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DOC Fix UserWarning in plot_gpr_prior_posterior #29268

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DOC Fix UserWarning in plot_gpr_prior_posterior #29268

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25 changes: 16 additions & 9 deletions 25 examples/gaussian_process/plot_gpr_prior_posterior.py
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Original file line number Diff line number Diff line change
Expand Up @@ -196,23 +196,30 @@ def plot_gpr_samples(gpr_model, n_samples, ax):
# %%
# Dot-product kernel
# ..................
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import ConstantKernel, DotProduct
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
Comment on lines +199 to +202
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matplotlib.pyplot is already imported in line 33
GaussianProcessRegressor is already imported in line 100

Suggested change
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import ConstantKernel, DotProduct
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
from sklearn.gaussian_process.kernels import ConstantKernel, DotProduct
from sklearn.preprocessing import StandardScaler


kernel = ConstantKernel(0.1, (0.01, 10.0)) * (
DotProduct(sigma_0=1.0, sigma_0_bounds=(0.1, 10.0)) ** 2
)
gpr = GaussianProcessRegressor(kernel=kernel, random_state=0)
# Scale the data
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)

fig, axs = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(10, 8))
# Define the kernel
kernel = ConstantKernel(0.1, (0.01, 10.0)) * (DotProduct(sigma_0=1.0, sigma_0_bounds=(0.1, 10.0)) ** 2)
Comment on lines +208 to +209
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Please avoid unrelated changes.

Suggested change
# Define the kernel
kernel = ConstantKernel(0.1, (0.01, 10.0)) * (DotProduct(sigma_0=1.0, sigma_0_bounds=(0.1, 10.0)) ** 2)
kernel = ConstantKernel(0.1, (0.01, 10.0)) * (
DotProduct(sigma_0=1.0, sigma_0_bounds=(0.1, 10.0)) ** 2
)


# plot prior
# Increase the number of iterations
gpr = GaussianProcessRegressor(kernel=kernel, random_state=0, n_restarts_optimizer=10, optimizer='fmin_l_bfgs_b')
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@ArturoAmorQ ArturoAmorQ Jun 17, 2024 •
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Adding comments to the code is mostly required when showing the reasoning behind a complex decision, for instance here, a comment would rather explain the need for the "fmin_l_bfgs_b" optimizer.

Also, try to keep lines under 88 characters (see the linter comment on this PR).


# Plot prior
fig, axs = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(10, 8))
plot_gpr_samples(gpr, n_samples=n_samples, ax=axs[0])
axs[0].set_title("Samples from prior distribution")

# plot posterior
gpr.fit(X_train, y_train)
# Fit the model and plot posterior
gpr.fit(X_train_scaled, y_train)
plot_gpr_samples(gpr, n_samples=n_samples, ax=axs[1])
axs[1].scatter(X_train[:, 0], y_train, color="red", zorder=10, label="Observations")
axs[1].scatter(X_train_scaled[:, 0], y_train, color="red", zorder=10, label="Observations")
axs[1].legend(bbox_to_anchor=(1.05, 1.5), loc="upper left")
axs[1].set_title("Samples from posterior distribution")

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