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['']=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 ##########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']) features.columns=pd.MultiIndex.from_product([["PROC"], features.columns]) procs=pd.DataFrame(columns=feat) for p,v in zip(feat,proc_val): procs[p]=v procs.columns=pd.MultiIndex.from_product([["PROC"], procs.columns]) proc_df = pd.concat([features,procs],ignore_index=True).fillna(0) 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']) features.columns=pd.MultiIndex.from_product([["OUT"], features.columns]) outs=pd.DataFrame(columns=feat) for o,v in zip(feat,out_val): outs[o]=v outs.columns=pd.MultiIndex.from_product([["OUT"], outs.columns]) out_df = pd.concat([features,outs],ignore_index=True).fillna(0) 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']) features.columns=pd.MultiIndex.from_product([["CHART"], features.columns]) chart=pd.DataFrame(columns=feat) for c,v in zip(feat,chart_val): chart[c]=v chart.columns=pd.MultiIndex.from_product([["CHART"], chart.columns]) chart_df = pd.concat([features,chart],ignore_index=True).fillna(0) 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']) features.columns=pd.MultiIndex.from_product([["LAB"], features.columns]) chart=pd.DataFrame(columns=feat) for c,v in zip(feat,chart_val): chart[c]=v chart.columns=pd.MultiIndex.from_product([["LAB"], chart.columns]) chart_df = pd.concat([features,chart],ignore_index=True).fillna(0) 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']) features.columns=pd.MultiIndex.from_product([["MEDS"], features.columns]) med=pd.DataFrame(columns=feat) for m,v in zip(feat,med_val): med[m]=v med.columns=pd.MultiIndex.from_product([["MEDS"], med.columns]) meds_df = pd.concat([features,med],ignore_index=True).fillna(0) 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() 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 y = int(demo['label']) 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 stat_df = torch.tensor(stat_df) stat_df = stat_df.type(torch.LongTensor) stat_df = stat_df.squeeze() demo_df = torch.tensor(demo_df) demo_df = demo_df.type(torch.LongTensor) demo_df=demo_df.squeeze() y_df = torch.tensor(y) y_df = y_df.type(torch.LongTensor) 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