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import pandas as pd
import pickle
import numpy as np
import torch
def create_vocab(file,task):
with open ('./data/dict/'+task+'/'+file, 'rb') as fp:
condVocab = pickle.load(fp)
condVocabDict={}
condVocabDict[0]=0
for val in range(len(condVocab)):
condVocabDict[condVocab[val]]= val+1
return condVocabDict
def gender_vocab():
genderVocabDict={}
genderVocabDict['<PAD>']=0
genderVocabDict['M']=1
genderVocabDict['F']=2
return genderVocabDict
def vocab(task,diag_flag,proc_flag,out_flag,chart_flag,med_flag,lab_flag):
condVocabDict={}
procVocabDict={}
medVocabDict={}
outVocabDict={}
chartVocabDict={}
labVocabDict={}
ethVocabDict={}
ageVocabDict={}
genderVocabDict={}
insVocabDict={}
ethVocabDict=create_vocab('ethVocab',task)
with open('./data/dict/'+task+'/ethVocabDict', 'wb') as fp:
pickle.dump(ethVocabDict, fp)
ageVocabDict=create_vocab('ageVocab',task)
with open('./data/dict/'+task+'/ageVocabDict', 'wb') as fp:
pickle.dump(ageVocabDict, fp)
genderVocabDict=gender_vocab()
with open('./data/dict/'+task+'/genderVocabDict', 'wb') as fp:
pickle.dump(genderVocabDict, fp)
insVocabDict=create_vocab('insVocab',task)
with open('./data/dict/'+task+'/insVocabDict', 'wb') as fp:
pickle.dump(insVocabDict, fp)
if diag_flag:
file='condVocab'
with open ('./data/dict/'+task+'/'+file, 'rb') as fp:
condVocabDict = pickle.load(fp)
if proc_flag:
file='procVocab'
with open ('./data/dict/'+task+'/'+file, 'rb') as fp:
procVocabDict = pickle.load(fp)
if med_flag:
file='medVocab'
with open ('./data/dict/'+task+'/'+file, 'rb') as fp:
medVocabDict = pickle.load(fp)
if out_flag:
file='outVocab'
with open ('./data/dict/'+task+'/'+file, 'rb') as fp:
outVocabDict = pickle.load(fp)
if chart_flag:
file='chartVocab'
with open ('./data/dict/'+task+'/'+file, 'rb') as fp:
chartVocabDict = pickle.load(fp)
if lab_flag:
file='labsVocab'
with open ('./data/dict/'+task+'/'+file, 'rb') as fp:
labVocabDict = pickle.load(fp)
return len(condVocabDict),len(procVocabDict),len(medVocabDict),len(outVocabDict),len(chartVocabDict),len(labVocabDict),ethVocabDict,genderVocabDict,ageVocabDict,insVocabDict
def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab):
meds=data['Med']
proc = data['Proc']
out = data['Out']
chart = data['Chart']
cond= data['Cond']['fids']
cond_df=pd.DataFrame()
proc_df=pd.DataFrame()
out_df=pd.DataFrame()
chart_df=pd.DataFrame()
meds_df=pd.DataFrame()
#demographic
demo=pd.DataFrame(columns=['Age','gender','ethnicity','label','insurance'])
new_row = {'Age': data['age'], 'gender': data['gender'], 'ethnicity': data['ethnicity'], 'label': data['label'], 'insurance': data['insurance']}
demo = demo.append(new_row, ignore_index=True)
##########COND#########
if (feat_cond):
#get all conds
with open("./data/dict/"+task+"/condVocab", 'rb') as fp:
conDict = pickle.load(fp)
conds=pd.DataFrame(conDict,columns=['COND'])
features=pd.DataFrame(np.zeros([1,len(conds)]),columns=conds['COND'])
#onehot encode
if(cond ==[]):
cond_df=pd.DataFrame(np.zeros([1,len(features)]),columns=features['COND'])
cond_df=cond_df.fillna(0)
else:
cond_df=pd.DataFrame(cond,columns=['COND'])
cond_df['val']=1
cond_df=(cond_df.drop_duplicates()).pivot(columns='COND',values='val').reset_index(drop=True)
cond_df=cond_df.fillna(0)
oneh = cond_df.sum().to_frame().T
combined_df = pd.concat([features,oneh],ignore_index=True).fillna(0)
combined_oneh=combined_df.sum().to_frame().T
cond_df=combined_oneh
for c in cond_df.columns :
if c not in features:
