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added ACS target columns
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@AndreFCruz
AndreFCruz committed Jun 18, 2024
1 parent 626da52 commit b0bca6d
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Showing 2 changed files with 92 additions and 15 deletions.
4 changes: 2 additions & 2 deletions folktexts/_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -91,7 +91,7 @@ def standardize_path(path: str | Path) -> str:
return Path(path).expanduser().resolve().as_posix()


def get_thresholded_column_name(column_name: str, threshold: float | int) -> str:
def get_thresholded_column_name(column_name: str, threshold: float | int, *, op: str = ">") -> str:
"""Standardizes naming of thresholded columns."""
threshold_str = f"{threshold:.2f}".replace(".", "_") if isinstance(threshold, float) else str(threshold)
return f"{column_name}_binary_{threshold_str}"
return f"{column_name}_{op}_{threshold_str}"
103 changes: 90 additions & 13 deletions folktexts/acs/acs_columns.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,13 +14,14 @@
ACS_ST_FILE = Path(__file__).parent / "data" / "ST-codes-acs.txt"


# Describing ACS columns and corresponding questions
# AGEP: Age
acs_age = ColumnToText(
"AGEP",
short_description="age",
value_map=lambda x: f"{int(x)} years old",
)

# COW: Class of Worker
acs_class_of_worker = ColumnToText(
"COW",
short_description="class of worker",
Expand All @@ -37,6 +38,7 @@
},
)

# SCHL: Educational Attainment
acs_schooling = ColumnToText(
"SCHL",
short_description="highest educational attainment",
Expand Down Expand Up @@ -68,6 +70,7 @@
},
)

# MAR: Marital Status
acs_marital_status = ColumnToText(
"MAR",
short_description="marital status",
Expand All @@ -80,6 +83,7 @@
},
)

# OCCP: Occupation
acs_occupation = ColumnToText(
"OCCP",
short_description="occupation",
Expand All @@ -90,17 +94,19 @@
),
)

# POBP: Place of Birth
acs_place_of_birth = ColumnToText(
"POBP",
short_description="place of birth",
value_map=partial(parse_pums_code, file=ACS_POBP_FILE),
)

# RELP: Relationship to Reference Person
acs_relationship = ColumnToText(
"RELP",
short_description="relationship to the reference person in the household",
short_description="relationship to the reference person in the survey",
value_map={
0: "The 'reference person' itself",
0: "The reference person itself",
1: "Husband/wife",
2: "Biological son or daughter",
3: "Adopted son or daughter",
Expand All @@ -121,13 +127,15 @@
},
)

# WKHP: Usual Hours Worked per Week
acs_work_hours = ColumnToText(
"WKHP",
short_description="usual number of hours worked per week",
missing_value_fill="N/A (less than 16 years old, or did not work during the past 12 months)",
value_map=lambda x: f"{int(x)} hours",
)

# SEX: Sex
acs_sex = ColumnToText(
"SEX",
short_description="sex",
Expand All @@ -137,6 +145,7 @@
},
)

# RAC1P: Race
acs_race = ColumnToText(
"RAC1P",
short_description="race",
Expand All @@ -156,14 +165,15 @@
},
)

# PINCP: Yearly Income
acs_income = ColumnToText(
"PINCP",
short_description="yearly income",
missing_value_fill="N/A (less than 15 years old)",
value_map=lambda x: f"${int(x):,}",
)

acs_income_binary_qa = MultipleChoiceQA(
acs_income_qa = MultipleChoiceQA(
column=get_thresholded_column_name("PINCP", 50_000),
text="What is this person's estimated yearly income?",
choices=[
Expand All @@ -182,13 +192,14 @@
num_forward_passes=2,
)

acs_income = ColumnToText(
acs_income_target_col = ColumnToText(
name=get_thresholded_column_name("PINCP", 50_000),
short_description="yearly income",
missing_value_fill="N/A (less than 15 years old)",
question=acs_income_binary_qa,
question=acs_income_qa,
)

"""
acs_income_brackets = ColumnToText(
"PINCP_brackets",
short_description="yearly income",
Expand All @@ -204,27 +215,30 @@
],
),
)
"""

# Note: yes/no values are flipped when using PUBCOV as the label column
acs_pubcov_binary_qa = MultipleChoiceQA(
# PUBCOV: Public Health Coverage
# NOTE: in folktables the negative choice has value `0` instead of `2`
acs_pubcov_qa = MultipleChoiceQA(
column="PUBCOV",
text="Does this person have public health insurance coverage?",
choices=[
Choice("No, individual is not covered by public health insurance", 0),
Choice("Yes, individual is covered by public health insurance", 1),
Choice("Yes, person is covered by public health insurance", 1),
Choice("No, person is not covered by public health insurance", 2),
],
)

acs_pubcov = ColumnToText(
acs_pubcov_target_col = ColumnToText(
"PUBCOV",
short_description="public health coverage status",
value_map={
1: "Covered by public health insurance",
2: "Not covered by public health insurance",
},
question=acs_pubcov_binary_qa,
question=acs_pubcov_qa,
)

