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import csv
import json
import os
import pandas as pd
import datasets
import sys
import pickle
import subprocess
import shutil
from urllib.request import urlretrieve
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import numpy as np
from tqdm import tqdm
import yaml
import torch
import time
from pathlib import Path
import importlib
_DESCRIPTION = """\
Dataset for mimic4 data, by default for the Mortality task.
Available tasks are: Mortality, Length of Stay, Readmission, Phenotype, Mortality Custom, Length of Stay Custom, Readmission Custom, Phenotype Custom.
The data is extracted from the mimic4 database using this pipeline: 'https://github.com/healthylaife/MIMIC-IV-Data-Pipeline/tree/main'
mimic path should have this form : "path/to/mimic4data/from/username/mimiciv/2.2"
If you choose a Custom task provide a configuration file for the Time series.
"""
_HOMEPAGE = "https://huggingface.co/datasets/thbndi/Mimic4Dataset"
_CITATION = "https://proceedings.mlr.press/v193/gupta22a.html"
_URL = "https://github.com/healthylaife/MIMIC-IV-Data-Pipeline"
_DATA_GEN = 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/data_generation_icu_modify.py'
_DAY_INT= 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/day_intervals_cohort_v22.py'
#_COHORT = 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/cohort.py'
_CONFIG_URLS = {'los' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/config/los.config',
'mortality' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/config/mortality.config',
'phenotype' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/config/phenotype.config',
'readmission' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/config/readmission.config'
}
def check_config(task,config_file):
with open(config_file) as f:
config = yaml.safe_load(f)
disease_label = config['disease_label']
time = config['timePrediction']
label = task
timeW = config['timeWindow']
include=int(timeW.split()[1])
bucket = config['timebucket']
radimp = config['radimp']
predW = config['predW']
disease_filter = config['disease_filter']
icu_no_icu = config['icu_no_icu']
groupingICD = config['groupingICD']
chart_flag = config['chart']
output_flag = config['output']
diag_flag= config['diagnosis']
proc_flag = config['proc']
meds_flag = config['meds']
select_diag= config['select_diag']
select_med= config['select_med']
select_proc= config['select_proc']
select_out = config['select_out']
select_chart = config['select_chart']
outlier_removal=config['outlier_removal']
thresh=config['outlier']
left_thresh=config['left_outlier']
assert (isinstance(select_diag,bool) and isinstance(select_med,bool) and isinstance(select_proc,bool) and isinstance(select_out,bool) and isinstance(select_chart,bool), " select_diag, select_chart, select_med, select_proc, select_out should be boolean")
assert (isinstance(chart_flag,bool) and isinstance(output_flag,bool) and isinstance(diag_flag,bool) and isinstance(proc_flag,bool) and isinstance(meds_flag,bool), "chart_flag, output_flag, diag_flag, proc_flag, meds_flag should be boolean")
if task=='Phenotype':
if disease_label=='Heart Failure':
label='Readmission'
time=30
disease_label='I50'
elif disease_label=='CAD':
label='Readmission'
time=30
disease_label='I25'
elif disease_label=='CKD':
label='Readmission'
time=30
disease_label='N18'
elif disease_label=='COPD':
label='Readmission'
time=30
disease_label='J44'
else :
raise ValueError('Disease label not correct provide one in the list: Heart Failure, CAD, CKD, COPD')
predW=0
assert (timeW[0]=='Last' and include<=72 and include>=24, "Time window should be between Last 24 and Last 72")
elif task=='Mortality':
time=0
disease_label=""
assert (predW<=8 and predW>=2, "Prediction window should be between 2 and 8")
assert (timeW[0]=='Fisrt' and include<=72 and include>=24, "Time window should be between First 24 and First 72")
elif task=='Length of Stay':
disease_label=""
