96 lines
3.1 KiB
Python
96 lines
3.1 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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#
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# Author: Chinmaya Pancholi <chinmayapancholi13@gmail.com>
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# Copyright (C) 2017 Radim Rehurek <radimrehurek@seznam.cz>
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# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
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"""Scikit learn interface for :class:`~gensim.models.rpmodel.RpModel`.
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Follows scikit-learn API conventions to facilitate using gensim along with scikit-learn.
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Examples
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--------
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>>> from gensim.sklearn_api.rpmodel import RpTransformer
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>>> from gensim.test.utils import common_dictionary, common_corpus
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>>>
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>>> # Initialize and fit the model.
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>>> model = RpTransformer(id2word=common_dictionary).fit(common_corpus)
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>>>
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>>> # Use the trained model to transform a document.
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>>> result = model.transform(common_corpus[3])
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"""
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import numpy as np
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from sklearn.base import TransformerMixin, BaseEstimator
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from sklearn.exceptions import NotFittedError
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from gensim import models
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from gensim import matutils
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class RpTransformer(TransformerMixin, BaseEstimator):
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"""Base Word2Vec module, wraps :class:`~gensim.models.rpmodel.RpModel`.
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For more information please have a look to `Random projection <https://en.wikipedia.org/wiki/Random_projection>`_.
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"""
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def __init__(self, id2word=None, num_topics=300):
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"""
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Parameters
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----------
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id2word : :class:`~gensim.corpora.dictionary.Dictionary`, optional
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Mapping `token_id` -> `token`, will be determined from corpus if `id2word == None`.
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num_topics : int, optional
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Number of dimensions.
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"""
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self.gensim_model = None
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self.id2word = id2word
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self.num_topics = num_topics
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def fit(self, X, y=None):
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"""Fit the model according to the given training data.
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Parameters
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----------
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X : iterable of list of (int, number)
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Input corpus in BOW format.
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Returns
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-------
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:class:`~gensim.sklearn_api.rpmodel.RpTransformer`
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The trained model.
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"""
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self.gensim_model = models.RpModel(corpus=X, id2word=self.id2word, num_topics=self.num_topics)
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return self
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def transform(self, docs):
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"""Find the Random Projection factors for `docs`.
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Parameters
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----------
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docs : {iterable of iterable of (int, int), list of (int, number)}
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Document or documents to be transformed in BOW format.
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Returns
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-------
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numpy.ndarray of shape [`len(docs)`, `num_topics`]
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RP representation for each input document.
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"""
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if self.gensim_model is None:
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raise NotFittedError(
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"This model has not been fitted yet. Call 'fit' with appropriate arguments before using this method."
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)
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# The input as array of array
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if isinstance(docs[0], tuple):
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docs = [docs]
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# returning dense representation for compatibility with sklearn
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# but we should go back to sparse representation in the future
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presentation = [matutils.sparse2full(self.gensim_model[doc], self.num_topics) for doc in docs]
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return np.reshape(np.array(presentation), (len(docs), self.num_topics))
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