mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
254 lines
9.7 KiB
254 lines
9.7 KiB
import statistics
|
|
from typing import Dict, List
|
|
|
|
import jieba
|
|
from bert_score import score
|
|
from nltk.translate.bleu_score import sentence_bleu
|
|
from nltk.translate.chrf_score import sentence_chrf
|
|
from rouge_chinese import Rouge as Rouge_cn
|
|
from rouge_score import rouge_scorer as Rouge_en
|
|
from sklearn.metrics import f1_score, precision_score, recall_score
|
|
from utils import preprocessing_text, remove_redundant_space
|
|
|
|
|
|
def bleu_score(preds: List[str], targets: List[str], language: str) -> Dict[str, float]:
|
|
"""Calculate BLEU Score Metric
|
|
|
|
The calculation includes BLEU-1 for unigram, BLEU-2 for bigram,
|
|
BLEU-3 for trigram and BLEU-4 for 4-gram. Unigram evaluates the
|
|
accuracy in word level, other n-gram evaluate the fluency in
|
|
sentence level.
|
|
"""
|
|
bleu_scores = {"bleu1": 0, "bleu2": 0, "bleu3": 0, "bleu4": 0}
|
|
cumulative_bleu = [0] * 4
|
|
weights = [(1. / 1., 0., 0., 0.), (1. / 2., 1. / 2., 0., 0.), (1. / 3., 1. / 3., 1. / 3., 0.),
|
|
(1. / 4., 1. / 4., 1. / 4., 1. / 4.)]
|
|
|
|
for pred, target in zip(preds, targets):
|
|
if language == "cn":
|
|
pred_list = ' '.join(jieba.cut(preprocessing_text(pred))).split()
|
|
target_list = [(' '.join(jieba.cut(preprocessing_text(target)))).split()]
|
|
elif language == "en":
|
|
pred_list = preprocessing_text(pred).split()
|
|
target_list = [preprocessing_text(target).split()]
|
|
|
|
bleu = sentence_bleu(target_list, pred_list, weights=weights)
|
|
cumulative_bleu = [a + b for a, b in zip(cumulative_bleu, bleu)]
|
|
|
|
for i in range(len(cumulative_bleu)):
|
|
bleu_scores[f"bleu{i+1}"] = cumulative_bleu[i] / len(preds)
|
|
|
|
return bleu_scores
|
|
|
|
|
|
def chrf_score(preds: List[str], targets: List[str], language: str) -> Dict[str, float]:
|
|
"""Calculate CHRF Score Metric in sentence level.
|
|
"""
|
|
chrf_score = {"chrf": 0}
|
|
cumulative_chrf = []
|
|
|
|
for pred, target in zip(preds, targets):
|
|
if language == "cn":
|
|
pred_list = ' '.join(jieba.cut(preprocessing_text(pred))).split()
|
|
target_list = ' '.join(jieba.cut(preprocessing_text(target))).split()
|
|
elif language == "en":
|
|
pred_list = preprocessing_text(pred).split()
|
|
target_list = preprocessing_text(target).split()
|
|
|
|
cumulative_chrf.append(sentence_chrf(target_list, pred_list))
|
|
|
|
chrf_score["chrf"] = statistics.mean(cumulative_chrf)
|
|
|
|
return chrf_score
|
|
|
|
|
|
def rouge_cn_score(preds: List[str], targets: List[str]) -> Dict[str, float]:
|
|
"""Calculate Chinese ROUGE Score Metric
|
|
|
|
The calculation includes ROUGE-1 for unigram, ROUGE-2 for bigram
|
|
and ROUGE-L. ROUGE-N evaluates the number of matching n-grams between
|
|
the preds and targets. ROUGE-L measures the number of matching
|
|
longest common subsequence (LCS) between preds and targets.