assert (timeW[0]=='Fisrt' and include<=72 and include>=24, "Time window should be between Fisrt 24 and Fisrt 72")
assert (time<=10 and time>=1, "Length of stay should be between 1 and 10")
predW=0
elif task=='Readmission':
disease_label=""
assert (timeW[0]=='Last' and include<=72 and include>=24, "Time window should be between Last 24 and Last 72")
assert (time<=150 and time>=10 and time%10==0, "Readmission window should be between 10 and 150 with a step of 10")
predW=0
else:
raise ValueError('Task not correct')
assert( disease_filter in ['Heart Failure','COPD','CKD','CAD',""], "Disease filter should be one of the following: Heart Failure, COPD, CKD, CAD or empty")
assert( icu_no_icu in ['ICU'], "Dataset currently only supports ICU data")
assert( groupingICD in ['Convert ICD-9 to ICD-10 and group ICD-10 codes','Keep both ICD-9 and ICD-10 codes','Convert ICD-9 to ICD-10 codes'], "Grouping ICD should be one of the following: Convert ICD-9 to ICD-10 and group ICD-10 codes, Keep both ICD-9 and ICD-10 codes, Convert ICD-9 to ICD-10 codes")
assert (bucket<=6 and bucket>=1 and isinstance(bucket, int), "Time bucket should be between 1 and 6 and an integer")
assert (radimp in ['No Imputation', 'forward fill and mean','forward fill and median'], "imputation should be one of the following: No Imputation, forward fill and mean, forward fill and median")
if chart_flag:
assert (left_thresh>=0 and left_thresh<=10 and isinstance(left_thresh, int), "Left outlier threshold should be between 0 and 10 and an integer")
assert (thresh>=90 and thresh<=99 and isinstance(thresh, int), "Outlier threshold should be between 90 and 99 and an integer")
assert (outlier_removal in ['No outlier detection','Impute Outlier (default:98)','Remove outliers (default:98)'], "Outlier removal should be one of the following: No outlier detection, Impute Outlier (default:98), Remove outliers (default:98)")
return label, time, disease_label, predW
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 ethVocabDict,genderVocabDict,ageVocabDict,insVocabDict
def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
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)
###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 getXY_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
meds, chart, out, proc, lab =[],[],[],[],[]
eth_vocab,gender_vocab,age_vocab,ins_vocab=vocab(task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,False)
dyn_df,cond_df,demo=concat_data(data,task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds)
keys=dyn_df.columns.levels[0]
dyn = dict.fromkeys(keys)
for key in range(len(keys)):
dyn_temp=dyn_df[keys[key]]
dyn_temp=dyn_temp.to_numpy()
dyn_temp=np.nan_to_num(dyn_temp,copy=False)
dyn_temp=dyn_temp.tolist()
dyn[key]=dyn_temp
for k in range(len(keys)):
if keys[k]=='MEDS':
meds=dyn[k]
if keys[k]=='CHART':
chart=dyn[k]
if keys[k]=='OUT':
out=dyn[k]
if keys[k]=='PROC':
proc=dyn[k]
if keys[k]=='LAB':
lab=dyn[k]
stat=cond_df
stat=stat.to_numpy()
stat=stat.tolist()
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.tolist()
return stat, demo, meds, chart, out, proc, lab, y
def getXY(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=list(dyna)
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
def encoding(X_data):
gen_encoder = LabelEncoder()
eth_encoder = LabelEncoder()
ins_encoder = LabelEncoder()
gen_encoder.fit(X_data['gender'])
eth_encoder.fit(X_data['ethnicity'])
ins_encoder.fit(X_data['insurance'])
X_data['gender']=gen_encoder.transform(X_data['gender'])
X_data['ethnicity']=eth_encoder.transform(X_data['ethnicity'])
X_data['insurance']=ins_encoder.transform(X_data['insurance'])
return X_data
def generate_split(path,task,concat,feat_cond,feat_chart,feat_proc, feat_meds, feat_out):
with open(path, 'rb') as fp:
dico = pickle.load(fp)
df = pd.DataFrame.from_dict(dico, orient='index')
X_df=pd.DataFrame()
taskf=task.replace(" ","_")
for _, data in tqdm(df.iterrows(),desc='Encoding Splits Data for '+task+' task'):
