Python rouge.rouge.Rouge() Examples
The following are 17
code examples of rouge.rouge.Rouge().
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Example #1
| Source File: tester.py From deep-summarization with MIT License | 6 votes |
|
def score(ref, hypo):
"""
ref, dictionary of reference sentences (id, sentence)
hypo, dictionary of hypothesis sentences (id, sentence)
score, dictionary of scores
"""
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Rouge(), "ROUGE_L"),
]
final_scores = {}
for scorer, method in scorers:
score, scores = scorer.compute_score(ref, hypo)
if type(score) == list:
for m, s in zip(method, score):
final_scores[m] = s
else:
final_scores[method] = score
return final_scores
Example #2
| Source File: eval.py From unilm with MIT License | 6 votes |
|
def evaluate(self):
output = []
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Meteor(), "METEOR"),
(Rouge(), "ROUGE_L"),
# (Cider(), "CIDEr")
]
# =================================================
# Compute scores
# =================================================
for scorer, method in scorers:
# print 'computing %s score...'%(scorer.method())
score, scores = scorer.compute_score(self.gts, self.res)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
print("%s: %0.5f" % (m, sc))
output.append(sc)
else:
print("%s: %0.5f" % (method, score))
output.append(score)
return output
Example #3
| Source File: eval_on_unilm_tokenized_ref.py From unilm with MIT License | 6 votes |
|
def evaluate(self):
output = []
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Meteor(), "METEOR"),
(Rouge(), "ROUGE_L"),
# (Cider(), "CIDEr")
]
# =================================================
# Compute scores
# =================================================
for scorer, method in scorers:
# print 'computing %s score...'%(scorer.method())
score, scores = scorer.compute_score(self.gts, self.res)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
print("%s: %0.5f" % (m, sc))
output.append(sc)
else:
print("%s: %0.5f" % (method, score))
output.append(score)
return output
Example #4
| Source File: eval.py From NQG_ASs2s with MIT License | 6 votes |
|
def evaluate(self):
output = []
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Meteor(),"METEOR"),
(Rouge(), "ROUGE_L"),
# (Cider(), "CIDEr")
]
# =================================================
# Compute scores
# =================================================
for scorer, method in scorers:
# print 'computing %s score...'%(scorer.method())
score, scores = scorer.compute_score(self.gts, self.res)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
print "%s: %0.5f"%(m, sc)
output.append(sc)
else:
print "%s: %0.5f"%(method, score)
output.append(score)
return output
Example #5
| Source File: eval.py From neural-question-generation with MIT License | 6 votes |
|
def evaluate(self):
output = []
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
# (Meteor(),"METEOR"),
# (Rouge(), "ROUGE_L"),
# (Cider(), "CIDEr")
]
# =================================================
# Compute scores
# =================================================
for scorer, method in scorers:
# print 'computing %s score...'%(scorer.method())
score, scores = scorer.compute_score(self.gts, self.res)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
print "%s: %0.5f"%(m, sc)
output.append(sc)
else:
print "%s: %0.5f"%(method, score)
output.append(score)
return output
Example #6
| Source File: eval.py From Zeroshot-QuestionGeneration with MIT License | 5 votes |
|
def evaluate(self):
# imgIds = self.coco.getImgIds()
gts = dict(zip(range(0, len(self.predicted_list)), self.predicted_list))
res = dict(zip(range(0, len(self.label_list)), self.label_list))
# =================================================
# Set up scorers
# =================================================
print 'tokenization...'
tokenizer = PTBTokenizer()
gts = tokenizer.tokenize(gts)
res = tokenizer.tokenize(res)
# =================================================
# Set up scorers
# =================================================
print 'setting up scorers...'
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Meteor(),"METEOR"),
(Rouge(), "ROUGE_L"),
(Cider(), "CIDEr")
]
# =================================================
# Compute scores
# =================================================
for scorer, method in scorers:
print 'computing %s score...'%(scorer.method())
score, scores = scorer.compute_score(gts, res)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
self.setEval(sc, m)
self.set_textid_to_eval(scs, gts.keys(), m)
print "%s: %0.3f"%(m, sc)
else:
self.setEval(score, method)
self.set_textid_to_eval(scores, gts.keys(), method)
print "%s: %0.3f"%(method, score)
self.set_eval()
Example #7
| Source File: eval.py From QG-Net with MIT License | 5 votes |
|
def evaluate(self):
output = []
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Meteor(),"METEOR"),
(Rouge(), "ROUGE_L"),
# (Cider(), "CIDEr")
]
# =================================================
# Compute scores
# =================================================
for scorer, method in scorers:
# print 'computing %s score...'%(scorer.method())
score, scores = scorer.compute_score(self.gts, self.res)
# set_trace()
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
print "%s: %0.5f"%(m, sc)
output.append(sc)
else:
print "%s: %0.5f"%(method, score)
output.append(score)
return output
Example #8
| Source File: album_eval.py From AREL with MIT License | 5 votes |
|
def evaluate(self, album_to_Gts, album_to_Res):
self.album_to_Res = album_to_Res
self.album_to_Gts = album_to_Gts
# =================================================
# Set up scorers
# =================================================
print 'setting up scorers...'
