Update acute_inflammation.py

acute_inflammation.py

@@ -47,6 +47,28 @@ features_types_per_config = {
         "has_inflammed_bladder": datasets.Value("bool"),
         "has_nephritis_of_renal_pelvis": datasets.Value("bool"),
         "has_acute_inflammation": datasets.ClassLabel(num_classes=2)
+    },
+    "nephritis": {
+        "temperature": datasets.Value("float64"),
+        "has_nausea": datasets.Value("bool"),
+        "has_lumbar_pain": datasets.Value("bool"),
+        "has_urine_pushing": datasets.Value("bool"),
+        "has_micturition_pains": datasets.Value("bool"),
+        "has_burnt_urethra": datasets.Value("bool"),
+        "has_inflammed_bladder": datasets.Value("bool"),
+        "has_acute_inflammation": datasets.Value("bool"),
+        "has_nephritis_of_renal_pelvis": datasets.ClassLabel(num_classes=2)
+    },
+    "bladder": {
+        "temperature": datasets.Value("float64"),
+        "has_nausea": datasets.Value("bool"),
+        "has_lumbar_pain": datasets.Value("bool"),
+        "has_urine_pushing": datasets.Value("bool"),
+        "has_micturition_pains": datasets.Value("bool"),
+        "has_burnt_urethra": datasets.Value("bool"),
+        "has_acute_inflammation": datasets.Value("bool"),
+        "has_nephritis_of_renal_pelvis": datasets.Value("bool"),
+        "has_inflammed_bladder": datasets.ClassLabel(num_classes=2), 
     }
 }
 features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
@@ -63,7 +85,11 @@ class Acute_Inflammation(datasets.GeneratorBasedBuilder):
     DEFAULT_CONFIG = "inflammation"
     BUILDER_CONFIGS = [
         Acute_InflammationConfig(name="inflammation",
-                    description="Binary classification of inflammation.")
+                                 description="Binary classification of inflammation."),
+        Acute_InflammationConfig(name="nephritis",
+                                 description="Binary classification of nephritis."),
+        Acute_InflammationConfig(name="bladder",
+                                 description="Binary classification of bladder inflammation."),
     ]
 
 
@@ -77,7 +103,7 @@ class Acute_Inflammation(datasets.GeneratorBasedBuilder):
         downloads = dl_manager.download_and_extract(urls_per_split)
 
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads[self.config.name]["train"]})
         ]
     
     def _generate_examples(self, filepath: str):
@@ -92,12 +118,18 @@ class Acute_Inflammation(datasets.GeneratorBasedBuilder):
     def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
         data.columns = _BASE_FEATURE_NAMES
         boolean_features = ["has_nausea", "has_lumbar_pain", "has_urine_pushing",
-                            "has_micturition_pains", "has_burnt_urethra", "has_inflammed_bladder",
-                            "has_nephritis_of_renal_pelvis", "has_acute_inflammation"]
+                                "has_micturition_pains", "has_burnt_urethra", "has_inflammed_bladder",
+                                "has_nephritis_of_renal_pelvis", "has_acute_inflammation"]
         for f in boolean_features:
             data.loc[:, f] = data[f].apply(lambda x: True if x == "yes" else False)
-
-        data = data.astype({f: "bool" for f in boolean_features})
-        data = data.astype({"has_acute_inflammation": int})
+        
+        if config == "inflammation":           
+            data = data.astype({"has_acute_inflammation": int})       
+        elif config == "nephritis"
+            data = data.astype({"has_nephritis_of_renal_pelvis": int})
+        elif config == "bladder":
+            data = data.astype({"has_inflammed_bladder": int})
+
+        data = data[list(features_types_per_config[config].keys())]       
         
         return data