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import json
from copy import deepcopy
from typing import Dict, List
import numpy as np
from sklearn.metrics import f1_score, precision_score, recall_score
from sklearn.preprocessing import MultiLabelBinarizer
def _transform(
y_pred: List[List],
y_true: List[List],
):
# 对 y_pred 做一个类型检查
y_pred = [[y if type(y) == str else "" for y in y_ls] for y_ls in y_pred]
# binary encoding
mlb = MultiLabelBinarizer()
tmp = deepcopy(y_true)
tmp.extend(y_pred)
mlb.fit(tmp)
y_true_binary = mlb.transform(y_true)
y_pred_binary = mlb.transform(y_pred)
return y_pred_binary, y_true_binary
class Metric:
# eval metrics for classifcation task
@classmethod
def averaged(
cls,
y_pred: List[List],
y_true: List[List],
metric_types: List[str] = ["macro"],
) -> Dict:
y_pred_binary, y_true_binary = _transform(y_pred, y_true)
resp = {}
for metric_type in metric_types:
assert metric_type in [
"micro",
"macro",
"samples",
"weighted",
], "metric type error."
# resp["{}-Averaged Precision".format(metric_type)] = round(precision_score(
# y_true_binary, y_pred_binary, average=metric_type
# ),3)
resp["{}-Averaged Recall".format(metric_type)] = round(
recall_score(y_true_binary, y_pred_binary, average=metric_type), 3
)
resp["{}-Averaged F1".format(metric_type)] = round(
f1_score(y_true_binary, y_pred_binary, average=metric_type), 3
)
return resp
@classmethod
def jaccard(
cls,
y_pred: List[List],
y_true: List[List],
) -> Dict:
def jaccard_similarity(l_pred: List, l_true: List) -> float:
intersection = len(set(l_pred) & set(l_true))
union = len(set(l_pred) | set(l_true))
if union == 0:
return 0
else:
return intersection / union
similarities = [
jaccard_similarity(l_pred, l_true) for l_pred, l_true in zip(y_pred, y_true)
]
jaccard = sum(similarities) / len(similarities)
return {"Jaccard Similarity": round(jaccard, 3)}
@classmethod
def hamming(
cls,
y_pred: List[List],
y_true: List[List],
) -> Dict:
y_pred_binary, y_true_binary = _transform(y_pred, y_true)
hamming_loss = np.sum(y_true_binary != y_pred_binary) / (
y_true_binary.shape[0] * y_true_binary.shape[1]
)
return {"Hamming Loss": round(hamming_loss, 3)}
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