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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.

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

X_new – Transformed array.

Return type:

numpy array of shape [n_samples, n_features_new]

get_params(deep=True)

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
partial_fit(X)

Train model over X.

set_params(**params)

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.

Returns:
Return type:self
transform(docs)

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)]