scorers = []
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Meteor(), "METEOR"),
(Rouge(), "ROUGE_L"),
(Cider(), "CIDEr") # df='VIST/VIST-train-words'
]
# =================================================
# Compute scores
# =================================================
for scorer, method in scorers:
print 'computing %s score ...' % (scorer.method())
score, scores = scorer.compute_score(self.album_to_Gts, self.album_to_Res)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
self.setEval(sc, m)
self.setAlbumToEval(scs, self.album_to_Gts.keys(), m)
print '%s: %.3f' % (m, sc)
else:
self.setEval(score, method)
self.setAlbumToEval(scores, self.album_to_Gts.keys(), method)
print '%s: %.3f' % (method, score)
self.setEvalAlbums()
Example #9
| Source File: eval.py From image_captioning with MIT License | 4 votes |
|
def evaluate(self):
imgIds = self.params['image_id']
# imgIds = self.coco.getImgIds()
gts = {}
res = {}
for imgId in imgIds:
gts[imgId] = self.coco.imgToAnns[imgId]
res[imgId] = self.cocoRes.imgToAnns[imgId]
# =================================================
# Set up scorers
# =================================================
print 'tokenization...'
tokenizer = PTBTokenizer()
gts = tokenizer.tokenize(gts)
res = tokenizer.tokenize(res)
# =================================================
# Set up scorers
# =================================================
print 'setting up scorers...'
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Meteor(),"METEOR"),
(Rouge(), "ROUGE_L"),
(Cider(), "CIDEr")
]
# =================================================
# Compute scores
# =================================================
for scorer, method in scorers:
print 'computing %s score...'%(scorer.method())
score, scores = scorer.compute_score(gts, res)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
self.setEval(sc, m)
self.setImgToEvalImgs(scs, gts.keys(), m)
print "%s: %0.3f"%(m, sc)
else:
self.setEval(score, method)
self.setImgToEvalImgs(scores, gts.keys(), method)
print "%s: %0.3f"%(method, score)
self.setEvalImgs()
Example #10
| Source File: eval.py From CommonSenseMultiHopQA with MIT License | 4 votes |
|
def evaluate(self):
imgIds = self.params['image_id']
# imgIds = self.coco.getImgIds()
gts = {}
res = {}
for imgId in imgIds:
gts[imgId] = self.coco.imgToAnns[imgId]
res[imgId] = self.cocoRes.imgToAnns[imgId]
# =================================================
# Set up scorers
# =================================================
print 'tokenization...'
tokenizer = PTBTokenizer()
gts = tokenizer.tokenize(gts)
res = tokenizer.tokenize(res)
# =================================================
# Set up scorers
# =================================================
print 'setting up scorers...'
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Meteor(),"METEOR"),
(Rouge(), "ROUGE_L"),
(Cider(), "CIDEr")
]
# =================================================
# Compute scores
# =================================================
for scorer, method in scorers:
print 'computing %s score...'%(scorer.method())
score, scores = scorer.compute_score(gts, res)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
self.setEval(sc, m)
self.setImgToEvalImgs(scs, gts.keys(), m)
print "%s: %0.3f"%(m, sc)
else:
self.setEval(score, method)
self.setImgToEvalImgs(scores, gts.keys(), method)
print "%s: %0.3f"%(method, score)
self.setEvalImgs()
Example #11
| Source File: eval.py From neural-image-captioning with MIT License | 4 votes |
|
def evaluate(self):
imgIds = self.params['image_id']
# imgIds = self.coco.getImgIds()
gts = {}
res = {}
for imgId in imgIds:
gts[imgId] = self.coco.imgToAnns[imgId]
res[imgId] = self.cocoRes.imgToAnns[imgId]
# =================================================
# Set up scorers
# =================================================
print 'tokenization...'
tokenizer = PTBTokenizer()
gts = tokenizer.tokenize(gts)
res = tokenizer.tokenize(res)
# =================================================
# Set up scorers
# =================================================
print 'setting up scorers...'
