# Illustration of prior and posterior Gaussian process for different kernels¶

This example illustrates the prior and posterior of a GaussianProcessRegressor with different kernels. Mean, standard deviation, and 5 samples are shown for both prior and posterior distributions.

Here, we only give some illustration. To know more about kernels’ formulation, refer to the User Guide.

# Authors: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
#          Guillaume Lemaitre <g.lemaitre58@gmail.com>


## Helper function¶

Before presenting each individual kernel available for Gaussian processes, we will define an helper function allowing us plotting samples drawn from the Gaussian process.

This function will take a GaussianProcessRegressor model and will drawn sample from the Gaussian process. If the model was not fit, the samples are drawn from the prior distribution while after model fitting, the samples are drawn from the posterior distribution.

import matplotlib.pyplot as plt
import numpy as np

def plot_gpr_samples(gpr_model, n_samples, ax):
"""Plot samples drawn from the Gaussian process model.

If the Gaussian process model is not trained then the drawn samples are
drawn from the prior distribution. Otherwise, the samples are drawn from
the posterior distribution. Be aware that a sample here corresponds to a
function.

Parameters
----------
gpr_model : GaussianProcessRegressor
A :class:~sklearn.gaussian_process.GaussianProcessRegressor model.
n_samples : int
The number of samples to draw from the Gaussian process distribution.
ax : matplotlib axis
The matplotlib axis where to plot the samples.
"""
x = np.linspace(0, 5, 100)
X = x.reshape(-1, 1)

y_mean, y_std = gpr_model.predict(X, return_std=True)
y_samples = gpr_model.sample_y(X, n_samples)

for idx, single_prior in enumerate(y_samples.T):
ax.plot(
x,
single_prior,
linestyle="--",
alpha=0.7,
label=f"Sampled function #{idx + 1}",
)
ax.plot(x, y_mean, color="black", label="Mean")
ax.fill_between(
x,
y_mean - y_std,
y_mean + y_std,
alpha=0.1,
color="black",
label=r"$\pm$ 1 std. dev.",
)
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_ylim([-3, 3])


## Dataset and Gaussian process generation¶

We will create a training dataset that we will use in the different sections.

rng = np.random.RandomState(4)
X_train = rng.uniform(0, 5, 10).reshape(-1, 1)
y_train = np.sin((X_train[:, 0] - 2.5) ** 2)
n_samples = 5


## Kernel cookbook¶

In this section, we illustrate some samples drawn from the prior and posterior distributions of the Gaussian process with different kernels.

from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF

kernel = 1.0 * RBF(length_scale=1.0, length_scale_bounds=(1e-1, 10.0))
gpr = GaussianProcessRegressor(kernel=kernel, random_state=0)

fig, axs = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(10, 8))

# plot prior
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)
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].legend(bbox_to_anchor=(1.05, 1.5), loc="upper left")
axs[1].set_title("Samples from posterior distribution")

plt.tight_layout()

print(f"Kernel parameters before fit:\n{kernel})")
print(
f"Kernel parameters after fit: \n{gpr.kernel_} \n"
f"Log-likelihood: {gpr.log_marginal_likelihood(gpr.kernel_.theta):.3f}"
)

Kernel parameters before fit:
1**2 * RBF(length_scale=1))
Kernel parameters after fit:
0.594**2 * RBF(length_scale=0.279)
Log-likelihood: -0.067


from sklearn.gaussian_process.kernels import RationalQuadratic

kernel = 1.0 * RationalQuadratic(length_scale=1.0, alpha=0.1, alpha_bounds=(1e-5, 1e15))
gpr = GaussianProcessRegressor(kernel=kernel, random_state=0)

fig, axs = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(10, 8))

