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Fix bug in evaluation for tie in scores. #10

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Fix bug in evaluation for tie in scores. #10

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3cf75cb
Fix bug in evaluation for tie in scores.
Jul 25, 2018
16480a0
Simplify rank computation.
Jul 26, 2018
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24 changes: 16 additions & 8 deletions eval_utils/rank_answerer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,14 +20,22 @@

def rankOptions(options, gtOptions, scores):
'''Rank a batch of examples against a list of options.'''
numOptions = options.size(1)
# Compute score of GT options in 'scores'
gtScores = scores.gather(1, gtOptions.unsqueeze(1))
# Sort all predicted scores
sortedScore, _ = torch.sort(scores, 1)
# In sorted scores, count how many are greater than the GT score
ranks = torch.sum(sortedScore.gt(gtScores).float(), 1)
return ranks + 1
# sort in descending order - largest score gets highest rank
sortedRanks, rankedIdx = scores.sort(1, descending=True)

# convert from rankedIdx to ranks
ranks = rankedIdx.clone().fill_(0)
for i in range(rankedIdx.size(0)):
for j in range(100):
ranks[i][rankedIdx[i][j]] = j
ranks += 1

gtOptions = gtOptions.view(-1)
gtRanks = torch.LongTensor(gtOptions.size(0))
for i in range(gtOptions.size(0)):
gtRanks[i] = int(ranks[i, gtOptions[i]])

return gtRanks


def rankABot(aBot, dataset, split, scoringFunction, exampleLimit=None):
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