Upload magic.py

magic.py

@@ -87,73 +87,10 @@ class Magic(datasets.GeneratorBasedBuilder):
         ]
     
     def _generate_examples(self, filepath: str):
-        if self.config.name == "encoding":
-            data = self.encodings()
-
-            for row_id, row in data.iterrows():
-                data_row = dict(row)
-
-                yield row_id, data_row
-
-        elif self.config.name in ["magic", "magic-no race", "race"]:               
-            data = pandas.read_csv(filepath)
-            data = self.preprocess(data, config=self.config.name)
-
-            for row_id, row in data.iterrows():
-                data_row = dict(row)
-
-                yield row_id, data_row
-
-        else:
-            raise ValueError(f"Unknown config: {self.config.name}")
-
-    def encodings(self):
-        data = [pandas.DataFrame([(feature, original_value, encoded_value)
-                                   for original_value, encoded_value in d.items()],
-                                 columns=["feature", "original_value", "encoded_value"])
-                for feature, d in _ENCODING_DICS.items()]
-        data.append(pandas.DataFrame([("race", original_value, encoded_value)
-                                       for original_value, encoded_value in _RACE_ENCODING.items()],
-                    columns=["feature", "original_value", "encoded_value"]))
-        data.append(pandas.DataFrame([("education", original_value, encoded_value)
-                                       for original_value, encoded_value in _EDUCATION_ENCODING.items()],
-                    columns=["feature", "original_value", "encoded_value"]))
-        data = pandas.concat(data, axis="rows").reset_index()
-        data.drop("index", axis="columns", inplace=True)
-
-        return data
-
-
-    def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
-        data.drop("education", axis="columns", inplace=True)
-        data = data.rename(columns={"threshold": "over_threshold", "sex": "is_male"})
-
-        data = data[["age", "capital_gain", "capital_loss", "education-num", "final_weight",
-                     "hours_per_week", "marital_status", "native_country", "occupation",
-                     "race", "relationship", "is_male", "workclass", "over_threshold"]]
-        data.columns = _BASE_FEATURE_NAMES
-
-        for feature in _ENCODING_DICS:
-            encoding_function = partial(self.encode, feature)
-            data.loc[:, feature] = data[feature].apply(encoding_function)
-        
-
-        if config == "magic":
-            return data[list(features_types_per_config["magic"].keys())]
-        elif config == "magic-no race":
-            return data[list(features_types_per_config["magic-no race"].keys())]
-        elif config =="race":
-            data.loc[:, "race"] = data.race.apply(self.encode_race)
-            data = data[list(features_types_per_config["race"].keys())]
-
-            return data
-        else:
-            raise ValueError(f"Unknown config: {config}")
-    
-    def encode(self, feature, value):
-        if feature in _ENCODING_DICS:
-            return _ENCODING_DICS[feature][value]
-        raise ValueError(f"Unknown feature: {feature}")
-    
-    def encode_race(self, race):
-        return _RACE_ENCODING[race]
+        data = pandas.read_csv(filepath)
+        data = self.preprocess(data, config=self.config.name)
+
+        for row_id, row in data.iterrows():
+            data_row = dict(row)
+
+            yield row_id, data_row