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columns.py
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columns.py
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import numpy as np
def get_all_columns():
"""
Returns the names of numerical and categorical columns.
Returns:
numerical_columns: names of numerical columns
categorical_columns: names of categorical columns
"""
categorical_columns = [
"_STATE",
"FMONTH",
"IMONTH",
"DISPCODE",
"CTELENUM",
"PVTRESD1",
"COLGHOUS",
"STATERES",
"CELLFON3",
"LADULT",
"CTELNUM1",
"CELLFON2",
"CADULT",
"PVTRESD2",
"CCLGHOUS",
"CSTATE",
"LANDLINE",
"GENHLTH",
"HLTHPLN1",
"PERSDOC2",
"MEDCOST",
"CHECKUP1",
"BPHIGH4",
"BPMEDS",
"BLOODCHO",
"CHOLCHK",
"TOLDHI2",
"CVDSTRK3",
"ASTHMA3",
"ASTHNOW",
"CHCSCNCR",
"CHCOCNCR",
"CHCCOPD1",
"HAVARTH3",
"ADDEPEV2",
"CHCKIDNY",
"DIABETE3",
"SEX",
"MARITAL",
"EDUCA",
"RENTHOM1",
"NUMHHOL2",
"NUMPHON2",
"CPDEMO1",
"VETERAN3",
"EMPLOY1",
"INCOME2",
"INTERNET",
"PREGNANT",
"QLACTLM2",
"USEEQUIP",
"BLIND",
"DECIDE",
"DIFFWALK",
"DIFFDRES",
"DIFFALON",
"SMOKE100",
"SMOKDAY2",
"STOPSMK2",
"LASTSMK2",
"USENOW3",
"EXERANY2",
"EXRACT11",
"EXRACT21",
"LMTJOIN3",
"ARTHDIS2",
"ARTHSOCL",
"SEATBELT",
"FLUSHOT6",
"IMFVPLAC",
"PNEUVAC3",
"HIVTST6",
"WHRTST10",
"PDIABTST",
"PREDIAB1",
"INSULIN",
"EYEEXAM",
"DIABEYE",
"DIABEDU",
"CAREGIV1",
"CRGVREL1",
"CRGVLNG1",
"CRGVHRS1",
"CRGVPRB1",
"CRGVPERS",
"CRGVHOUS",
"CRGVMST2",
"CRGVEXPT",
"VIDFCLT2",
"VIREDIF3",
"VIPRFVS2",
"VINOCRE2",
"VIEYEXM2",
"VIINSUR2",
"VICTRCT4",
"VIGLUMA2",
"VIMACDG2",
"CIMEMLOS",
"CDHOUSE",
"CDASSIST",
"CDHELP",
"CDSOCIAL",
"CDDISCUS",
"WTCHSALT",
"DRADVISE",
"ASATTACK",
"ASYMPTOM",
"ASNOSLEP",
"ASTHMED3",
"ASINHALR",
"HAREHAB1",
"STREHAB1",
"CVDASPRN",
"ASPUNSAF",
"RLIVPAIN",
"RDUCHART",
"RDUCSTRK",
"ARTTODAY",
"ARTHWGT",
"ARTHEXER",
"ARTHEDU",
"TETANUS",
"HPVADVC2",
"SHINGLE2",
"HADMAM",
"HOWLONG",
"HADPAP2",
"LASTPAP2",
"HPVTEST",
"HPLSTTST",
"HADHYST2",
"PROFEXAM",
"LENGEXAM",
"BLDSTOOL",
"LSTBLDS3",
"HADSIGM3",
"HADSGCO1",
"LASTSIG3",
"PCPSAAD2",
"PCPSADI1",
"PCPSARE1",
"PSATEST1",
"PSATIME",
"PCPSARS1",
"PCPSADE1",
"SCNTMNY1",
"SCNTMEL1",
"SCNTPAID",
"SCNTLPAD",
"SXORIENT",
"TRNSGNDR",
"RCSGENDR",
"RCSRLTN2",
"CASTHDX2",
"CASTHNO2",
"EMTSUPRT",
"LSATISFY",
"MISTMNT",
"ADANXEV",
"QSTVER",
"MSCODE",
"_CHISPNC",
"_CRACE1",
"_CPRACE",
"_DUALUSE",
"_RFHLTH",
"_HCVU651",
"_RFHYPE5",
"_CHOLCHK",
"_RFCHOL",
"_LTASTH1",
"_CASTHM1",
"_ASTHMS1",
"_DRDXAR1",
"_PRACE1",
"_MRACE1",
"_HISPANC",
"_RACE",
"_RACEG21",
"_RACEGR3",
"_RACE_G1",
"_AGEG5YR",
"_AGE65YR",
"_AGE_G",
"_BMI5CAT",
"_RFBMI5",
"_CHLDCNT",
"_EDUCAG",
"_INCOMG",
"_SMOKER3",
"_RFSMOK3",
"DRNKANY5",
"_RFBING5",
"_RFDRHV5",
"_FRTRESP",
"_VEGRESP",
"_FRTLT1",
"_VEGLT1",
"_FRT16",
"_VEG23",
"_FRUITEX",
"_VEGETEX",
"_TOTINDA",
"ACTIN11_",
"ACTIN21_",
"PAMISS1_",
"_PACAT1",