# plot prior
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)
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].legend(bbox_to_anchor=(1.05, 1.5), loc="upper left")
axs[1].set_title("Samples from posterior distribution")

plt.tight_layout()

print(f"Kernel parameters before fit:\n{kernel})")
print(
f"Kernel parameters after fit: \n{gpr.kernel_} \n"
f"Log-likelihood: {gpr.log_marginal_likelihood(gpr.kernel_.theta):.3f}"
)

Kernel parameters before fit:
Kernel parameters after fit:
Log-likelihood: -0.067


### Exp-Sine-Squared kernel¶

from sklearn.gaussian_process.kernels import ExpSineSquared

kernel = 1.0 * ExpSineSquared(
length_scale=1.0,
periodicity=3.0,
length_scale_bounds=(0.1, 10.0),
periodicity_bounds=(1.0, 10.0),
)
gpr = GaussianProcessRegressor(kernel=kernel, random_state=0)

fig, axs = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(10, 8))

# plot prior
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)
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].legend(bbox_to_anchor=(1.05, 1.5), loc="upper left")
axs[1].set_title("Samples from posterior distribution")

fig.suptitle("Exp-Sine-Squared kernel", fontsize=18)
plt.tight_layout()

print(f"Kernel parameters before fit:\n{kernel})")
print(
f"Kernel parameters after fit: \n{gpr.kernel_} \n"
f"Log-likelihood: {gpr.log_marginal_likelihood(gpr.kernel_.theta):.3f}"
)

Kernel parameters before fit:
1**2 * ExpSineSquared(length_scale=1, periodicity=3))
Kernel parameters after fit:
0.799**2 * ExpSineSquared(length_scale=0.791, periodicity=2.87)
Log-likelihood: 3.394


### Dot-product kernel¶

from sklearn.gaussian_process.kernels import ConstantKernel, DotProduct

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)

fig, axs = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(10, 8))

# plot prior
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)
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].legend(bbox_to_anchor=(1.05, 1.5), loc="upper left")
axs[1].set_title("Samples from posterior distribution")

fig.suptitle("Dot-product kernel", fontsize=18)
plt.tight_layout()

/home/circleci/project/sklearn/gaussian_process/_gpr.py:663: ConvergenceWarning:

lbfgs failed to converge (status=2):
ABNORMAL_TERMINATION_IN_LNSRCH.

Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html

/home/circleci/project/sklearn/gaussian_process/_gpr.py:479: UserWarning:

Predicted variances smaller than 0. Setting those variances to 0.

print(f"Kernel parameters before fit:\n{kernel})")
print(
f"Kernel parameters after fit: \n{gpr.kernel_} \n"
f"Log-likelihood: {gpr.log_marginal_likelihood(gpr.kernel_.theta):.3f}"
)

Kernel parameters before fit:
0.316**2 * DotProduct(sigma_0=1) ** 2)
Kernel parameters after fit:
2.68**2 * DotProduct(sigma_0=8.47) ** 2
Log-likelihood: -7337046907.481


### Matérn kernel¶

from sklearn.gaussian_process.kernels import Matern

kernel = 1.0 * Matern(length_scale=1.0, length_scale_bounds=(1e-1, 10.0), nu=1.5)
gpr = GaussianProcessRegressor(kernel=kernel, random_state=0)

fig, axs = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(10, 8))

# plot prior
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)
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].legend(bbox_to_anchor=(1.05, 1.5), loc="upper left")
axs[1].set_title("Samples from posterior distribution")

fig.suptitle("Matérn kernel", fontsize=18)
plt.tight_layout()

print(f"Kernel parameters before fit:\n{kernel})")
print(
f"Kernel parameters after fit: \n{gpr.kernel_} \n"
f"Log-likelihood: {gpr.log_marginal_likelihood(gpr.kernel_.theta):.3f}"
)

Kernel parameters before fit:
1**2 * Matern(length_scale=1, nu=1.5))
Kernel parameters after fit:
0.609**2 * Matern(length_scale=0.484, nu=1.5)
Log-likelihood: -1.185


Total running time of the script: (0 minutes 1.582 seconds)

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