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metrics.py
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metrics.py
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import numpy as np
import string
import re
import torch
from collections import Counter
from sklearn.metrics import matthews_corrcoef, f1_score
from scipy.stats import pearsonr
from rouge import Rouge
from torch.nn import CrossEntropyLoss, Softmax
import pdb
from transformers import T5Tokenizer
cross_entropy = CrossEntropyLoss()
softmax = Softmax()
METRICS = {
"rt-polarity": ["ACC"],
"isear": ["ACC"],
"openbook": ["ACC"],
"fever": ["ACC"],
}
class Metric:
"""
It's used only for dev / test evaluation
Not during training
"""
def __init__(self, args, soft=False, online=False):
self.task_name = args.task_name
self.tokenizer = T5Tokenizer.from_pretrained(
args.model_name_or_path, model_max_length=args.max_length
)
self.predictions = []
self.references = []
self.online = online
self.args = args
self.soft = soft
def reset(self):
self.predictions = []
self.references = []
def add_batch(self, predictions, references):
if not self.soft:
if type(predictions[0]) != str:
predictions = self.tokenizer.batch_decode(
predictions,
skip_special_tokens=True,
clean_up_tokenization_spaces=True,
)
if type(references[0]) != str:
references = self.tokenizer.batch_decode(
references,
skip_special_tokens=True,
clean_up_tokenization_spaces=True,
)
self.predictions = self.predictions + predictions
self.references = self.references + references
def compute(self):
if self.soft:
metrics = evaluate_soft(
self.predictions, self.references, self.args.temperature
)
else:
metrics = evaluate_hard(self.predictions, self.references, self.task_name)
self.reset()
return metrics
def evaluate_soft(predictions, data, temperature=1):
cross_entropy_score = []
accuracy = []
for idx, pred in enumerate(predictions):
predictions[idx] = softmax(torch.tensor(pred).cuda().float())
data[idx] = softmax(torch.tensor(data[idx]).cuda().float() / temperature)
cross_entropy_score.append(cross_entropy(predictions[idx], data[idx]))
predictions[idx].argmax() == data[idx].argmax()
accuracy.append(1 * (predictions[idx].argmax() == data[idx].argmax()).tolist())
return [sum(accuracy) / len(data), sum(cross_entropy_score) / len(data)]
def evaluate_hard(predictions, data, task):
metrics = METRICS[task]
tmp_metrics = []
assert len(predictions) == len(data)
if "ACC" in metrics:
accs = []
for prediction, dp in zip(predictions, data):
accs.append(get_accruacy_over_list(prediction, dp))
tmp_metrics.append(np.mean(accs))
if "hatexplain" in metrics:
accs = []
for prediction, dp in zip(predictions, data):
accs.append(get_accuracy_hatexplain(prediction, dp))
tmp_metrics.append(np.mean(accs))
if "QA-F1" in metrics:
f1s = []
for prediction, dp in zip(predictions, data):
f1s.append(get_f1_over_list(prediction, dp))
tmp_metrics.append(np.mean(f1s))
if "Classification-F1" in metrics:
if isinstance(data[0], list):
data = [dat[0] for dat in data]
return tmp_metrics.append(f1_score(data, predictions, average="macro"))
return tmp_metrics
def qa_f1_score(prediction, ground_truth):
prediction_tokens = normalize_answer(prediction).split()
ground_truth_tokens = normalize_answer(ground_truth).split()
common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
num_same = sum(common.values())
if num_same == 0:
return 0
precision = 1.0 * num_same / len(prediction_tokens)
recall = 1.0 * num_same / len(ground_truth_tokens)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def accuracy(prediction, ground_truth):
return prediction.lower() == ground_truth.lower()
def get_accuracy_hatexplain(prediction, groundtruth):
prediction = prediction.split(" ")
groundtruth = groundtruth.split(" ")
precision = 0
f1 = None
if len(prediction[1:]):
for pred in prediction[1:]:
precision += get_accruacy_over_list(pred, groundtruth[1:])
precision = precision / len(prediction[1:])
f1 = precision
recall = 0
if len(groundtruth[1:]):
for truth in groundtruth[1:]:
recall += get_accruacy_over_list(truth, prediction[1:])
recall = recall / len(groundtruth[1:])
if f1 is None:
f1 = recall
else:
f1 = 0.5 * precision + 0.5 * recall
if f1 is None:
return accuracy(prediction[0], groundtruth[0])
return 0.5 * accuracy(prediction[0], groundtruth[0]) + 0.5 * f1
def get_accruacy_over_list(prediction, groundtruth):
if isinstance(groundtruth, list):
if len(groundtruth) == 0:
return 0
return np.max([accuracy(prediction, gt) for gt in groundtruth])
return accuracy(prediction, groundtruth)
def get_f1_over_list(prediction, groundtruth):
if isinstance(groundtruth, list):
if len(groundtruth) == 0:
return 0
return np.max([qa_f1_score(prediction, gt) for gt in groundtruth])
return qa_f1_score(prediction, groundtruth)
def get_exact_match_over_list(prediction, groundtruth):
if isinstance(groundtruth, list):
if len(groundtruth) == 0:
return 0
return np.max([get_exact_match_over_list(prediction, gt) for gt in groundtruth])
return normalize_answer(prediction) == normalize_answer(groundtruth)
def normalize_answer(s):
def remove_articles(text):
return re.sub(r"\b(a|an|the)\b", " ", text)
def remove_trivial_white_space(text):
while len(text) and text[0] == " ":
text = text[1:]
while len(text) and text[-1] == " ":
text = text[:-1]
return text
def white_space_fix(text):
return " ".join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(
remove_trivial_white_space(remove_articles(remove_punc(lower(s))))
)