import numpy as np
import pandas as pd
from tqdm import tqdm
from datetime import datetime
from sklearn.preprocessing import LabelEncoder
import pickle
import datetime
import os
import sys
from pathlib import Path
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + './../..')
if not os.path.exists("./data/dict"):
    os.makedirs("./data/dict")
if not os.path.exists("./data/csv"):
    os.makedirs("./data/csv")
    
class Generator():
    def __init__(self,task,cohort_output,if_mort,if_admn,if_los,feat_cond,feat_proc,feat_out,feat_chart,feat_med,impute,include_time=24,bucket=1,predW=6):
        self.feat_cond,self.feat_proc,self.feat_out,self.feat_chart,self.feat_med = feat_cond,feat_proc,feat_out,feat_chart,feat_med
        self.cohort_output=cohort_output
        self.impute=impute
        self.task = task
        self.data = self.generate_adm()
        if not os.path.exists("./data/dict/"+self.task):
            os.makedirs("./data/dict/"+self.task)
        print("[ READ COHORT ]")
        
        self.generate_feat()
        print("[ READ ALL FEATURES ]")
        
        if if_mort:
            self.mortality_length(include_time,predW)
            print("[ PROCESSED TIME SERIES TO EQUAL LENGTH  ]")
        elif if_admn:
            self.readmission_length(include_time)
            print("[ PROCESSED TIME SERIES TO EQUAL LENGTH  ]")
        elif if_los:
            self.los_length(include_time)
            print("[ PROCESSED TIME SERIES TO EQUAL LENGTH  ]")
        
        self.smooth_meds(bucket)
        print("[ SUCCESSFULLY SAVED DATA DICTIONARIES ]")
    
    def generate_feat(self):
        if(self.feat_cond):
            print("[ ======READING DIAGNOSIS ]")
            self.generate_cond()
        if(self.feat_proc):
            print("[ ======READING PROCEDURES ]")
            self.generate_proc()
        if(self.feat_out):
            print("[ ======READING OUT EVENTS ]")
            self.generate_out()
        if(self.feat_chart):
            print("[ ======READING CHART EVENTS ]")
            self.generate_chart()
        if(self.feat_med):
            print("[ ======READING MEDICATIONS ]")
            self.generate_meds()

    def generate_adm(self):
        data=pd.read_csv(f"./data/cohort/{self.cohort_output}.csv.gz", compression='gzip', header=0, index_col=None)
        data['intime'] = pd.to_datetime(data['intime'])
        data['outtime'] = pd.to_datetime(data['outtime'])
        data['los']=pd.to_timedelta(data['outtime']-data['intime'],unit='h')
        data['los']=data['los'].astype(str)
        data[['days', 'dummy','hours']] = data['los'].str.split(' ', expand=True)
        data[['hours','min','sec']] = data['hours'].str.split(':', expand=True)
        data['los']=pd.to_numeric(data['days'])*24+pd.to_numeric(data['hours'])
        data=data.drop(columns=['days', 'dummy','hours','min','sec'])
        data=data[data['los']>0]
        data['Age']=data['Age'].astype(int)
        #print(data.head())
        #print(data.shape)
        return data
    
    def generate_cond(self):
        cond=pd.read_csv("./data/features/preproc_diag_icu.csv.gz", compression='gzip', header=0, index_col=None)
        cond=cond[cond['stay_id'].isin(self.data['stay_id'])]
        cond_per_adm = cond.groupby('stay_id').size().max()
        self.cond, self.cond_per_adm = cond, cond_per_adm
    
    def generate_proc(self):
        proc=pd.read_csv("./data/features/preproc_proc_icu.csv.gz", compression='gzip', header=0, index_col=None)
        proc=proc[proc['stay_id'].isin(self.data['stay_id'])]
        proc[['start_days', 'dummy','start_hours']] = proc['event_time_from_admit'].str.split(' ', -1, expand=True)
        proc[['start_hours','min','sec']] = proc['start_hours'].str.split(':', -1, expand=True)
        proc['start_time']=pd.to_numeric(proc['start_days'])*24+pd.to_numeric(proc['start_hours'])
        proc=proc.drop(columns=['start_days', 'dummy','start_hours','min','sec'])
        proc=proc[proc['start_time']>=0]
        
