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sklearn_api.rpmodel – Scikit learn wrapper for Random Projection model

sklearn_api.rpmodel – Scikit learn wrapper for Random Projection model

Scikit learn interface for RpModel.

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


>>> from gensim.sklearn_api.rpmodel import RpTransformer
>>> from gensim.test.utils import common_dictionary, common_corpus
>>> # Initialize and fit the model.
>>> model = RpTransformer(id2word=common_dictionary).fit(common_corpus)
>>> # Use the trained model to transform a document.
>>> result = model.transform(common_corpus[3])
class gensim.sklearn_api.rpmodel.RpTransformer(id2word=None, num_topics=300)

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

Base Word2Vec module, wraps RpModel.

For more information please have a look to Random projection.

  • id2word (Dictionary, optional) – Mapping token_id -> token, will be determined from corpus if id2word == None.
  • num_topics (int, optional) – Number of dimensions.
fit(X, y=None)

Fit the model according to the given training data.

Parameters:X (iterable of list of (int, number)) – Input corpus in BOW format.
Returns:The trained model.
Return type:RpTransformer
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

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

Find the Random Projection factors for docs.

Parameters:docs ({iterable of iterable of (int, int), list of (int, number)}) – Document or documents to be transformed in BOW format.
Returns:RP representation for each input document.
Return type:numpy.ndarray of shape [len(docs), num_topics]