sklearn_api.d2vmodel – Scikit learn wrapper for paragraph2vec model

sklearn_api.d2vmodel – Scikit learn wrapper for paragraph2vec model

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

class gensim.sklearn_api.d2vmodel.D2VTransformer(dm_mean=None, dm=1, dbow_words=0, dm_concat=0, dm_tag_count=1, docvecs=None, docvecs_mapfile=None, comment=None, trim_rule=None, size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None, sample=0.001, seed=1, workers=3, min_alpha=0.0001, hs=0, negative=5, cbow_mean=1, hashfxn=<built-in function hash>, iter=5, sorted_vocab=1, batch_words=10000)

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

Base Doc2Vec module

Sklearn api for Doc2Vec model. See gensim.models.Doc2Vec and gensim.models.Word2Vec for parameter details.

fit(X, y=None)

Fit the model according to the given training data. Calls gensim.models.Doc2Vec

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

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)

Return the vector representations for the input documents. The input docs should be a list of lists like : [ [‘calculus’, ‘mathematical’], [‘geometry’, ‘operations’, ‘curves’] ] or a single document like : [‘calculus’, ‘mathematical’]