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Rescaling linguistic isolation (#1750)

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Rescales linguistic isolation to drop puerto rico
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Emma Nechamkin 2022-08-02 16:28:05 -04:00 • committed by Emma Nechamkin
parent e98282dcef
commit 29419dd2fd
1 changed files with 57 additions and 56 deletions
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111
data/data-pipeline/data_pipeline/etl/score/etl_score.py
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@ -264,6 +264,7 @@ class ScoreETL(ExtractTransformLoad):
df: pd.DataFrame,
input_column_name: str,
output_column_name_root: str,
drop_tracts: list = None,
ascending: bool = True,
) -> pd.DataFrame:
"""Creates percentiles.
@ -298,10 +299,15 @@ class ScoreETL(ExtractTransformLoad):
something like "3rd grade reading proficiency" and `output_column_name_root`
may be something like "Low 3rd grade reading proficiency".
"""
if (
output_column_name_root
!= field_names.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD
):
# We have two potential options for assessing how to calculate percentiles.
# For the vast majority of columns, we will simply calculate percentiles overall.
# However, for Linguistic Isolation and Agricultural Value Loss, there exist conditions
# for which we drop out tracts from consideration in the percentile. More details on those
# are below, for them, we provide a list of tracts to not include.
# Because of the fancy transformations below, I have removed the urban / rural percentiles,
# which are now deprecated.
if not drop_tracts:
# Create the "basic" percentile.
df[
f"{output_column_name_root}"
@ -309,62 +315,23 @@ class ScoreETL(ExtractTransformLoad):
] = df[input_column_name].rank(pct=True, ascending=ascending)
else:
# For agricultural loss, we are using whether there is value at all to determine percentile and then
# filling places where the value is False with 0
tmp_series = df[input_column_name].where(
~df[field_names.GEOID_TRACT_FIELD].isin(drop_tracts),
np.nan,
)
logger.info(
f"Creating special case column for percentiles from {input_column_name}"
)
df[
f"{output_column_name_root}"
f"{field_names.PERCENTILE_FIELD_SUFFIX}"
] = (
df.where(
df[field_names.AGRICULTURAL_VALUE_BOOL_FIELD].astype(float)
== 1.0
)[input_column_name]
.rank(ascending=ascending, pct=True)
.fillna(
df[field_names.AGRICULTURAL_VALUE_BOOL_FIELD].astype(float)
)
)
] = tmp_series.rank(ascending=ascending, pct=True)
# Create the urban/rural percentiles.
urban_rural_percentile_fields_to_combine = []
for (urban_or_rural_string, urban_heuristic_bool) in [
("urban", True),
("rural", False),
]:
# Create a field with only those values
this_category_only_value_field = (
f"{input_column_name} (value {urban_or_rural_string} only)"
)
df[this_category_only_value_field] = np.where(
df[field_names.URBAN_HEURISTIC_FIELD] == urban_heuristic_bool,
df[input_column_name],
None,
)
# Calculate the percentile for only this category
this_category_only_percentile_field = (
# Check that "drop tracts" were dropped (quicker than creating a fixture?)
assert df[df[field_names.GEOID_TRACT_FIELD].isin(drop_tracts)][
f"{output_column_name_root}"
f"(percentile {urban_or_rural_string} only)"
)
df[this_category_only_percentile_field] = df[
this_category_only_value_field
].rank(
pct=True,
# Set ascending to the parameter value.
ascending=ascending,
)
# Add the field name to this list. Later, we'll combine this list.
urban_rural_percentile_fields_to_combine.append(
this_category_only_percentile_field
)
# Combine both urban and rural into one field:
df[
f"{output_column_name_root}{field_names.PERCENTILE_URBAN_RURAL_FIELD_SUFFIX}"
] = df[urban_rural_percentile_fields_to_combine].mean(
axis=1, skipna=True
)
f"{field_names.PERCENTILE_FIELD_SUFFIX}"
].isna().sum() == len(drop_tracts), "Not all tracts were dropped"
return df
@ -523,13 +490,47 @@ class ScoreETL(ExtractTransformLoad):
df_copy[numeric_columns] = df_copy[numeric_columns].apply(pd.to_numeric)
# Convert all columns to numeric and do math
# Note that we have a few special conditions here, that we handle explicitly.
# For *Linguistic Isolation*, we do NOT want to include Puerto Rico in the percentile
# calculation. This is because linguistic isolation as a category doesn't make much sense
# in Puerto Rico, where Spanish is a recognized language. Thus, we construct a list
# of tracts to drop from the percentile calculation.
#
# For *Expected Agricultural Loss*, we only want to include in the percentile tracts
# in which there is some agricultural value. This helps us adjust the data such that we have
# the ability to discern which tracts truly are at the 90th percentile, since many tracts have 0 value.
for numeric_column in numeric_columns:
drop_tracts = []
if (
numeric_column
== field_names.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD
):
drop_tracts = df_copy[
~df_copy[field_names.AGRICULTURAL_VALUE_BOOL_FIELD]
.astype(bool)
.fillna(False)
][field_names.GEOID_TRACT_FIELD].to_list()
logger.info(
f"Dropping {len(drop_tracts)} tracts from Agricultural Value Loss"
)
elif numeric_column == field_names.LINGUISTIC_ISO_FIELD:
drop_tracts = df_copy[
# 72 is the FIPS code for Puerto Rico
df_copy[field_names.GEOID_TRACT_FIELD].str.startswith("72")
][field_names.GEOID_TRACT_FIELD].to_list()
logger.info(
f"Dropping {len(drop_tracts)} tracts from Linguistic Isolation"
)
df_copy = self._add_percentiles_to_df(
df=df_copy,
input_column_name=numeric_column,
# For this use case, the input name and output name root are the same.
output_column_name_root=numeric_column,
ascending=True,
drop_tracts=drop_tracts,
)
# Min-max normalization: