DOC improve random_state docstring from decomposition module (#11902)
* [MRG] Fix random_state docstrings in decomposition module * Add more details, and specify reproducibility * [MRG] docs for random_state in decomposition module Update the wording for reproducible results. * [MRG] Update doc for optional parameter to match numpy doc style * apply new style Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
This commit is contained in:
Sarat Addepalli
Guillaume Lemaitre
parent
cb86aae1ed
commit
cd6e4d52d9
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@ -361,11 +361,10 @@ def _update_dict(dictionary, Y, code, verbose=False, return_r2=False,
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Whether to compute and return the residual sum of squares corresponding
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to the computed solution.
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random_state : int, RandomState instance or None, optional (default=None)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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random_state : int, RandomState instance, default=None
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Used for randomly initializing the dictionary. Pass an int for
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reproducible results across multiple function calls.
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See :term:`Glossary <random_state>`.
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positive : boolean, optional
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Whether to enforce positivity when finding the dictionary.
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@ -483,10 +482,9 @@ def dict_learning(X, n_components, alpha, max_iter=100, tol=1e-8,
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To control the verbosity of the procedure.
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random_state : int, RandomState instance or None, optional (default=None)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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Used for randomly initializing the dictionary. Pass an int for
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reproducible results across multiple function calls.
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See :term:`Glossary <random_state>`.
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return_n_iter : bool
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Whether or not to return the number of iterations.
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@ -690,10 +688,11 @@ def dict_learning_online(X, n_components=2, alpha=1, n_iter=100,
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initialization.
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random_state : int, RandomState instance or None, optional (default=None)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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Used for initializing the dictionary when ``dict_init`` is not
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specified, randomly shuffling the data when ``shuffle`` is set to
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``True``, and updating the dictionary. Pass an int for reproducible
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results across multiple function calls.
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See :term:`Glossary <random_state>`.
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return_inner_stats : boolean, optional
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Return the inner statistics A (dictionary covariance) and B
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@ -1132,11 +1131,12 @@ class DictionaryLearning(SparseCodingMixin, BaseEstimator):
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its negative part and its positive part. This can improve the
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performance of downstream classifiers.
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random_state : int, RandomState instance or None, default=None
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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random_state : int, RandomState instance or None, optional (default=None)
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Used for initializing the dictionary when ``dict_init`` is not
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specified, randomly shuffling the data when ``shuffle`` is set to
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``True``, and updating the dictionary. Pass an int for reproducible
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results across multiple function calls.
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See :term:`Glossary <random_state>`.
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positive_code : bool, default=False
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Whether to enforce positivity when finding the code.
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@ -1323,10 +1323,11 @@ class MiniBatchDictionaryLearning(SparseCodingMixin, BaseEstimator):
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performance of downstream classifiers.
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random_state : int, RandomState instance or None, optional (default=None)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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Used for initializing the dictionary when ``dict_init`` is not
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specified, randomly shuffling the data when ``shuffle`` is set to
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``True``, and updating the dictionary. Pass an int for reproducible
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results across multiple function calls.
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See :term:`Glossary <random_state>`.
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positive_code : bool
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Whether to enforce positivity when finding the code.
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@ -89,11 +89,10 @@ class FactorAnalysis(TransformerMixin, BaseEstimator):
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Number of iterations for the power method. 3 by default. Only used
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if ``svd_method`` equals 'randomized'
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random_state : int, RandomState instance or None, optional (default=0)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`. Only used when ``svd_method`` equals 'randomized'.
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random_state : int, RandomState instance, default=None
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Only used when ``svd_method`` equals 'randomized'. Pass an int for
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reproducible results across multiple function calls.
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See :term:`Glossary <random_state>`.
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Attributes
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----------
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@ -202,11 +202,11 @@ def fastica(X, n_components=None, algorithm="parallel", whiten=True,
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Initial un-mixing array of dimension (n.comp,n.comp).
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If None (default) then an array of normal r.v.'s is used.
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random_state : int, RandomState instance or None, optional (default=None)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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random_state : int, RandomState instance, default=None
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Used to initialize ``w_init`` when not specified, with a
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normal distribution. Pass an int, for reproducible results
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across multiple function calls.
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See :term:`Glossary <random_state>`.
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return_X_mean : bool, optional
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If True, X_mean is returned too.
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@ -341,11 +341,11 @@ class FastICA(TransformerMixin, BaseEstimator):
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w_init : None of an (n_components, n_components) ndarray
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The mixing matrix to be used to initialize the algorithm.
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random_state : int, RandomState instance or None, optional (default=None)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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random_state : int, RandomState instance, default=None
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Used to initialize ``w_init`` when not specified, with a
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normal distribution. Pass an int, for reproducible results
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across multiple function calls.
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See :term:`Glossary <random_state>`.
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Attributes
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----------
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@ -76,11 +76,10 @@ class KernelPCA(TransformerMixin, BaseEstimator):
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When n_components is None, this parameter is ignored and components
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with zero eigenvalues are removed regardless.
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random_state : int, RandomState instance or None, optional (default=None)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`. Used when ``eigen_solver`` == 'arpack'.