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Meteor(),"METEOR"),
(Rouge(), "ROUGE_L"),
(Cider(), "CIDEr")
]
# =================================================
# Compute scores
# =================================================
eval = {}
for scorer, method in scorers:
print 'computing %s score...'%(scorer.method())
score, scores = scorer.compute_score(gts, res)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
self.setEval(sc, m)
self.setImgToEvalImgs(scs, imgIds, m)
print "%s: %0.3f"%(m, sc)
else:
self.setEval(score, method)
self.setImgToEvalImgs(scores, imgIds, method)
print "%s: %0.3f"%(method, score)
self.setEvalImgs()
Example #12
| Source File: eval.py From captionGAN with MIT License | 4 votes |
|
def evaluate(self):
imgIds = self.params[self.imgidStr]
# imgIds = self.coco.getImgIds()
gts = {}
res = {}
for imgId in imgIds:
gts[imgId] = self.coco.imgToAnns[imgId]
res[imgId] = self.cocoRes.imgToAnns[imgId]
# =================================================
# Set up scorers
# =================================================
print 'tokenization...'
tokenizer = PTBTokenizer()
gts = tokenizer.tokenize(gts)
res = tokenizer.tokenize(res)
# =================================================
# Set up scorers
# =================================================
print 'setting up scorers...'
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Meteor(),"METEOR"),
(Rouge(), "ROUGE_L"),
(Cider(), "CIDEr"),
(Spice(), "SPICE")
]
# =================================================
# Compute scores
# =================================================
eval = {}
for scorer, method in scorers:
print 'computing %s score...'%(scorer.method())
score, scores = scorer.compute_score(gts, res)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
self.setEval(sc, m)
self.setImgToEvalImgs(scs, gts.keys(), m)
print "%s: %0.3f"%(m, sc)
else:
self.setEval(score, method)
self.setImgToEvalImgs(scores, gts.keys(), method)
print "%s: %0.3f"%(method, score)
self.setEvalImgs()
Example #13
| Source File: eval.py From deepQuest with BSD 3-Clause "New" or "Revised" License | 4 votes |
|
def evaluate(self):
imgIds = self.params['image_id']
# imgIds = self.coco.getImgIds()
gts = {}
res = {}
for imgId in imgIds:
gts[imgId] = self.coco.imgToAnns[imgId]
res[imgId] = self.cocoRes.imgToAnns[imgId]
# =================================================
# Set up scorers
# =================================================
print 'tokenization...'
tokenizer = PTBTokenizer()
gts = tokenizer.tokenize(gts)
res = tokenizer.tokenize(res)
# =================================================
# Set up scorers
# =================================================
print 'setting up scorers...'
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Meteor(), "METEOR"),
(Rouge(), "ROUGE_L"),
(Cider(), "CIDEr")
]
# =================================================
# Compute scores
# =================================================
for scorer, method in scorers:
print 'computing %s score...' % (scorer.method())
score, scores = scorer.compute_score(gts, res)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
self.setEval(sc, m)
self.setImgToEvalImgs(scs, gts.keys(), m)
print "%s: %0.3f" % (m, sc)
else:
self.setEval(score, method)
self.setImgToEvalImgs(scores, gts.keys(), method)
print "%s: %0.3f" % (method, score)
self.setEvalImgs()
Example #14
| Source File: eval.py From DialoGPT with MIT License | 4 votes |
|
def evaluate(self):
imgIds = self.params['image_id']
# imgIds = self.coco.getImgIds()
gts = {}
res = {}
for imgId in imgIds:
gts[imgId] = self.coco.imgToAnns[imgId]
res[imgId] = self.cocoRes.imgToAnns[imgId]
# =================================================
# Set up scorers
# =================================================
print('tokenization...')
tokenizer = PTBTokenizer()
gts = tokenizer.tokenize(gts)
res = tokenizer.tokenize(res)
# =================================================
# Set up scorers
# =================================================
print('setting up scorers...')