        ###Remove where event time is after discharge time
        proc=pd.merge(proc,self.data[['stay_id','los']],on='stay_id',how='left')
        proc['sanity']=proc['los']-proc['start_time']
        proc=proc[proc['sanity']>0]
        del proc['sanity']
        
        self.proc=proc
        
    def generate_out(self):
        out=pd.read_csv("./data/features/preproc_out_icu.csv.gz", compression='gzip', header=0, index_col=None)
        out=out[out['stay_id'].isin(self.data['stay_id'])]
        out[['start_days', 'dummy','start_hours']] = out['event_time_from_admit'].str.split(' ', -1, expand=True)
        out[['start_hours','min','sec']] = out['start_hours'].str.split(':', -1, expand=True)
        out['start_time']=pd.to_numeric(out['start_days'])*24+pd.to_numeric(out['start_hours'])
        out=out.drop(columns=['start_days', 'dummy','start_hours','min','sec'])
        out=out[out['start_time']>=0]
        
        ###Remove where event time is after discharge time
        out=pd.merge(out,self.data[['stay_id','los']],on='stay_id',how='left')
        out['sanity']=out['los']-out['start_time']
        out=out[out['sanity']>0]
        del out['sanity']
        
        self.out=out
        
        
    def generate_chart(self):
        chunksize = 5000000
        final=pd.DataFrame()
        for chart in tqdm(pd.read_csv("./data/features/preproc_chart_icu.csv.gz", compression='gzip', header=0, index_col=None,chunksize=chunksize)):
            chart=chart[chart['stay_id'].isin(self.data['stay_id'])]
            chart[['start_days', 'dummy','start_hours']] = chart['event_time_from_admit'].str.split(' ', -1, expand=True)
            chart[['start_hours','min','sec']] = chart['start_hours'].str.split(':', -1, expand=True)
            chart['start_time']=pd.to_numeric(chart['start_days'])*24+pd.to_numeric(chart['start_hours'])
            chart=chart.drop(columns=['start_days', 'dummy','start_hours','min','sec','event_time_from_admit'])
            chart=chart[chart['start_time']>=0]

            ###Remove where event time is after discharge time
            chart=pd.merge(chart,self.data[['stay_id','los']],on='stay_id',how='left')
            chart['sanity']=chart['los']-chart['start_time']
            chart=chart[chart['sanity']>0]
            del chart['sanity']
            del chart['los']
            
            if final.empty:
                final=chart
            else:
                final=final.append(chart, ignore_index=True)
        
        self.chart=final
        
        
        
    def generate_meds(self):
        meds=pd.read_csv("./data/features/preproc_med_icu.csv.gz", compression='gzip', header=0, index_col=None)
        meds[['start_days', 'dummy','start_hours']] = meds['start_hours_from_admit'].str.split(' ', -1, expand=True)
        meds[['start_hours','min','sec']] = meds['start_hours'].str.split(':', -1, expand=True)
        meds['start_time']=pd.to_numeric(meds['start_days'])*24+pd.to_numeric(meds['start_hours'])
        meds[['start_days', 'dummy','start_hours']] = meds['stop_hours_from_admit'].str.split(' ', -1, expand=True)
        meds[['start_hours','min','sec']] = meds['start_hours'].str.split(':', -1, expand=True)
        meds['stop_time']=pd.to_numeric(meds['start_days'])*24+pd.to_numeric(meds['start_hours'])
        meds=meds.drop(columns=['start_days', 'dummy','start_hours','min','sec'])
        #####Sanity check
        meds['sanity']=meds['stop_time']-meds['start_time']
        meds=meds[meds['sanity']>0]
        del meds['sanity']
        #####Select hadm_id as in main file
        meds=meds[meds['stay_id'].isin(self.data['stay_id'])]
        meds=pd.merge(meds,self.data[['stay_id','los']],on='stay_id',how='left')

