def f(process_id):
print('Process Id: {}'.format(os.getpid()))
process = psutil.Process(os.getpid())
new_model = Word2Vec.load('/tmp/mymodel.pkl')
vector = new_model.wv["science"]
annoy_index = AnnoyIndexer()
annoy_index.load('/tmp/mymodel.index')
annoy_index.model = new_model
approximate_neighbors = new_model.wv.most_similar([vector],
topn=5,
indexer=annoy_index)
print('\nMemory used by process {}: {}\n---'.format(
os.getpid(), process.memory_info()))
def summary(x: str, word2vec, vocabulary):
'''Summary of the 10 nearest similar words based on Word2Vec'''
annoy_index = AnnoyIndexer(word2vec, 100)
vector = vocabulary[x]
# The instance of AnnoyIndexer we just created is passed
approximate_neighbors = vocabulary.most_similar(
[vector], topn=11, indexer=annoy_index)
# Neatly print the approximate_neighbors and their corresponding cosine similarity values
print("Approximate Neighbors")
for neighbor in approximate_neighbors:
print(neighbor)
normal_neighbors = vocabulary.most_similar([vector], topn=11)
print("\nExact Neighbors")
for neighbor in normal_neighbors:
print(neighbor)
# `here <https://github.com/spotify/annoy#how-does-it-work>`__. The relationship
# between ``num_trees``\ , build time, and accuracy will be investigated later
# in the tutorial.
#
# Now that we are ready to make a query, lets find the top 5 most similar words
# to "science" in the Text8 corpus. To make a similarity query we call
# ``Word2Vec.most_similar`` like we would traditionally, but with an added
# parameter, ``indexer``.
#
# Apart from Annoy, Gensim also supports the NMSLIB indexer. NMSLIB is a similar library to
# Annoy – both support fast, approximate searches for similar vectors.
#
from gensim.similarities.annoy import AnnoyIndexer
# 100 trees are being used in this example
annoy_index = AnnoyIndexer(model, 100)
# Derive the vector for the word "science" in our model
vector = wv["science"]
# The instance of AnnoyIndexer we just created is passed
approximate_neighbors = wv.most_similar([vector], topn=11, indexer=annoy_index)
# Neatly print the approximate_neighbors and their corresponding cosine similarity values
print("Approximate Neighbors")
for neighbor in approximate_neighbors:
print(neighbor)
normal_neighbors = wv.most_similar([vector], topn=11)
print("\nExact Neighbors")
for neighbor in normal_neighbors:
print(neighbor)
###############################################################################