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metrics.py
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metrics.py
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Metrics class.
"""
from collections import Counter
from nltk.translate import bleu_score
from nltk.translate.bleu_score import SmoothingFunction
import numpy as np
def distinct(seqs):
""" Calculate intra/inter distinct 1/2. """
batch_size = len(seqs)
intra_dist1, intra_dist2 = [], []
unigrams_all, bigrams_all = Counter(), Counter()
for seq in seqs:
unigrams = Counter(seq)
bigrams = Counter(zip(seq, seq[1:]))
intra_dist1.append((len(unigrams)+1e-12) / (len(seq)+1e-5))
intra_dist2.append((len(bigrams)+1e-12) / (max(0, len(seq)-1)+1e-5))
unigrams_all.update(unigrams)
bigrams_all.update(bigrams)
inter_dist1 = (len(unigrams_all)+1e-12) / (sum(unigrams_all.values())+1e-5)
inter_dist2 = (len(bigrams_all)+1e-12) / (sum(bigrams_all.values())+1e-5)
intra_dist1 = np.average(intra_dist1)
intra_dist2 = np.average(intra_dist2)
return intra_dist1, intra_dist2, inter_dist1, inter_dist2
def bleu(hyps, refs):
""" Calculate bleu 1/2. """
bleu_1 = []
bleu_2 = []
for hyp, ref in zip(hyps, refs):
try:
score = bleu_score.sentence_bleu(
[ref], hyp,
smoothing_function=SmoothingFunction().method7,
weights=[1, 0, 0, 0])
except:
score = 0
bleu_1.append(score)
try:
score = bleu_score.sentence_bleu(
[ref], hyp,
smoothing_function=SmoothingFunction().method7,
weights=[0.5, 0.5, 0, 0])
except:
score = 0
bleu_2.append(score)
bleu_1 = np.average(bleu_1)
bleu_2 = np.average(bleu_2)
return bleu_1, bleu_2