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Speed up example on plot_gradient_boosting_categorical.py #21634

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Nov 24, 2021
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Speed up example on plot_gradient_boosting_categorical.py #21634

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sped up the example by reducing the number of columns and the number …
pedugnat Nov 11, 2021
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removed warning filter as it breaks the CI
pedugnat Nov 11, 2021
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pedugnat Nov 11, 2021
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fixed typo
pedugnat Nov 11, 2021
ab22fa6
made code more robust as per PR comment
pedugnat Nov 11, 2021
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pedugnat Nov 12, 2021
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73 changes: 55 additions & 18 deletions examples/ensemble/plot_gradient_boosting_categorical.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,14 +26,44 @@
# %%
# Load Ames Housing dataset
# -------------------------
# First, we load the ames housing data as a pandas dataframe. The features
# 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=41211, as_frame=True, return_X_y=True)

n_categorical_features = (X.dtypes == "category").sum()
n_numerical_features = (X.dtypes == "float").sum()
# Select only a subset of features of X to make the example faster to run
categorical_columns_subset = [
"Bldg_Type",
"Garage_Finish",
"Lot_Config",
"Functional",
"Mas_Vnr_Type",
"House_Style",
"Fireplace_Qu",
"Exter_Cond",
"Exter_Qual",
"Pool_QC",
]

numerical_columns_subset = [
"Three_season_porch",
"Fireplaces",
"Bsmt_Half_Bath",
"Half_Bath",
"Garage_Cars",
"TotRms_AbvGrd",
"BsmtFin_SF_1",
"BsmtFin_SF_2",
"Gr_Liv_Area",
"Screen_Porch",
]

X = X[categorical_columns_subset + numerical_columns_subset]

n_categorical_features = X.select_dtypes(include="category").shape[1]
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}")
Expand Down Expand Up @@ -114,6 +144,7 @@

# The ordinal encoder will first output the categorical features, and then the
# continuous (passed-through) features

categorical_mask = [True] * n_categorical_features + [False] * n_numerical_features
hist_native = make_pipeline(
ordinal_encoder,
Expand All @@ -134,18 +165,20 @@
import matplotlib.pyplot as plt

scoring = "neg_mean_absolute_percentage_error"
dropped_result = cross_validate(hist_dropped, X, y, cv=3, scoring=scoring)
one_hot_result = cross_validate(hist_one_hot, X, y, cv=3, scoring=scoring)
ordinal_result = cross_validate(hist_ordinal, X, y, cv=3, scoring=scoring)
native_result = cross_validate(hist_native, X, y, cv=3, scoring=scoring)
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, (0, 0.20)),
("test_score", "Mean Absolute Percentage Error", ax2, None),
]

x, width = np.arange(4), 0.9
Expand All @@ -156,11 +189,15 @@ def plot_results(figure_title):
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,
[np.mean(np.abs(item)) for item in items],
width,
yerr=[np.std(item) for item in items],
x=x,
height=mape_cv_mean,
width=width,
yerr=mape_cv_std,
color=["C0", "C1", "C2", "C3"],
)
ax.set(
Expand All @@ -173,7 +210,7 @@ def plot_results(figure_title):
fig.suptitle(figure_title)


plot_results("Gradient Boosting on Adult Census")
plot_results("Gradient Boosting on Ames Housing")

# %%
# We see that the model with one-hot-encoded data is by far the slowest. This
Expand Down Expand Up @@ -219,12 +256,12 @@ def plot_results(figure_title):
histgradientboostingregressor__max_iter=15,
)

dropped_result = cross_validate(hist_dropped, X, y, cv=3, scoring=scoring)
one_hot_result = cross_validate(hist_one_hot, X, y, cv=3, scoring=scoring)
ordinal_result = cross_validate(hist_ordinal, X, y, cv=3, scoring=scoring)
native_result = cross_validate(hist_native, X, y, cv=3, scoring=scoring)
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 Adult Census (few and small trees)")
plot_results("Gradient Boosting on Ames Housing (few and small trees)")

plt.show()

Expand Down