concat_cols=[]
sample=data
dyn_df,cond_df,demo=concat_data(sample,taskf,feat_cond,feat_chart,feat_proc, feat_meds, feat_out)
dyn=dyn_df.copy()
dyn.columns=dyn.columns.droplevel(0)
cols=dyn.columns
time=dyn.shape[0]
for t in range(time):
cols_t = [str(x) + "_"+str(t) for x in cols]
concat_cols.extend(cols_t)
X= getXY(dyn_df,cond_df,demo,concat_cols,concat)
if X_df.empty:
X_df=pd.concat([X_df,X],axis=1)
else:
X_df = pd.concat([X_df, X], axis=0)
X_df=X_df.fillna(0)
X_df = encoding(X_df)
return X_df
def generate_split_deep(path,task,feat_cond,feat_chart,feat_proc, feat_meds, feat_out):
with open(path, 'rb') as fp:
dico = pickle.load(fp)
X = pd.DataFrame.from_dict(dico, orient='index')
X_dict = {}
taskf=task.replace(" ","_")
for hid, data in tqdm(X.iterrows(),desc='Encoding Splits Data for '+task+' task'):
stat, demo, meds, chart, out, proc, lab, y = getXY_deep(data, taskf, feat_cond, feat_proc, feat_out, feat_chart,feat_meds)
X_dict[hid] = {'stat': stat, 'demo': demo, 'meds': meds, 'chart': chart, 'out': out, 'proc': proc, 'lab': lab, 'label': y}
return X_dict
def task_cohort(task, mimic_path, config_path):
sys.path.append('./preprocessing/day_intervals_preproc')
sys.path.append('./utils')
sys.path.append('./preprocessing/hosp_module_preproc')
sys.path.append('./model')
import day_intervals_cohort_v22
import day_intervals_cohort
import feature_selection_icu
import data_generation_icu_modify
root_dir = os.path.dirname(os.path.abspath('UserInterface.ipynb'))
config_path='./config/'+config_path
with open(config_path) as f:
config = yaml.safe_load(f)
version_path = mimic_path+'/'
version = mimic_path.split('/')[-1][0]
start = time.time()
#----------------------------------------------config----------------------------------------------------
label, tim, disease_label, predW = check_config(task,config_path)
timeW = config['timeWindow']
include=int(timeW.split()[1])
bucket = config['timebucket']
radimp = config['radimp']
diag_flag = config['diagnosis']
out_flag = config['output']
chart_flag = config['chart']
proc_flag= config['proc']
med_flag = config['meds']
disease_filter = config['disease_filter']
icu_no_icu = config['icu_no_icu']
groupingICD = config['groupingICD']
select_diag= config['select_diag']
select_med= config['select_med']
select_proc= config['select_proc']
#select_lab= config['select_lab']
select_out= config['select_out']
select_chart= config['select_chart']
# -------------------------------------------------------------------------------------------------------------
data_icu=icu_no_icu=="ICU"
data_mort=label=="Mortality"
data_admn=label=='Readmission'
data_los=label=='Length of Stay'
if (disease_filter=="Heart Failure"):
icd_code='I50'
elif (disease_filter=="CKD"):
icd_code='N18'
elif (disease_filter=="COPD"):
icd_code='J44'
elif (disease_filter=="CAD"):
icd_code='I25'
else:
icd_code='No Disease Filter'
#-----------------------------------------------EXTRACT MIMIC-----------------------------------------------------
if version == '2':
cohort_output = day_intervals_cohort_v22.extract_data(icu_no_icu,label,tim,icd_code, root_dir,version_path,disease_label)
elif version == '1':
cohort_output = day_intervals_cohort.extract_data(icu_no_icu,label,tim,icd_code, root_dir,version_path,disease_label)
#----------------------------------------------FEATURES-------------------------------------------------------
print(data_icu)
if data_icu :
feature_selection_icu.feature_icu(cohort_output, version_path,diag_flag,out_flag,chart_flag,proc_flag,med_flag)
#----------------------------------------------GROUPING-------------------------------------------------------
if data_icu:
if diag_flag:
group_diag=groupingICD
feature_selection_icu.preprocess_features_icu(cohort_output, diag_flag, group_diag,False,False,False,0,0)
#----------------------------------------------SUMMARY-------------------------------------------------------
if data_icu:
