roc_auc\", data=dfad, \n",
+    "                        order=Order, #,'FeatLR_longitudinal'], \n",
+    "                        hue='cutoff')\n",
+    "    ax.legend_.remove() \n",
+    "    ax.set_title(disease)\n",
+    "    ax.set_xlabel('')\n",
+    "    if (i) < 4:\n",
+    "        plt.setp( ax.get_xticklabels(), visible=False) \n",
+    "    if (i) % 4 != 0:\n",
+    "        ax.set_ylabel('')\n",
+    "        ax.set_yticklabels([]) \n",
+    "    else:\n",
+    "        ax.set_ylabel('AUROC',size=14)\n",
+    "    ax.set_ylim(0.75,1.0)\n",
+    "\n",
+    "ax = h.add_subplot(2,4,8)\n",
+    "sns.barplot(ax=ax,data=df_ranks,y='rank_auc',x='algorithm',edgecolor=(0,0,0),capsize=0.1,errwidth=1,\n",
+    "            fill=True,\n",
+    "            hue='cutoff',\n",
+    "            order=Order)\n",
+    "plt.legend(loc='lower left',title='Horizon',fontsize=14,framealpha=1)\n",
+    "#             order=['RF','KernelRidge','','CN','Corr','SXO','CNSXO','CorrSXO'])\n",
+    "# # plt.ylim([-1,1])\n",
+    "plt.xticks(size=12)\n",
+    "plt.title('All Diseases')\n",
+    "plt.ylabel('Median AUROC Rank',size=14)\n",
+    "plt.xlabel('')\n",
+    "plt.tight_layout()\n",
+    "# h.tight_layout()\n",
+    "plt.show() \n",
+    "# h.savefig('../paper/figs/'+resdir.split('/')[-1] + '/roc_auc_by_year_ranking.pdf',bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Alzheimer's disease algorithm\n",
+      "LR     0.813527\n",
+      "RF     0.837598\n",
+      "XGB    0.845855\n",
+      "Name: roc_auc, dtype: float64\n",
+      "Diabetes algorithm\n",
+      "LR     0.868763\n",
+      "RF     0.884364\n",
+      "XGB    0.908355\n",
+      "Name: roc_auc, dtype: float64\n",
+      "Diabetes with renal manifestations algorithm\n",
+      "LR     0.947293\n",
+      "RF     0.948200\n",
+      "XGB    0.958312\n",
+      "Name: roc_auc, dtype: float64\n",
+      "Esophageal reflux algorithm\n",
+      "LR     0.788947\n",
+      "RF     0.827983\n",
+      "XGB    0.847063\n",
+      "Name: roc_auc, dtype: float64\n",
+      "Kidney disease algorithm\n",
+      "LR     0.889198\n",
+      "RF     0.893287\n",
+      "XGB    0.913925\n",
+      "Name: roc_auc, dtype: float64\n",
+      "Liver disease algorithm\n",
+      "LR     0.858015\n",
+      "RF     0.857535\n",
+      "XGB    0.876419\n",
+      "Name: roc_auc, dtype: float64\n",
+      "Sleep apnea algorithm\n",
+      "LR     0.848485\n",
+      "RF     0.845963\n",
+      "XGB    0.866720\n",
+      "Name: roc_auc, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "for disease,dfg in dfs.groupby('disease'):\n",
+    "    print(disease,dfg.loc[dfg.cutoff=='1 year'].groupby('algorithm')['roc_auc'].median())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}