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sklearn_api.hdp – Scikit learn wrapper for Hierarchical Dirichlet Process model

sklearn_api.hdp – Scikit learn wrapper for Hierarchical Dirichlet Process model

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

class gensim.sklearn_api.hdp.HdpTransformer(id2word, max_chunks=None, max_time=None, chunksize=256, kappa=1.0, tau=64.0, K=15, T=150, alpha=1, gamma=1, eta=0.01, scale=1.0, var_converge=0.0001, outputdir=None, random_state=None)

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

Base HDP module

Sklearn api for HDP model. See gensim.models.HdpModel for parameter details.

fit(X, y=None)

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

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)]