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README.md

@@ -28,17 +28,17 @@ The dataset has four configurations:
 - `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$`, `0` otherwise.
+- `age` `[int64]` Age of the person;
+- `capital_gain` `[float64]` Capital gained by the person;
+- `capital_loss` `[float64]` Capital lost by the person;
+- `education` `[int8]` Education level: the higher, the more educated the person;
+- `final_weight` `[int64]`
+- `hours_per_week` `[int64]` Hours worked per week;
+- `marital_status` `[string]` Marital status of the person;
+- `native_country` `[string]` Native country of the person;
+- `occupation` `[string]` Job of the person;
+- `race` `[string]` Race of the person;
+- `relationship` `[string]`
+- `sex` `[int8]` Sex of the person;
+- `workclass` `[string]` Type of job of the person;
+- `over_threshold` `int8` `1` for income `>= 50k$`, `0` otherwise.

adult.py

@@ -55,6 +55,24 @@ _RACE_ENCODING = {
     "Amer-Indian-Eskimo": 3,
     "Other": 4,
 }
+_EDUCATION_ENCODING = {
+	"Preschool": 0,
+	"1st-4th": 1,
+	"5th-6th": 2,
+	"7th-8th": 3,
+	"9th": 4,
+	"10th": 5,
+	"11th": 6,
+	"12th": 7,
+	"HS-grad": 8,
+	"Assoc-acdm": 9,
+	"Assoc-voc": 10,
+	"Some-college": 11,
+    "Bachelors": 12,
+	"Masters": 13,
+	"Doctorate": 14,
+	"Prof-school": 15
+}
 
 DESCRIPTION = "Adult dataset from the UCI ML repository."
 _HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Adult"
@@ -207,6 +225,9 @@ class Adult(datasets.GeneratorBasedBuilder):
         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)