cond_df=cond_df.drop(columns=[c])
##########PROC#########
if (feat_proc):
with open("./data/dict/"+task+"/procVocab", 'rb') as fp:
procDic = pickle.load(fp)
if proc :
feat=proc.keys()
proc_val=[proc[key] for key in feat]
procedures=pd.DataFrame(procDic,columns=['PROC'])
features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC'])
procs=pd.DataFrame(columns=feat)
for p,v in zip(feat,proc_val):
procs[p]=v
features=features.drop(columns=procs.columns.to_list())
proc_df = pd.concat([features,procs],axis=1).fillna(0)
proc_df.columns=pd.MultiIndex.from_product([["PROC"], proc_df.columns])
else:
procedures=pd.DataFrame(procDic,columns=['PROC'])
features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC'])
features.columns=pd.MultiIndex.from_product([["PROC"], features.columns])
proc_df=features.fillna(0)
##########OUT#########
if (feat_out):
with open("./data/dict/"+task+"/outVocab", 'rb') as fp:
outDic = pickle.load(fp)
if out :
feat=out.keys()
out_val=[out[key] for key in feat]
outputs=pd.DataFrame(outDic,columns=['OUT'])
features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT'])
outs=pd.DataFrame(columns=feat)
for o,v in zip(feat,out_val):
outs[o]=v
features=features.drop(columns=outs.columns.to_list())
out_df = pd.concat([features,outs],axis=1).fillna(0)
out_df.columns=pd.MultiIndex.from_product([["OUT"], out_df.columns])
else:
outputs=pd.DataFrame(outDic,columns=['OUT'])
features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT'])
features.columns=pd.MultiIndex.from_product([["OUT"], features.columns])
out_df=features.fillna(0)
##########CHART#########
if (feat_chart):
with open("./data/dict/"+task+"/chartVocab", 'rb') as fp:
chartDic = pickle.load(fp)
if chart:
charts=chart['val']
feat=charts.keys()
chart_val=[charts[key] for key in feat]
charts=pd.DataFrame(chartDic,columns=['CHART'])
features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART'])
chart=pd.DataFrame(columns=feat)
for c,v in zip(feat,chart_val):
chart[c]=v
features=features.drop(columns=chart.columns.to_list())
chart_df = pd.concat([features,chart],axis=1).fillna(0)
chart_df.columns=pd.MultiIndex.from_product([["CHART"], chart_df.columns])
else:
charts=pd.DataFrame(chartDic,columns=['CHART'])
features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART'])
features.columns=pd.MultiIndex.from_product([["CHART"], features.columns])
chart_df=features.fillna(0)
##########LAB#########
if (feat_lab):
with open("./data/dict/"+task+"/labsVocab", 'rb') as fp:
chartDic = pickle.load(fp)
if chart:
charts=chart['val']
feat=charts.keys()
chart_val=[charts[key] for key in feat]
charts=pd.DataFrame(chartDic,columns=['LAB'])
features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB'])
chart=pd.DataFrame(columns=feat)
for c,v in zip(feat,chart_val):
chart[c]=v
features=features.drop(columns=chart.columns.to_list())
chart.columns=pd.MultiIndex.from_product([["LAB"], chart.columns])
chart_df = pd.concat([features,chart],axis=1).fillna(0)
chart_df.columns=pd.MultiIndex.from_product([["LAB"], chart_df.columns])
else:
charts=pd.DataFrame(chartDic,columns=['LAB'])
features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB'])
features.columns=pd.MultiIndex.from_product([["LAB"], features.columns])
chart_df=features.fillna(0)
###MEDS
if (feat_meds):
with open("./data/dict/"+task+"/medVocab", 'rb') as fp:
medDic = pickle.load(fp)
if meds:
feat=meds['signal'].keys()
med_val=[meds['amount'][key] for key in feat]
meds=pd.DataFrame(medDic,columns=['MEDS'])
