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sklearn_api.atmodel – Scikit learn wrapper for Author-topic model

sklearn_api.atmodel – Scikit learn wrapper for Author-topic model

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

class gensim.sklearn_api.atmodel.AuthorTopicTransformer(num_topics=100, id2word=None, author2doc=None, doc2author=None, chunksize=2000, passes=1, iterations=50, decay=0.5, offset=1.0, alpha='symmetric', eta='symmetric', update_every=1, eval_every=10, gamma_threshold=0.001, serialized=False, serialization_path=None, minimum_probability=0.01, random_state=None)

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

Base AuthorTopic module

Sklearn wrapper for AuthorTopic model. See gensim.models.AuthorTopicModel for parameter details.

fit(X, y=None)

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

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, author2doc=None, doc2author=None)

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(author_names)

Return topic distribution for input authors as a list of (topic_id, topic_probabiity) 2-tuples.