feature_selection_icu.generate_summary_icu(diag_flag,proc_flag,med_flag,out_flag,chart_flag)
#----------------------------------------------FEATURE SELECTION---------------------------------------------
feature_selection_icu.features_selection_icu(cohort_output, diag_flag,proc_flag,med_flag,out_flag, chart_flag,select_diag,select_med,select_proc,select_out,select_chart)
#---------------------------------------CLEANING OF FEATURES-----------------------------------------------
thresh=0
if data_icu:
if chart_flag:
outlier_removal=config['outlier_removal']
clean_chart=outlier_removal!='No outlier detection'
impute_outlier_chart=outlier_removal=='Impute Outlier (default:98)'
thresh=config['outlier']
left_thresh=config['left_outlier']
feature_selection_icu.preprocess_features_icu(cohort_output, False, False,chart_flag,clean_chart,impute_outlier_chart,thresh,left_thresh)
# ---------------------------------------tim-Series Representation--------------------------------------------
if radimp == 'forward fill and mean' :
impute='Mean'
elif radimp =='forward fill and median':
impute = 'Median'
else :
impute = False
if data_icu:
gen=data_generation_icu_modify.Generator(task,cohort_output,data_mort,data_admn,data_los,diag_flag,proc_flag,out_flag,chart_flag,med_flag,impute,include,bucket,predW)
end = time.time()
print("Time elapsed : ", round((end - start)/60,2),"mins")
print("[============TASK COHORT SUCCESSFULLY CREATED============]")
#############################################DATASET####################################################################
class Mimic4DatasetConfig(datasets.BuilderConfig):
"""BuilderConfig for Mimic4Dataset."""
def __init__(
self,
**kwargs,
):
super().__init__(**kwargs)
class Mimic4Dataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def __init__(self, **kwargs):
self.mimic_path = kwargs.pop("mimic_path", None)
self.encoding = kwargs.pop("encoding",'raw')
self.config_path = kwargs.pop("config_path",None)
self.test_size = kwargs.pop("test_size",0.2)
self.val_size = kwargs.pop("val_size",0.1)
super().__init__(**kwargs)
BUILDER_CONFIGS = [
Mimic4DatasetConfig(
name="Phenotype",
version=VERSION,
description="Dataset for mimic4 Phenotype task"
),
Mimic4DatasetConfig(
name="Readmission",
version=VERSION,
description="Dataset for mimic4 Readmission task"
),
Mimic4DatasetConfig(
name="Length of Stay",
version=VERSION,
description="Dataset for mimic4 Length of Stay task"
),
Mimic4DatasetConfig(
name="Mortality",
version=VERSION,
description="Dataset for mimic4 Mortality task"
),
Mimic4DatasetConfig(
name="Phenotype Custom",
version=VERSION,
description="Dataset for mimic4 Custom Phenotype task"
),
Mimic4DatasetConfig(
name="Readmission Custom",
version=VERSION,
description="Dataset for mimic4 Custom Readmission task"
),
Mimic4DatasetConfig(
name="Length of Stay Custom",
version=VERSION,
description="Dataset for mimic4 Custom Length of Stay task"
),
Mimic4DatasetConfig(
name="Mortality Custom",
version=VERSION,
description="Dataset for mimic4 Custom Mortality task"
),
]
DEFAULT_CONFIG_NAME = "Mortality"
def map_dtype(self,dtype):
if pd.api.types.is_integer_dtype(dtype):
return datasets.Value('int64')
elif pd.api.types.is_float_dtype(dtype):
return datasets.Value('float64')
elif pd.api.types.is_string_dtype(dtype):
return datasets.Value('string')
else:
raise ValueError(f"Unsupported dtype: {dtype}")
def create_cohort(self):
if self.config.name == 'Phenotype' : self.config_path = _CONFIG_URLS['phenotype']
if self.config.name == 'Readmission' : self.config_path = _CONFIG_URLS['readmission']
if self.config.name == 'Length of Stay' : self.config_path = _CONFIG_URLS['los']
if self.config.name == 'Mortality' : self.config_path = _CONFIG_URLS['mortality']
if self.config.name in ['Phenotype Custom','Readmission Custom','Length of Stay Custom','Mortality Custom'] and self.config.name==None:
raise ValueError('Please provide a config file')
version = self.mimic_path.split('/')[-1]
