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bootstrap.py
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#!/usr/bin/env python
import os
import urllib
import logging
import pandas as pd
from sklearn.cross_validation import train_test_split
logging.basicConfig(level=logging.INFO)
def download_data(data_dir):
file_list = [
('1999-2000', 'DEMO'), ('2001-2002', 'DEMO_B'), ('2003-2004', 'DEMO_C'),
('1999-2000', 'DIQ'), ('2001-2002', 'DIQ_B'), ('2003-2004', 'DIQ_C'),
('1999-2000', 'LAB10AM'), ('2001-2002', 'L10AM_B'), ('2003-2004', 'L10AM_C'),
('1999-2000', 'ALQ'), ('2001-2002', 'ALQ_B'), ('2003-2004', 'ALQ_C'),
('1999-2000', 'SMQ'), ('2001-2002', 'SMQ_B'), ('2003-2004', 'SMQ_C'),
('1999-2000', 'BMX'), ('2001-2002', 'BMX_B') , ('2003-2004', 'BMX_C'),
('1999-2000', 'BPQ'), ('2001-2002', 'BPQ_B'), ('2003-2004', 'BPQ_C'),
('1999-2000', 'MCQ'), ('2001-2002', 'MCQ_B'), ('2003-2004', 'MCQ_C'),
('1999-2000', 'PAQ'), ('2001-2002', 'PAQ_B'), ('2003-2004', 'PAQ_C'),
('1999-2000', 'LAB13'), ('2001-2002', 'L13_B'), ('2003-2004', 'L13_C'),
]
if not os.path.exists(data_dir):
os.makedirs(data_dir)
for (year, data_file) in file_list:
sub_dir = os.path.join(data_dir, year)
if not os.path.exists(sub_dir):
os.makedirs(sub_dir)
url = 'http://wwwn.cdc.gov/Nchs/Nhanes/{0}/{1}.XPT'.format(year, data_file)
file_name = os.path.join(sub_dir, data_file + '.XPT')
if not os.path.exists(file_name):
logging.info('Downloading: {}'.format(url))
urllib.request.urlretrieve(url, file_name)
else:
logging.info('File exists: {}'.format(file_name))
def merge_data(data_dir):
'''Import data'''
logging.info('Loading XPT data...')
DEMO = pd.read_sas(os.path.join(data_dir, '1999-2000', 'DEMO.XPT'))
DEMO_B = pd.read_sas(os.path.join(data_dir, '2001-2002', 'DEMO_B.XPT'))
DEMO_C = pd.read_sas(os.path.join(data_dir, '2003-2004', 'DEMO_C.XPT'))
DEMO_cols = ['SEQN', 'RIDAGEYR', 'RIAGENDR', 'RIDRETH1', 'DMDEDUC2', 'INDHHINC']
DIQ = pd.read_sas(os.path.join(data_dir, '1999-2000', 'DIQ.XPT'))
DIQ_B = pd.read_sas(os.path.join(data_dir, '2001-2002', 'DIQ_B.XPT'))
DIQ_C = pd.read_sas(os.path.join(data_dir, '2003-2004', 'DIQ_C.XPT'))
DIQ_cols = ['SEQN', 'DIQ010']
LAB10AM = pd.read_sas(os.path.join(data_dir, '1999-2000', 'LAB10AM.XPT'))
LAB10AM_B = pd.read_sas(os.path.join(data_dir, '2001-2002', 'L10AM_B.XPT'))
LAB10AM_C = pd.read_sas(os.path.join(data_dir, '2003-2004', 'L10AM_C.XPT'))
LAB10AM_cols = ['SEQN', 'LBXGLU']
ALQ = pd.read_sas(os.path.join(data_dir, '1999-2000', 'ALQ.XPT'))
ALQ_B = pd.read_sas(os.path.join(data_dir, '2001-2002', 'ALQ_B.XPT'))
ALQ_C = pd.read_sas(os.path.join(data_dir, '2003-2004', 'ALQ_C.XPT'))
ALQ_cols = ['SEQN', 'ALQ120Q']
SMQ = pd.read_sas(os.path.join(data_dir, '1999-2000', 'SMQ.XPT'))
SMQ_B = pd.read_sas(os.path.join(data_dir, '2001-2002', 'SMQ_B.XPT'))
