val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?n_components:int ->
?doc_topic_prior:float ->
?topic_word_prior:float ->
?learning_method:[ `Batch | `Online ] ->
?learning_decay:float ->
?learning_offset:float ->
?max_iter:int ->
?batch_size:int ->
?evaluate_every:int ->
?total_samples:int ->
?perp_tol:float ->
?mean_change_tol:float ->
?max_doc_update_iter:int ->
?n_jobs:[ `Int of int | `None ] ->
?verbose:int ->
?random_state:[ `Int of int | `RandomState of Py.Object.t | `None ] ->
unit ->
tLatent Dirichlet Allocation with online variational Bayes algorithm
.. versionadded:: 0.17
Read more in the :ref:`User Guide <LatentDirichletAllocation>`.
Parameters ---------- n_components : int, optional (default=10) Number of topics.
doc_topic_prior : float, optional (default=None) Prior of document topic distribution `theta`. If the value is None, defaults to `1 / n_components`. In 1_, this is called `alpha`.
topic_word_prior : float, optional (default=None) Prior of topic word distribution `beta`. If the value is None, defaults to `1 / n_components`. In 1_, this is called `eta`.
learning_method : 'batch' | 'online', default='batch' Method used to update `_component`. Only used in :meth:`fit` method. In general, if the data size is large, the online update will be much faster than the batch update.
Valid options::
'batch': Batch variational Bayes method. Use all training data in each EM update. Old `components_` will be overwritten in each iteration. 'online': Online variational Bayes method. In each EM update, use mini-batch of training data to update the ``components_`` variable incrementally. The learning rate is controlled by the ``learning_decay`` and the ``learning_offset`` parameters.
.. versionchanged:: 0.20 The default learning method is now ``"batch"``.
learning_decay : float, optional (default=0.7) It is a parameter that control learning rate in the online learning method. The value should be set between (0.5, 1.0] to guarantee asymptotic convergence. When the value is 0.0 and batch_size is ``n_samples``, the update method is same as batch learning. In the literature, this is called kappa.
learning_offset : float, optional (default=10.) A (positive) parameter that downweights early iterations in online learning. It should be greater than 1.0. In the literature, this is called tau_0.
max_iter : integer, optional (default=10) The maximum number of iterations.
batch_size : int, optional (default=128) Number of documents to use in each EM iteration. Only used in online learning.
evaluate_every : int, optional (default=0) How often to evaluate perplexity. Only used in `fit` method. set it to 0 or negative number to not evaluate perplexity in training at all. Evaluating perplexity can help you check convergence in training process, but it will also increase total training time. Evaluating perplexity in every iteration might increase training time up to two-fold.
total_samples : int, optional (default=1e6) Total number of documents. Only used in the :meth:`partial_fit` method.
perp_tol : float, optional (default=1e-1) Perplexity tolerance in batch learning. Only used when ``evaluate_every`` is greater than 0.
mean_change_tol : float, optional (default=1e-3) Stopping tolerance for updating document topic distribution in E-step.
max_doc_update_iter : int (default=100) Max number of iterations for updating document topic distribution in the E-step.
n_jobs : int or None, optional (default=None) The number of jobs to use in the E-step. ``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 : int, optional (default=0) Verbosity level.
random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`.
Attributes ---------- components_ : array, n_components, n_features Variational parameters for topic word distribution. Since the complete conditional for topic word distribution is a Dirichlet, ``components_i, j`` can be viewed as pseudocount that represents the number of times word `j` was assigned to topic `i`. It can also be viewed as distribution over the words for each topic after normalization: ``model.components_ / model.components_.sum(axis=1):, np.newaxis``.
n_batch_iter_ : int Number of iterations of the EM step.
n_iter_ : int Number of passes over the dataset.
bound_ : float Final perplexity score on training set.
doc_topic_prior_ : float Prior of document topic distribution `theta`. If the value is None, it is `1 / n_components`.
topic_word_prior_ : float Prior of topic word distribution `beta`. If the value is None, it is `1 / n_components`.
Examples -------- >>> from sklearn.decomposition import LatentDirichletAllocation >>> from sklearn.datasets import make_multilabel_classification >>> # This produces a feature matrix of token counts, similar to what >>> # CountVectorizer would produce on text. >>> X, _ = make_multilabel_classification(random_state=0) >>> lda = LatentDirichletAllocation(n_components=5, ... random_state=0) >>> lda.fit(X) LatentDirichletAllocation(...) >>> # get topics for some given samples: >>> lda.transform(X-2:) array([0.00360392, 0.25499205, 0.0036211 , 0.64236448, 0.09541846],
[0.15297572, 0.00362644, 0.44412786, 0.39568399, 0.003586 ])
References ---------- .. 1 "Online Learning for Latent Dirichlet Allocation", Matthew D. Hoffman, David M. Blei, Francis Bach, 2010
2 "Stochastic Variational Inference", Matthew D. Hoffman, David M. Blei, Chong Wang, John Paisley, 2013
3 Matthew D. Hoffman's onlineldavb code. Link: https://github.com/blei-lab/onlineldavb
val fit :
?y:Py.Object.t ->
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
tLearn model for the data X with variational Bayes method.
When `learning_method` is 'online', use mini-batch update. Otherwise, use batch update.
Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Document word matrix.
y : Ignored
Returns ------- self
val fit_transform :
?y:Ndarray.t ->
?fit_params:(string * Py.Object.t) list ->
x:Ndarray.t ->
t ->
Ndarray.tFit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters ---------- X : numpy array of shape n_samples, n_features Training set.
y : numpy array of shape n_samples Target values.
**fit_params : dict Additional fit parameters.
Returns ------- X_new : numpy array of shape n_samples, n_features_new Transformed array.
val get_params : ?deep:bool -> t -> Py.Object.tGet parameters for this estimator.
Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns ------- params : mapping of string to any Parameter names mapped to their values.
val partial_fit :
?y:Py.Object.t ->
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Py.Object.tOnline VB with Mini-Batch update.
Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Document word matrix.
y : Ignored
Returns ------- self
val perplexity :
?sub_sampling:bool ->
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
floatCalculate approximate perplexity for data X.
Perplexity is defined as exp(-1. * log-likelihood per word)
.. versionchanged:: 0.19 *doc_topic_distr* argument has been deprecated and is ignored because user no longer has access to unnormalized distribution
Parameters ---------- X : array-like or sparse matrix, n_samples, n_features Document word matrix.
sub_sampling : bool Do sub-sampling or not.
Returns ------- score : float Perplexity score.
val score :
?y:Py.Object.t ->
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
floatCalculate approximate log-likelihood as score.
Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Document word matrix.
y : Ignored
Returns ------- score : float Use approximate bound as score.
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
val transform :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tTransform data X according to the fitted model.
.. versionchanged:: 0.18 *doc_topic_distr* is now normalized
Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Document word matrix.
Returns ------- doc_topic_distr : shape=(n_samples, n_components) Document topic distribution for X.
val n_batch_iter_ : t -> intAttribute n_batch_iter_: see constructor for documentation
val n_iter_ : t -> intAttribute n_iter_: see constructor for documentation
val bound_ : t -> floatAttribute bound_: see constructor for documentation
val doc_topic_prior_ : t -> floatAttribute doc_topic_prior_: see constructor for documentation
val topic_word_prior_ : t -> floatAttribute topic_word_prior_: see constructor for documentation
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.