features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS'])
med=pd.DataFrame(columns=feat)
for m,v in zip(feat,med_val):
med[m]=v
features=features.drop(columns=med.columns.to_list())
meds_df = pd.concat([features,med],axis=1).fillna(0)
meds_df.columns=pd.MultiIndex.from_product([["MEDS"], meds_df.columns])
else:
meds=pd.DataFrame(medDic,columns=['MEDS'])
features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS'])
features.columns=pd.MultiIndex.from_product([["MEDS"], features.columns])
meds_df=features.fillna(0)
dyn_df = pd.concat([meds_df,proc_df,out_df,chart_df], axis=1)
return dyn_df,cond_df,demo
def generate_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab):
stat_df = torch.zeros(size=(1,0))
demo_df = torch.zeros(size=(1,0))
meds = torch.zeros(size=(0,0))
charts = torch.zeros(size=(0,0))
proc = torch.zeros(size=(0,0))
out = torch.zeros(size=(0,0))
lab = torch.zeros(size=(0,0))
stat_df = torch.zeros(size=(1,0))
demo_df = torch.zeros(size=(1,0))
size_cond, size_proc, size_meds, size_out, size_chart, size_lab, eth_vocab,gender_vocab,age_vocab,ins_vocab=vocab(task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,False)
dyn,cond_df,demo=concat_data(data,task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab)
if feat_chart:
charts = dyn['CHART']
charts=charts.to_numpy()
charts = torch.tensor(charts, dtype=torch.long)
charts = charts.tolist()
if feat_meds:
meds = dyn['MEDS']
meds=meds.to_numpy()
meds = torch.tensor(meds, dtype=torch.long)
meds = meds.tolist()
if feat_proc:
proc = dyn['PROC']
proc=proc.to_numpy()
proc = torch.tensor(proc, dtype=torch.long)
proc = proc.tolist()
if feat_out:
out = dyn['OUT']
out=out.to_numpy()
out = torch.tensor(out, dtype=torch.long)
out = out.tolist()
if feat_lab:
lab = dyn['LAB']
lab=lab.to_numpy()
lab = torch.tensor(lab, dtype=torch.long)
lab = lab.tolist()
if feat_cond:
stat=cond_df
stat = stat.to_numpy()
stat = torch.tensor(stat)
if stat_df[0].nelement():
stat_df = torch.cat((stat_df,stat),0)
else:
stat_df = stat
stat_df = torch.tensor(stat_df)
stat_df = stat_df.type(torch.LongTensor)
stat_df = stat_df.squeeze()
y = int(demo['label'])
y_df = torch.tensor(y)
y_df = y_df.type(torch.LongTensor)
demo["gender"].replace(gender_vocab, inplace=True)
demo["ethnicity"].replace(eth_vocab, inplace=True)
demo["insurance"].replace(ins_vocab, inplace=True)
demo["Age"].replace(age_vocab, inplace=True)
demo=demo[["gender","ethnicity","insurance","Age"]]
demo = demo.values
demo = torch.tensor(demo)
if demo_df[0].nelement():
demo_df = torch.cat((demo_df,demo),0)
else:
demo_df = demo
demo_df = torch.tensor(demo_df)
demo_df = demo_df.type(torch.LongTensor)
demo_df=demo_df.squeeze()
return stat_df, demo_df, meds, charts, out, proc, lab, y
def generate_ml(dyn,stat,demo,concat_cols,concat):
X_df=pd.DataFrame()
if concat:
dyna=dyn.copy()
dyna.columns=dyna.columns.droplevel(0)
dyna=dyna.to_numpy()
dyna=np.nan_to_num(dyna, copy=False)
dyna=dyna.reshape(1,-1)
dyn_df=pd.DataFrame(data=dyna,columns=concat_cols)
else:
dyn_df=pd.DataFrame()
for key in dyn.columns.levels[0]:
dyn_temp=dyn[key]
if ((key=="CHART") or (key=="MEDS")):
agg=dyn_temp.aggregate("mean")
agg=agg.reset_index()
else:
agg=dyn_temp.aggregate("max")
agg=agg.reset_index()
if dyn_df.empty:
dyn_df=agg
else:
dyn_df=pd.concat([dyn_df,agg],axis=0)
dyn_df=dyn_df.T
dyn_df.columns = dyn_df.iloc[0]
dyn_df=dyn_df.iloc[1:,:]
X_df=pd.concat([dyn_df,stat],axis=1)
X_df=pd.concat([X_df,demo],axis=1)
return X_df |