|
|
"""
|
|
rouge_scores = {"rouge1": 0, "rouge2": 0, "rougeL": 0}
|
|
all_preds = []
|
|
all_targets = []
|
|
|
|
for pred, target in zip(preds, targets):
|
|
pred_list = remove_redundant_space(' '.join(jieba.cut(preprocessing_text(pred))))
|
|
target_list = remove_redundant_space(' '.join(jieba.cut(preprocessing_text(target))))
|
|
all_preds.append(pred_list)
|
|
all_targets.append(target_list)
|
|
|
|
rouge_cn = Rouge_cn()
|
|
rouge_avg = rouge_cn.get_scores(all_preds, all_targets, avg=True)
|
|
|
|
rouge_scores["rouge1"] = rouge_avg["rouge-1"]["f"]
|
|
rouge_scores["rouge2"] = rouge_avg["rouge-2"]["f"]
|
|
rouge_scores["rougeL"] = rouge_avg["rouge-l"]["f"]
|
|
|
|
return rouge_scores
|
|
|
|
|
|
def rouge_en_score(preds: List[str], targets: List[str]) -> Dict[str, float]:
|
|
"""Calculate English ROUGE Score Metric
|
|
|
|
The calculation includes ROUGE-1 for unigram, ROUGE-2 for bigram
|
|
and ROUGE-L. ROUGE-N evaluates the number of matching n-grams between
|
|
the preds and targets. ROUGE-L measures the number of matching
|
|
longest common subsequence (LCS) between preds and targets.
|
|
"""
|
|
rouge_scores = {"rouge1": 0, "rouge2": 0, "rougeL": 0}
|
|
all_preds = []
|
|
all_targets = []
|
|
|
|
rouge_en = Rouge_en.RougeScorer(["rouge1", "rouge2", "rougeL"], use_stemmer=False)
|
|
|
|
for pred, target in zip(preds, targets):
|
|
score = rouge_en.score(preprocessing_text(pred), preprocessing_text(target))
|
|
rouge_scores["rouge1"] += score['rouge1'].fmeasure
|
|
rouge_scores["rouge2"] += score['rouge2'].fmeasure
|
|
rouge_scores["rougeL"] += score['rougeL'].fmeasure
|
|
|
|
rouge_scores["rouge1"] = rouge_scores["rouge1"] / len(preds)
|
|
rouge_scores["rouge2"] = rouge_scores["rouge2"] / len(preds)
|
|
rouge_scores["rougeL"] = rouge_scores["rougeL"] / len(preds)
|
|
|
|
return rouge_scores
|
|
|
|
|
|
def rouge_score(preds: List[str], targets: List[str], language: str) -> Dict[str, float]:
|
|
"""Calculate ROUGE Score Metric"""
|
|
if language == "cn":
|
|
return rouge_cn_score(preds, targets)
|
|
elif language == "en":
|
|
return rouge_en_score(preds, targets)
|
|
|
|
|
|
def distinct_score(preds: List[str], language: str) -> Dict[str, float]:
|
|
"""Calculate Distinct Score Metric
|
|
|
|
This metric refers to https://arxiv.org/abs/1510.03055.
|
|
It evaluates the diversity of generation text by counting
|
|
the unique n-grams.
|
|
"""
|
|
distinct_score = {"distinct": 0}
|
|
cumulative_distinct = []
|
|
|
|
for pred in preds:
|
|
if language == "cn":
|
|
pred_seg_list = ' '.join(jieba.cut(pred)).split()
|
|
count_segs = len(pred_seg_list)
|
|
unique_segs = set(pred_seg_list)
|
|
count_unique_chars = len(unique_segs)
|
|
# prevent denominator from being 0
|
|
cumulative_distinct.append(count_unique_chars / (count_segs + 1e-6))
|
|
elif language == "en":
|
|
# calculate distinct 1-gram, 2-gram, 3-gram
|
|
unique_ngram = [set() for _ in range(0, 3)]
|
|
all_ngram_count = [0 for _ in range(0, 3)]
|
|
|
|
split_pred = preprocessing_text(pred).split()
|
|
for n in range(0, 3):
|
|
for i in range(0, len(split_pred) - n):
|
|
ngram = ' '.join(split_pred[i:i + n + 1])
|
|
unique_ngram[n].add(ngram)
|
|
all_ngram_count[n] += 1
|
|
|
|
# Sometimes the answer may contain only one word. For 2-gram and 3-gram, the gram count(denominator) may be zero.
|
|
avg_distinct = [len(a) / (b + 1e-6) for a, b in zip(unique_ngram, all_ngram_count)]
|
|
|
|
cumulative_distinct.append(statistics.mean(avg_distinct))
|
|
|
|
distinct_score["distinct"] = statistics.mean(cumulative_distinct)
|
|
|
|
return distinct_score
|
|
|
|
|
|
def bert_score(preds: List[str], targets: List[str], language: str) -> Dict[str, float]:
|
|
"""Calculate BERTScore Metric
|
|
|
|
The BERTScore evaluates the semantic similarity between
|
|
tokens of preds and targets with BERT.
