"""
================================================
Categorical Feature Support in Gradient Boosting
================================================
.. currentmodule:: sklearn
In this example, we will compare the training times and prediction
performances of :class:`~ensemble.HistGradientBoostingRegressor` with
different encoding strategies for categorical features. In
particular, we will evaluate:
- dropping the categorical features
- using a :class:`~preprocessing.OneHotEncoder`
- using an :class:`~preprocessing.OrdinalEncoder` and treat categories as
ordered, equidistant quantities
- using an :class:`~preprocessing.OrdinalEncoder` and rely on the :ref:`native
category support ` of the
:class:`~ensemble.HistGradientBoostingRegressor` estimator.
We will work with the Ames Iowa Housing dataset which consists of numerical
and categorical features, where the houses' sales prices is the target.
"""
# %%
# Load Ames Housing dataset
# -------------------------
# First, we load the Ames Housing data as a pandas dataframe. The features
# are either categorical or numerical:
from sklearn.datasets import fetch_openml
X, y = fetch_openml(data_id=42165, as_frame=True, return_X_y=True)
# Select only a subset of features of X to make the example faster to run
categorical_columns_subset = [
"BldgType",
"GarageFinish",
"LotConfig",
"Functional",
"MasVnrType",
"HouseStyle",
"FireplaceQu",
"ExterCond",
"ExterQual",
"PoolQC",
]
numerical_columns_subset = [
"3SsnPorch",
"Fireplaces",
"BsmtHalfBath",
"HalfBath",
"GarageCars",
"TotRmsAbvGrd",
"BsmtFinSF1",
"BsmtFinSF2",
"GrLivArea",
"ScreenPorch",
]
X = X[categorical_columns_subset + numerical_columns_subset]
X[categorical_columns_subset] = X[categorical_columns_subset].astype("category")
categorical_columns = X.select_dtypes(include="category").columns
n_categorical_features = len(categorical_columns)
n_numerical_features = X.select_dtypes(include="number").shape[1]
print(f"Number of samples: {X.shape[0]}")
print(f"Number of features: {X.shape[1]}")
print(f"Number of categorical features: {n_categorical_features}")
print(f"Number of numerical features: {n_numerical_features}")
# %%
# Gradient boosting estimator with dropped categorical features
# -------------------------------------------------------------
# As a baseline, we create an estimator where the categorical features are
# dropped:
from sklearn.compose import make_column_selector, make_column_transformer
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.pipeline import make_pipeline
dropper = make_column_transformer(
("drop", make_column_selector(dtype_include="category")), remainder="passthrough"
)
hist_dropped = make_pipeline(dropper, HistGradientBoostingRegressor(random_state=42))
# %%
# Gradient boosting estimator with one-hot encoding
# -------------------------------------------------
# Next, we create a pipeline that will one-hot encode the categorical features
# and let the rest of the numerical data to passthrough:
from sklearn.preprocessing import OneHotEncoder
one_hot_encoder = make_column_transformer(
(
OneHotEncoder(sparse_output=False, handle_unknown="ignore"),
make_column_selector(dtype_include="category"),
),
remainder="passthrough",
)
hist_one_hot = make_pipeline(
one_hot_encoder, HistGradientBoostingRegressor(random_state=42)
)
# %%
# Gradient boosting estimator with ordinal encoding
# -------------------------------------------------
# Next, we create a pipeline that will treat categorical features as if they
# were ordered quantities, i.e. the categories will be encoded as 0, 1, 2,
# etc., and treated as continuous features.
import numpy as np
from sklearn.preprocessing import OrdinalEncoder
ordinal_encoder = make_column_transformer(
(
OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=np.nan),
make_column_selector(dtype_include="category"),
),
remainder="passthrough",
# Use short feature names to make it easier to specify the categorical
# variables in the HistGradientBoostingRegressor in the next step
# of the pipeline.
verbose_feature_names_out=False,
)
hist_ordinal = make_pipeline(
ordinal_encoder, HistGradientBoostingRegressor(random_state=42)
)
