8000 DOC Ensures that GaussianProcessRegressor passes numpydoc validation by baam25simo · Pull Request #20981 · scikit-learn/scikit-learn · GitHub
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DOC Ensures that GaussianProcessRegressor passes numpydoc validation #20981

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Merged
merged 6 commits into from
Sep 9, 2021
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DOC Ensures that GaussianProcessRegressor passes numpydoc validation #20981

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a9fc2ae
`GaussianProcessRegressor` removed from `DOCSTRING_IGNORE_LIST` in te…
baam25simo Sep 8, 2021
5e09413
In `GaussianProcessRegressor`: "See Also" section added.
baam25simo Sep 8, 2021
8ba21d9
In `GaussianProcessRegressor.fit`: `self` description added.
baam25simo Sep 8, 2021
15c73a0
In `GaussianProcessRegressor.log_marginal_likelihood`: summary fixed.
baam25simo Sep 8, 2021
5da2ad3
In `GaussianProcessRegressor.predict`: summary fixed.
baam25simo Sep 8, 2021
bc24e6e
In `GaussianProcessRegressor.sample_y`: `n_sample` description fixed.
baam25simo Sep 8, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Expand Up @@ -9,7 +9,6 @@

# List of modules ignored when checking for numpydoc validation.
DOCSTRING_IGNORE_LIST = [
"GaussianProcessRegressor",
"GaussianRandomProjection",
"GridSearchCV",
"HalvingGridSearchCV",
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16 changes: 11 additions & 5 deletions sklearn/gaussian_process/_gpr.py
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Expand Up @@ -82,7 +82,7 @@ def optimizer(obj_func, initial_theta, bounds):

Per default, the L-BFGS-B algorithm from `scipy.optimize.minimize`
is used. If None is passed, the kernel's parameters are kept fixed.
Available internal optimizers are: `{'fmin_l_bfgs_b'}`
Available internal optimizers are: `{'fmin_l_bfgs_b'}`.

n_restarts_optimizer : int, default=0
The number of restarts of the optimizer for finding the kernel's
Expand Down Expand Up @@ -146,6 +146,11 @@ def optimizer(obj_func, initial_theta, bounds):

.. versionadded:: 1.0

See Also
--------
GaussianProcessClassifier : Gaussian process classification (GPC)
based on Laplace approximation.

References
----------
.. [1] `Rasmussen, Carl Edward.
Expand Down Expand Up @@ -200,7 +205,8 @@ def fit(self, X, y):

Returns
-------
self : returns an instance of self.
self : object
GaussianProcessRegressor class instance.
"""
if self.kernel is None: # Use an RBF kernel as default
self.kernel_ = C(1.0, constant_value_bounds="fixed") * RBF(
Expand Down Expand Up @@ -318,7 +324,7 @@ def obj_func(theta, eval_gradient=True):
return self

def predict(self, X, return_std=False, return_cov=False):
"""Predict using the Gaussian process regression model
"""Predict using the Gaussian process regression model.

We can also predict based on an unfitted model by using the GP prior.
In addition to the mean of the predictive distribution, optionally also
Expand Down Expand Up @@ -433,7 +439,7 @@ def sample_y(self, X, n_samples=1, random_state=0):
Query points where the GP is evaluated.

n_samples : int, default=1
Number of samples drawn from the Gaussian process per query point
Number of samples drawn from the Gaussian process per query point.

random_state : int, RandomState instance or None, default=0
Determines random number generation to randomly draw samples.
Expand Down Expand Up @@ -464,7 +470,7 @@ def sample_y(self, X, n_samples=1, random_state=0):
def log_marginal_likelihood(
self, theta=None, eval_gradient=False, clone_kernel=True
):
"""Returns log-marginal likelihood of theta for training data.
"""Return log-marginal likelihood of theta for training data.

Parameters
----------
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