        #####Remove where start time is after end of visit
        meds['sanity']=meds['los']-meds['start_time']
        meds=meds[meds['sanity']>0]
        del meds['sanity']
        ####Any stop_time after end of visit is set at end of visit
        meds.loc[meds['stop_time'] > meds['los'],'stop_time']=meds.loc[meds['stop_time'] > meds['los'],'los']
        del meds['los']
        
        meds['rate']=meds['rate'].apply(pd.to_numeric, errors='coerce')
        meds['amount']=meds['amount'].apply(pd.to_numeric, errors='coerce')
        
        self.meds=meds
    
    def mortality_length(self,include_time,predW):
        print("include_time",include_time)
        self.los=include_time
        self.data=self.data[(self.data['los']>=include_time+predW)]
        self.hids=self.data['stay_id'].unique()
        
        if(self.feat_cond):
            self.cond=self.cond[self.cond['stay_id'].isin(self.data['stay_id'])]
        
        self.data['los']=include_time

        ####Make equal length input time series and remove data for pred window if needed
        
        ###MEDS
        if(self.feat_med):
            self.meds=self.meds[self.meds['stay_id'].isin(self.data['stay_id'])]
            self.meds=self.meds[self.meds['start_time']<=include_time]
            self.meds.loc[self.meds.stop_time >include_time, 'stop_time']=include_time
                    
        
        ###PROCS
        if(self.feat_proc):
            self.proc=self.proc[self.proc['stay_id'].isin(self.data['stay_id'])]
            self.proc=self.proc[self.proc['start_time']<=include_time]
            
        ###OUT
        if(self.feat_out):
            self.out=self.out[self.out['stay_id'].isin(self.data['stay_id'])]
            self.out=self.out[self.out['start_time']<=include_time]
            
       ###CHART
        if(self.feat_chart):
            self.chart=self.chart[self.chart['stay_id'].isin(self.data['stay_id'])]
            self.chart=self.chart[self.chart['start_time']<=include_time]
        
        #self.los=include_time
    def los_length(self,include_time):
        print("include_time",include_time)
        self.los=include_time
        self.data=self.data[(self.data['los']>=include_time)]
        self.hids=self.data['stay_id'].unique()
        
        if(self.feat_cond):
            self.cond=self.cond[self.cond['stay_id'].isin(self.data['stay_id'])]
        
        self.data['los']=include_time

        ####Make equal length input time series and remove data for pred window if needed
        
        ###MEDS
        if(self.feat_med):
            self.meds=self.meds[self.meds['stay_id'].isin(self.data['stay_id'])]
            self.meds=self.meds[self.meds['start_time']<=include_time]
            self.meds.loc[self.meds.stop_time >include_time, 'stop_time']=include_time
                    
        
        ###PROCS
        if(self.feat_proc):
            self.proc=self.proc[self.proc['stay_id'].isin(self.data['stay_id'])]
            self.proc=self.proc[self.proc['start_time']<=include_time]
            
        ###OUT
        if(self.feat_out):
            self.out=self.out[self.out['stay_id'].isin(self.data['stay_id'])]
            self.out=self.out[self.out['start_time']<=include_time]
            
       ###CHART
        if(self.feat_chart):
            self.chart=self.chart[self.chart['stay_id'].isin(self.data['stay_id'])]
            self.chart=self.chart[self.chart['start_time']<=include_time]
            
    def readmission_length(self,include_time):
        self.los=include_time
        self.data=self.data[(self.data['los']>=include_time)]
        self.hids=self.data['stay_id'].unique()
        
        if(self.feat_cond):
            self.cond=self.cond[self.cond['stay_id'].isin(self.data['stay_id'])]
        self.data['select_time']=self.data['los']-include_time
        self.data['los']=include_time

