184 lines
8.2 KiB
Python
184 lines
8.2 KiB
Python
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#!/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.word2vec.Word2Vec`.
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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.test.utils import common_texts
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>>> from gensim.sklearn_api import W2VTransformer
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>>>
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>>> # Create a model to represent each word by a 10 dimensional vector.
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>>> model = W2VTransformer(size=10, min_count=1, seed=1)
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>>>
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>>> # What is the vector representation of the word 'graph'?
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>>> wordvecs = model.fit(common_texts).transform(['graph', 'system'])
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>>> assert wordvecs.shape == (2, 10)
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"""
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import numpy as np
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import six
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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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class W2VTransformer(TransformerMixin, BaseEstimator):
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"""Base Word2Vec module, wraps :class:`~gensim.models.word2vec.Word2Vec`.
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For more information please have a look to `Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean: "Efficient
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Estimation of Word Representations in Vector Space" <https://arxiv.org/abs/1301.3781>`_.
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"""
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def __init__(self, size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None, sample=1e-3, seed=1,
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workers=3, min_alpha=0.0001, sg=0, hs=0, negative=5, cbow_mean=1, hashfxn=hash, iter=5, null_word=0,
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trim_rule=None, sorted_vocab=1, batch_words=10000):
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"""
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Parameters
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----------
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size : int
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Dimensionality of the feature vectors.
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alpha : float
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The initial learning rate.
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window : int
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The maximum distance between the current and predicted word within a sentence.
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min_count : int
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Ignores all words with total frequency lower than this.
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max_vocab_size : int
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Limits the RAM during vocabulary building; if there are more unique
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words than this, then prune the infrequent ones. Every 10 million word types need about 1GB of RAM.
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Set to `None` for no limit.
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sample : float
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The threshold for configuring which higher-frequency words are randomly downsampled,
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useful range is (0, 1e-5).
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seed : int
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Seed for the random number generator. Initial vectors for each word are seeded with a hash of
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the concatenation of word + `str(seed)`. Note that for a fully deterministically-reproducible run,
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you must also limit the model to a single worker thread (`workers=1`), to eliminate ordering jitter
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from OS thread scheduling. (In Python 3, reproducibility between interpreter launches also requires
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use of the `PYTHONHASHSEED` environment variable to control hash randomization).
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workers : int
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Use these many worker threads to train the model (=faster training with multicore machines).
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min_alpha : float
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Learning rate will linearly drop to `min_alpha` as training progresses.
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sg : int {1, 0}
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Defines the training algorithm. If 1, CBOW is used, otherwise, skip-gram is employed.
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hs : int {1,0}
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If 1, hierarchical softmax will be used for model training.
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If set to 0, and `negative` is non-zero, negative sampling will be used.
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negative : int
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If > 0, negative sampling will be used, the int for negative specifies how many "noise words"
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should be drawn (usually between 5-20).
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If set to 0, no negative sampling is used.
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cbow_mean : int {1,0}
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If 0, use the sum of the context word vectors. If 1, use the mean, only applies when cbow is used.
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hashfxn : callable (object -> int), optional
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A hashing function. Used to create an initial random reproducible vector by hashing the random seed.
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iter : int
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Number of iterations (epochs) over the corpus.
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null_word : int {1, 0}
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If 1, a null pseudo-word will be created for padding when using concatenative L1 (run-of-words)
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trim_rule : function
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Vocabulary trimming rule, specifies whether certain words should remain in the vocabulary,
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be trimmed away, or handled using the default (discard if word count < min_count).
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Can be None (min_count will be used, look to :func:`~gensim.utils.keep_vocab_item`),
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or a callable that accepts parameters (word, count, min_count) and returns either
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:attr:`gensim.utils.RULE_DISCARD`, :attr:`gensim.utils.RULE_KEEP` or :attr:`gensim.utils.RULE_DEFAULT`.
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Note: The rule, if given, is only used to prune vocabulary during build_vocab() and is not stored as part
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of the model.
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sorted_vocab : int {1,0}
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If 1, sort the vocabulary by descending frequency before assigning word indexes.
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batch_words : int
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Target size (in words) for batches of examples passed to worker threads (and
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thus cython routines).(Larger batches will be passed if individual
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texts are longer than 10000 words, but the standard cython code truncates to that maximum.)
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"""
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self.gensim_model = None
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self.size = size
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self.alpha = alpha
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self.window = window
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self.min_count = min_count
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self.max_vocab_size = max_vocab_size
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self.sample = sample
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self.seed = seed
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self.workers = workers
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self.min_alpha = min_alpha
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self.sg = sg
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self.hs = hs
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self.negative = negative
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self.cbow_mean = int(cbow_mean)
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self.hashfxn = hashfxn
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self.iter = iter
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self.null_word = null_word
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self.trim_rule = trim_rule
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self.sorted_vocab = sorted_vocab
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self.batch_words = batch_words
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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 iterables of str
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The input corpus. X can be simply a list of lists of tokens, but for larger corpora,
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consider an iterable that streams the sentences directly from disk/network.
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See :class:`~gensim.models.word2vec.BrownCorpus`, :class:`~gensim.models.word2vec.Text8Corpus`
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or :class:`~gensim.models.word2vec.LineSentence` in :mod:`~gensim.models.word2vec` module for such examples.
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Returns
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-------
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:class:`~gensim.sklearn_api.w2vmodel.W2VTransformer`
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The trained model.
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"""
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self.gensim_model = models.Word2Vec(
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sentences=X, size=self.size, alpha=self.alpha,
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window=self.window, min_count=self.min_count, max_vocab_size=self.max_vocab_size,
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sample=self.sample, seed=self.seed, workers=self.workers, min_alpha=self.min_alpha,
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sg=self.sg, hs=self.hs, negative=self.negative, cbow_mean=self.cbow_mean,
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hashfxn=self.hashfxn, iter=self.iter, null_word=self.null_word, trim_rule=self.trim_rule,
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sorted_vocab=self.sorted_vocab, batch_words=self.batch_words
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)
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return self
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def transform(self, words):
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"""Get the word vectors the input words.
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Parameters
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----------
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words : {iterable of str, str}
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Word or a collection of words to be transformed.
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Returns
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-------
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np.ndarray of shape [`len(words)`, `size`]
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A 2D array where each row is the vector of one word.
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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(words, six.string_types):
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words = [words]
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vectors = [self.gensim_model[word] for word in words]
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return np.reshape(np.array(vectors), (len(words), self.size))
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def partial_fit(self, X):
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raise NotImplementedError(
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"'partial_fit' has not been implemented for W2VTransformer. "
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"However, the model can be updated with a fixed vocabulary using Gensim API call."
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)
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