8000 [MRG+2] DOC Correct default n_jobs & reference the glossary by qinhanmin2014 · Pull Request #11808 · scikit-learn/scikit-learn · GitHub
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[MRG+2] DOC Correct default n_jobs & reference the glossary #11808

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7 changes: 5 additions & 2 deletions doc/glossary.rst
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Expand Up @@ -1479,8 +1479,11 @@ functions or non-estimator constructors.

``n_jobs`` is an int, specifying the maximum number of concurrently
running jobs. If set to -1, all CPUs are used. If 1 is given, no
parallel computing code is used at all. For n_jobs below -1, (n_cpus +
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The statement already here is not quite true. Better to say no job-level parallelism or something, and perhaps to note that other parallelism may be done by the numerical processing libraries (as per the FAQ's coverage of this)...

1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.
joblib level parallelism is used at all, which is useful for
debugging. Even with ``n_jobs = 1``, parallelism may occur due to
numerical processing libraries (see :ref:`FAQ <faq_mkl_threading>`).
For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for
``n_jobs = -2``, all CPUs but one are used.

``n_jobs=None`` means *unset*; it will generally be interpreted as
``n_jobs=1``, unless the current :class:`joblib.Parallel` backend
Expand Down
9 changes: 6 additions & 3 deletions examples/model_selection/plot_learning_curve.py
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Expand Up @@ -25,7 +25,7 @@


def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):
"""
Generate a simple plot of the test and training learning curve.

Expand Down Expand Up @@ -63,8 +63,11 @@ def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
Refer :ref:`User Guide <cross_validation>` for the various
cross-validators that can be used here.

n_jobs : integer, optional
Number of jobs to run in parallel (default 1).
n_jobs : int or None, optional (default=None)
Number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

train_sizes : array-like, shape (n_ticks,), dtype float or int
Relative or absolute numbers of training examples that will be used to
Expand Down
18 changes: 8 additions & 10 deletions sklearn/cluster/bicluster.py
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Expand Up @@ -228,15 +228,14 @@ class SpectralCoclustering(BaseSpectral):
chosen and the algorithm runs once. Otherwise, the algorithm
is run for each initialization and the best solution chosen.

n_jobs : int, optional, default: 1
n_jobs : int or None, optional (default=None)
The number of jobs to use for the computation. This works by breaking
down the pairwise matrix into n_jobs even slices and computing them in
parallel.

If -1 all CPUs are used. If 1 is given, no parallel computing code is
used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
are used.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

random_state : int, RandomState instance or None (default)
Used for randomizing the singular value decomposition and the k-means
Expand Down Expand Up @@ -375,15 +374,14 @@ class SpectralBiclustering(BaseSpectral):
chosen and the algorithm runs once. Otherwise, the algorithm
is run for each initialization and the best solution chosen.

n_jobs : int, optional, default: 1
n_jobs : int or None, optional (default=None)
The number of jobs to use for the computation. This works by breaking
down the pairwise matrix into n_jobs even slices and computing them in
parallel.

If -1 all CPUs are used. If 1 is given, no parallel computing code is
used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
are used.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

random_state : int, RandomState instance or None (default)
Used for randomizing the singular value decomposition and the k-means
Expand Down
12 changes: 8 additions & 4 deletions sklearn/cluster/dbscan_.py
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Expand Up @@ -76,9 +76,11 @@ def dbscan(X, eps=0.5, min_samples=5, metric='minkowski', metric_params=None,
weight may inhibit its eps-neighbor from being core.
Note that weights are absolute, and default to 1.

n_jobs : int, optional (default = 1)
n_jobs : int or None, optional (default=None)
The number of parallel jobs to run for neighbors search.
If ``-1``, then the number of jobs is set to the number of CPU cores.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

Returns
-------
Expand Down Expand Up @@ -229,9 +231,11 @@ class DBSCAN(BaseEstimator, ClusterMixin):
The power of the Minkowski metric to be used to calculate distance
between points.

n_jobs : int, optional (default = 1)
n_jobs : int or None, optional (default=None)
The number of parallel jobs to run.
If ``-1``, then the number of jobs is set to the number of CPU cores.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

Attributes
----------
Expand Down
18 changes: 8 additions & 10 deletions sklearn/cluster/k_means_.py
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10000
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Expand Up @@ -261,14 +261,13 @@ def k_means(X, n_clusters, sample_weight=None, init='k-means++',
the data mean, in this case it will also not ensure that data is
C-contiguous which may cause a significant slowdown.

n_jobs : int
n_jobs : int or None, optional (default=None)
The number of jobs to use for the computation. This works by computing
each of the n_init runs in parallel.

