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 HdpModel.

Follows scikit-learn API conventions to facilitate using gensim along with scikit-learn.

Examples

>>> from gensim.test.utils import common_dictionary, common_corpus
>>> from gensim.sklearn_api import HdpTransformer
>>>
>>> # Lets extract the distribution of each document in topics
>>> model = HdpTransformer(id2word=common_dictionary)
>>> distr = model.fit_transform(common_corpus)

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, wraps HdpModel.

The inner workings of this class heavily depends on Wang, Paisley, Blei: “Online Variational Inference for the Hierarchical Dirichlet Process, JMLR (2011)”.

Parameters

• id2word (Dictionary, optional) – Mapping between a words ID and the word itself in the vocabulary.

• max_chunks (int, optional) – Upper bound on how many chunks to process.It wraps around corpus beginning in another corpus pass, if there are not enough chunks in the corpus.

• max_time (int, optional) – Upper bound on time in seconds for which model will be trained.

• chunksize (int, optional) – Number of documents to be processed by the model in each mini-batch.

• kappa (float, optional) –

  Learning rate, see Wang, Paisley, Blei: “Online Variational Inference for the Hierarchical Dirichlet Process, JMLR (2011)”.

• tau (float, optional) –

  Slow down parameter, see Wang, Paisley, Blei: “Online Variational Inference for the Hierarchical Dirichlet Process, JMLR (2011)”.

• K (int, optional) –

  Second level truncation level, see Wang, Paisley, Blei: “Online Variational Inference for the Hierarchical Dirichlet Process, JMLR (2011)”.

• T (int, optional) –

  Top level truncation level, see Wang, Paisley, Blei: “Online Variational Inference for the Hierarchical Dirichlet Process, JMLR (2011)”.

• alpha (int, optional) –

  Second level concentration, see Wang, Paisley, Blei: “Online Variational Inference for the Hierarchical Dirichlet Process, JMLR (2011)”.

• gamma (int, optional) –

  First level concentration, see Wang, Paisley, Blei: “Online Variational Inference for the Hierarchical Dirichlet Process, JMLR (2011)”.

• eta (float, optional) –

  The topic Dirichlet, see Wang, Paisley, Blei: “Online Variational Inference for the Hierarchical Dirichlet Process, JMLR (2011)”.

• scale (float, optional) – Weights information from the mini-chunk of corpus to calculate rhot.

• var_converge (float, optional) – Lower bound on the right side of convergence. Used when updating variational parameters for a single document.

• outputdir (str, optional) – Path to a directory where topic and options information will be stored.

• random_state (int, optional) – Seed used to create a RandomState. Useful for obtaining reproducible results.

fit(X, y=None)

Fit the model according to the given training data.

Parameters

X ({iterable of list of (int, number), scipy.sparse matrix}) – A collection of documents in BOW format used for training the model.

Returns

The trained model.

Return type

HdpTransformer

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 a potentially incomplete set of documents.

Uses the parameters set in the constructor. This method can be used in two ways: * On an unfitted model in which case the model is initialized and trained on X. * On an already fitted model in which case the model is updated by X.

Parameters

X ({iterable of list of (int, number), scipy.sparse matrix}) – A collection of documents in BOW format used for training the model.

Returns

The trained model.

Return type

HdpTransformer

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)

Infer a matrix of topic distribution for the given document bow, where a_ij indicates (topic_i, topic_probability_j).

Parameters

docs ({iterable of list of (int, number), list of (int, number)}) – Document or sequence of documents in BOW format.

Returns

Topic distribution for docs.

Return type

numpy.ndarray of shape [len(docs), num_topics]