def main():
"""
Indicators for DOC sentences by crime type
"""
sql = """
SELECT DISTINCT offense_type AS offense
FROM {doc_sentences}
WHERE offense_type IS NOT NULL
ORDER BY offense
""".format(**globals())
with cx_Oracle.connect("/") as cxn:
offenses = pd.read_sql(sql, cxn)
print(offenses)
pivot = ",".join([
"'{0}' AS doc_sentenced_{0}".format(offense)
for offense in offenses["OFFENSE"]
])
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
sql = """
SELECT *
FROM (
SELECT pop.riipl_id,
ds.offense_type AS offense,
1 AS sentenced
FROM {population} pop
LEFT JOIN {lookback} lb
ON pop.riipl_id = lb.riipl_id
LEFT JOIN {dim_date} dd
ON lb.yrmo = dd.yrmo
INNER JOIN {doc_sentences} ds
ON pop.riipl_id = ds.riipl_id AND
dd.date_dt = ds.imposed_date
GROUP BY pop.riipl_id, ds.offense_type
)
PIVOT (MAX(sentenced) FOR offense IN ({pivot}))
""".format(pivot=pivot, **globals())
with cx_Oracle.connect("/") as cxn:
features = features.join(pd.read_sql(sql, cxn).set_index(index))
# Drop offense types that are missing for the entire population.
features = features.dropna(axis=1, how="all").fillna(0).astype(int)
labels = {}
for offense in offenses["OFFENSE"]:
label = "sentenced for {} during lookback period".format(offense)
labels["DOC_SENTENCED_{}".format(offense.upper())] = label
SaveFeatures(features,
out,
manifest,
population,
labels,
bool_features=list(labels))
def main():
"""
Industry worked in (NAICS code) from DLT wages
"""
sql = """
SELECT DISTINCT SUBSTR(naics4, 0, 2) AS naics2
FROM {dlt_wage}
ORDER BY naics2
""".format(**globals())
with cx_Oracle.connect("/") as cxn:
naics = pd.read_sql(sql, cxn)
pivot = ",".join(["'{0}' AS naics_{0}".format(n) for n in naics["NAICS2"]])
index = ["RIIPL_ID"]
features = CachePopulation(population, index).set_index(index)
sql = """
SELECT *
FROM (
SELECT pop.riipl_id,
SUBSTR(dw.naics4, 0, 2) AS naics2,
1 AS worked
FROM {population} pop
LEFT JOIN (
SELECT DISTINCT
riipl_id,
yyq
FROM {lookback}
) lb
ON pop.riipl_id = lb.riipl_id
INNER JOIN {dlt_wage} dw
ON pop.riipl_id = dw.riipl_id AND
lb.yyq = dw.yyq
GROUP BY pop.riipl_id, SUBSTR(dw.naics4, 0, 2)
)
PIVOT (MAX(worked) FOR naics2 IN ({pivot}))
""".format(pivot=pivot, **globals())
with cx_Oracle.connect("/") as cxn:
features = features.join(pd.read_sql(sql, cxn).set_index(index))
# Drop NAICS categories that are missing for the entire population.
features = features.dropna(axis=1, how="all").fillna(0).astype(int)
# Missing indicator not needed - provided by WAGES_MISSING
labels = {}
naics2_labels = {
"00": "Unknown",
"02": "Unknown",
"11": "Agriculture, Forestry, Fishing, and Hunting",
"21": "Mining, Quarrying, and Oil and Gas Extraction",
"22": "Utilities",
"23": "Construction",
"24": "Unknown",
"31": "Manufacturing",
"32": "Manufacturing",
"33": "Manufacturing",
"42": "Wholesale Trade",
"44": "Retail Trade",
"45": "Retail Trade",
"48": "Transportation and Warehousing",
"49": "Transportation and Warehousing",
"50": "Unknown",
"51": "Information",
"52": "Finance and Insurance",
"53": "Real Estate and Rental and Leasing",
"54": "Professional, Scientific, and Technical Services",
"55": "Management of Companies and Enterprises",
"56":
"Administrative and Support and Waste Management and Remediation Services",
"61": "Educational Services",
"62": "Health Care and Social Assistance",
"71": "Arts, Entertainment, and Recreation",
"72": "Accommodation and Food Services",
"81": "Other Services (except Public Administration)",
"84": "Unknown",
"92": "Public Administration",
"94": "Unknown",
"99": "Unknown"
}
for n, sector in naics2_labels.items():
label = "worked in {} sector (NAICS {}) during lookback period".format(
sector, n)
labels["NAICS_{}".format(n)] = label
SaveFeatures(features,
out,
manifest, (population, index),
labels,
bool_features=list(labels))