SMQ_C = pd.read_sas(os.path.join(data_dir, '2003-2004', 'SMQ_C.XPT'))
SMQ_cols = ['SEQN', 'SMD030']
BMX = pd.read_sas(os.path.join(data_dir, '1999-2000', 'BMX.XPT'))
BMX_B = pd.read_sas(os.path.join(data_dir, '2001-2002', 'BMX_B.XPT'))
BMX_C = pd.read_sas(os.path.join(data_dir, '2003-2004', 'BMX_C.XPT'))
BMX_cols = ['SEQN', 'BMXHT', 'BMXWAIST', 'BMXBMI', 'BMXWT', 'BMXLEG']
BPQ = pd.read_sas(os.path.join(data_dir, '1999-2000', 'BPQ.XPT'))
BPQ_B = pd.read_sas(os.path.join(data_dir, '2001-2002', 'BPQ_B.XPT'))
BPQ_C = pd.read_sas(os.path.join(data_dir, '2003-2004', 'BPQ_C.XPT'))
BPQ_cols = ['SEQN', 'BPQ020']
MCQ = pd.read_sas(os.path.join(data_dir, '1999-2000', 'MCQ.XPT'))
MCQ_B = pd.read_sas(os.path.join(data_dir, '2001-2002', 'MCQ_B.XPT'))
MCQ_C = pd.read_sas(os.path.join(data_dir, '2003-2004', 'MCQ_C.XPT'))
MCQ_cols = ['SEQN', 'MCQ250A']
PAQ = pd.read_sas(os.path.join(data_dir, '1999-2000', 'PAQ.XPT'))
PAQ_B = pd.read_sas(os.path.join(data_dir, '2001-2002', 'PAQ_B.XPT'))
PAQ_C = pd.read_sas(os.path.join(data_dir, '2003-2004', 'PAQ_C.XPT'))
PAQ_cols = ['SEQN', 'PAQ180']
LAB13 = pd.read_sas(os.path.join(data_dir, '1999-2000', 'LAB13.XPT'))
LAB13_B = pd.read_sas(os.path.join(data_dir, '2001-2002', 'L13_B.XPT'))
LAB13_C = pd.read_sas(os.path.join(data_dir, '2003-2004', 'L13_C.XPT'))
LAB13_cols = ['SEQN', 'LBXTC']
'''Merge Datasets
Age >= 20 and non-pregnant population
'''
logging.info('Merging data...')
age = 20
df_00 = DEMO.loc[((DEMO.RIAGENDR == 2) & (DEMO.RIDAGEYR >= age) & (DEMO.RIDEXPRG == 2)) |
((DEMO.RIAGENDR == 1) & (DEMO.RIDAGEYR >= age)),
DEMO_cols] \
.merge(ALQ[ALQ_cols], on='SEQN') \
.merge(BMX[BMX_cols], on='SEQN') \
.merge(BPQ[BPQ_cols], on='SEQN') \
.merge(PAQ[PAQ_cols], on='SEQN') \
.merge(MCQ[MCQ_cols], on='SEQN') \
.merge(SMQ[SMQ_cols], on='SEQN') \
.merge(LAB13[LAB13_cols], on='SEQN')
df_02 = DEMO_B.loc[((DEMO_B.RIAGENDR == 2) & (DEMO_B.RIDAGEYR >= age) & (DEMO_B.RIDEXPRG == 2)) |
((DEMO_B.RIAGENDR == 1) & (DEMO_B.RIDAGEYR >= age)),
DEMO_cols] \
.merge(ALQ_B[ALQ_cols], on='SEQN') \
.merge(BMX_B[BMX_cols], on='SEQN') \
.merge(BPQ_B[BPQ_cols], on='SEQN') \
.merge(PAQ_B[PAQ_cols], on='SEQN') \
.merge(MCQ_B[MCQ_cols], on='SEQN') \
.merge(SMQ_B[SMQ_cols], on='SEQN') \
.merge(LAB13_B[LAB13_cols], on='SEQN')
df_04 = DEMO_C.loc[((DEMO_C.RIAGENDR == 2) & (DEMO_C.RIDAGEYR >= age) & (DEMO_C.RIDEXPRG == 2)) |
((DEMO_C.RIAGENDR == 1) & (DEMO_C.RIDAGEYR >= age)),
DEMO_cols] \
.merge(ALQ_C[ALQ_cols], on='SEQN') \
.merge(BMX_C[BMX_cols], on='SEQN') \
.merge(BPQ_C[BPQ_cols], on='SEQN') \
.merge(PAQ_C[PAQ_cols], on='SEQN') \
.merge(MCQ_C[MCQ_cols], on='SEQN') \
.merge(SMQ_C[SMQ_cols], on='SEQN') \
.merge(LAB13_C[LAB13_cols], on='SEQN')
df_pop = pd.concat([df_00, df_02, df_04])
'''Diagnosed Diabetes (total should be 1,266)
The next questions are about specific medical conditions.
