piskvorky/gensim

WordEmbeddingsKeyedVectors.add() doesn't clear `vectors_norm`, causing `IndexError` on later `most_similar()`

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#2,532 opened on Jun 18, 2019

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Description

As reported in a StackOverflow question/answer: https://stackoverflow.com/a/56641265/130288

An adapted version of the asker's minimal test case (which could become a unit test):

import numpy as np
from gensim.models.keyedvectors import WordEmbeddingsKeyedVectors

kv = WordEmbeddingsKeyedVectors(vector_size=3)
kv.add(entities=['a', 'b'],
       weights=[np.random.rand(3), np.random.rand(3)])
kv.most_similar('a')  # works

kv.add(entities=['c'], weights=[np.random.rand(3)])
kv.most_similar('c')  # fails with `IndexError`

Clearing the vectors_norm property (with either del or assignment-to-None) should be sufficient to trigger re-calculation upon the next most_similar().

Contributor guide

Research direction
Investigate the `vectors norm` property in `WordEmbeddingsKeyedVectors` and ensure it is cleared when `add()` is called.
Tech stack
python
Domain
machine learning
Issue type
Bug
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2
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1-3 hours
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Active
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Clear
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PythonNumPy
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75