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random_state : int, RandomState instance, default=None
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Used when ``eigen_solver`` == 'arpack'. Pass an int for reproducible
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results across multiple function calls.
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See :term:`Glossary <random_state>`.
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.. versionadded:: 0.18
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@ -222,11 +222,9 @@ class LatentDirichletAllocation(TransformerMixin, BaseEstimator):
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verbose : int, optional (default=0)
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Verbosity level.
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random_state : int, RandomState instance or None, optional (default=None)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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random_state : int, RandomState instance, default=None
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Pass an int for reproducible results across multiple function calls.
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See :term:`Glossary <random_state>`.
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Attributes
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----------
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@ -287,11 +287,10 @@ def _initialize_nmf(X, n_components, init=None, eps=1e-6,
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eps : float
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Truncate all values less then this in output to zero.
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random_state : int, RandomState instance or None, optional, default: None
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`. Used when ``random`` == 'nndsvdar' or 'random'.
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random_state : int, RandomState instance, default=None
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Used when ``init`` == 'nndsvdar' or 'random'. Pass an int for
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reproducible results across multiple function calls.
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See :term:`Glossary <random_state>`.
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Returns
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-------
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@ -472,11 +471,11 @@ def _fit_coordinate_descent(X, W, H, tol=1e-4, max_iter=200, l1_reg_W=0,
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shuffle : boolean, default: False
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If true, randomize the order of coordinates in the CD solver.
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random_state : int, RandomState instance or None, optional, default: None
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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random_state : int, RandomState instance, default=None
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Used to randomize the coordinates in the CD solver, when
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``shuffle`` is set to ``True``. Pass an int for reproducible
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results across multiple function calls.
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See :term:`Glossary <random_state>`.
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Returns
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-------
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@ -963,11 +962,11 @@ def non_negative_factorization(X, W=None, H=None, n_components=None,
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Select whether the regularization affects the components (H), the
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transformation (W), both or none of them.
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random_state : int, RandomState instance or None, optional, default: None
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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random_state : int, RandomState instance, default=None
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Used for NMF initialisation (when ``init`` == 'nndsvdar' or
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'random'), and in Coordinate Descent. Pass an int for reproducible
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results across multiple function calls.
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See :term:`Glossary <random_state>`.
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verbose : integer, default: 0
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The verbosity level.
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@ -1156,11 +1155,11 @@ class NMF(TransformerMixin, BaseEstimator):
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max_iter : integer, default: 200
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Maximum number of iterations before timing out.
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random_state : int, RandomState instance or None, optional, default: None
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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random_state : int, RandomState instance, default=None
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Used for initialisation (when ``init`` == 'nndsvdar' or
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'random'), and in Coordinate Descent. Pass an int for reproducible
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results across multiple function calls.
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See :term:`Glossary <random_state>`.
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alpha : double, default: 0.
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Constant that multiplies the regularization terms. Set it to zero to
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@ -189,11 +189,10 @@ class PCA(_BasePCA):
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.. versionadded:: 0.18.0
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random_state : int, RandomState instance or None, optional (default None)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`. Used when ``svd_solver`` == 'arpack' or 'randomized'.
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random_state : int, RandomState instance, default=None
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Used when ``svd_solver`` == 'arpack' or 'randomized'. Pass an int
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for reproducible results across multiple function calls.
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See :term:`Glossary <random_state>`.
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.. versionadded:: 0.18.0
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@ -79,11 +79,10 @@ class SparsePCA(TransformerMixin, BaseEstimator):
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verbose : int
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Controls the verbosity; the higher, the more messages. Defaults to 0.
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random_state : int, RandomState instance or None, optional (default=None)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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random_state : int, RandomState instance, default=None
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Used during dictionary learning. Pass an int for reproducible results
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across multiple function calls.
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See :term:`Glossary <random_state>`.
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normalize_components : 'deprecated'
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This parameter does not have any effect. The components are always
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@ -282,11 +281,11 @@ class MiniBatchSparsePCA(SparsePCA):
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Lasso solution (linear_model.Lasso). Lars will be faster if
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the estimated components are sparse.
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random_state : int, RandomState instance or None, optional (default=None)
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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random_state : int, RandomState instance, default=None
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Used for random shuffling when ``shuffle`` is set to ``True``,
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during online dictionary learning. Pass an int for reproducible results
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across multiple function calls.
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See :term:`Glossary <random_state>`.
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normalize_components : 'deprecated'
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This parameter does not have any effect. The components are always
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@ -56,11 +56,10 @@ class TruncatedSVD(TransformerMixin, BaseEstimator):
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`~sklearn.utils.extmath.randomized_svd` to handle sparse matrices that
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may have large slowly decaying spectrum.
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random_state : int, RandomState instance or None, optional, default = None
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If int, random_state is the seed used by the random number generator;
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If RandomState instance, random_state is the random number generator;
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If None, the random number generator is the RandomState instance used
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by `np.random`.
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random_state : int, RandomState instance, default=None
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Used during randomized svd. Pass an int for reproducible results across
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multiple function calls.
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See :term:`Glossary <random_state>`.
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tol : float, optional
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Tolerance for ARPACK. 0 means machine precision. Ignored by randomized
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