How to get document vectors of two text documents using Doc2vec? I am new to this, so it would be helpful if someone could point me in right direction/help me with some tutorial
I am using gensim python library.
doc1=["This is a sentence","This is another sentence"] documents1=[doc.strip().split(" ") for doc in doc1 ] model = doc2vec.Doc2Vec(documents1, size = 100, window = 300, min_count = 10, workers=4) I get AttributeError: 'list' object has no attribute 'words' whenever I run this
Gensim was updated. The syntax of LabeledSentence does not contain labels. There are now tags - see documentation for LabeledSentence https://radimrehurek.com/gensim/models/doc2vec.html
However, @bee2502 was right with
docvec = model.docvecs[99] It will should the 100th vector's value for trained model, it works with integers and strings.
If you want to train Doc2Vec model, your data set needs to contain lists of words (similar to Word2Vec format) and tags (id of documents). It can also contain some additional info (see https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/doc2vec-IMDB.ipynb for more information).
# Import libraries from gensim.models import doc2vec from collections import namedtuple # Load data doc1 = ["This is a sentence", "This is another sentence"] # Transform data (you can add more data preprocessing steps) docs = [] analyzedDocument = namedtuple('AnalyzedDocument', 'words tags') for i, text in enumerate(doc1): words = text.lower().split() tags = [i] docs.append(analyzedDocument(words, tags)) # Train model (set min_count = 1, if you want the model to work with the provided example data set) model = doc2vec.Doc2Vec(docs, size = 100, window = 300, min_count = 1, workers = 4) # Get the vectors model.docvecs[0] model.docvecs[1] UPDATE (how to train in epochs): Doc2Vec function contains alpha and min_alpha parameters, but that means that the learning rate decays during one epoch from alpha to min_alpha. To train several epochs, set the learning rate manually, like this:
from gensim.models import doc2vec import random alpha_val = 0.025 # Initial learning rate min_alpha_val = 1e-4 # Minimum for linear learning rate decay passes = 15 # Number of passes of one document during training alpha_delta = (alpha_val - min_alpha_val) / (passes - 1) model = doc2vec.Doc2Vec( size = 100 # Model initialization , window = 300 , min_count = 1 , workers = 4) model.build_vocab(docs) # Building vocabulary for epoch in range(passes): # Shuffling gets better results random.shuffle(docs) # Train model.alpha, model.min_alpha = alpha_val, alpha_val model.train(docs) # Logs print('Completed pass %i at alpha %f' % (epoch + 1, alpha_val)) # Next run alpha alpha_val -= alpha_delta doc=["This is a sentence","This is another sentence"] documents=[doc.strip().split(" ") for doc in doc1 ] model = doc2vec.Doc2Vec(documents, size = 100, window = 300, min_count = 10, workers=4) I got AttributeError: 'list' object has no attribute 'words' because the input documents to the Doc2vec() was not in correct LabeledSentence format. I hope this below example will help you understand the format.
documents = LabeledSentence(words=[u'some', u'words', u'here'], labels=[u'SENT_1']) More details are here : http://rare-technologies.com/doc2vec-tutorial/ However, I solved the problem by taking input data from file using TaggedLineDocument().
File format: one document = one line = one TaggedDocument object. Words are expected to be already preprocessed and separated by whitespace, tags are constructed automatically from the document line number.
sentences=doc2vec.TaggedLineDocument(file_path) model = doc2vec.Doc2Vec(sentences,size = 100, window = 300, min_count = 10, workers=4) To get document vector : You can use docvecs. More details here : https://radimrehurek.com/gensim/models/doc2vec.html#gensim.models.doc2vec.TaggedDocument
docvec = model.docvecs[99] where 99 is the document id whose vector we want. If labels are in integer format (by default, if you load using TaggedLineDocument() ), directly use integer id like I did. If labels are in string format,use "SENT_99" .This is similar to Word2vec