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DOC Fix sphinx's links to Glossary #21094

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Sep 21, 2021
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DOC Fix sphinx's links to Glossary #21094

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d8e10a1
Fix sphinx's links to Glossary
jmloyola Sep 19, 2021
7b18fd9
Merge 'upstream/main' into fix_glossary_terms
jmloyola Sep 21, 2021
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2 changes: 1 addition & 1 deletion sklearn/covariance/_elliptic_envelope.py
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Expand Up @@ -41,7 +41,7 @@ class EllipticEnvelope(OutlierMixin, MinCovDet):
random_state : int, RandomState instance or None, default=None
Determines the pseudo random number generator for shuffling
the data. Pass an int for reproducible results across multiple function
calls. See :term: `Glossary <random_state>`.
calls. See :term:`Glossary <random_state>`.

Attributes
----------
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8 changes: 4 additions & 4 deletions sklearn/covariance/_robust_covariance.py
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Expand Up @@ -71,7 +71,7 @@ def c_step(
random_state : int, RandomState instance or None, default=None
Determines the pseudo random number generator for shuffling the data.
Pass an int for reproducible results across multiple function calls.
See :term: `Glossary <random_state>`.
See :term:`Glossary <random_state>`.

Returns
-------
Expand Down Expand Up @@ -269,7 +269,7 @@ def select_candidates(
random_state : int, RandomState instance or None, default=None
Determines the pseudo random number generator for shuffling the data.
Pass an int for reproducible results across multiple function calls.
See :term: `Glossary <random_state>`.
See :term:`Glossary <random_state>`.

See Also
---------
Expand Down Expand Up @@ -381,7 +381,7 @@ def fast_mcd(
random_state : int, RandomState instance or None, default=None
Determines the pseudo random number generator for shuffling the data.
Pass an int for reproducible results across multiple function calls.
See :term: `Glossary <random_state>`.
See :term:`Glossary <random_state>`.

Returns
-------
Expand Down Expand Up @@ -610,7 +610,7 @@ class MinCovDet(EmpiricalCovariance):
random_state : int, RandomState instance or None, default=None
Determines the pseudo random number generator for shuffling the data.
Pass an int for reproducible results across multiple function calls.
See :term: `Glossary <random_state>`.
See :term:`Glossary <random_state>`.

Attributes
----------
Expand Down
2 changes: 1 addition & 1 deletion sklearn/ensemble/_hist_gradient_boosting/binning.py
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Expand Up @@ -115,7 +115,7 @@ class _BinMapper(TransformerMixin, BaseEstimator):
Pseudo-random number generator to control the random sub-sampling.
Pass an int for reproducible output across multiple
function calls.
See :term: `Glossary <random_state>`.
See :term:`Glossary <random_state>`.
n_threads : int, default=None
Number of OpenMP threads to use. `_openmp_effective_n_threads` is called
to determine the effective number of threads use, which takes cgroups CPU
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4 changes: 2 additions & 2 deletions sklearn/gaussian_process/_gpc.py
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Expand Up @@ -110,7 +110,7 @@ def optimizer(obj_func, initial_theta, bounds):
random_state : int, RandomState instance or None, default=None
Determines random number generation used to initialize the centers.
Pass an int for reproducible results across multiple function calls.
See :term: `Glossary <random_state>`.
See :term:`Glossary <random_state>`.

Attributes
----------
Expand Down Expand Up @@ -559,7 +559,7 @@ def optimizer(obj_func, initial_theta, bounds):
random_state : int, RandomState instance or None, default=None
Determines random number generation used to initialize the centers.
Pass an int for reproducible results across multiple function calls.
See :term: `Glossary <random_state>`.
See :term:`Glossary <random_state>`.

