Feature engineering pipeline for categorical data

The script showcases how to keep the categorical data encoding consistent across training and inference. There are many ways to attain the same goal, this script can be used as a starting point.

See Also

• Tutorial

• Getting started with categorical data

• Train XGBoost with cat_in_the_dat dataset

from typing import List, Tuple

import numpy as np
import pandas as pd
from sklearn.compose import make_column_selector, make_column_transformer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OrdinalEncoder

import xgboost as xgb


def make_example_data() -> Tuple[pd.DataFrame, pd.Series, List[str]]:
    """Generate data for demo."""
    n_samples = 2048
    rng = np.random.default_rng(1994)

    # We have three categorical features, while the rest are numerical.
    categorical_features = ["brand_id", "retailer_id", "category_id"]

    df = pd.DataFrame(
        np.random.randint(32, 96, size=(n_samples, 3)),
        columns=categorical_features,
    )

    df["price"] = rng.integers(100, 200, size=(n_samples,))
    df["stock_status"] = rng.choice([True, False], n_samples)
    df["on_sale"] = rng.choice([True, False], n_samples)
    df["label"] = rng.normal(loc=0.0, scale=1.0, size=n_samples)

    X = df.drop(["label"], axis=1)
    y = df["label"]

    return X, y, categorical_features


def native() -> None:
    """Using the native XGBoost interface."""
    X, y, cat_feats = make_example_data()

    X_train, X_test, y_train, y_test = train_test_split(
        X, y, random_state=1994, test_size=0.2
    )

    # Create an encoder based on training data.
    enc = OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=np.nan)
    enc.set_output(transform="pandas")
    enc = enc.fit(X_train[cat_feats])

    def enc_transform(X: pd.DataFrame) -> pd.DataFrame:
        # don't make change inplace so that we can have demonstrations for encoding
        X = X.copy()
        cat_cols = enc.transform(X[cat_feats])
        for i, name in enumerate(cat_feats):
            # create pd.Series based on the encoder
            cat_cols[name] = pd.Categorical.from_codes(
                codes=cat_cols[name].astype(np.int32), categories=enc.categories_[i]
            )
        X[cat_feats] = cat_cols
        return X

    # Encode the data based on fitted encoder.
    X_train_enc = enc_transform(X_train)
    X_test_enc = enc_transform(X_test)
    # Train XGBoost model using the native interface.
    Xy_train = xgb.QuantileDMatrix(X_train_enc, y_train, enable_categorical=True)
    Xy_test = xgb.QuantileDMatrix(
        X_test_enc, y_test, enable_categorical=True, ref=Xy_train
    )
    booster = xgb.train({}, Xy_train)
    booster.predict(Xy_test)

    # Following shows that data are encoded consistently.

    # We first obtain result from newly encoded data
    predt0 = booster.inplace_predict(enc_transform(X_train.head(16)))
    # then we obtain result from already encoded data from training.
    predt1 = booster.inplace_predict(X_train_enc.head(16))

    np.testing.assert_allclose(predt0, predt1)


def pipeline() -> None:
    """Using the sklearn pipeline."""
    X, y, cat_feats = make_example_data()

    X_train, X_test, y_train, y_test = train_test_split(
        X, y, random_state=3, test_size=0.2
    )

    enc = make_column_transformer(
        (
            OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=np.nan),
            # all categorical feature names end with "_id"
            make_column_selector(pattern=".*_id"),
        ),
        remainder="passthrough",
        verbose_feature_names_out=False,
    )
    # No need to set pandas output, we use `feature_types` to indicate the type of
    # features.

    # enc.set_output(transform="pandas")

    feature_types = ["c" if fn in cat_feats else "q" for fn in X_train.columns]
    reg = xgb.XGBRegressor(
        feature_types=feature_types, enable_categorical=True, n_estimators=10
    )
    p = make_pipeline(enc, reg)
    p.fit(X_train, y_train)
    # check XGBoost is using the feature type correctly.
    model_types = reg.get_booster().feature_types
    assert model_types is not None
    for a, b in zip(model_types, feature_types):
        assert a == b

    # Following shows that data are encoded consistently.

    # We first create a slice of data that doesn't contain all the categories
    predt0 = p.predict(X_train.iloc[:16, :])
    # Then we use the dataframe that contains all the categories
    predt1 = p.predict(X_train)[:16]

    # The resulting encoding is the same
    np.testing.assert_allclose(predt0, predt1)


if __name__ == "__main__":
    pipeline()
    native()