        ####Make equal length input time series and remove data for pred window if needed
        
        ###MEDS
        if(self.feat_med):
            self.meds=self.meds[self.meds['stay_id'].isin(self.data['stay_id'])]
            self.meds=pd.merge(self.meds,self.data[['stay_id','select_time']],on='stay_id',how='left')
            self.meds['stop_time']=self.meds['stop_time']-self.meds['select_time']
            self.meds['start_time']=self.meds['start_time']-self.meds['select_time']
            self.meds=self.meds[self.meds['stop_time']>=0]
            self.meds.loc[self.meds.start_time <0, 'start_time']=0
        
        ###PROCS
        if(self.feat_proc):
            self.proc=self.proc[self.proc['stay_id'].isin(self.data['stay_id'])]
            self.proc=pd.merge(self.proc,self.data[['stay_id','select_time']],on='stay_id',how='left')
            self.proc['start_time']=self.proc['start_time']-self.proc['select_time']
            self.proc=self.proc[self.proc['start_time']>=0]
            
        ###OUT
        if(self.feat_out):
            self.out=self.out[self.out['stay_id'].isin(self.data['stay_id'])]
            self.out=pd.merge(self.out,self.data[['stay_id','select_time']],on='stay_id',how='left')
            self.out['start_time']=self.out['start_time']-self.out['select_time']
            self.out=self.out[self.out['start_time']>=0]
            
       ###CHART
        if(self.feat_chart):
            self.chart=self.chart[self.chart['stay_id'].isin(self.data['stay_id'])]
            self.chart=pd.merge(self.chart,self.data[['stay_id','select_time']],on='stay_id',how='left')
            self.chart['start_time']=self.chart['start_time']-self.chart['select_time']
            self.chart=self.chart[self.chart['start_time']>=0]
        
            
    def smooth_meds(self,bucket):
        final_meds=pd.DataFrame()
        final_proc=pd.DataFrame()
        final_out=pd.DataFrame()
        final_chart=pd.DataFrame()
        
        if(self.feat_med):
            self.meds=self.meds.sort_values(by=['start_time'])
        if(self.feat_proc):
            self.proc=self.proc.sort_values(by=['start_time'])
        if(self.feat_out):
            self.out=self.out.sort_values(by=['start_time'])
        if(self.feat_chart):
            self.chart=self.chart.sort_values(by=['start_time'])
        
        t=0
        for i in tqdm(range(0,self.los,bucket)): 
            ###MEDS
             if(self.feat_med):
                sub_meds=self.meds[(self.meds['start_time']>=i) & (self.meds['start_time']<i+bucket)].groupby(['stay_id','itemid','orderid']).agg({'stop_time':'max','subject_id':'max','rate':np.nanmean,'amount':np.nanmean})
                sub_meds=sub_meds.reset_index()
                sub_meds['start_time']=t
                sub_meds['stop_time']=sub_meds['stop_time']/bucket
                if final_meds.empty:
                    final_meds=sub_meds
                else:
                    final_meds=final_meds.append(sub_meds)
            
            ###PROC
             if(self.feat_proc):
                sub_proc=self.proc[(self.proc['start_time']>=i) & (self.proc['start_time']<i+bucket)].groupby(['stay_id','itemid']).agg({'subject_id':'max'})
                sub_proc=sub_proc.reset_index()
                sub_proc['start_time']=t
                if final_proc.empty:
                    final_proc=sub_proc
                else:    
                    final_proc=final_proc.append(sub_proc)
                    