multi_class : {'one_vs_rest', 'one_vs_one'}, default='one_vs_rest'
Specifies how multi-class classification problems are handled.
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2 changes: 1 addition & 1 deletion sklearn/inspection/_permutation_importance.py
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Expand Up @@ -171,7 +171,7 @@ def permutation_importance(
Pseudo-random number generator to control the permutations of each
feature.
Pass an int to get reproducible results across function calls.
See :term: `Glossary <random_state>`.
See :term:`Glossary <random_state>`.

sample_weight : array-like of shape (n_samples,), default=None
Sample weights used in scoring.
Expand Down
6 changes: 3 additions & 3 deletions sklearn/manifold/_locally_linear.py
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Expand Up @@ -154,7 +154,7 @@ def null_space(
random_state : int, RandomState instance, default=None
Determines the random number generator when ``solver`` == 'arpack'.
Pass an int for reproducible results across multiple function calls.
See :term: `Glossary <random_state>`.
See :term:`Glossary <random_state>`.
"""
if eigen_solver == "auto":
if M.shape[0] > 200 and k + k_skip < 10:
Expand Down Expand Up @@ -268,7 +268,7 @@ def locally_linear_embedding(
random_state : int, RandomState instance, default=None
Determines the random number generator when ``solver`` == 'arpack'.
Pass an int for reproducible results across multiple function calls.
See :term: `Glossary <random_state>`.
See :term:`Glossary <random_state>`.

n_jobs : int or None, default=None
The number of parallel jobs to run for neighbors search.
Expand Down Expand Up @@ -608,7 +608,7 @@ class LocallyLinearEmbedding(TransformerMixin, _UnstableArchMixin, BaseEstimator
random_state : int, RandomState instance, default=None
Determines the random number generator when
``eigen_solver`` == 'arpack'. Pass an int for reproducible results
across multiple function calls. See :term: `Glossary <random_state>`.
across multiple function calls. See :term:`Glossary <random_state>`.

n_jobs : int or None, default=None
The number of parallel jobs to run.
Expand Down
6 changes: 3 additions & 3 deletions sklearn/manifold/_mds.py
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Expand Up @@ -61,7 +61,7 @@ def _smacof_single(
random_state : int, RandomState instance or None, default=None
Determines the random number generator used to initialize the centers.
Pass an int for reproducible results across multiple function calls.
See :term: `Glossary <random_state>`.
See :term:`Glossary <random_state>`.

Returns
-------
Expand Down Expand Up @@ -220,7 +220,7 @@ def smacof(
random_state : int, RandomState instance or None, default=None
Determines the random number generator used to initialize the centers.
Pass an int for reproducible results across multiple function calls.
See :term: `Glossary <random_state>`.
See :term:`Glossary <random_state>`.

return_n_iter : bool, default=False
Whether or not to return the number of iterations.
Expand Down Expand Up @@ -347,7 +347,7 @@ class MDS(BaseEstimator):
random_state : int, RandomState instance or None, default=None
Determines the random number generator used to initialize the centers.
Pass an int for reproducible results across multiple function calls.
See :term: `Glossary <random_state>`.
See :term:`Glossary <random_state>`.

dissimilarity : {'euclidean', 'precomputed'}, default='euclidean'
Dissimilarity measure to use:
Expand Down
2 changes: 1 addition & 1 deletion sklearn/manifold/_t_sne.py
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Expand Up @@ -619,7 +619,7 @@ class TSNE(BaseEstimator):
Determines the random number generator. Pass an int for reproducible
results across multiple function calls. Note that different
initializations might result in different local minima of the cost
function. See :term: `Glossary <random_state>`.
function. See :term:`Glossary <random_state>`.

method : str, default='barnes_hut'
By default the gradient calculation algorithm uses Barnes-Hut
Expand Down
2 changes: 1 addition & 1 deletion sklearn/neighbors/_kde.py
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Expand Up @@ -281,7 +281,7 @@ def sample(self, n_samples=1, random_state=None):
Determines random number generation used to generate
random samples. Pass an int for reproducible results
across multiple function calls.
See :term: `Glossary <random_state>`.
See :term:`Glossary <random_state>`.

Returns
-------
Expand Down