gensim logo

gensim tagline

Get Expert Help

• machine learning, NLP, data mining

• custom SW design, development, optimizations

• corporate trainings & IT consulting

sklearn_api.lsimodel – Scikit learn wrapper for Latent Semantic Indexing

sklearn_api.lsimodel – Scikit learn wrapper for Latent Semantic Indexing

Scikit learn interface for gensim for easy use of gensim with scikit-learn Follows scikit-learn API conventions

class gensim.sklearn_api.lsimodel.LsiTransformer(num_topics=200, id2word=None, chunksize=20000, decay=1.0, onepass=True, power_iters=2, extra_samples=100)

Bases: sklearn.base.TransformerMixin, sklearn.base.BaseEstimator

Base LSI module

Sklearn wrapper for LSI model. See gensim.model.LsiModel for parameter details.

fit(X, y=None)

Fit the model according to the given training data. Calls gensim.models.LsiModel

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.

  • X (numpy array of shape [n_samples, n_features]) – Training set.
  • y (numpy array of shape [n_samples]) – Target values.

X_new – Transformed array.

Return type:

numpy array of shape [n_samples, n_features_new]


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 – Parameter names mapped to their values.
Return type:mapping of string to any

Train model over X.


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.

Return type:self

Takes a list of documents as input (‘docs’). Returns a matrix of topic distribution for the given document bow, where a_ij indicates (topic_i, topic_probability_j). The input docs should be in BOW format and can be a list of documents like [[(4, 1), (7, 1)], [(9, 1), (13, 1)], [(2, 1), (6, 1)]] or a single document like : [(4, 1), (7, 1)]