Create project1.py

project1.py

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+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