hypertools.tools.gensim_models.Doc2VecVectorizer¶

class hypertools.tools.gensim_models.Doc2VecVectorizer(vector_size=100, window=5, min_count=1, epochs=10, seed=0, workers=None)[source]¶

Vectorizer-stage model: a document’s vector is either its trained gensim Doc2Vec tag vector (for documents seen during fit) or an inferred vector (Doc2Vec.infer_vector) for new documents at transform time. To keep transform consistent regardless of whether a document was part of the training corpus, this wrapper always uses infer_vector.

Note that while two identically-seeded fits produce identical transform outputs, repeated transform() calls on the SAME fitted instance are not bitwise-identical: gensim’s infer_vector advances the model’s internal random-number state on every call.

Parameters:

vector_sizeint: Dimensionality of the document vectors. Default: 100.
windowint: Maximum distance between the current and predicted word within a sentence. Default: 5.
min_countint: Ignores all words with total frequency lower than this. Default: 1.
epochsint: Number of training epochs (also used as the number of inference steps in infer_vector). Default: 10.
seedint or None: Random seed for reproducible training/inference. Default: 0. See Word2VecVectorizer for the single-worker determinism note.
workersint or None: Number of worker threads used during training. Default: None (resolves per _effective_workers).

Attributes:

model_gensim.models.doc2vec.Doc2Vec: The trained Doc2Vec model (set by fit).

Methods

`fit`(X[, y])	Train a Doc2Vec model on the given documents.
`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)	Infer document vectors for the given documents.

__init__(vector_size=100, window=5, min_count=1, epochs=10, seed=0, workers=None)[source]¶

Methods

`__init__`([vector_size, window, min_count, ...])
`fit`(X[, y])	Train a Doc2Vec model on the given documents.
`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)	Infer document vectors for the given documents.