              ###OUT
             if(self.feat_out):
                sub_out=self.out[(self.out['start_time']>=i) & (self.out['start_time']<i+bucket)].groupby(['stay_id','itemid']).agg({'subject_id':'max'})
                sub_out=sub_out.reset_index()
                sub_out['start_time']=t
                if final_out.empty:
                    final_out=sub_out
                else:    
                    final_out=final_out.append(sub_out)
                    
                    
              ###CHART
             if(self.feat_chart):
                sub_chart=self.chart[(self.chart['start_time']>=i) & (self.chart['start_time']<i+bucket)].groupby(['stay_id','itemid']).agg({'valuenum':np.nanmean})
                sub_chart=sub_chart.reset_index()
                sub_chart['start_time']=t
                if final_chart.empty:
                    final_chart=sub_chart
                else:    
                    final_chart=final_chart.append(sub_chart)
            
             t=t+1
        print("bucket",bucket)
        los=int(self.los/bucket)
        
        
        ###MEDS
        if(self.feat_med):
            f2_meds=final_meds.groupby(['stay_id','itemid','orderid']).size()
            self.med_per_adm=f2_meds.groupby('stay_id').sum().reset_index()[0].max()                 
            self.medlength_per_adm=final_meds.groupby('stay_id').size().max()
        
        ###PROC
        if(self.feat_proc):
            f2_proc=final_proc.groupby(['stay_id','itemid']).size()
            self.proc_per_adm=f2_proc.groupby('stay_id').sum().reset_index()[0].max()       
            self.proclength_per_adm=final_proc.groupby('stay_id').size().max()
            
        ###OUT
        if(self.feat_out):
            f2_out=final_out.groupby(['stay_id','itemid']).size()
            self.out_per_adm=f2_out.groupby('stay_id').sum().reset_index()[0].max() 
            self.outlength_per_adm=final_out.groupby('stay_id').size().max()
            
            
        ###chart
        if(self.feat_chart):
            f2_chart=final_chart.groupby(['stay_id','itemid']).size()
            self.chart_per_adm=f2_chart.groupby('stay_id').sum().reset_index()[0].max()             
            self.chartlength_per_adm=final_chart.groupby('stay_id').size().max()
        
        print("[ PROCESSED TIME SERIES TO EQUAL TIME INTERVAL ]")
        ###CREATE DICT
#         if(self.feat_chart):
#             self.create_chartDict(final_chart,los)
#         else:
        self.create_Dict(final_meds,final_proc,final_out,final_chart,los)
        

    def create_Dict(self,meds,proc,out,chart,los):
        dataDic={}

        for hid in self.hids:
            grp=self.data[self.data['stay_id']==hid]
            dataDic[hid]={'Cond':{},'Proc':{},'Med':{},'Out':{},'Chart':{},'ethnicity':grp['ethnicity'].iloc[0],'age':int(grp['Age']),'gender':grp['gender'].iloc[0],'label':int(grp['label']),'insurance':grp['insurance'].iloc[0]}
            

        for hid in tqdm(self.hids):
            grp=self.data[self.data['stay_id']==hid]
            
            ###MEDS
            if(self.feat_med):
                feat=meds['itemid'].unique()
                df2=meds[meds['stay_id']==hid]
                if df2.shape[0]==0:
                    amount=pd.DataFrame(np.zeros([los,len(feat)]),columns=feat)
                    amount=amount.fillna(0)
                    amount.columns=pd.MultiIndex.from_product([["MEDS"], amount.columns])
                else:
                    rate=df2.pivot_table(index='start_time',columns='itemid',values='rate')
                    #print(rate)
                    amount=df2.pivot_table(index='start_time',columns='itemid',values='amount')
                    df2=df2.pivot_table(index='start_time',columns='itemid',values='stop_time')
                    #print(df2.shape)
                    add_indices = pd.Index(range(los)).difference(df2.index)
                    add_df = pd.DataFrame(index=add_indices, columns=df2.columns).fillna(np.nan)
                    df2=pd.concat([df2, add_df])
                    df2=df2.sort_index()
                    df2=df2.ffill()
                    df2=df2.fillna(0)