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Meteor(),"METEOR"),
(Rouge(), "ROUGE_L"),
(Cider(), "CIDEr")
]
# =================================================
# Compute scores
# =================================================
for scorer, method in scorers:
print('computing %s score...'%(scorer.method()))
score, scores = scorer.compute_score(gts, res)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
self.setEval(sc, m)
self.setImgToEvalImgs(scs, gts.keys(), m)
print("%s: %0.3f"%(m, sc))
else:
self.setEval(score, method)
self.setImgToEvalImgs(scores, gts.keys(), method)
print("%s: %0.3f"%(method, score))
self.setEvalImgs()
Example #15
| Source File: eval.py From TGIF-Release with BSD 3-Clause "New" or "Revised" License | 4 votes |
|
def main():
import sys
res_path = sys.argv[1]
gt_path = osp.join(this_dir, 'tgif-v1.0.tsv')
test_list_path = osp.join(this_dir, 'splits', 'test.txt')
test_keys = load_list(test_list_path)
all_sents = load_sentences(gt_path)
res = load_sentences(res_path)
# make sure res has and only has single sentence
# for all testing keys
gts = {}
for key in test_keys:
gts[key] = all_sents[key]
if key in res:
res[key] = [res[key][0]]
else:
res[key] = [""]
# =================================================
# Convert to COCO format
# =================================================
gts = to_coco(gts, res.keys())
res = to_coco(res, res.keys())
# =================================================
# Set up scorers
# =================================================
print 'tokenization...'
tokenizer = PTBTokenizer()
gts = tokenizer.tokenize(gts)
res = tokenizer.tokenize(res)
# =================================================
# Set up scorers
# =================================================
print 'setting up scorers...'
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Meteor(),"METEOR"),
(Rouge(), "ROUGE_L"),
(Cider(), "CIDEr")
]
# =================================================
# Compute scores
# =================================================
eval = {}
for scorer, method in scorers:
print 'computing %s score...'%(scorer.method())
score, scores = scorer.compute_score(gts, res)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
print "%s: %0.3f"%(m, sc)
else:
print "%s: %0.3f"%(method, score)
Example #16
| Source File: eval.py From densecap-tensorflow with MIT License | 4 votes |
|
def evaluate(self):
imgIds = self.params['image_id']
# imgIds = self.coco.getImgIds()
gts = {}
res = {}
for imgId in imgIds:
gts[imgId] = self.coco.imgToAnns[imgId]
res[imgId] = self.cocoRes.imgToAnns[imgId]
# =================================================
# Set up scorers
# =================================================
print 'tokenization...'
tokenizer = PTBTokenizer()
gts = tokenizer.tokenize(gts)
res = tokenizer.tokenize(res)
# =================================================
# Set up scorers
# =================================================
print 'setting up scorers...'
scorers = [
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
(Meteor(),"METEOR"),
(Rouge(), "ROUGE_L"),
(Cider(), "CIDEr")
]
# =================================================
# Compute scores
# =================================================
eval = {}
for scorer, method in scorers:
print 'computing %s score...'%(scorer.method())
score, scores = scorer.compute_score(gts, res)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
self.setEval(sc, m)
self.setImgToEvalImgs(scs, imgIds, m)
print "%s: %0.3f"%(m, sc)
else:
self.setEval(score, method)
self.setImgToEvalImgs(scores, imgIds, method)
print "%s: %0.3f"%(method, score)
self.setEvalImgs()
Example #17
| Source File: tester.py From deep-summarization with MIT License | 4 votes |
|
def main():
# Feed in the directory where the hypothesis summary and true summary is stored
hyp_file = glob.glob('metrics/hypothesis/*')
ref_file = glob.glob('metrics/reference/*')
BLEU_1 = 0.
BLEU_2 = 0.
BLEU_3 = 0.
BLEU_4 = 0.
ROUGE_L = 0.
num_files = 0
for reference_file, hypothesis_file in zip(ref_file, hyp_file):
num_files += 1
with open(reference_file) as rf:
reference = rf.readlines()
with open(hypothesis_file) as hf:
hypothesis = hf.readlines()
ref, hypo = load_textfiles(reference, hypothesis)
score_map = score(ref, hypo)
BLEU_1 += score_map['Bleu_1']
BLEU_2 += score_map['Bleu_2']
BLEU_3 += score_map['Bleu_3']
BLEU_4 += score_map['Bleu_4']
ROUGE_L += score_map['ROUGE_L']
BLEU_1 = BLEU_1/num_files
BLEU_2 = BLEU_2/num_files
BLEU_3 = BLEU_3/num_files
BLEU_4 = BLEU_4/num_files
ROUGE_L = ROUGE_L/num_files
print 'Average Metric Score for All Review Summary Pairs:'
print 'Bleu - 1gram:', BLEU_1
print 'Bleu - 2gram:', BLEU_2
print 'Bleu - 3gram:', BLEU_3
print 'Bleu - 4gram:', BLEU_4
print 'Rouge:', ROUGE_L
return BLEU_1,BLEU_2,BLEU_3, BLEU_4, ROUGE_L