# DIS: Disability Status
acs_disability = ColumnToText(
"DIS",
short_description="disability status",
Expand All @@ -234,6 +248,7 @@
},
)

# ESP: Employment Status of Parents
acs_emp_parents = ColumnToText(
"ESP",
short_description="employment status of parents",
Expand All @@ -250,6 +265,7 @@
missing_value_fill="N/A (not own child of householder, and not child in subfamily)",
)

# CIT: Citizenship Status
acs_citizenship = ColumnToText(
"CIT",
short_description="citizenship status",
Expand All @@ -262,6 +278,7 @@
},
)

# MIG: Mobility Status
acs_mobility = ColumnToText(
"MIG",
short_description="mobility status over the last year",
Expand All @@ -272,6 +289,23 @@
},
)

acs_mobility_qa = MultipleChoiceQA(
column=get_thresholded_column_name("MIG", 1, op="=="),
text="Has this person moved in the last year?",
choices=[
Choice("No, person has lived in the same house for the last year", 1),
Choice("Yes, person has moved in the last year", 0),
],
)

acs_mobility_target_col = ColumnToText(
name=get_thresholded_column_name("MIG", 1, op="=="),
short_description="mobility status over the last year",
question=acs_mobility_qa,
use_value_map_only=True,
)

# MIL: Military Service Status
acs_military = ColumnToText(
"MIL",
short_description="military service status",
Expand All @@ -284,6 +318,7 @@
missing_value_fill="N/A (less than 17 years old)",
)

# ANC: Ancestry
acs_ancestry = ColumnToText(
"ANC",
short_description="ancestry",
Expand All @@ -295,6 +330,7 @@
},
)

# NATIVITY: Nativity
acs_nativity = ColumnToText(
"NATIVITY",
short_description="nativity",
Expand All @@ -304,6 +340,7 @@
},
)

# DEAR: Hearing Status
acs_hearing = ColumnToText(
"DEAR",
short_description="hearing status",
Expand All @@ -313,6 +350,7 @@
},
)

# DEYE: Vision Status
acs_vision = ColumnToText(
"DEYE",
short_description="vision status",
Expand All @@ -322,6 +360,7 @@
},
)

# DREM: Cognitive Status
acs_cognitive = ColumnToText(
"DREM",
short_description="cognition status",
Expand All @@ -332,6 +371,7 @@
missing_value_fill="N/A (less than 5 years old)",
)

# ESR: Employment Status
acs_employment = ColumnToText(
"ESR",
short_description="employment status",
Expand All @@ -346,16 +386,34 @@
missing_value_fill="N/A (less than 16 years old)",
)

acs_employment_qa = MultipleChoiceQA(
column=get_thresholded_column_name("ESR", 1, op="=="),
text="What is this person's employment status?",
choices=[
Choice("Employed civilian", 1),
Choice("Unemployed or in the military", 0),
],
)

acs_employment_target_col = ColumnToText(
name=get_thresholded_column_name("ESR", 1, op="=="),
short_description="employment status",
question=acs_employment_qa,
use_value_map_only=True,
)

# ST: State
acs_state = ColumnToText(
"ST",
short_description="state",
short_description="resident state",
value_map=partial(
parse_pums_code,
file=ACS_ST_FILE,
postprocess=lambda x: x[:x.find("/")].strip(),
),
)

# FER: Parenthood Status
acs_parenthood = ColumnToText(
"FER",
short_description="person has given birth within the last year",
Expand All @@ -367,13 +425,31 @@
missing_value_fill="N/A (less than 15 years old, or greater than 50 years old, or male)",
)

# JWMNP: Commute Time
acs_commute_time = ColumnToText(
"JWMNP",
short_description="commute time",
value_map=lambda x: f"{int(x)} minutes",
missing_value_fill="N/A (not a worker, or worker who worked at home)",
)

acs_commute_time_qa = MultipleChoiceQA(
column=get_thresholded_column_name("JWMNP", 20),
text="What is this person's commute time?",
choices=[
Choice("Longer than 20 minutes", 1),
Choice("Less than 20 minutes", 0),
],
)

acs_travel_time_target_col = ColumnToText(
name=get_thresholded_column_name("JWMNP", 20),
short_description="commute time",
question=acs_commute_time_qa,
use_value_map_only=True,
)

# JWTR: Commute Method
acs_commute_method = ColumnToText(
"JWTR",
short_description="means of transportation to work",
Expand All @@ -393,6 +469,7 @@
},
)

# POVPIP: Income-to-Poverty Ratio
acs_poverty_ratio = ColumnToText(
"POVPIP",
short_description="income-to-poverty ratio",
Expand Down

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