Doc2Vec Get most similar documents

Question

I am trying to build a document retrieval model that returns most documents ordered by their relevancy with respect to a query or a search string. For this I trained a doc2vec model using the Doc2Vec model in gensim. My dataset is in the form of a pandas dataset which has each document stored as a string on each line. This is the code I have so far

import gensim, re import pandas as pd  # TOKENIZER def tokenizer(input_string):     return re.findall(r"[\w']+", input_string)  # IMPORT DATA data = pd.read_csv('mp_1002_prepd.txt') data.columns = ['merged'] data.loc[:, 'tokens'] = data.merged.apply(tokenizer) sentences= [] for item_no, line in enumerate(data['tokens'].values.tolist()):     sentences.append(LabeledSentence(line,[item_no]))  # MODEL PARAMETERS dm = 1 # 1 for distributed memory(default); 0 for dbow  cores = multiprocessing.cpu_count() size = 300 context_window = 50 seed = 42 min_count = 1 alpha = 0.5 max_iter = 200  # BUILD MODEL model = gensim.models.doc2vec.Doc2Vec(documents = sentences, dm = dm, alpha = alpha, # initial learning rate seed = seed, min_count = min_count, # ignore words with freq less than min_count max_vocab_size = None, #  window = context_window, # the number of words before and after to be used as context size = size, # is the dimensionality of the feature vector sample = 1e-4, # ? negative = 5, # ? workers = cores, # number of cores iter = max_iter # number of iterations (epochs) over the corpus)  # QUERY BASED DOC RANKING ?? 

The part where I am struggling is in finding documents that are most similar/relevant to the query. I used the infer_vector but then realised that it considers the query as a document, updates the model and returns the results. I tried using the most_similar and most_similar_cosmul methods but I get words along with a similarity score(I guess) in return. What I want to do is when I enter a search string(a query), I should get the documents (ids) that are most relevant along with a similarity score(cosine etc). How do I get this part done?

Answer 1

You need to use infer_vector to get a document vector of the new text - which does not alter the underlying model.

Here is how you do it:

tokens = "a new sentence to match".split()  new_vector = model.infer_vector(tokens) sims = model.docvecs.most_similar([new_vector]) #gives you top 10 document tags and their cosine similarity 

Edit:

Here is an example of how the underlying model does not change after infer_vec is called.

import numpy as np  words = "king queen man".split()  len_before =  len(model.docvecs) #number of docs  #word vectors for king, queen, man w_vec0 = model[words[0]] w_vec1 = model[words[1]] w_vec2 = model[words[2]]  new_vec = model.infer_vector(words)  len_after =  len(model.docvecs)  print np.array_equal(model[words[0]], w_vec0) # True print np.array_equal(model[words[1]], w_vec1) # True print np.array_equal(model[words[2]], w_vec2) # True  print len_before == len_after #True