|
|
"""
|
|
bert_score = {"bert_score": 0}
|
|
pred_list = []
|
|
target_list = []
|
|
|
|
for pred, target in zip(preds, targets):
|
|
pred_list.append(pred)
|
|
target_list.append(target)
|
|
|
|
if language == "cn":
|
|
_, _, F = score(pred_list, target_list, lang="zh", verbose=True)
|
|
elif language == "en":
|
|
_, _, F = score(pred_list, target_list, lang="en", verbose=True)
|
|
|
|
bert_score["bert_score"] = F.mean().item()
|
|
|
|
return bert_score
|
|
|
|
|
|
def calculate_precision_recall_f1(preds: List[str], targets: List[str], language: str) -> Dict[str, float]:
|
|
"""Precision, Recall and F1-Score Calculation
|
|
|
|
The calculation of precision, recall and f1-score is realized by counting
|
|
the number f overlaps between the preds and target. The comparison length
|
|
limited by the shorter one of preds and targets.
|
|
"""
|
|
precision_recall_f1 = {"precision": 0, "recall": 0, "f1_score": 0}
|
|
precision_scores = []
|
|
recall_scores = []
|
|
f1_scores = []
|
|
|
|
for pred, target in zip(preds, targets):
|
|
if language == "cn":
|
|
pred_list = [char for char in ' '.join(jieba.cut(preprocessing_text(pred))).split()]
|
|
target_list = [char for char in ' '.join(jieba.cut(preprocessing_text(target))).split()]
|
|
elif language == "en":
|
|
pred_list = [char for char in preprocessing_text(pred).split()]
|
|
target_list = [char for char in preprocessing_text(target).split()]
|
|
|
|
target_labels = [1] * min(len(target_list), len(pred_list))
|
|
pred_labels = [int(pred_list[i] == target_list[i]) for i in range(0, min(len(target_list), len(pred_list)))]
|
|
|
|
precision_scores.append(precision_score(target_labels, pred_labels, zero_division=0))
|
|
recall_scores.append(recall_score(target_labels, pred_labels, zero_division=0))
|
|
f1_scores.append(f1_score(target_labels, pred_labels, zero_division=0))
|
|
|
|
precision_recall_f1["precision"] = statistics.mean(precision_scores)
|
|
precision_recall_f1["recall"] = statistics.mean(recall_scores)
|
|
precision_recall_f1["f1_score"] = statistics.mean(f1_scores)
|
|
|
|
return precision_recall_f1
|
|
|
|
|
|
def precision(preds: List[str], targets: List[str], language: str) -> Dict[str, float]:
|
|
"""Calculate Precision Metric
|
|
|
|
Calculating precision by counting the number of overlaps between the preds and target.
|
|
"""
|
|
precision = {"precision": 0}
|
|
precision["precision"] = calculate_precision_recall_f1(preds, targets, language)["precision"]
|
|
return precision
|
|
|
|
|
|
def recall(preds: List[str], targets: List[str], language: str) -> Dict[str, float]:
|
|
"""Calculate Recall Metric
|
|
|
|
Calculating recall by counting the number of overlaps between the preds and target.
|
|
"""
|
|
recall = {"recall": 0}
|
|
recall["recall"] = calculate_precision_recall_f1(preds, targets, language)["recall"]
|
|
return recall
|
|
|
|
|
|
def F1_score(preds: List[str], targets: List[str], language: str) -> Dict[str, float]:
|
|
"""Calculate F1-score Metric
|
|
|
|
Calculating f1-score by counting the number of overlaps between the preds and target.
|
|
"""
|
|
f1 = {"f1_score": 0}
|
|
f1["f1_score"] = calculate_precision_recall_f1(preds, targets, language)["f1_score"]
|
|
return f1
|