If -1 all CPUs are used. If 1 is given, no parallel computing code is
used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
are used.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

algorithm : "auto", "full" or "elkan", default="auto"
K-means algorithm to use. The classical EM-style algorithm is "full".
Expand Down Expand Up @@ -834,14 +833,13 @@ class KMeans(BaseEstimator, ClusterMixin, TransformerMixin):
the data mean, in this case it will also not ensure that data is
C-contiguous which may cause a significant slowdown.

n_jobs : int
n_jobs : int or None, optional (default=None)
The number of jobs to use for the computation. This works by computing
each of the n_init runs in parallel.

If -1 all CPUs are used. If 1 is given, no parallel computing code is
used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
are used.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

algorithm : "auto", "full" or "elkan", default="auto"
K-means algorithm to use. The classical EM-style algorithm is "full".
Expand Down
24 changes: 12 additions & 12 deletions sklearn/cluster/mean_shift_.py
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Expand Up @@ -53,9 +53,11 @@ def estimate_bandwidth(X, quantile=0.3, n_samples=None, random_state=0,
deterministic.
See :term:`Glossary <random_state>`.

n_jobs : int, optional (default = 1)
n_jobs : int or None, optional (default=None)
The number of parallel jobs to run for neighbors search.
If ``-1``, then the number of jobs is set to the number of CPU cores.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

Returns
-------
Expand Down Expand Up @@ -152,14 +154,13 @@ def mean_shift(X, bandwidth=None, seeds=None, bin_seeding=False,
Maximum number of iterations, per seed point before the clustering
operation terminates (for that seed point), if has not converged yet.

n_jobs : int
n_jobs : int or None, optional (default=None)
The number of jobs to use for the computation. This works by computing
each of the n_init runs in parallel.

If -1 all CPUs are used. If 1 is given, no parallel computing code is
used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
are used.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

.. versionadded:: 0.17
Parallel Execution using *n_jobs*.
Expand Down Expand Up @@ -334,14 +335,13 @@ class MeanShift(BaseEstimator, ClusterMixin):
not within any kernel. Orphans are assigned to the nearest kernel.
If false, then orphans are given cluster label -1.

n_jobs : int
n_jobs : int or None, optional (default=None)
The number of jobs to use for the computation. This works by computing
each of the n_init runs in parallel.

If -1 all CPUs are used. If 1 is given, no parallel computing code is
used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
are used.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

Attributes
----------
Expand Down
12 changes: 8 additions & 4 deletions sklearn/cluster/optics_.py
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Expand Up @@ -118,9 +118,11 @@ def optics(X, min_samples=5, max_bound=np.inf, metric='euclidean',
required to store the tree. The optimal value depends on the
nature of the problem.

n_jobs : int, optional (default=1)
n_jobs : int or None, optional (default=None)
The number of parallel jobs to run for neighbors search.
If ``-1``, then the number of jobs is set to the number of CPU cores.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

Returns
-------
Expand Down Expand Up @@ -243,9 +245,11 @@ class OPTICS(BaseEstimator, ClusterMixin):
required to store the tree. The optimal value depends on the
nature of the problem.

n_jobs : int, optional (default=1)
n_jobs : int or None, optional (default=None)
The number of parallel jobs to run for neighbors search.
If ``-1``, then the number of jobs is set to the number of CPU cores.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

Attributes
----------
Expand Down
6 changes: 4 additions & 2 deletions sklearn/cluster/spectral.py
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Expand Up @@ -358,9 +358,11 @@ class SpectralClustering(BaseEstimator, ClusterMixin):
Parameters (keyword arguments) and values for kernel passed as
callable object. Ignored by other kernels.

n_jobs : int, optional (default = 1)
n_jobs : int or None, optional (default=None)
The number of parallel jobs to run.
If ``-1``, then the number of jobs is set to the number of CPU cores.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

Attributes
----------
Expand Down
14 changes: 10 additions & 4 deletions sklearn/compose/_column_transformer.py
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Expand Up @@ -93,8 +93,11 @@ class ColumnTransformer(_BaseComposition, TransformerMixin):
the stacked result will be sparse or dense, respectively, and this
keyword will be ignored.

n_jobs : int, optional
Number of jobs to run in parallel (default 1).
n_jobs : int or None, optional (default=None)
Number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

transformer_weights : dict, optional
Multiplicative weights for features per transformer. The output of the
Expand Down Expand Up @@ -666,8 +669,11 @@ def make_column_transformer(*transformers, **kwargs):
non-specified columns will use the ``remainder`` estimator. The
estimator must support `fit` and `transform`.

