def sex_datasource():
path = "input_data/ACS_14_5YR_S0101_with_ann.csv"
def extract(row):
return {
"total": float(row["HC01_EST_VC01"]),
"male": float(row["HC02_EST_VC01"]),
"female": float(row["HC03_EST_VC01"])
}
def aggregate(values):
total = 0
male = 0
female = 0
for value in values:
total += value["total"]
male += value["male"]
female += value["female"]
return {
"percentage_male": male / (total + 1),
"percentage_female": female / (total + 1)
}
feature_extractor = extractor.FeatureExtractor(extract, aggregate)
return DataSource(path=path, feature_extractors=[feature_extractor])
def zipcode_feature_extractor():
def extract(row):
return {"zipcode": std_aggregation_key(row)}
def aggregate(values):
return values[0]
return extractor.FeatureExtractor(extract, aggregate)
def employment_datasource():
path = "input_data/ACS_14_5YR_S2301_with_ann.csv"
def extract(row):
return {"unemployment_rate": float(row["HC04_EST_VC01"])}
def aggregate(values):
num = len(values)
total = sum(i["unemployment_rate"] for i in values)
return {"unemployment_rate": total / num}
feature_extractor = extractor.FeatureExtractor(extract, aggregate)
return DataSource(path=path, feature_extractors=[feature_extractor])
def median_income_datasource():
path = "input_data/ACS_14_5YR_S1901_with_ann.csv"
def extract(row):
return {"median_income": int(row["HC02_EST_VC02"])}
def aggregate(values):
num = len(values)
total = sum(i["median_income"] for i in values)
return {"median_income": total / num}
median_income_feature_extractor = extractor.FeatureExtractor(
extract, aggregate)
return DataSource(path=path,
feature_extractors=[median_income_feature_extractor])
def language_datasource():
path = "input_data/ACS_14_5YR_S1601_with_ann.csv"
def extract(row):
return {
"total":
float(row["HC01_EST_VC01"]),
"english_well":
float(row["HC02_EST_VC01"]) * float(row["HC01_EST_VC01"]),
"english_poor":
float(row["HC03_EST_VC01"]) * float(row["HC01_EST_VC01"]),
"spanish_lang":
float(row["HC01_EST_VC04"]),
"asian_lang":
float(row["HC01_EST_VC06"]) * float(row["HC01_EST_VC01"]),
}
def aggregate(values):
total = 0
english_well = 0
english_poor = 0
spanish_lang = 0
asian_lang = 0
for value in values:
total += value["total"]
english_well += value["english_well"]
english_poor += value["english_poor"]
spanish_lang += value["spanish_lang"]
asian_lang += value["asian_lang"]
return {
"english_well_per": english_well / total,
"english_poor_per": english_poor / total,
"spanish_lang": spanish_lang,
"asian_lang": asian_lang
}
feature_extractor = extractor.FeatureExtractor(extract, aggregate)
return DataSource(path=path, feature_extractors=[feature_extractor])
def race_datasource():
path = "input_data/ACS_14_5YR_B02001_with_ann.csv"
def extract(row):
return {
"total": float(row["HD01_VD01"]),
"white": float(row["HD01_VD02"]),
"black": float(row["HD01_VD03"]),
"asian": float(row["HD01_VD05"]),
"other": float(row["HD01_VD07"]),
"two_or_more": float(row["HD01_VD08"])
}
def aggregate(values):
total = 0
white = 0
black = 0
asian = 0
other = 0
two_or_more = 0
for value in values:
total += value["total"]
white += value["white"]
black += value["black"]
asian += value["asian"]
other += value["other"]
two_or_more += value["two_or_more"]
return {
"percentage_white": white / (total + 1),
"percentage_black": black / (total + 1),
"percentage_asian": asian / (total + 1),
"percentage_other": other / (total + 1),
"percentage_two_or_more": two_or_more / (total + 1)
}
feature_extractor = extractor.FeatureExtractor(extract, aggregate)
return DataSource(path=path, feature_extractors=[feature_extractor])
def create_extractor(desc_type, desc_param, patch_size, data_file=None):
desc = Factory.create_descriptor(desc_type, desc_param)
ext = extractor.FeatureExtractor(desc, patch_size, data_file)
return ext