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import datasets |
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import pandas as pd |
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import numpy as np |
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_CITATION = """\ |
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@InProceedings{huggingface:dataset, |
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title = {A great new dataset}, |
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author={huggingface, Inc. |
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}, |
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year={2020} |
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} |
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""" |
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_DESCRIPTION = """\ |
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This new dataset is designed to solve this great NLP task and is crafted with a lot of care. |
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""" |
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_HOMEPAGE = "" |
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_LICENSE = "" |
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class HealthStatisticsDataset(datasets.GeneratorBasedBuilder): |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"Year": datasets.Value("int32"), |
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"LocationAbbr": datasets.Value("string"), |
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"LocationDesc": datasets.Value("string"), |
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"Latitude": datasets.Value("float32"), |
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"Longitude": datasets.Value("float32"), |
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"Disease_Type": datasets.Value("int32"), |
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"Data_Value_Type": datasets.Value("int32"), |
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"Data_Value": datasets.Value("float32"), |
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"Break_Out_Category": datasets.Value("string"), |
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"Break_Out_Details": datasets.Value("string"), |
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"Break_Out_Type": datasets.Value("int32"), |
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"Life_Expectancy": datasets.Value("float32") |
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} |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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data = pd.read_csv(dl_manager.download_and_extract("https://docs.google.com/uc?export=download&id=1eChYmZ3RMq1v-ek1u6DD2m_dGIrz3sbi&confirm=t")) |
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processed_data = self.preprocess_data(data) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"data": processed_data}, |
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), |
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] |
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def _generate_examples(self, data): |
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for key, row in data.iterrows(): |
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year = int(row['Year']) if 'Year' in row else None |
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latitude, longitude = None, None |
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if isinstance(row['Geolocation'], str): |
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geo_str = row['Geolocation'].replace('POINT (', '').replace(')', '') |
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longitude, latitude = map(float, geo_str.split()) |
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yield key, { |
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"Year": year, |
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"LocationAbbr": row.get('LocationAbbr', None), |
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"LocationDesc": row.get('LocationDesc', None), |
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"Latitude": latitude, |
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"Longitude": longitude, |
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"Disease_Type": int(row["Disease_Type"]) if "Disease_Type" in row else None, |
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"Data_Value_Type": int(row["Data_Value_Type"]) if "Data_Value_Type" in row else None, |
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"Data_Value": float(row["Data_Value"]) if "Data_Value" in row else None, |
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"Break_Out_Category": row.get("Break_Out_Category", None), |
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"Break_Out_Details": row.get("Break_Out_Details", None), |
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"Break_Out_Type": int(row["Break_Out_Type"]) if 'Break_Out_Type' in row else None, |
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"Life_Expectancy": float(row["Life_Expectancy"]) if row.get("Life_Expectancy") else None |
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} |
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@staticmethod |
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def preprocess_data(data): |
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data = data[['YearStart', 'LocationAbbr', 'LocationDesc', 'Geolocation', 'Topic', 'Question', 'Data_Value_Type', 'Data_Value', 'Data_Value_Alt', |
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'Low_Confidence_Limit', 'High_Confidence_Limit', 'Break_Out_Category', 'Break_Out']] |
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pd.options.mode.chained_assignment = None |
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disease_columns = [ |
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'Major cardiovascular disease mortality rate among US adults (18+); NVSS', |
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'Diseases of the heart (heart disease) mortality rate among US adults (18+); NVSS', |
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'Acute myocardial infarction (heart attack) mortality rate among US adults (18+); NVSS', |
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'Coronary heart disease mortality rate among US adults (18+); NVSS', |