# %%
# Gradient boosting estimator with native categorical support
# -----------------------------------------------------------
# We now create a :class:`~ensemble.HistGradientBoostingRegressor` estimator
# that will natively handle categorical features. This estimator will not treat
# categorical features as ordered quantities. We set
# `categorical_features="from_dtype"` such that features with categorical dtype
# are considered categorical features.
#
# The main difference between this estimator and the previous one is that in
# this one, we let the :class:`~ensemble.HistGradientBoostingRegressor` detect
# which features are categorical from the DataFrame columns' dtypes.
hist_native = HistGradientBoostingRegressor(
random_state=42, categorical_features="from_dtype"
)
# %%
# Model comparison
# ----------------
# Finally, we evaluate the models using cross validation. Here we compare the
# models performance in terms of
# :func:`~metrics.mean_absolute_percentage_error` and fit times.
import matplotlib.pyplot as plt
from sklearn.model_selection import cross_validate
scoring = "neg_mean_absolute_percentage_error"
n_cv_folds = 3
dropped_result = cross_validate(hist_dropped, X, y, cv=n_cv_folds, scoring=scoring)
one_hot_result = cross_validate(hist_one_hot, X, y, cv=n_cv_folds, scoring=scoring)
ordinal_result = cross_validate(hist_ordinal, X, y, cv=n_cv_folds, scoring=scoring)
native_result = cross_validate(hist_native, X, y, cv=n_cv_folds, scoring=scoring)
def plot_results(figure_title):
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8))
plot_info = [
("fit_time", "Fit times (s)", ax1, None),
("test_score", "Mean Absolute Percentage Error", ax2, None),
]
x, width = np.arange(4), 0.9
for key, title, ax, y_limit in plot_info:
items = [
dropped_result[key],
one_hot_result[key],
ordinal_result[key],
native_result[key],
]
mape_cv_mean = [np.mean(np.abs(item)) for item in items]
mape_cv_std = [np.std(item) for item in items]
ax.bar(
x=x,
height=mape_cv_mean,
width=width,
yerr=mape_cv_std,
color=["C0", "C1", "C2", "C3"],
)
ax.set(
xlabel="Model",
title=title,
xticks=x,
xticklabels=["Dropped", "One Hot", "Ordinal", "Native"],
ylim=y_limit,
)
fig.suptitle(figure_title)
plot_results("Gradient Boosting on Ames Housing")
# %%
# We see that the model with one-hot-encoded data is by far the slowest. This
# is to be expected, since one-hot-encoding creates one additional feature per
# category value (for each categorical feature), and thus more split points
# need to be considered during fitting. In theory, we expect the native
# handling of categorical features to be slightly slower than treating
# categories as ordered quantities ('Ordinal'), since native handling requires
# :ref:`sorting categories `. Fitting times should
# however be close when the number of categories is small, and this may not
# always be reflected in practice.
#
# In terms of prediction performance, dropping the categorical features leads
# to poorer performance. The three models that use categorical features have
# comparable error rates, with a slight edge for the native handling.
# %%
# Limiting the number of splits
# -----------------------------
# In general, one can expect poorer predictions from one-hot-encoded data,
# especially when the tree depths or the number of nodes are limited: with
# one-hot-encoded data, one needs more split points, i.e. more depth, in order
# to recover an equivalent split that could be obtained in one single split
# point with native handling.
#
# This is also true when categories are treated as ordinal quantities: if
# categories are `A..F` and the best split is `ACF - BDE` the one-hot-encoder
# model will need 3 split points (one per category in the left node), and the
# ordinal non-native model will need 4 splits: 1 split to isolate `A`, 1 split
# to isolate `F`, and 2 splits to isolate `C` from `BCDE`.
#
# How strongly the models' performances differ in practice will depend on the
# dataset and on the flexibility of the trees.
#
# To see this, let us re-run the same analysis with under-fitting models where
# we artificially limit the total number of splits by both limiting the number
# of trees and the depth of each tree.
for pipe in (hist_dropped, hist_one_hot, hist_ordinal, hist_native):
if pipe is hist_native:
# The native model does not use a pipeline so, we can set the parameters
# directly.
pipe.set_params(max_depth=3, max_iter=15)
else:
pipe.set_params(
histgradientboostingregressor__max_depth=3,
histgradientboostingregressor__max_iter=15,
)
dropped_result = cross_validate(hist_dropped, X, y, cv=n_cv_folds, scoring=scoring)
one_hot_result = cross_validate(hist_one_hot, X, y, cv=n_cv_folds, scoring=scoring)
ordinal_result = cross_validate(hist_ordinal, X, y, cv=n_cv_folds, scoring=scoring)
native_result = cross_validate(hist_native, X, y, cv=n_cv_folds, scoring=scoring)
plot_results("Gradient Boosting on Ames Housing (few and small trees)")
plt.show()
# %%
# The results for these under-fitting models confirm our previous intuition:
# the native category handling strategy performs the best when the splitting
# budget is constrained. The two other strategies (one-hot encoding and
# treating categories as ordinal values) lead to error values comparable
# to the baseline model that just dropped the categorical features altogether.