Doc2Vec - Finding document similarity in test data
It turns out there is a function called similarity_unseen_docs(...) which can be used to find the similarity of 2 documents in the test data. 
However, I will leave the question unsolved for now as it is not very optimal since I would need manually compare the specific document with every other document in the test data. Also, it compares the words in the documents instead of the vectors which could affect accuracy.
I am trying to train a doc2vec model using training data, then finding the similarity of every document in the test data for a specific document in the test data using the trained doc2vec model. However, I am unable to determine how to do this.
I currently using model.docvecs.most_similar(...). However, this function only finds the similarity of every document in the training data for a specific document in the test data. 
I have tried manually comparing the inferred vector of a specific document in the test data with the inferred vectors of every other document in the test data using model.docvecs.n_similarity(inferred_vector.tolist(), testvectors[i].tolist()) but this returns KeyError: "tag '-0.3502606451511383' not seen in training corpus/invalid" as there are vectors not in the dictionary.