mojolearn.LatentDirichletAllocation#

class mojolearn.LatentDirichletAllocation(n_components=10, *, doc_topic_prior=None, topic_word_prior=None, learning_method='batch', 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=None, verbose=0, random_state=None, numeric_mode=None)#

Latent Dirichlet allocation topic model, fitted by variational EM.

Follows scikit-learn’s LatentDirichletAllocation. X is a document-term count matrix (non-negative; a scipy.sparse matrix is densified). Random draws come from mojolearn’s own generator, so topics differ from scikit-learn’s for the same seed. Online learning takes the mini-batches in row order.

Parameters:

n_components (int, default 10) – Number of topics.
doc_topic_prior (float or None, default None) – Prior on the document-topic distribution; None uses 1 / n_components.
topic_word_prior (float or None, default None) – Prior on the topic-word distribution; None uses 1 / n_components.
learning_method ({“batch”, “online”}, default “batch”) – Use all data in each update, or mini-batches.
learning_decay (float, default 0.7) – Online learning rate decay.
learning_offset (float, default 10.0) – Online learning rate offset (down-weights early iterations).
max_iter (int, default 10) – Maximum passes over the data.
batch_size (int, default 128) – Documents per mini-batch for online learning; must be a positive int.
evaluate_every (int, default -1) – Compute perplexity every this many iterations and stop once it improves by less than perp_tol; 0 or negative never evaluates.
total_samples (float, default 1e6) – Total number of documents, used by partial_fit and perplexity(sub_sampling=True).
perp_tol (float, default 0.1) – Perplexity tolerance for evaluate_every.
mean_change_tol (float, default 1e-3) – Stopping tolerance of the per-document E-step.
max_doc_update_iter (int, default 100) – Maximum E-step iterations per document.
n_jobs (ignored)
verbose (ignored)
random_state (int or None, default None) – Seed; None means 0.
numeric_mode ({“identical”, “fast”} or None, default None) – Numeric mode for this estimator. None uses the package default ("identical" unless changed). In identical mode, mojolearn returns the same bits on Apple, NVIDIA and AMD GPUs. "fast" is available on Apple GPUs only.

Variables:

components_ (ndarray of shape (n_components, n_features)) – Variational topic-word parameters.
exp_dirichlet_component_ (ndarray of shape (n_components, n_features))
doc_topic_prior_ (float)
topic_word_prior_ (float)
bound_ (float) – Final perplexity on the training data.
n_iter_ (int)
n_batch_iter_ (int)
n_features_in_ (int)

Raises:

ValueError – If X has negative values, or learning_method or batch_size is invalid.

Examples

>>> import numpy as np, mojolearn
>>> X = np.random.default_rng(0).integers(0, 5, (200, 30)).astype(np.float32)
>>> lda = mojolearn.LatentDirichletAllocation(n_components=4, random_state=0).fit(X)
>>> doc_topics = lda.transform(X)

Choosing an estimator

Methods

`fit`(X[, y])	Fit the topic model to a document-term matrix.
`fit_transform`(X[, y, normalize])	Fit the model, then return `transform(X, normalize=normalize)`.
`get_params`([deep])	Return this estimator's constructor parameters.
`partial_fit`(X[, y])	Update the model with one batch of documents (online learning).
`perplexity`(X[, sub_sampling])	Return the approximate perplexity of X.
`score`(X[, y])	Return the approximate log-likelihood (variational bound) of X.
`set_params`(**params)	Set constructor parameters by keyword.
`transform`(X, *[, normalize])	Return the topic distribution of each document.

fit(X, y=None)#

Fit the topic model to a document-term matrix.

Parameters:

X (array-like or scipy.sparse matrix of shape (n_samples, n_features)) – Non-negative word counts.
y (ignored)

Returns:

self

partial_fit(X, y=None)#

Update the model with one batch of documents (online learning).

Parameters:

X (array-like or scipy.sparse matrix of shape (n_samples, n_features)) – Non-negative word counts, split into mini-batches of batch_size.
y (ignored)

Returns:

self

transform(X, *, normalize=True)#

Return the topic distribution of each document.

Parameters:

X (array-like or scipy.sparse matrix of shape (n_samples, n_features)) – Non-negative word counts.
normalize (bool, default True) – Normalize each row to sum to 1.

Returns:

ndarray of shape (n_samples, n_components), float32

fit_transform(X, y=None, *, normalize=True)#

Fit the model, then return transform(X, normalize=normalize).

Parameters:

X (array-like or scipy.sparse matrix of shape (n_samples, n_features))
y (ignored)
normalize (bool, default True)

Returns:

ndarray of shape (n_samples, n_components), float32

score(X, y=None)#

Return the approximate log-likelihood (variational bound) of X.

Parameters:

X (array-like or scipy.sparse matrix of shape (n_samples, n_features))
y (ignored)

Returns:

float

perplexity(X, sub_sampling=False)#

Return the approximate perplexity of X.

Parameters:

X (array-like or scipy.sparse matrix of shape (n_samples, n_features))
sub_sampling (bool, default False) – Scale the bound as if X were a sample of total_samples documents.

Returns:

float – Infinity when X has no words.

get_params(deep=True)#

Return this estimator’s constructor parameters.

Parameters:: deep (bool, default True) – Accepted for scikit-learn compatibility; it has no effect.
Returns:: dict – Parameter name to current value.

set_params(**params)#

Set constructor parameters by keyword.

Raises:: ValueError – If a keyword is not a parameter of this estimator.
Returns:: self