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Silence warning

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Paul O'Leary McCann 2021-06-12 19:31:35 +09:00
parent 7efbc721a1
commit e728b0e45d
1 changed files with 9 additions and 8 deletions
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17
spacy/ml/models/coref.py
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@ -1,4 +1,5 @@
from dataclasses import dataclass from dataclasses import dataclass
import warnings
from thinc.api import Model, Linear, Relu, Dropout, chain, noop from thinc.api import Model, Linear, Relu, Dropout, chain, noop
from thinc.types import Floats2d, Floats1d, Ints2d, Ragged from thinc.types import Floats2d, Floats1d, Ints2d, Ragged
@ -366,14 +367,14 @@ def ant_scorer_forward(
# make a mask so antecedents precede referrents # make a mask so antecedents precede referrents
ant_range = xp.arange(0, cvecs.shape[0]) ant_range = xp.arange(0, cvecs.shape[0])
# TODO use python warning
# with xp.errstate(divide="ignore"): # This will take the log of 0, which causes a warning, but we're doing
# mask = xp.log( # it on purpose so we can just ignore the warning.
# (xp.expand_dims(ant_range, 1) - xp.expand_dims(ant_range, 0)) >= 1 with warnings.catch_warnings():
# ).astype(float) warnings.filterwarnings('ignore', category=RuntimeWarning)
mask = xp.log( mask = xp.log(
(xp.expand_dims(ant_range, 1) - xp.expand_dims(ant_range, 0)) >= 1 (xp.expand_dims(ant_range, 1) - xp.expand_dims(ant_range, 0)) >= 1
).astype(float) ).astype(float)
scores = pw_prod + pw_sum + mask scores = pw_prod + pw_sum + mask