n_jobs : int, optional
Number of jobs to run in parallel (default 1).
n_jobs : int or None, optional (default=None)
Number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

Returns
-------
Expand Down
14 changes: 10 additions & 4 deletions sklearn/covariance/graph_lasso_.py
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Expand Up @@ -520,8 +520,11 @@ class GraphicalLassoCV(GraphicalLasso):
than number of samples. Elsewhere prefer cd which is more numerically
stable.

n_jobs : int, optional
number of jobs to run in parallel (default 1).
n_jobs : int or None, optional (default=None)
number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

verbose : boolean, optional
If verbose is True, the objective function and duality gap are
Expand Down Expand Up @@ -927,8 +930,11 @@ class GraphLassoCV(GraphicalLassoCV):
than number of samples. Elsewhere prefer cd which is more numerically
stable.

n_jobs : int, optional
number of jobs to run in parallel (default 1).
n_jobs : int or None, optional (default=None)
number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

verbose : boolean, optional
If verbose is True, the objective function and duality gap are
Expand Down
40 changes: 29 additions & 11 deletions sklearn/decomposition/dict_learning.py
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Expand Up @@ -246,8 +246,11 @@ def sparse_encode(X, dictionary, gram=None, cov=None, algorithm='lasso_lars',
max_iter : int, 1000 by default
Maximum number of iterations to perform if `algorithm='lasso_cd'`.

n_jobs : int, optional
n_jobs : int or None, optional (default=None)
Number of parallel jobs to run.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

check_input : boolean, optional
If False, the input arrays X and dictionary will not be checked.
Expand Down Expand Up @@ -459,8 +462,11 @@ def dict_learning(X, n_components, alpha, max_iter=100, tol=1e-8,
Lasso solution (linear_model.Lasso). Lars will be faster if
the estimated components are sparse.

n_jobs : int,
Number of parallel jobs to run, or -1 to autodetect.
n_jobs : int or None, optional (default=None)
Number of parallel jobs to run.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

dict_init : array of shape (n_components, n_features),
Initial value for the dictionary for warm restart scenarios.
Expand Down Expand Up @@ -654,8 +660,11 @@ def dict_learning_online(X, n_components=2, alpha=1, n_iter=100,
shuffle : boolean,
Whether to shuffle the data before splitting it in batches.

n_jobs : int,
Number of parallel jobs to run, or -1 to autodetect.
n_jobs : int or None, optional (default=None)
Number of parallel jobs to run.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

method : {'lars', 'cd'}
lars: uses the least angle regression method to solve the lasso problem
Expand Down Expand Up @@ -949,8 +958,11 @@ class SparseCoder(BaseEstimator, SparseCodingMixin):
its negative part and its positive part. This can improve the
performance of downstream classifiers.

n_jobs : int,
number of parallel jobs to run
n_jobs : int or None, optional (default=None)
Number of parallel jobs to run.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

positive_code : bool
Whether to enforce positivity when finding the code.
Expand Down Expand Up @@ -1069,8 +1081,11 @@ class DictionaryLearning(BaseEstimator, SparseCodingMixin):
the reconstruction error targeted. In this case, it overrides
`n_nonzero_coefs`.

n_jobs : int,
number of parallel jobs to run
n_jobs : int or None, optional (default=None)
Number of parallel jobs to run.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

code_init : array of shape (n_samples, n_components),
initial value for the code, for warm restart
Expand Down Expand Up @@ -1220,8 +1235,11 @@ class MiniBatchDictionaryLearning(BaseEstimator, SparseCodingMixin):
Lasso solution (linear_model.Lasso). Lars will be faster if
the estimated components are sparse.

n_jobs : int,
number of parallel jobs to run
n_jobs : int or None, optional (default=None)
Number of parallel jobs to run.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

batch_size : int,
number of samples in each mini-batch
Expand Down
6 changes: 4 additions & 2 deletions sklearn/decomposition/kernel_pca.py
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Expand Up @@ -89,9 +89,11 @@ class KernelPCA(BaseEstimator, TransformerMixin):

.. versionadded:: 0.18

n_jobs : int, default=1
n_jobs : int or None, optional (default=None)
The number of parallel jobs to run.
If `-1`, then the number of jobs is set to the number of CPU cores.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

.. versionadded:: 0.18

Expand Down
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