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I tried to import word2vec using nlpia module. But it took almost forever. I thought it was a memory issue. But my laptop has 16G RAM and the memory usage was fine when I run the code below.
from nlpia.data.loaders import get_data
word_vectors = get_data('word2vec')
I test the time of the evaluation using the test code below. I found the evaluation took 6 minutes. This evaluation need to be run for 6 times in the entire code. That mean, we need to wait at least for half a hour to get the result. That seems not efficient. Maybe we should make the regular expression more efficient or only evaluate the the file name, not the full file path.
import re
import timeit
filepath = fplower = '/users/kelly/.pyenv/versions/nlpia/lib/python3.8/site-packages/nlpia/bigdata/googlenews-vectors-negative300.bin.gz'
ext, newext = '.tgz', '.tar.gz'
r = ext.lower().replace('.', r'\.') + r'$'
r = r'^[.]?([^.]*)\.([^.]{1,10})*' + r
start_time = timeit.default_timer()
if re.match(r, fplower) and not fplower.endswith(newext):
filepath = filepath[:-len(ext)] + newext
elapsed = timeit.default_timer() - start_time
print('elapsed time is: ', elapsed)
The text was updated successfully, but these errors were encountered:
I tried to import
word2vec
using nlpia module. But it took almost forever. I thought it was a memory issue. But my laptop has 16G RAM and the memory usage was fine when I run the code below.When I traced the source code, I found the evaluation of the regular expression in futil.py took too long.
https://github.com/totalgood/nlpia/blob/master/src/nlpia/futil.py#L275
I test the time of the evaluation using the test code below. I found the evaluation took 6 minutes. This evaluation need to be run for 6 times in the entire code. That mean, we need to wait at least for half a hour to get the result. That seems not efficient. Maybe we should make the regular expression more efficient or only evaluate the the file name, not the full file path.
The text was updated successfully, but these errors were encountered: