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README.md

@@ -11,9 +11,34 @@ size_categories:
 task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
 - tabular-classification
 configs:
+- encoding
 - income
 - income-no race
 - race
 ---
 # Adult
-The [Adult dataset](https://archive.ics.uci.edu/ml/datasets/Adult) is cool.
+The [Adult dataset](https://archive.ics.uci.edu/ml/datasets/Adult) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
+Census dataset including income threshold.
+
+# Configurations and tasks
+The dataset has four configurations:
+- `encoding`, which holds the encoding dictionaries mapping binary and ordinal features to their value;
+- `income`, for binary classification of the individual's income;
+- `income-no race`, as `income`, but the `race` feature is removed;
+- `race`, multiclass classification to predict the `race` of the individual.
+
+# Features
+- `age` Age of the person;
+- `capital_gain` Capital gained by the person;
+- `capital_loss` Capital lost by the person;
+- `education` Education level: the higher, the more educated the person;
+- `final_weight`
+- `hours_per_week` Hours worked per week;
+- `marital_status` Marital status of the person;
+- `native_country` Native country of the person;
+- `occupation` Job of the person;
+- `race` Race of the person;
+- `relationship`
+- `sex` Sex of the person;
+- `workclass` Type of job of the person;
+- `over_threshold` `1` for income `>= 50k$`, `0` otherwise.

adult.py

@@ -1,6 +1,7 @@
 """Adult: A Census Dataset"""
 
 from typing import List
+from functools import partial
 
 import datasets
 
@@ -39,8 +40,22 @@ _BASE_FEATURE_NAMES = [
     "relationship",
     "sex",
     "workclass",
-    "threshold",
+    "over_threshold",
 ]
+_ENCODINGS = {
+    "sex": {
+            "Male": 0,
+            "Female": 1
+        }
+}
+_RACE_ENCODING = {
+    "White": 0,
+    "Black": 1,
+    "Asian-Pac-Islander": 2,
+    "Amer-Indian-Eskimo": 3,
+    "Other": 4,
+}
+
 DESCRIPTION = "Adult dataset from the UCI ML repository."
 _HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Adult"
 _URLS = ("https://huggingface.co/datasets/mstz/adult/raw/adult.csv")
@@ -82,7 +97,7 @@ features_types_per_config = {
                "relationship": datasets.Value("string"),
                "sex": datasets.Value("int8"),
                "workclass": datasets.Value("string"),
-               "threshold": datasets.ClassLabel(num_classes=2, names=("no", "yes"))},
+               "over_threshold": datasets.ClassLabel(num_classes=2, names=("no", "yes"))},
     "income-no race": {"age": datasets.Value("int64"),
                        "capital_gain": datasets.Value("float64"),
                        "capital_loss": datasets.Value("float64"),
@@ -95,7 +110,7 @@ features_types_per_config = {
                        "relationship": datasets.Value("string"),
                        "sex": datasets.Value("int8"),
                        "workclass": datasets.Value("string"),
-                       "threshold": datasets.ClassLabel(num_classes=2, names=("no", "yes"))},
+                       "over_threshold": datasets.ClassLabel(num_classes=2, names=("no", "yes"))},
     "race": {"age": datasets.Value("int64"),
              "capital_gain": datasets.Value("float64"),
              "capital_loss": datasets.Value("float64"),
@@ -124,6 +139,8 @@ class Adult(datasets.GeneratorBasedBuilder):
     # dataset versions
     DEFAULT_CONFIG = "income"
     BUILDER_CONFIGS = [
+        AdultConfig(name="encoding",
+                    description="Encoding dictionaries."),
         AdultConfig(name="income",
                     description="Adult for income threshold binary classification."),
         AdultConfig(name="income-no race",
@@ -151,91 +168,61 @@ class Adult(datasets.GeneratorBasedBuilder):
         ]
     
     def _generate_examples(self, filepath: str):
-        data = pandas.read_csv(filepath)
-        data = self.preprocess(data, config=self.config.name)
+        if self.config.name == "encoding":
+            data = self.encodings()
+        elif self.config.name in ["income", "income-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 in _ENCODINGS]
+        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 = pandas.concat(data, axis="rows").reset_index()
+        data.drop("index", axis="columns", inplace=True)
 
-        for row_id, row in data.iterrows():
-            data_row = dict(row)
+        return data
 
-            yield row_id, data_row
 
     def preprocess(self, data: pandas.DataFrame, config: str = "income") -> pandas.DataFrame:
         data.drop("education", axis="columns", inplace=True)
+
         data = data[["age", "capital_gain", "capital_loss", "education-num", "final_weight",
                      "hours_per_week", "marital_status", "native_country", "occupation",
-                     "race", "relationship", "sex", "workclass", "threshold"]]
+                     "race", "relationship", "sex", "workclass", "over_threshold"]]
         data.columns = _BASE_FEATURE_NAMES
 
-        # binarize features
-        data.loc[:, "sex"] = data.sex.apply(self.encode_sex)
+        for feature in _ENCODINGS:
+            encoding_function = partial(self.encode, feature)
+            data.loc[:, feature] = data[feature].apply(encoding_function)
         
         if config == "income":
-            return self.income_preprocessing(data)
+            return data[list(features_types_per_config["income"].keys())]
         elif config == "income-no race":
-            return self.income_norace_preprocessing(data)
+            return data[list(features_types_per_config["income-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 self.race_preprocessing(data)
         else:
             raise ValueError(f"Unknown config: {config}")
     
-    def income_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame:
-        data = data[list(features_types_per_config["income"].keys())]
-
-        return data
-        
-    def income_norace_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame:
-        data = data[list(features_types_per_config["income-no race"].keys())]
-
-        return data
+    def encode(self, feature, value):
+        if feature in _ENCODING_DICS:
+            return _ENCODING_DICS[feature][value]
+        raise ValueError(f"Unknown feature: {feature}")
     
-    def race_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame:
-        features = list(features_types_per_config["race"].keys())
-        features[features.index("over_threshold")] = "threshold"
-        data.loc[:, "race"] = data.race.apply(self.encode_race)
-        data = data[features]
-        data.columns = ["age", "capital_gain", "capital_loss", "education", "final_weight",
-                        "hours_per_week", "marital_status", "native_country", "occupation", "relationship", "sex", "workclass", "over_threshold", "race"]
-
-        return data
-
     def encode_race(self, race):
-        return self.race_encoding_dic()[race]
-
-    def decode_race(self, code):
-        return self.race_decoding_dic()[code]
-    
-    def race_decoding_dic(self):
-        return {
-            0: "White",
-            1: "Black",
-            2: "Asian-Pac-Islander",
-            3: "Amer-Indian-Eskimo",
-            4: "Other",
-        }
-
-    def race_encoding_dic(self):
-        return {
-            "White": 0,
-            "Black": 1,
-            "Asian-Pac-Islander": 2,
-            "Amer-Indian-Eskimo": 3,
-            "Other": 4,
-        }
-
-    def encode_sex(self, sex):
-        return self.sex_encoding_dic()[sex]
-
-    def decode_sex(self, code):
-        return self.sex_decoding_dic()[code]
-
-    def sex_encoding_dic(self):
-        return {
-            "Male": 0,
-            "Female": 1
-        }
-    
-    def sex_decoding_dic(self):
-        return {
-            0: "Male",
-            1: "Female"
-        }
+        return _RACE_ENCODING[race]