|
def _info(self): |
|
|
|
if self.config.name == "first_domain": |
|
features = datasets.Features( |
|
{ |
|
"year": datasets.Value("int32"), |
|
"locationabbr": datasets.Value("string"), |
|
"locationdesc": datasets.Value("string"), |
|
"geolocation": datasets.Features({"latitude": datasets.Value("float32"), "longitude": datasets.Value("float32")}), |
|
"disease_type": datasets.Value("int32"), |
|
"data_value_type": datasets.Value("int32"), |
|
"data_value": datasets.Value("float32"), |
|
"break_out_category": datasets.Value("string"), |
|
"break_out_details": datasets.Value("string"), |
|
"break_out_type": datasets.Value("int32"), |
|
"life_expectancy": datasets.Value("float32") |
|
|
|
} |
|
) |
|
return datasets.DatasetInfo( |
|
|
|
description=_DESCRIPTION, |
|
|
|
features=features, |
|
|
|
|
|
|
|
|
|
homepage=_HOMEPAGE, |
|
|
|
license=_LICENSE, |
|
|
|
citation=_CITATION, |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
|
|
|
|
|
|
|
|
|
|
|
|
dl_paths = dl_manager.download_and_extract({ |
|
'train_csv': 'https://drive.google.com/file/d/1eChYmZ3RMq1v-ek1u6DD2m_dGIrz3sbi/view?usp=sharing' |
|
}) |
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, |
|
gen_kwargs={ |
|
"csvpath": dl_paths['train_csv'], |
|
}, |
|
), |
|
] |
|
|
|
def _generate_examples(self, csvpath): |
|
with open(csvpath, encoding="utf-8") as f: |
|
reader = csv.DictReader(f) |
|
|
|
for key, row in enumerate(reader): |
|
yield key, { |
|
"year": int(row["Year"]), |
|
"location_abbr": row["LocationAbbr"], |
|
"location_desc": row["LocationDesc"], |
|
"geolocation": { |
|
"latitude": float(row["latitude"]), |
|
"longitude": float(row["longitude"]) |
|
}, |
|
"disease_type": int(row["Disease_Type"]), |
|
"data_value_type": int(row["Data_Value_Type"]), |
|
"data_value": float(row["Data_Value"]), |
|
"break_out_category": row["Break_Out_Category"], |
|
"break_out_details": row["Break_Out_Details"], |
|
"break_out_type": int(row["Break_Out_Type"]), |
|
"life_expectancy": float(row["Life_Expectancy"]) if row["Life_Expectancy"] else None |
|
} |
|
|
|
@staticmethod |
|
def preprocess_data(filepath): |
|
data = pd.read_csv("/content/drive/MyDrive/National_Vital_Statistics_System__NVSS__-_National_Cardiovascular_Disease_Surveillance_Data_20240129.csv") |
|
data = data[['YearStart', 'LocationAbbr', 'LocationDesc', 'Geolocation', 'Topic', 'Question', 'Data_Value_Type', 'Data_Value', 'Data_Value_Alt', |
|
'Low_Confidence_Limit', 'High_Confidence_Limit', 'Break_Out_Category', 'Break_Out']] |
|
def convert_to_tuple(geo_str): |
|
if isinstance(geo_str, str): |
|
geo_str = geo_str.replace('POINT (', '').replace(')', '') |
|
lon, lat = map(float, geo_str.split()) |
|
return (lon, lat) |
|
else: |
|
return geo_str |
|
|
|
data['Geolocation'] = data['Geolocation'].apply(convert_to_tuple) |
|
disease_columns = [ |
|
'Major cardiovascular disease mortality rate among US adults (18+); NVSS', |
|
'Diseases of the heart (heart disease) mortality rate among US adults (18+); NVSS', |
|
'Acute myocardial infarction (heart attack) mortality rate among US adults (18+); NVSS', |
|
'Coronary heart disease mortality rate among US adults (18+); NVSS', |
|
'Heart failure mortality rate among US adults (18+); NVSS', |
|
'Cerebrovascular disease (stroke) mortality rate among US adults (18+); NVSS', |
|
'Ischemic stroke mortality rate among US adults (18+); NVSS', |
|
'Hemorrhagic stroke mortality rate among US adults (18+); NVSS' |