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'Heart failure mortality rate among US adults (18+); NVSS', |
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'Cerebrovascular disease (stroke) mortality rate among US adults (18+); NVSS', |
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'Ischemic stroke mortality rate among US adults (18+); NVSS', |
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'Hemorrhagic stroke mortality rate among US adults (18+); NVSS' |
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] |
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disease_column_mapping = {column_name: index for index, column_name in enumerate(disease_columns)} |
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data['Question'] = data['Question'].apply(lambda x: disease_column_mapping.get(x, -1)) |
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sex_columns = ['Male', 'Female'] |
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sex_column_mapping = {column_name: index + 1 for index, column_name in enumerate(sex_columns)} |
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age_columns = ['18-24', '25-44', '45-64', '65+'] |
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age_column_mapping = {column_name: index + 1 for index, column_name in enumerate(age_columns)} |
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race_columns = ['Non-Hispanic White', 'Non-Hispanic Black', 'Hispanic', 'Other'] |
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race_column_mapping = {column_name: index + 1 for index, column_name in enumerate(race_columns)} |
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def map_break_out_category(value): |
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if value in sex_column_mapping: |
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return sex_column_mapping[value] |
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elif value in age_column_mapping: |
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return age_column_mapping[value] |
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elif value in race_column_mapping: |
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return race_column_mapping[value] |
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else: |
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return value |
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data['Break_Out_Type'] = data['Break_Out'].apply(map_break_out_category) |
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data.drop(columns=['Topic', 'Low_Confidence_Limit', 'High_Confidence_Limit', 'Data_Value_Alt'], axis=1, inplace=True) |
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data['Data_Value_Type'] = data['Data_Value_Type'].apply(lambda x: 1 if x == 'Age-Standardized' else 0) |
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data.rename(columns={'Question':'Disease_Type', 'YearStart':'Year', 'Break_Out':'Break_Out_Details'}, inplace=True) |
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data['Break_Out_Type'] = data['Break_Out_Type'].replace('Overall', 0) |
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pd.options.mode.chained_assignment = 'warn' |
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lt2000 = pd.read_csv("https://docs.google.com/uc?export=download&id=1ktRNl7jg0Z83rkymD9gcsGLdVqVaFtd-&confirm=t") |
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lt2000 = lt2000[(lt2000['race_name'] == 'Total') & (lt2000['age_name'] == '<1 year')] |
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lt2000 = lt2000[['location_name', 'val']] |
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lt2000.rename(columns={'val':'Life_Expectancy'}, inplace=True) |
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lt2005 = pd.read_csv("https://docs.google.com/uc?export=download&id=1xZqeOgj32-BkOhDTZVc4k_tp1ddnOEh7&confirm=t") |
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lt2005 = lt2005[(lt2005['race_name'] == 'Total') & (lt2005['age_name'] == '<1 year')] |
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lt2005 = lt2005[['location_name', 'val']] |
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lt2005.rename(columns={'val':'Life_Expectancy'}, inplace=True) |
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lt2010 = pd.read_csv("https://docs.google.com/uc?export=download&id=1ItqHBuuUa38PVytfahaAV8NWwbhHMMg8&confirm=t") |
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lt2010 = lt2010[(lt2010['race_name'] == 'Total') & (lt2010['age_name'] == '<1 year')] |
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lt2010 = lt2010[['location_name', 'val']] |
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lt2010.rename(columns={'val':'Life_Expectancy'}, inplace=True) |
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lt2015 = pd.read_csv("https://docs.google.com/uc?export=download&id=1rOgQY1RQiry2ionTKM_UWgT8cYD2E0vX&confirm=t") |
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lt2015 = lt2015[(lt2015['race_name'] == 'Total') & (lt2015['age_name'] == '<1 year')] |
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lt2015 = lt2015[['location_name', 'val']] |
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lt2015.rename(columns={'val':'Life_Expectancy'}, inplace=True) |
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lt_data = pd.concat([lt2000, lt2005, lt2010, lt2015]) |
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lt_data.drop_duplicates(subset=['location_name'], inplace=True) |
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data2 = pd.merge(data, lt_data, how='inner', left_on='LocationDesc', right_on='location_name') |
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data2.drop(columns=['location_name'], axis=1, inplace=True) |
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data2 = data2[(data2['Break_Out_Details'] != '75+') & (data2['Break_Out_Details'] != '35+')] |
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data2.rename(columns={'Question':'Disease_Type'}, inplace=True) |
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data2['Life_Expectancy'] = np.where(data2['Break_Out_Type'] == 0, data2['Life_Expectancy'], np.nan) |
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data2 = data2.reset_index(drop=True) |
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return data2 |
