My commit

Long-Term-Care-Aggregated-Data.py

@@ -122,67 +122,75 @@ class LongTermCareAggregatedData(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # URLs of the raw CSV files on GitHub using the raw content feature
-        urls_to_download = {
-            "train_incidence": "https://raw.githubusercontent.com/justinkao44/STA663_Project_1/main/train_filtered_incidence_df.csv",
-            "train_termination": "https://raw.githubusercontent.com/justinkao44/STA663_Project_1/main/train_filtered_termination_df.csv",
-            "validation_incidence": "https://raw.githubusercontent.com/justinkao44/STA663_Project_1/main/validation_filtered_incidence_df.csv",
-            "validation_termination": "https://raw.githubusercontent.com/justinkao44/STA663_Project_1/main/validation_filtered_termination_df.csv",
-        }
 
-        # Use dl_manager to download and extract files
-        downloaded_files = dl_manager.download(urls_to_download)
+        downloaded_files = dl_manager.download(_URLS)
 
         return [
             datasets.SplitGenerator(
                 name="train_incidence",
                 gen_kwargs={
-                    "filepath": downloaded_files["train_incidence"],
-                    "split": "incidence"
+                    "data_file": downloaded_files["train_incidence"],
+                    "split": "incidence",
                 },
             ),
             datasets.SplitGenerator(
                 name="validation_incidence",
                 gen_kwargs={
-                    "filepath": downloaded_files["validation_incidence"],
-                    "split": "incidence"
+                    "data_file": downloaded_files["validation_incidence"],
+                    "split": "incidence",
                 },
             ),
             datasets.SplitGenerator(
                 name="train_termination",
                 gen_kwargs={
-                    "filepath": downloaded_files["train_termination"],
-                    "split": "termination"
+                    "data_file": downloaded_files["train_termination"],
+                    "split": "termination",
                 },
             ),
             datasets.SplitGenerator(
                 name="validation_termination",
                 gen_kwargs={
-                    "filepath": downloaded_files["validation_termination"],
-                    "split": "termination"
+                    "data_file": downloaded_files["validation_termination"],
+                    "split": "termination",
                 },
             ),
         ]
-    def _generate_examples(self, incidence_filepath, termination_filepath, split):
-        if self.config.name == "incidence":
-            dataframe = pd.read_csv(incidence_filepath)
-            feature_columns = [
-                "Group_Indicator", "Gender", "Issue_Age_Bucket", "Incurred_Age_Bucket",
-                "Issue_Year_Bucket", "Policy_Year", "Marital_Status", "Premium_Class",
-                "Underwriting_Type", "Coverage_Type_Bucket", "Tax_Qualification_Status",
-                "Inflation_Rider", "Rate_Increase_Flag", "Restoration_of_Benefits",
-                "NH_Orig_Daily_Ben_Bucket", "ALF_Orig_Daily_Ben_Bucket", "HHC_Orig_Daily_Ben_Bucket",
-                "NH_Ben_Period_Bucket", "ALF_Ben_Period_Bucket", "HHC_Ben_Period_Bucket",
-                "NH_EP_Bucket", "ALF_EP_Bucket", "HHC_EP_Bucket", "Region",
-                "Active_Exposure", "Total_Exposure", "Claim_Count", "Count_NH", "Count_ALF", "Count_HHC", "Count_Unk",
-            ]
-        elif 'termination' in split:
-            feature_columns = [
-                "Gender", "Incurred_Age_Bucket", "Incurred_Year_Bucket", "Claim_Type",
-                "Region", "Diagnosis_Category", "Claim_Duration", "Exposure", "Deaths",
-                "Recovery", "Terminations", "Benefit_Expiry", "Others_Terminations",
-            ]
 
+    def _generate_examples(self, data_file, split):
+        # Read the CSV file for the given split
+        dataframe = pd.read_csv(data_file)
+
+        # Determine the feature columns based on the split type
+        feature_columns = self._get_feature_columns(split)
+
+        # Yield examples
         for idx, row in dataframe.iterrows():
             feature_dict = {column: row[column] for column in feature_columns}
-            yield idx, feature_dict
+            yield idx, feature_dict
+
+    def _get_feature_columns(self, split):
+        # Define the feature columns for 'incidence'
+        incidence_columns = [
+            "Group_Indicator", "Gender", "Issue_Age_Bucket", "Incurred_Age_Bucket",
+            "Issue_Year_Bucket", "Policy_Year", "Marital_Status", "Premium_Class",
+            "Underwriting_Type", "Coverage_Type_Bucket", "Tax_Qualification_Status",
+            "Inflation_Rider", "Rate_Increase_Flag", "Restoration_of_Benefits",
+            "NH_Orig_Daily_Ben_Bucket", "ALF_Orig_Daily_Ben_Bucket", "HHC_Orig_Daily_Ben_Bucket",
+            "NH_Ben_Period_Bucket", "ALF_Ben_Period_Bucket", "HHC_Ben_Period_Bucket",
+            "NH_EP_Bucket", "ALF_EP_Bucket", "HHC_EP_Bucket", "Region",
+            "Active_Exposure", "Total_Exposure", "Claim_Count", "Count_NH", "Count_ALF", "Count_HHC", "Count_Unk",
+        ]
+
+        # Define the feature columns for 'termination'
+        termination_columns = [
+            "Gender", "Incurred_Age_Bucket", "Incurred_Year_Bucket", "Claim_Type",
+            "Region", "Diagnosis_Category", "Claim_Duration", "Exposure", "Deaths",
+            "Recovery", "Terminations", "Benefit_Expiry", "Others_Terminations",
+        ]
+
+        if split == "incidence":
+            return incidence_columns
+        elif split == "termination":
+            return termination_columns
+        else:
+            raise ValueError(f"Split name not recognized: {split}")