mimic_folder= self.mimic_path.split('/')[-2]
mimic_complete_path='/'+mimic_folder+'/'+version
current_directory = os.getcwd()
if os.path.exists(os.path.dirname(current_directory)+'/MIMIC-IV-Data-Pipeline-main'):
dir =os.path.dirname(current_directory)
os.chdir(dir)
else:
#move to parent directory of mimic data
dir = self.mimic_path.replace(mimic_complete_path,'')
if dir[-1]!='/':
dir=dir+'/'
elif dir=='':
dir="./"
parent_dir = os.path.dirname(self.mimic_path)
os.chdir(parent_dir)
#####################clone git repo if doesnt exists
repo_url='https://github.com/healthylaife/MIMIC-IV-Data-Pipeline'
if os.path.exists('MIMIC-IV-Data-Pipeline-main'):
path_bench = './MIMIC-IV-Data-Pipeline-main'
else:
path_bench ='./MIMIC-IV-Data-Pipeline-main'
subprocess.run(["git", "clone", repo_url, path_bench])
os.makedirs(path_bench+'/mimic-iv')
shutil.move(version,path_bench+'/mimic-iv')
os.chdir(path_bench)
self.mimic_path = './mimic-iv/'+version
####################Get configurations param
#download config file if not custom
if self.config_path[0:4] == 'http':
c = self.config_path.split('/')[-1]
file_path, head = urlretrieve(self.config_path,c)
else :
file_path = self.config_path
if not os.path.exists('./config'):
os.makedirs('config')
#save config file in config folder
conf='./config/'+file_path.split('/')[-1]
if not os.path.exists(conf):
shutil.move(file_path,'./config')
with open(conf) as f:
config = yaml.safe_load(f)
feat_cond, feat_chart, feat_proc, feat_meds, feat_out = config['diagnosis'], config['chart'], config['proc'], config['meds'], config['output']
#####################downloads modules from hub
if not os.path.exists('./model/data_generation_icu_modify.py'):
file_path, head = urlretrieve(_DATA_GEN, "data_generation_icu_modify.py")
shutil.move(file_path, './model')
if not os.path.exists('./preprocessing/day_intervals_preproc/day_intervals_cohort_v22.py'):
file_path, head = urlretrieve(_DAY_INT, "day_intervals_cohort_v22.py")
shutil.move(file_path, './preprocessing/day_intervals_preproc')
data_dir = "./data/dict/"+self.config.name.replace(" ","_")+"/dataDic"
sys.path.append(path_bench)
config = self.config_path.split('/')[-1]
#####################create task cohort
task_cohort(self.config.name.replace(" ","_"),self.mimic_path,config)
#####################Split data into train, test and val
with open(data_dir, 'rb') as fp:
dataDic = pickle.load(fp)
data = pd.DataFrame.from_dict(dataDic)
data=data.T
train_data, test_data = train_test_split(data, test_size=self.test_size, random_state=42)
train_data, val_data = train_test_split(train_data, test_size=self.val_size, random_state=42)
dict_dir = "./data/dict/"+self.config.name.replace(" ","_")
train_dic = train_data.to_dict('index')
test_dic = test_data.to_dict('index')
val_dic = val_data.to_dict('index')
train_path = dict_dir+'/train_data.pkl'
test_path = dict_dir+'/test_data.pkl'
val_path = dict_dir+'/val_data.pkl'
with open(train_path, 'wb') as f:
pickle.dump(train_dic, f)
with open(val_path, 'wb') as f:
pickle.dump(val_dic, f)
with open(test_path, 'wb') as f:
pickle.dump(test_dic, f)
return feat_cond, feat_chart, feat_proc, feat_meds, feat_out, dict_dir
###########################################################RAW##################################################################
def _info_raw(self):
features = datasets.Features(
{
"label": datasets.ClassLabel(num_classes=2,names=["0", "1"]),
"gender": datasets.Value("string"),
"ethnicity": datasets.Value("string"),
"insurance": datasets.Value("string"),
"age": datasets.Value("int32"),
"COND": datasets.Sequence(datasets.Value("string")),
"MEDS": {
"signal":
{
"id": datasets.Sequence(datasets.Value("int32")),
"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
}
,
"rate":
{
"id": datasets.Sequence(datasets.Value("int32")),
"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
}
,
"amount":
{
"id": datasets.Sequence(datasets.Value("int32")),
"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
}
},