"_PAINDX1",
"_PA150R2",
"_PA300R2",
"_PA30021",
"_PASTRNG",
"_PAREC1",
"_PASTAE1",
"_LMTACT1",
"_LMTWRK1",
"_LMTSCL1",
"_RFSEAT2",
"_RFSEAT3",
"_FLSHOT6",
"_PNEUMO2",
"_AIDTST3",
]
numerical_columns = [
"SEQNO",
"FLSHTMY2",
"_STSTR",
"_STRWT",
"_RAWRAKE",
"_WT2RAKE",
"_DUALCOR",
"_PSU",
"_CLLCPWT",
"_LLCPWT",
"IDATE",
"IDAY",
"IYEAR",
"NUMADULT",
"NUMMEN",
"NUMWOMEN",
"HHADULT",
"PHYSHLTH",
"MENTHLTH",
"POORHLTH",
"DIABAGE2",
"CHILDREN",
"WEIGHT2",
"HEIGHT3",
"ALCDAY5",
"AVEDRNK2",
"DRNK3GE5",
"MAXDRNKS",
"FRUITJU1",
"FRUIT1",
"FVBEANS",
"FVGREEN",
"FVORANG",
"VEGETAB1",
"EXEROFT1",
"EXERHMM1",
"EXEROFT2",
"EXERHMM2",
"STRENGTH",
"JOINPAIN",
"HIVTSTD3",
"BLDSUGAR",
"FEETCHK2",
"DOCTDIAB",
"CHKHEMO3",
"FEETCHK",
"LONGWTCH",
"ASTHMAGE",
"ASERVIST",
"ASDRVIST",
"ASRCHKUP",
"ASACTLIM",
"HPVADSHT",
"PCDMDECN",
"SCNTWRK1",
"SCNTLWK1",
"ADPLEASR",
"ADDOWN",
"ADSLEEP",
"ADENERGY",
"ADEAT1",
"ADFAIL",
"ADTHINK",
"ADMOVE",
"QSTLANG",
"_AGE80",
"HTIN4",
"HTM4",
"WTKG3",
"_BMI5",
"DROCDY3_",
"_DRNKWEK",
"FTJUDA1_",
"FRUTDA1_",
"BEANDAY_",
"GRENDAY_",
"ORNGDAY_",
"VEGEDA1_",
"_MISFRTN",
"_MISVEGN",
"_FRUTSUM",
"_VEGESUM",
"METVL11_",
"METVL21_",
"MAXVO2_",
"FC60_",
"PADUR1_",
"PADUR2_",
"PAFREQ1_",
"PAFREQ2_",
"_MINAC11",
"_MINAC21",
"STRFREQ_",
"PAMIN11_",
"PAMIN21_",
"PA1MIN_",
"PAVIG11_",
"PAVIG21_",
"PA1VIGM_",
]
return numerical_columns, categorical_columns
def get_all_binary_categorical_columns():
"""
Returns the list of binary categorical columns.
Returns:
list: binary categorical column names
"""
binary_categorical_columns = [
"DISPCODE",
"CTELENUM",
"COLGHOUS",
"CELLFON3",
"LADULT",
"CTELNUM1",
"CELLFON2",
"CADULT",
"PVTRESD2",
"CCLGHOUS",
"CSTATE",
"LANDLINE",
"PHYSHLTH",
"MENTHLTH",
"POORHLTH",
"HLTHPLN1",
"MEDCOST",
"BPMEDS",
"BLOODCHO",
"TOLDHI2",
"CVDSTRK3",
"ASTHMA3",
"ASTHNOW",
"CHCSCNCR",
"CHCOCNCR",
"CHCCOPD1",
"HAVARTH3",
"ADDEPEV2",
"CHCKIDNY",
"DIABAGE2",
"SEX",
"NUMHHOL2",
"CPDEMO1",
"VETERAN3",
"CHILDREN",
"INTERNET",
"PREGNANT",
"QLACTLM2",
"USEEQUIP",
"BLIND",
"DECIDE",
"DIFFWALK",
"DIFFDRES",
"DIFFALON",
"SMOKE100",
"STOPSMK2",
"AVEDRNK2",
"DRNK3GE5",
"MAXDRNKS",
"EXERANY2",
"LMTJOIN3",
"ARTHDIS2",
"FLUSHOT6",
"PNEUVAC3",
"HIVTST6",
"PDIABTST",
"INSULIN",
"DOCTDIAB",
"FEETCHK",
"DIABEYE",
"DIABEDU",
"CRGVPERS",
"CRGVHOUS",
"CRGVEXPT",
"CIMEMLOS",
"CDDISCUS",
"WTCHSALT",
"DRADVISE",
"ASATTACK",
"HAREHAB1",
"STREHAB1",
"CVDASPRN",
"RLIVPAIN",
"RDUCHART",
"RDUCSTRK",
"ARTHWGT",
"ARTHEXER",
"ARTHEDU",
"HPVADSHT",
"SHINGLE2",
"HADMAM",
"HADPAP2",
"HPVTEST",
"HADHYST2",
"PROFEXAM",
"BLDSTOOL",
"HADSIGM3",
"HADSGCO1",
"PCPSAAD2",
"PCPSADI1",
"PCPSARE1",
"PSATEST1",
"RCSGENDR",
"CASTHDX2",
"CASTHNO2",
"ADPLEASR",
"ADDOWN",
"ADSLEEP",
"ADENERGY",
"ADEAT1",
"ADFAIL",
"ADTHINK",
"ADMOVE",
"MISTMNT",
"ADANXEV",
"_CHISPNC",
"_RFHLTH",
"_HCVU651",
"_RFHYPE5",
"_RFCHOL",
"_LTASTH1",
