sklearn.decomposition.LatentDirichletAllocation

class sklearn.decomposition.LatentDirichletAllocation(n_components=10, doc_topic_prior=None, topic_word_prior=None, learning_method=None, learning_decay=0.7, learning_offset=10.0, max_iter=10, batch_size=128, evaluate_every=-1, total_samples=1000000.0, perp_tol=0.1, mean_change_tol=0.001, max_doc_update_iter=100, n_jobs=1, verbose=0, random_state=None, n_topics=None)

Latent Dirichlet Allocation with online variational Bayes algorithm

New in version 0.17.

Read more in the User Guide.

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 the literature, 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 the literature, this is called eta.

learning_method : ‘batch’ | ‘online’, default=’online’

Method used to update _component. Only used in fit method. In general, if the data size is large, the online update will be much faster than the batch update. The default learning method is going to be changed to ‘batch’ in the 0.20 release. 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.

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 evalute 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 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, optional (default=1)

The number of jobs to use in the E-step. If -1, all CPUs are used. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used.

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.

n_topics : int, optional (default=None)

This parameter has been renamed to n_components and will be removed in version 0.21. .. deprecated:: 0.19

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.

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:

http://matthewdhoffman.com//code/onlineldavb.tar

Methods

fit(X[, y])	Learn model for the data X with variational Bayes method.
fit_transform(X[, y])	Fit to data, then transform it.
get_params([deep])	Get parameters for this estimator.
partial_fit(X[, y])	Online VB with Mini-Batch update.
perplexity(X[, doc_topic_distr, sub_sampling])	Calculate approximate perplexity for data X.
score(X[, y])	Calculate approximate log-likelihood as score.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Transform data X according to the fitted model.

__init__(n_components=10, doc_topic_prior=None, topic_word_prior=None, learning_method=None, learning_decay=0.7, learning_offset=10.0, max_iter=10, batch_size=128, evaluate_every=-1, total_samples=1000000.0, perp_tol=0.1, mean_change_tol=0.001, max_doc_update_iter=100, n_jobs=1, verbose=0, random_state=None, n_topics=None)

fit(X, y=None)

Learn 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 :

fit_transform(X, y=None, **fit_params)

Fit 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.

Returns:

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep : boolean, optional

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.

partial_fit(X, y=None)

Online VB with Mini-Batch update.

Parameters:

X : array-like or sparse matrix, shape=(n_samples, n_features)

Document word matrix.

y : Ignored.

Returns:

self :

perplexity(X, doc_topic_distr=’deprecated’, sub_sampling=False)

Calculate approximate perplexity for data X.

Perplexity is defined as exp(-1. * log-likelihood per word)

Changed in version 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.

doc_topic_distr : None or array, shape=(n_samples, n_components)

Document topic distribution. This argument is deprecated and is currently being ignored.

Deprecated since version 0.19.

sub_sampling : bool

Do sub-sampling or not.

Returns:

score : float

Perplexity score.

score(X, y=None)

Calculate 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.

set_params(**params)

Set 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.

Returns:self :

transform(X)

Transform data X according to the fitted model.

Changed in version 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.

Examples using sklearn.decomposition.LatentDirichletAllocation

Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation