hypertools.tools.gensim_models.LdaVectorizer

class hypertools.tools.gensim_models.LdaVectorizer(num_topics=20, passes=1, seed=0)

Semantic-stage model: gensim’s LdaModel (Latent Dirichlet Allocation) trained over a bag-of-words corpus built internally from a dense document-term matrix (e.g. the output of a vectorizer-stage model). transform returns dense (n_docs, num_topics) topic proportions – each row sums to (approximately) 1, mirroring sklearn.decomposition.LatentDirichletAllocation.

Parameters:

num_topicsint

Number of latent topics. Default: 20 (matches hypertools’ scikit-learn LDA/NMF default).

passesint

Number of passes through the corpus during training. Default: 1.

seedint or None

Random state for reproducible training (gensim’s random_state). Default: 0.

Attributes:

model_gensim.models.LdaModel

The trained LDA model (set by fit).

n_features_int

Vocabulary size (number of document-term matrix columns) seen during fit; transform requires the same width.

Methods

fit(X[, y])	Fit an LDA model to a dense document-term matrix.
fit_transform(X[, y])	Fit to data, then transform it.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Compute topic proportions for a dense document-term matrix.

__init__(num_topics=20, passes=1, seed=0)

Methods

__init__([num_topics, passes, seed])
fit(X[, y])	Fit an LDA model to a dense document-term matrix.
fit_transform(X[, y])	Fit to data, then transform it.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Compute topic proportions for a dense document-term matrix.