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Benchmarking native categorical support
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| from sklearn.model_selection import cross_validate | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from sklearn.datasets import fetch_openml | |
| from sklearn.experimental import enable_hist_gradient_boosting # noqa | |
| from sklearn.ensemble import HistGradientBoostingClassifier | |
| from sklearn.pipeline import make_pipeline | |
| from sklearn.compose import make_column_transformer | |
| from sklearn.compose import make_column_selector | |
| from sklearn.preprocessing import OneHotEncoder | |
| from sklearn.impute import SimpleImputer | |
| X, y = fetch_openml(data_id=179, as_frame=True, return_X_y=True) | |
| # does not support categories in encoding y yet | |
| y = y.cat.codes | |
| n_features = X.shape[1] | |
| n_categorical_features = (X.dtypes == 'category').sum() | |
| n_numerical_features = (X.dtypes == 'float').sum() | |
| print(f"Number of features: {X.shape[1]}") | |
| print(f"Number of categorical featuers: {n_categorical_features}") | |
| print(f"Number of numerical featuers: {n_numerical_features}") | |
| cat_pipe = make_pipeline( | |
| SimpleImputer(strategy='constant', fill_value='missing'), | |
| OneHotEncoder(sparse=False, handle_unknown='ignore')) | |
| preprocessor = make_column_transformer( | |
| (cat_pipe, make_column_selector(dtype_include='category')), | |
| remainder='passthrough') | |
| hist_one_hot = make_pipeline(preprocessor, | |
| HistGradientBoostingClassifier(random_state=0)) | |
| hist_native = HistGradientBoostingClassifier(categorical='pandas', | |
| random_state=0) | |
| one_hot_result = cross_validate(hist_one_hot, X, y) | |
| native_result = cross_validate(hist_native, X, y) | |
| fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(12, 8)) | |
| plot_info = [('fit_time', 'Fit times (s)', ax1), | |
| ('score_time', 'Score times (s)', ax2), | |
| ('test_score', 'Test Scores (accuracy)', ax3)] | |
| x, width = np.arange(2), 0.9 | |
| for key, title, ax in plot_info: | |
| items = [native_result[key], one_hot_result[key]] | |
| ax.bar(x, [np.mean(item) for item in items], | |
| width, | |
| yerr=[np.std(item) for item in items], | |
| color=['b', 'r']) | |
| ax.set(xlabel='Split number', | |
| title=title, | |
| xticks=[0, 1], | |
| xticklabels=['Native', "One Hot"]) | |
| fig.suptitle("Adult dataset") | |
| plt.show() |
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