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import ipywidgets as widgets
import sys
from pathlib import Path
import os
import importlib
import shutil
import time
import yaml
def task_cohort(task,mimic_path,path_benchmark, config_path):
root_dir = path_benchmark
config_path=path_benchmark+'/config/'+config_path
with open(config_path) as f:
config = yaml.safe_load(f)
version_path = mimic_path
version = version_path.split('/')[-1][0]
start = time.time()
#----------------------------------------------config----------------------------------------------------
disease_label = config['disease_label']
tim = config['time']
label = config['label']
timeW = config['timeW']
include=int(timeW.split()[1])
bucket = config['bucket']
radimp = config['radimp']
predW = config['predW']
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']
# -------------------------------------------------------------------------------------------------------------
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, version_path,disease_label)
elif version == '1':
cohort_output = day_intervals_cohort.extract_data(icu_no_icu,label,tim,icd_code, version_path,disease_label)
end = time.time()
print("Time elapsed : ", round((end - start)/60,2),"mins")
#----------------------------------------------FEATURES-------------------------------------------------------
if data_icu :
feature_selection_icu.feature_icu(cohort_output, version_path,diag_flag,out_flag,chart_flag,proc_flag,med_flag)
end = time.time()
print("Time elapsed : ", round((end - start)/60,2),"mins")
#----------------------------------------------GROUPING-------------------------------------------------------
group_diag=False
group_med=False
group_proc=False
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)
end = time.time()
print("Time elapsed : ", round((end - start)/60,2),"mins")
#----------------------------------------------SUMMARY-------------------------------------------------------
if data_icu:
feature_selection_icu.generate_summary_icu(cohort_output,diag_flag,proc_flag,med_flag,out_flag,chart_flag)
end = time.time()
print("Time elapsed : ", round((end - start)/60,2),"mins")
#----------------------------------------------FEATURE SELECTION---------------------------------------------
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']
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)
end = time.time()
print("Time elapsed : ", round((end - start)/60,2),"mins")
#---------------------------------------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)
end = time.time()
print("Time elapsed : ", round((end - start)/60,2),"mins")
# ---------------------------------------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)
print("[============TASK COHORT SUCCESSFULLY CREATED============]")
if __name__ == '__main__':
task = sys.argv[1]
mimic_path = sys.argv[2]
path_benchmark = sys.argv[3]
config = sys.argv[4]
sys.path.append(path_benchmark+'/preprocessing/day_intervals_preproc')
sys.path.append(path_benchmark+'/utils')
sys.path.append(path_benchmark+'/preprocessing/hosp_module_preproc')
sys.path.append(path_benchmark+'/model')
import day_intervals_cohort_v22
import day_intervals_cohort
import feature_selection_icu
import data_generation_icu_modify
task_cohort(task, mimic_path, path_benchmark, config) |