import datasets import pandas as pd import numpy as np _CITATION = """\ @InProceedings{huggingface:dataset, title = {A great new dataset}, author={huggingface, Inc. }, year={2020} } """ _DESCRIPTION = """\ This new dataset is designed to solve this great NLP task and is crafted with a lot of care. """ _HOMEPAGE = "" _LICENSE = "" class HealthStatisticsDataset(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "Year": datasets.Value("int32"), "LocationAbbr": datasets.Value("string"), "LocationDesc": datasets.Value("string"), "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") } ), supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): data = pd.read_csv(dl_manager.download_and_extract("https://docs.google.com/uc?export=download&id=1eChYmZ3RMq1v-ek1u6DD2m_dGIrz3sbi&confirm=t")) processed_data = self.preprocess_data(data) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"data": processed_data}, ), ] def _generate_examples(self, data): for key, row in data.iterrows(): year = int(row['Year']) if 'Year' in row else None latitude, longitude = None, None if isinstance(row['Geolocation'], str): geo_str = row['Geolocation'].replace('POINT (', '').replace(')', '') longitude, latitude = map(float, geo_str.split()) yield key, { "Year": year, "LocationAbbr": row.get('LocationAbbr', None), "LocationDesc": row.get('LocationDesc', None), "Latitude": latitude, "Longitude": longitude, "Disease_Type": int(row["Disease_Type"]) if "Disease_Type" in row else None, "Data_Value_Type": int(row["Data_Value_Type"]) if "Data_Value_Type" in row else None, "Data_Value": float(row["Data_Value"]) if "Data_Value" in row else None, "Break_Out_Category": row.get("Break_Out_Category", None), "Break_Out_Details": row.get("Break_Out_Details", None), "Break_Out_Type": int(row["Break_Out_Type"]) if 'Break_Out_Type' in row else None, "Life_Expectancy": float(row["Life_Expectancy"]) if row.get("Life_Expectancy") else None } @staticmethod def preprocess_data(data): 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']] pd.options.mode.chained_assignment = None 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) pd.options.mode.chained_assignment = 'warn' lt2000 = pd.read_csv("https://docs.google.com/uc?export=download&id=1ktRNl7jg0Z83rkymD9gcsGLdVqVaFtd-&confirm=t") 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://docs.google.com/uc?export=download&id=1xZqeOgj32-BkOhDTZVc4k_tp1ddnOEh7&confirm=t") 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://docs.google.com/uc?export=download&id=1ItqHBuuUa38PVytfahaAV8NWwbhHMMg8&confirm=t") 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://docs.google.com/uc?export=download&id=1rOgQY1RQiry2ionTKM_UWgT8cYD2E0vX&confirm=t") 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) data2 = data2.reset_index(drop=True) return data2