Update README.md

README.md

@@ -170,3 +170,109 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+# Dataset Card for Census Income (Adult)
+
+<!-- Provide a quick summary of the dataset. -->
+
+This dataset is a precise version of [Adult](https://archive.ics.uci.edu/dataset/2/adult) or [Census Income](https://archive.ics.uci.edu/dataset/20/census+income). This dataset from UCI somehow happens to occupy two links, but we checked and confirm that they are identical.
+
+
+We used the following python script to create this Hugging Face dataset.
+```python
+import pandas as pd
+from datasets import Dataset, DatasetDict, Features, Value, ClassLabel
+
+# URLs
+url1 = "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"
+url2 = "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test"
+
+# Column names
+columns = [
+    "age", "workclass", "fnlwgt", "education", "education-num", "marital-status",
+    "occupation", "relationship", "race", "sex", "capital-gain", "capital-loss",
+    "hours-per-week", "native-country", "income"
+]
+
+
+# Load datasets
+df_train = pd.read_csv(url1, names=columns, skipinitialspace=True)
+df_test = pd.read_csv(url2, names=columns, skipinitialspace=True, skiprows=1)
+
+# Convert continuous columns to float
+continuous_columns = ["age", "fnlwgt", "education-num", "capital-gain", "capital-loss", "hours-per-week"]
+for col in continuous_columns:
+    df_train[col] = pd.to_numeric(df_train[col], errors='coerce')
+    df_test[col] = pd.to_numeric(df_test[col], errors='coerce')
+
+df_test['income'] = df_test['income'].str.rstrip('.') # This is somewhat critical.
+
+# Define categorical columns
+categorical_columns = [
+    "workclass", "education", "marital-status", "occupation", "relationship",
+    "race", "sex", "native-country", "income"
+]
+
+# Dictionary to store category mappings
+category_mappings = {}
+
+for col in categorical_columns:
+    # Convert train column to category and extract categories
+    df_train[col] = df_train[col].astype("category")
+    category_mappings[col] = df_train[col].cat.categories.to_list()  # Store category order
+
+    # Apply the same category mapping to test
+    df_test[col] = pd.Categorical(df_test[col], categories=category_mappings[col])
+
+    # Convert to integer codes
+    df_train[col] = df_train[col].cat.codes
+    df_test[col] = df_test[col].cat.codes
+
+# Define Hugging Face dataset schema
+hf_features = Features({
+    "age": Value("int64"),
+    "workclass": ClassLabel(names=category_mappings["workclass"]),
+    "fnlwgt": Value("int64"),
+    "education": ClassLabel(names=category_mappings["education"]),
+    "education-num": Value("int64"),
+    "marital-status": ClassLabel(names=category_mappings["marital-status"]),
+    "occupation": ClassLabel(names=category_mappings["occupation"]),
+    "relationship": ClassLabel(names=category_mappings["relationship"]),
+    "race": ClassLabel(names=category_mappings["race"]),
+    "sex": ClassLabel(names=category_mappings["sex"]),
+    "capital-gain": Value("int64"),
+    "capital-loss": Value("int64"),
+    "hours-per-week": Value("int64"),
+    "native-country": ClassLabel(names=category_mappings["native-country"]),
+    "income": ClassLabel(names=category_mappings["income"])
+})
+
+# Convert pandas DataFrame to Hugging Face Dataset
+hf_train = Dataset.from_pandas(df_train, features=hf_features)
+hf_test = Dataset.from_pandas(df_test, features=hf_features)
+
+# Create a dataset dictionary
+hf_dataset = DatasetDict({
+    "train": hf_train,
+    "test": hf_test
+})
+
+# Print dataset structure
+print(hf_dataset)
+```
+
+The printed output could look like
+
+```
+DatasetDict({
+    train: Dataset({
+        features: ['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'income'],
+        num_rows: 32561
+    })
+    test: Dataset({
+        features: ['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'income'],
+        num_rows: 16281
+    })
+})
+```
+