hypertools.tools.gensim_models.LsiVectorizer¶

class hypertools.tools.gensim_models.LsiVectorizer(num_topics=20, seed=0)[source]¶

Semantic-stage model: gensim’s LsiModel (Latent Semantic Indexing) trained over a bag-of-words corpus built internally from a dense document-term matrix. transform returns dense (n_docs, num_topics) projections (unlike LDA these are signed real values, not probabilities – they do not sum to 1).

Parameters:

num_topicsint: Number of latent dimensions. Default: 20.
seedint or None: Random seed for reproducible training (gensim’s random_seed). Default: 0.

Attributes:

model_gensim.models.LsiModel: The trained LSI model (set by fit).
n_features_int: Vocabulary size seen during fit; transform requires the same width.

Methods

`fit`(X[, y])	Fit an LSI 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 LSI projections for a dense document-term matrix.

__init__(num_topics=20, seed=0)[source]¶

Methods

`__init__`([num_topics, seed])
`fit`(X[, y])	Fit an LSI 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 LSI projections for a dense document-term matrix.