|
] |
|
|
|
disease_column_mapping = {column_name: index for index, column_name in enumerate(disease_columns)} |
|
data['Question'] = data['Question'].apply(lambda x: disease_column_mapping.get(x, -1)) |
|
|
|
sex_columns = ['Male', 'Female'] |
|
sex_column_mapping = {column_name: index + 1 for index, column_name in enumerate(sex_columns)} |
|
|
|
age_columns = ['18-24', '25-44', '45-64', '65+'] |
|
age_column_mapping = {column_name: index + 1 for index, column_name in enumerate(age_columns)} |
|
|
|
race_columns = ['Non-Hispanic White', 'Non-Hispanic Black', 'Hispanic', 'Other'] |
|
race_column_mapping = {column_name: index + 1 for index, column_name in enumerate(race_columns)} |
|
|
|
def map_break_out_category(value): |
|
if value in sex_column_mapping: |
|
return sex_column_mapping[value] |
|
elif value in age_column_mapping: |
|
return age_column_mapping[value] |
|
elif value in race_column_mapping: |
|
return race_column_mapping[value] |
|
else: |
|
return value |
|
|
|
data['Break_Out_Type'] = data['Break_Out'].apply(map_break_out_category) |
|
data.drop(columns=['Topic', 'Low_Confidence_Limit', 'High_Confidence_Limit', 'Data_Value_Alt'], axis=1, inplace=True) |
|
data['Data_Value_Type'] = data['Data_Value_Type'].apply(lambda x: 1 if x == 'Age-Standardized' else 0) |
|
data.rename(columns={'Question':'Disease_Type', 'YearStart':'Year', 'Break_Out':'Break_Out_Details'}, inplace=True) |
|
data['Break_Out_Type'] = data['Break_Out_Type'].replace('Overall', 0) |
|
|
|
lt2000 = pd.read_csv("https://drive.google.com/file/d/1ktRNl7jg0Z83rkymD9gcsGLdVqVaFtd-/view?usp=drive_link") |
|
lt2000 = lt2000[(lt2000['race_name'] == 'Total') & (lt2000['age_name'] == '<1 year')] |
|
lt2000 = lt2000[['location_name', 'val']] |
|
lt2000.rename(columns={'val':'Life_Expectancy'}, inplace=True) |
|
|
|
lt2005 = pd.read_csv("https://drive.google.com/file/d/1xZqeOgj32-BkOhDTZVc4k_tp1ddnOEh7/view?usp=drive_link") |
|
lt2005 = lt2005[(lt2005['race_name'] == 'Total') & (lt2005['age_name'] == '<1 year')] |
|
lt2005 = lt2005[['location_name', 'val']] |
|
lt2005.rename(columns={'val':'Life_Expectancy'}, inplace=True) |
|
|
|
lt2010 = pd.read_csv("https://drive.google.com/file/d/1ItqHBuuUa38PVytfahaAV8NWwbhHMMg8/view?usp=drive_link") |
|
lt2010 = lt2010[(lt2010['race_name'] == 'Total') & (lt2010['age_name'] == '<1 year')] |
|
lt2010 = lt2010[['location_name', 'val']] |
|
lt2010.rename(columns={'val':'Life_Expectancy'}, inplace=True) |
|
|
|
lt2015 = pd.read_csv("https://drive.google.com/file/d/1rOgQY1RQiry2ionTKM_UWgT8cYD2E0vX/view?usp=drive_link") |
|
lt2015 = lt2015[(lt2015['race_name'] == 'Total') & (lt2015['age_name'] == '<1 year')] |
|
lt2015 = lt2015[['location_name', 'val']] |
|
lt2015.rename(columns={'val':'Life_Expectancy'}, inplace=True) |
|
|
|
lt_data = pd.concat([lt2000, lt2005, lt2010, lt2015]) |
|
lt_data.drop_duplicates(subset=['location_name'], inplace=True) |
|
|
|
data2 = pd.merge(data, lt_data, how='inner', left_on='LocationDesc', right_on='location_name') |
|
data2.drop(columns=['location_name'], axis=1, inplace=True) |
|
data2 = data2[(data2['Break_Out_Details'] != '75+') & (data2['Break_Out_Details'] != '35+')] |
|
data2.rename(columns={'Question':'Disease_Type'}, inplace=True) |
|
data2['Life_Expectancy'] = np.where(data2['Break_Out_Type'] == 0, data2['Life_Expectancy'], np.nan) |
|
processed_filepath = '/content/drive/MyDrive/my_processed_data.csv' |
|
return processed_filepath |