{Other than during pregnancy, {have you/has SP}/{Have you/Has SP}}
ever been told by a doctor or health professional that
{you have/{he/she/SP} has} diabetes or sugar diabetes?
'''
df_00_diag = df_00.merge(DIQ.loc[DIQ.DIQ010 == 1, DIQ_cols], on="SEQN")
df_02_diag = df_02.merge(DIQ_B.loc[DIQ_B.DIQ010 == 1, DIQ_cols], on="SEQN")
df_04_diag = df_04.merge(DIQ_C.loc[DIQ_C.DIQ010 == 1, DIQ_cols], on="SEQN")
diag_total = pd.concat([df_00_diag, df_02_diag, df_04_diag])
diag_total.loc[:,'status'] = 1
logging.info('Diagnosed subject count: {}'.format(diag_total.shape[0]))
'''Undiagnosed Diabetes (total should be 195)'''
df_00_undiag = df_00.merge(DIQ.loc[DIQ.DIQ010 == 2, DIQ_cols] \
.merge(LAB10AM.loc[LAB10AM.LBXGLU >= 126, LAB10AM_cols], on='SEQN'),
on='SEQN')
df_02_undiag = df_02.merge(DIQ_B.loc[DIQ_B.DIQ010 == 2, DIQ_cols] \
.merge(LAB10AM_B.loc[LAB10AM_B.LBXGLU >= 126, LAB10AM_cols], on='SEQN'),
on='SEQN')
df_04_undiag = df_04.merge(DIQ_C.loc[DIQ_C.DIQ010 == 2, DIQ_cols] \
.merge(LAB10AM_C.loc[LAB10AM_C.LBXGLU >= 126, LAB10AM_cols], on='SEQN'),
on='SEQN')
undiag_total = pd.concat([df_00_undiag, df_02_undiag, df_04_undiag])
undiag_total.loc[:,'status'] = 1
logging.info('Undiagnosed subject count: {}'.format(undiag_total.shape[0]))
'''Pre-diabetes (total should be 1,576)'''
df_00_prediab = df_00.merge(LAB10AM.loc[(LAB10AM.LBXGLU >= 100) & \
(LAB10AM.LBXGLU <= 125),
LAB10AM_cols], on='SEQN')
df_02_prediab = df_02.merge(LAB10AM_B.loc[(LAB10AM_B.LBXGLU >= 100) & \
(LAB10AM_B.LBXGLU <= 125),
LAB10AM_cols], on='SEQN')
df_04_prediab = df_04.merge(LAB10AM_C.loc[(LAB10AM_C.LBXGLU >= 100) & \
(LAB10AM_C.LBXGLU <= 125),
LAB10AM_cols], on='SEQN')
prediab_total = pd.concat([df_00_prediab, df_02_prediab, df_04_prediab])
prediab_total.loc[:,'status'] = 0
logging.info('Pre-diabetic subject count: {}'.format(prediab_total.shape[0]))
'''No Diabetes (total should be 3,277)'''
df_00_nodiab = df_00.merge(LAB10AM.loc[LAB10AM.LBXGLU < 100, LAB10AM_cols], on='SEQN')
df_02_nodiab = df_02.merge(LAB10AM_B.loc[LAB10AM_B.LBXGLU < 100, LAB10AM_cols], on='SEQN')
df_04_nodiab = df_04.merge(LAB10AM_C.loc[LAB10AM_C.LBXGLU < 100, LAB10AM_cols], on='SEQN')
nodiab_total = pd.concat([df_00_nodiab, df_02_nodiab, df_04_nodiab])
nodiab_total.loc[:,'status'] = 0
logging.info('Diabetes-free subject count: {}'.format(nodiab_total.shape[0]))
'''Join and split data'''
df = pd.concat([diag_total, undiag_total, prediab_total, nodiab_total], ignore_index=True)
df = df.drop(['SEQN', 'LBXGLU','DIQ010'], axis=1)
df_train, df_test = train_test_split(df, test_size=0.2, random_state=289)
'''Save data'''
fname_train = os.path.join(data_dir, 'diabetes_data_train.csv')
fname_test = os.path.join(data_dir, 'diabetes_data_test.csv')
df_train.to_csv(fname_train, index=False, float_format='%.1f')
logging.info('Training set saved: {}'.format(fname_train))
df_test.to_csv(fname_test, index=False, float_format='%.1f')
logging.info('Test set saved: {}'.format(fname_test))
if __name__ == '__main__':
data_dir = 'data'
download_data(data_dir)
merge_data(data_dir)