Upload heart.py

heart.py

@@ -40,60 +40,9 @@ _CITATION = """
 
 # Dataset info
 urls_per_split = {
-    "cleveland": {"train": "https://huggingface.co/datasets/mstz/heart/raw/main/processed.cleveland.data"},
     "hungary": {"train": "https://huggingface.co/datasets/mstz/heart/raw/main/processed.hungarian.data"},
-    "switzerland": {"train": "https://huggingface.co/datasets/mstz/heart/raw/main/processed.switzerland.data"},
-    "va": {"train": "https://huggingface.co/datasets/mstz/heart/raw/main/processed.va.data"}
 }
 features_types_per_config = {
-    "cleveland": {
-        "age": datasets.Value("int8"),
-        "is_male": datasets.Value("bool"),
-        "type_of_chest_pain": datasets.Value("string"),
-        "resting_blood_pressure": datasets.Value("float32"),
-        "serum_cholesterol": datasets.Value("float32"),
-        "fasting_blood_sugar": datasets.Value("float32"),
-        "rest_electrocardiographic_type": datasets.Value("string"),
-        "maximum_heart_rate": datasets.Value("float32"),
-        "has_exercise_induced_angina": datasets.Value("bool"),
-        "depression_induced_by_exercise": datasets.Value("float32"),
-        "slope_of_peak_exercise": datasets.Value("float32"),
-        "number_of_major_vessels_colored_by_flourosopy": datasets.Value("int16"),
-        "thal": datasets.Value("float32"),
-        "has_hearth_disease": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
-    },
-    "va": {
-        "age": datasets.Value("int8"),
-        "is_male": datasets.Value("bool"),
-        "type_of_chest_pain": datasets.Value("string"),
-        "resting_blood_pressure": datasets.Value("float32"),
-        "serum_cholesterol": datasets.Value("float32"),
-        "fasting_blood_sugar": datasets.Value("float32"),
-        "rest_electrocardiographic_type": datasets.Value("string"),
-        "maximum_heart_rate": datasets.Value("float32"),
-        "has_exercise_induced_angina": datasets.Value("bool"),
-        "depression_induced_by_exercise": datasets.Value("float32"),
-        "slope_of_peak_exercise": datasets.Value("float32"),
-        "number_of_major_vessels_colored_by_flourosopy": datasets.Value("int16"),
-        "thal": datasets.Value("float32"),
-        "has_hearth_disease": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
-    },
-    "switzerland": {
-        "age": datasets.Value("int8"),
-        "is_male": datasets.Value("bool"),
-        "type_of_chest_pain": datasets.Value("string"),
-        "resting_blood_pressure": datasets.Value("float32"),
-        "serum_cholesterol": datasets.Value("float32"),
-        "fasting_blood_sugar": datasets.Value("float32"),
-        "rest_electrocardiographic_type": datasets.Value("string"),
-        "maximum_heart_rate": datasets.Value("float32"),
-        "has_exercise_induced_angina": datasets.Value("bool"),
-        "depression_induced_by_exercise": datasets.Value("float32"),
-        "slope_of_peak_exercise": datasets.Value("float32"),
-        "number_of_major_vessels_colored_by_flourosopy": datasets.Value("int16"),
-        "thal": datasets.Value("float32"),
-        "has_hearth_disease": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
-    },
     "hungary": {
         "age": datasets.Value("int8"),
         "is_male": datasets.Value("bool"),
@@ -127,14 +76,8 @@ class HeartConfig(datasets.BuilderConfig):
 
 class Heart(datasets.GeneratorBasedBuilder):
     # dataset versions
-    DEFAULT_CONFIG = "cleveland"
+    DEFAULT_CONFIG = "hungary"
     BUILDER_CONFIGS = [
-        HeartConfig(name="cleveland",
-                    description="Heart for binary classification, dataset."),
-        HeartConfig(name="va",
-                    description="Heart for binary classification, va dataset."),
-        HeartConfig(name="switzerland",
-                    description="Heart for binary classification, switzerland dataset."),
         HeartConfig(name="hungary",
                     description="Heart for binary classification, hungary dataset.")
     ]
@@ -158,12 +101,6 @@ class Heart(datasets.GeneratorBasedBuilder):
         data.columns = _BASE_FEATURE_NAMES
         data = self.preprocess(data, self.config.name)
 
-        print(data.head())
-        print(data.dtypes)
-        for f in data.columns:
-            print(f)
-            print(data[f].unique())
-
         for row_id, row in data.iterrows():
             data_row = dict(row)
 
@@ -176,47 +113,26 @@ class Heart(datasets.GeneratorBasedBuilder):
 
         data[["age"]].applymap(int)
 
-        if config == "hungary":
-            data.drop("slope_of_peak_exercise", axis="columns", inplace=True)
-            data.drop("number_of_major_vessels_colored_by_flourosopy", axis="columns", inplace=True)
-            data.drop("thal", axis="columns", inplace=True)
-            data = data[data.serum_cholesterol != "?"]
-
-            data = data.infer_objects()
-
-            data = data[data.resting_blood_pressure != "?"]
-            data = data[data.fasting_blood_sugar != "?"]
-            data = data[data.rest_electrocardiographic_type != "?"]
-            data = data[data.maximum_heart_rate != "?"]
-            data = data[data.has_exercise_induced_angina != "?"]
-
-            for f in data.columns:
-                if "?" in data[f].values:
-                    print(f)
-            data = data.astype({"is_male": bool, "has_exercise_induced_angina": bool,
-                                "serum_cholesterol": float, "maximum_heart_rate": float,
-                                "resting_blood_pressure": float, "fasting_blood_sugar": float})
-        else:
-            data = data[data.thal != "?"]
-            data = data[data.number_of_major_vessels_colored_by_flourosopy != "?"]
-            data = data[data.resting_blood_pressure != "?"]
-            data = data[data.fasting_blood_sugar != "?"]
-            data = data[data.rest_electrocardiographic_type != "?"]
-            data = data[data.maximum_heart_rate != "?"]
-            data = data[data.has_exercise_induced_angina != "?"]
-            
-            for f in data.columns:
-                if "?" in data[f].values:
-                    print(f)
-            data = data.astype({"is_male": bool, "has_exercise_induced_angina": bool,
-                                "serum_cholesterol": float, "maximum_heart_rate": float,
-                                "resting_blood_pressure": float, "fasting_blood_sugar": float,
-                                "thal": float, "number_of_major_vessels_colored_by_flourosopy": float})
-        
+        data.drop("slope_of_peak_exercise", axis="columns", inplace=True)
+        data.drop("number_of_major_vessels_colored_by_flourosopy", axis="columns", inplace=True)
+        data.drop("thal", axis="columns", inplace=True)
+        data = data[data.serum_cholesterol != "?"]
+
+        data = data.infer_objects()
+
+        data = data[data.resting_blood_pressure != "?"]
+        data = data[data.fasting_blood_sugar != "?"]
+        data = data[data.rest_electrocardiographic_type != "?"]
+        data = data[data.maximum_heart_rate != "?"]
+        data = data[data.has_exercise_induced_angina != "?"]
+
+        data = data.astype({"is_male": bool, "has_exercise_induced_angina": bool,
+                            "serum_cholesterol": float, "maximum_heart_rate": float,
+                            "resting_blood_pressure": float, "fasting_blood_sugar": float})       
 
         return data
 
     def encode(self, feature, value):
         if feature in _ENCODING_DICS:
             return _ENCODING_DICS[feature][value]
-        raise ValueError(f"Unknown feature: {feature}")
+        raise ValueError(f"Unknown feature: {feature}")