                    rate=pd.concat([rate, add_df])
                    rate=rate.sort_index()
                    rate=rate.ffill()
                    rate=rate.fillna(-1)

                    amount=pd.concat([amount, add_df])
                    amount=amount.sort_index()
                    amount=amount.ffill()
                    amount=amount.fillna(-1)
                    #print(df2.head())
                    df2.iloc[:,0:]=df2.iloc[:,0:].sub(df2.index,0)
                    df2[df2>0]=1
                    df2[df2<0]=0
                    rate.iloc[:,0:]=df2.iloc[:,0:]*rate.iloc[:,0:]
                    amount.iloc[:,0:]=df2.iloc[:,0:]*amount.iloc[:,0:]
                    #print(df2.head())
                    dataDic[hid]['Med']['signal']=df2.iloc[:,0:].to_dict(orient="list")
                    dataDic[hid]['Med']['rate']=rate.iloc[:,0:].to_dict(orient="list")
                    dataDic[hid]['Med']['amount']=amount.iloc[:,0:].to_dict(orient="list")
    
            ###PROCS
            if(self.feat_proc):
                feat=proc['itemid'].unique()
                df2=proc[proc['stay_id']==hid]
                if df2.shape[0]==0:
                    df2=pd.DataFrame(np.zeros([los,len(feat)]),columns=feat)
                    df2=df2.fillna(0)
                    df2.columns=pd.MultiIndex.from_product([["PROC"], df2.columns])
                else:
                    df2['val']=1
                    #print(df2)
                    df2=df2.pivot_table(index='start_time',columns='itemid',values='val')
                    #print(df2.shape)
                    add_indices = pd.Index(range(los)).difference(df2.index)
                    add_df = pd.DataFrame(index=add_indices, columns=df2.columns).fillna(np.nan)
                    df2=pd.concat([df2, add_df])
                    df2=df2.sort_index()
                    df2=df2.fillna(0)
                    df2[df2>0]=1
                    #print(df2.head())
                    dataDic[hid]['Proc']=df2.to_dict(orient="list")
    
            ###OUT
            if(self.feat_out):
                feat=out['itemid'].unique()
                df2=out[out['stay_id']==hid]
                if df2.shape[0]==0:
                    df2=pd.DataFrame(np.zeros([los,len(feat)]),columns=feat)
                    df2=df2.fillna(0)
                    df2.columns=pd.MultiIndex.from_product([["OUT"], df2.columns])
                else:
                    df2['val']=1
                    df2=df2.pivot_table(index='start_time',columns='itemid',values='val')
                    #print(df2.shape)
                    add_indices = pd.Index(range(los)).difference(df2.index)
                    add_df = pd.DataFrame(index=add_indices, columns=df2.columns).fillna(np.nan)
                    df2=pd.concat([df2, add_df])
                    df2=df2.sort_index()
                    df2=df2.fillna(0)
                    df2[df2>0]=1
                    #print(df2.head())
                    dataDic[hid]['Out']=df2.to_dict(orient="list")
        
            ###CHART
            if(self.feat_chart):
                feat=chart['itemid'].unique()
                df2=chart[chart['stay_id']==hid]
                if df2.shape[0]==0:
                    val=pd.DataFrame(np.zeros([los,len(feat)]),columns=feat)
                    val=val.fillna(0)
                    val.columns=pd.MultiIndex.from_product([["CHART"], val.columns])
                else:
                    val=df2.pivot_table(index='start_time',columns='itemid',values='valuenum')
                    df2['val']=1
                    df2=df2.pivot_table(index='start_time',columns='itemid',values='val')
                    #print(df2.shape)
                    add_indices = pd.Index(range(los)).difference(df2.index)
                    add_df = pd.DataFrame(index=add_indices, columns=df2.columns).fillna(np.nan)
                    df2=pd.concat([df2, add_df])
                    df2=df2.sort_index()
                    df2=df2.fillna(0)

