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[MRG] fixed SpectralCoclustering and SpectralBiclustering default value doc #15778

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Dec 10, 2019
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[MRG] fixed SpectralCoclustering and SpectralBiclustering default value doc #15778

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07ac8af
fixed default values
Dec 4, 2019
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glemaitre Dec 10, 2019
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70 changes: 37 additions & 33 deletions sklearn/cluster/_bicluster.py
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Original file line number Diff line number Diff line change
Expand Up @@ -191,39 +191,40 @@ class SpectralCoclustering(BaseSpectral):

Parameters
----------
n_clusters : integer, optional, default: 3
n_clusters : int, default=3
The number of biclusters to find.

svd_method : string, optional, default: 'randomized'
svd_method : {'randomized', 'arpack'}, default='randomized'
Selects the algorithm for finding singular vectors. May be
'randomized' or 'arpack'. If 'randomized', use
:func:`sklearn.utils.extmath.randomized_svd`, which may be faster
for large matrices. If 'arpack', use
:func:`scipy.sparse.linalg.svds`, which is more accurate, but
possibly slower in some cases.

n_svd_vecs : int, optional, default: None
n_svd_vecs : int, default=None
Number of vectors to use in calculating the SVD. Corresponds
to `ncv` when `svd_method=arpack` and `n_oversamples` when
`svd_method` is 'randomized`.

mini_batch : bool, optional, default: False
mini_batch : bool, default=False
Whether to use mini-batch k-means, which is faster but may get
different results.

init : {'k-means++', 'random' or an ndarray}
Method for initialization of k-means algorithm; defaults to
'k-means++'.
init : {'k-means++', 'random', or ndarray of shape \
(n_clusters, n_features), default='k-means++'
Method for initialization of k-means algorithm; defaults to
'k-means++'.

n_init : int, optional, default: 10
n_init : int, default=10
Number of random initializations that are tried with the
k-means algorithm.

If mini-batch k-means is used, the best initialization is
chosen and the algorithm runs once. Otherwise, the algorithm
is run for each initialization and the best solution chosen.

n_jobs : int or None, optional (default=None)
n_jobs : int, 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.
Expand All @@ -232,24 +233,24 @@ class SpectralCoclustering(BaseSpectral):
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

random_state : int, RandomState instance or None (default)
random_state : int, RandomState instance, default=None
Used for randomizing the singular value decomposition and the k-means
initialization. Use an int to make the randomness deterministic.
See :term:`Glossary <random_state>`.

Attributes
----------
rows_ : array-like, shape (n_row_clusters, n_rows)
rows_ : array-like of shape (n_row_clusters, n_rows)
Results of the clustering. `rows[i, r]` is True if
cluster `i` contains row `r`. Available only after calling ``fit``.

columns_ : array-like, shape (n_column_clusters, n_columns)
columns_ : array-like of shape (n_column_clusters, n_columns)
Results of the clustering, like `rows`.

row_labels_ : array-like, shape (n_rows,)
row_labels_ : array-like of shape (n_rows,)
The bicluster label of each row.

column_labels_ : array-like, shape (n_cols,)
column_labels_ : array-like of shape (n_cols,)
The bicluster label of each column.

Examples
Expand Down Expand Up @@ -319,55 +320,58 @@ class SpectralBiclustering(BaseSpectral):

Parameters
----------
n_clusters : integer or tuple (n_row_clusters, n_column_clusters)
n_clusters : int or tuple (n_row_clusters, n_column_clusters), default=3
The number of row and column clusters in the checkerboard
structure.

method : string, optional, default: 'bistochastic'
method : {'bistochastic', 'scale', 'log'}, default='bistochastic'
Method of normalizing and converting singular vectors into
biclusters. May be one of 'scale', 'bistochastic', or 'log'.
The authors recommend using 'log'. If the data is sparse,
however, log normalization will not work, which is why the
default is 'bistochastic'. CAUTION: if `method='log'`, the
data must not be sparse.
default is 'bistochastic'.

n_components : integer, optional, default: 6
.. warning::
if `method='log'`, the data must be sparse.

n_components : int, default=6
Number of singular vectors to check.

n_best : integer, optional, default: 3
n_best : int, default=3
Number of best singular vectors to which to project the data
for clustering.

svd_method : string, optional, default: 'randomized'
svd_method : {'randomized', 'arpack'}, default='randomized'
Selects the algorithm for finding singular vectors. May be
'randomized' or 'arpack'. If 'randomized', uses
:func:`~sklearn.utils.extmath.randomized_svd`, which may be faster
for large matrices. If 'arpack', uses
`scipy.sparse.linalg.svds`, which is more accurate, but
possibly slower in some cases.

n_svd_vecs : int, optional, default: None
n_svd_vecs : int, default=None
Number of vectors to use in calculating the SVD. Corresponds
to `ncv` when `svd_method=arpack` and `n_oversamples` when
`svd_method` is 'randomized`.

mini_batch : bool, optional, default: False
mini_batch : bool, default=False
Whether to use mini-batch k-means, which is faster but may get
different results.

init : {'k-means++', 'random' or an ndarray}
Method for initialization of k-means algorithm; defaults to
'k-means++'.
init : {'k-means++', 'random'} or ndarray of (n_clusters, n_features), \
default='k-means++'
Method for initialization of k-means algorithm; defaults to
'k-means++'.

n_init : int, optional, default: 10
n_init : int, default=10
Number of random initializations that are tried with the
k-means algorithm.

If mini-batch k-means is used, the best initialization is
chosen and the algorithm runs once. Otherwise, the algorithm
is run for each initialization and the best solution chosen.

n_jobs : int or None, optional (default=None)
n_jobs : int, 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.
Expand All @@ -376,24 +380,24 @@ class SpectralBiclustering(BaseSpectral):
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

random_state : int, RandomState instance or None (default)
random_state : int, RandomState instance, default=None
Used for randomizing the singular value decomposition and the k-means
initialization. Use an int to make the randomness deterministic.
See :term:`Glossary <random_state>`.

Attributes
----------
rows_ : array-like, shape (n_row_clusters, n_rows)
rows_ : array-like of shape (n_row_clusters, n_rows)
Results of the clustering. `rows[i, r]` is True if
cluster `i` contains row `r`. Available only after calling ``fit``.

columns_ : array-like, shape (n_column_clusters, n_columns)
columns_ : array-like of shape (n_column_clusters, n_columns)
Results of the clustering, like `rows`.

row_labels_ : array-like, shape (n_rows,)
row_labels_ : array-like of shape (n_rows,)
Row partition labels.

column_labels_ : array-like, shape (n_cols,)
column_labels_ : array-like of shape (n_cols,)
Column partition labels.

Examples
Expand Down