"PROC": {
"id": datasets.Sequence(datasets.Value("int32")),
"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
},
"CHART":
{
"signal" : {
"id": datasets.Sequence(datasets.Value("int32")),
"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
},
"val" : {
"id": datasets.Sequence(datasets.Value("int32")),
"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
},
},
"OUT": {
"id": datasets.Sequence(datasets.Value("int32")),
"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
},
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def __split_generators_raw(self):
csv_dir = "./data/dict/"+self.config.name.replace(" ","_")
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": csv_dir+'/train_data.pkl'}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": csv_dir+'/val_data.pkl'}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": csv_dir+'/test_data.pkl'}),
]
def _generate_examples_raw(self, filepath):
with open(filepath, 'rb') as fp:
dataDic = pickle.load(fp)
for hid, data in dataDic.items():
proc_features = data['Proc']
chart_features = data['Chart']
meds_features = data['Med']
out_features = data['Out']
cond_features = data['Cond']['fids']
eth= data['ethnicity']
age = data['age']
gender = data['gender']
label = data['label']
insurance=data['insurance']
items = list(proc_features.keys())
values =[proc_features[i] for i in items ]
procs = {"id" : items,
"value": values}
items_outs = list(out_features.keys())
values_outs =[out_features[i] for i in items_outs ]
outs = {"id" : items_outs,
"value": values_outs}
#chart signal
if ('signal' in chart_features):
items_chart_sig = list(chart_features['signal'].keys())
values_chart_sig =[chart_features['signal'][i] for i in items_chart_sig ]
chart_sig = {"id" : items_chart_sig,
"value": values_chart_sig}
else:
chart_sig = {"id" : [],
"value": []}
#chart val
if ('val' in chart_features):
items_chart_val = list(chart_features['val'].keys())
values_chart_val =[chart_features['val'][i] for i in items_chart_val ]
chart_val = {"id" : items_chart_val,
"value": values_chart_val}
else:
chart_val = {"id" : [],
"value": []}
charts = {"signal" : chart_sig,
"val" : chart_val}
#meds signal
if ('signal' in meds_features):
items_meds_sig = list(meds_features['signal'].keys())
values_meds_sig =[meds_features['signal'][i] for i in items_meds_sig ]
meds_sig = {"id" : items_meds_sig,
"value": values_meds_sig}
else:
meds_sig = {"id" : [],
"value": []}
#meds rate
if ('rate' in meds_features):
items_meds_rate = list(meds_features['rate'].keys())
values_meds_rate =[meds_features['rate'][i] for i in items_meds_rate ]
meds_rate = {"id" : items_meds_rate,
"value": values_meds_rate}
else:
meds_rate = {"id" : [],
"value": []}
#meds amount
if ('amount' in meds_features):
items_meds_amount = list(meds_features['amount'].keys())
values_meds_amount =[meds_features['amount'][i] for i in items_meds_amount ]
meds_amount = {"id" : items_meds_amount,
"value": values_meds_amount}
else:
meds_amount = {"id" : [],
"value": []}
meds = {"signal" : meds_sig,
"rate" : meds_rate,
"amount" : meds_amount}
yield int(hid), {
"label" : label,
"gender" : gender,
"ethnicity" : eth,
"insurance" : insurance,
"age" : age,
"COND" : cond_features,
"PROC" : procs,
"CHART" : charts,
"OUT" : outs,
"MEDS" : meds
}
###########################################################ENCODED##################################################################
def _info_encoded(self):
X_train_encoded=generate_split(self.path+'/train_data.pkl',self.config.name,True,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
X_test_encoded=generate_split(self.path+'/test_data.pkl',self.config.name,True,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
X_val_encoded=generate_split(self.path+'/val_data.pkl',self.config.name,True,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
X_train_encoded.to_csv(self.path+"/X_train_encoded.csv", index=False)
X_test_encoded.to_csv(self.path+"/X_test_encoded.csv", index=False)