                    val=pd.concat([val, add_df])
                    val=val.sort_index()
                    if self.impute=='Mean':
                        val=val.ffill()
                        val=val.bfill()
                        val=val.fillna(val.mean())
                    elif self.impute=='Median':
                        val=val.ffill()
                        val=val.bfill()
                        val=val.fillna(val.median())
                    val=val.fillna(0)


                    df2[df2>0]=1
                    df2[df2<0]=0
                    #print(df2.head())
                    dataDic[hid]['Chart']['signal']=df2.iloc[:,0:].to_dict(orient="list")
                    dataDic[hid]['Chart']['val']=val.iloc[:,0:].to_dict(orient="list")
            
            ##########COND#########
            if(self.feat_cond):
                feat=self.cond['new_icd_code'].unique()
                grp=self.cond[self.cond['stay_id']==hid]
                if(grp.shape[0]==0):
                    dataDic[hid]['Cond']={'fids':list(['<PAD>'])}
                else:
                    dataDic[hid]['Cond']={'fids':list(grp['new_icd_code'])}
            
                
        ######SAVE DICTIONARIES##############
        metaDic={'Cond':{},'Proc':{},'Med':{},'Out':{},'Chart':{},'LOS':{}}
        metaDic['LOS']=los
        with open("./data/dict/"+self.task+"/dataDic", 'wb') as fp:
            pickle.dump(dataDic, fp)

        with open("./data/dict/"+self.task+"/hadmDic", 'wb') as fp:
            pickle.dump(self.hids, fp)
        
        with open("./data/dict/"+self.task+"/ethVocab", 'wb') as fp:
            pickle.dump(list(self.data['ethnicity'].unique()), fp)
            self.eth_vocab = self.data['ethnicity'].nunique()
            
        with open("./data/dict/"+self.task+"/ageVocab", 'wb') as fp:
            pickle.dump(list(self.data['Age'].unique()), fp)
            self.age_vocab = self.data['Age'].nunique()
            
        with open("./data/dict/"+self.task+"/insVocab", 'wb') as fp:
            pickle.dump(list(self.data['insurance'].unique()), fp)
            self.ins_vocab = self.data['insurance'].nunique()
            
        if(self.feat_med):
            with open("./data/dict/"+self.task+"/medVocab", 'wb') as fp:
                pickle.dump(list(meds['itemid'].unique()), fp)
            self.med_vocab = meds['itemid'].nunique()
            metaDic['Med']=self.med_per_adm
            
        if(self.feat_out):
            with open("./data/dict/"+self.task+"/outVocab", 'wb') as fp:
                pickle.dump(list(out['itemid'].unique()), fp)
            self.out_vocab = out['itemid'].nunique()
            metaDic['Out']=self.out_per_adm
            
        if(self.feat_chart):
            with open("./data/dict/"+self.task+"/chartVocab", 'wb') as fp:
                pickle.dump(list(chart['itemid'].unique()), fp)
            self.chart_vocab = chart['itemid'].nunique()
            metaDic['Chart']=self.chart_per_adm
        
        if(self.feat_cond):
            with open("./data/dict/"+self.task+"/condVocab", 'wb') as fp:
                pickle.dump(list(self.cond['new_icd_code'].unique()), fp)
            self.cond_vocab = self.cond['new_icd_code'].nunique()
            metaDic['Cond']=self.cond_per_adm
        
        if(self.feat_proc):    
            with open("./data/dict/"+self.task+"/procVocab", 'wb') as fp:
                pickle.dump(list(proc['itemid'].unique()), fp)
            self.proc_vocab = proc['itemid'].nunique()
            metaDic['Proc']=self.proc_per_adm
            
        with open("./data/dict/"+self.task+"/metaDic", 'wb') as fp:
            pickle.dump(metaDic, fp)