Create test_dataset.py

test_dataset.py

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+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            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")
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        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