X_val_encoded.to_csv(self.path+"/X_val_encoded.csv", index=False)
columns = {col: self.map_dtype(X_train_encoded[col].dtype) for col in X_train_encoded.columns}
features = datasets.Features(columns)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def __split_generators_encoded(self):
data_dir = "./data/dict/"+self.config.name.replace(" ","_")
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir+'/X_train_encoded.csv'}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_dir+'/X_val_encoded.csv'}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir+'/X_test_encoded.csv'}),
]
def _generate_examples_encoded(self, filepath):
df = pd.read_csv(filepath, header=0)
for i, row in df.iterrows():
yield i, row.to_dict()
######################################################DEEP###############################################################
def _info_deep(self):
X_train_deep = generate_split_deep(self.path+'/train_data.pkl',self.config.name,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
X_test_deep = generate_split_deep(self.path+'/test_data.pkl',self.config.name,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
X_val_deep = generate_split_deep(self.path+'/val_data.pkl',self.config.name,self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out)
with open(self.path+"/X_train_deep.pkl", 'wb') as f:
pickle.dump(X_train_deep, f)
with open(self.path+"/X_test_deep.pkl", 'wb') as f:
pickle.dump(X_test_deep, f)
with open(self.path+"/X_val_deep.pkl", 'wb') as f:
pickle.dump(X_val_deep, f)
features = datasets.Features(
{
"label": datasets.ClassLabel(num_classes=2,names=["0", "1"]),
"DEMO": datasets.Sequence(datasets.Sequence(datasets.Value("int32"))),
"COND" : datasets.Sequence(datasets.Sequence(datasets.Value("float64"))) ,
"MEDS" : datasets.Sequence(datasets.Sequence(datasets.Value("float64"))) ,
"PROC" : datasets.Sequence(datasets.Sequence(datasets.Value("float64"))) ,
"CHART" : datasets.Sequence(datasets.Sequence(datasets.Value("float64"))) ,
"OUT" : datasets.Sequence(datasets.Sequence(datasets.Value("float64"))) ,
"LAB" : datasets.Sequence(datasets.Sequence(datasets.Value("float64"))) ,
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def __split_generators_deep(self):
data_dir = "./data/dict/"+self.config.name.replace(" ","_")
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir+'/X_train_deep.pkl'}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_dir+'/X_val_deep.pkl'}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir+'/X_test_deep.pkl'}),
]
def _generate_examples_deep(self, filepath):
with open(filepath, 'rb') as fp:
dico = pickle.load(fp)
for key, data in dico.items():
proc_features = data['proc']
chart_features = data['chart']
meds_features = data['meds']
out_features = data['out']
cond_features = data['stat']
demo= data['demo']
label = data['label']
lab=data['lab']
yield int(key), {
'label': label,
'DEMO': demo,
'COND': cond_features,
'MEDS': meds_features,
'PROC': proc_features,
'CHART': chart_features,
'OUT': out_features,
'LAB': lab,
}
#############################################################################################################################
def _info(self):
self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out,self.path = self.create_cohort()
if self.encoding == 'onehot' :
return self._info_encoded()
elif self.encoding == 'deep' :
return self._info_deep()
else:
return self._info_raw()
def _split_generators(self, dl_manager):
if self.encoding == 'onehot' :
return self.__split_generators_encoded()
elif self.encoding == 'deep' :
return self.__split_generators_deep()
else:
return self.__split_generators_raw()
def _generate_examples(self, filepath):
if self.encoding == 'onehot' :
yield from self._generate_examples_encoded(filepath)
elif self.encoding == 'deep' :
yield from self._generate_examples_deep(filepath)
else :
yield from self._generate_examples_raw(filepath)
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