Python Doc2Vec 获取最相似的文档
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Doc2Vec Get most similar documents
提问by Clock Slave
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 Doc2Vecmodel 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
我正在尝试构建一个文档检索模型，该模型返回按查询或搜索字符串的相关性排序的大多数文档。为此，我使用 gensim 中的模型训练了 doc2vec 模型Doc2Vec。我的数据集采用 Pandas 数据集的形式，其中每个文档都存储为每一行的字符串。这是我到目前为止的代码
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_vectorbut then realised that it considers the query as a document, updates the model and returns the results. I tried using the most_similarand most_similar_cosmulmethods 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?
我挣扎的部分是查找与查询最相似/最相关的文档。我使用了infer_vector但后来意识到它将查询视为文档，更新模型并返回结果。我尝试使用most_similar和most_similar_cosmul方法，但作为回报，我得到了单词和相似度分数（我猜）。我想要做的是当我输入搜索字符串（查询）时，我应该获取最相关的文档（id）以及相似度分数（余弦等）。我如何完成这部分？
回答by Erock
You need to use infer_vectorto get a document vector of the new text - which does not alter the underlying model.
您需要使用infer_vector来获取新文本的文档向量 - 它不会改变底层模型。
Here is how you do it:
这是你如何做到的：
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:
编辑：
Here is an example of how the underlying model does not change after infer_vecis called.
这infer_vec是调用后底层模型如何不改变的示例。
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