"_CASTHM1",
"_DRDXAR1",
"_HISPANC",
"_RACEG21",
"HTIN4",
"HTM4",
"WTKG3",
"_RFBMI5",
"_RFSMOK3",
"DRNKANY5",
"_RFBING5",
"_RFDRHV5",
"_MISFRTN",
"_MISVEGN",
"_FRTRESP",
"_VEGRESP",
"_FRTLT1",
"_VEGLT1",
"_FRT16",
"_VEG23",
"_TOTINDA",
"METVL11_",
"METVL21_",
"MAXVO2_",
"FC60_",
"PAFREQ1_",
"PAFREQ2_",
"_MINAC11",
"_MINAC21",
"STRFREQ_",
"PAMISS1_",
"_PAINDX1",
"_PA30021",
"_PASTRNG",
"_PASTAE1",
"_RFSEAT2",
"_RFSEAT3",
"_FLSHOT6",
"_PNEUMO2",
"_AIDTST3",
]
return binary_categorical_columns
def get_selected_columns():
"""
Returns the names of numerical and categorical columns.
Returns:
numerical_columns: names of numerical columns
categorical_columns: names of categorical columns
"""
categorical_columns = [
"HLTHPLN1",
"MEDCOST",
"EDUCA",
"INCOME2",
"CHOLCHK",
"PNEUVAC3",
"EMPLOY1",
"DIFFWALK",
"DIFFDRES",
"DIFFALON",
"USEEQUIP",
"_AGEG5YR",
"GENHLTH",
"_TOTINDA",
"_RACE",
"CVDSTRK3",
"ASTHMA3",
"CHCSCNCR",
"CHCOCNCR",
"CHCCOPD1",
"HAVARTH3",
"ADDEPEV2",
"CHCKIDNY",
"_SMOKER3",
"_RFSMOK3",
"DRNKANY5",
"_RFBING5",
"_RFDRHV5",
"_RFHYPE5",
"_RFCHOL",
"DIABETE3",
]
numerical_columns = [
"_BMI5",
"CHILDREN",
"FTJUDA1_",
"FRUTDA1_",
"BEANDAY_",
"GRENDAY_",
"ORNGDAY_",
"VEGEDA1_",
"PHYSHLTH",
"MENTHLTH",
"HTM4",
"WTKG3",
"_DRNKWEK",
]
return numerical_columns, categorical_columns
def get_selected_binary_categorical_columns():
"""
Returns the list of binary categorical columns.
Returns:
list: binary categorical column names
"""
binary_categorical_columns = [
"HLTHPLN1",
"MEDCOST",
"PNEUVAC3",
"DIFFWALK",
"DIFFDRES",
"DIFFALON",
"USEEQUIP",
"_TOTINDA",
"_RACE",
"CVDSTRK3",
"ASTHMA3",
"CHCSCNCR",
"CHCOCNCR",
"CHCCOPD1",
"HAVARTH3",
"ADDEPEV2",
"CHCKIDNY",
"_RFSMOK3",
"DRNKANY5",
"_RFBING5",
"_RFDRHV5",
"_RFHYPE5",
"_RFCHOL",
"DIABETE3",
]
return binary_categorical_columns
def get_random_column_subset(percentage=100):
"""
Returns a random subset of all columns (numerical, categorical, binary) based on the specified percentage.
Args:
percentage (int): Percentage of columns to return.
Returns:
tuple: (selected_numerical_columns, selected_categorical_columns, selected_binary_columns)
"""
numerical_columns, categorical_columns = get_all_columns()
binary_categorical_columns = get_all_binary_categorical_columns()
all_columns = list(set(numerical_columns + categorical_columns))
num_columns = int(len(all_columns) * (percentage / 100))
selected_columns = np.random.choice(
all_columns, num_columns, replace=False
)
selected_numerical_columns = [
col for col in selected_columns if col in numerical_columns
]
selected_categorical_columns = [
col for col in selected_columns if col in categorical_columns
]
selected_binary_columns = [
col for col in selected_columns if col in binary_categorical_columns
]
return (
selected_numerical_columns,
selected_categorical_columns,
selected_binary_columns,
)