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Update functional.py #8

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17 changes: 9 additions & 8 deletions klue_baseline/metrics/functional.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
import numpy as np
import sklearn
from scipy.stats import pearsonr
from sklearn.metrics import f1_score as skl_f1_score
from seqeval.metrics import f1_score as ner_f1_score
from seqeval.scheme import IOB2

Expand All @@ -21,7 +22,7 @@


def ynat_macro_f1(preds: np.ndarray, targets: np.ndarray) -> Any:
return sklearn.metrics.f1_score(targets, preds, average="macro") * 100.0
return skl_f1_score(targets, preds, average="macro") * 100.0


def klue_nli_acc(preds: np.ndarray, targets: np.ndarray) -> Any:
Expand All @@ -36,7 +37,7 @@ def klue_sts_f1(preds: np.ndarray, labels: np.ndarray) -> Any:
threshold = 3
preds = np.where(preds >= threshold, 1, 0)
labels = np.where(labels >= threshold, 1, 0)
return sklearn.metrics.f1_score(labels, preds, average="binary") * 100.0
return skl_f1_score(labels, preds, average="binary") * 100.0


def klue_ner_entity_macro_f1(preds: np.ndarray, labels: np.ndarray, label_list: List[str]) -> Any:
Expand All @@ -60,15 +61,15 @@ def klue_ner_char_macro_f1(preds: np.ndarray, labels: np.ndarray, label_list: Li
label_indices = list(range(len(label_list)))
preds = np.array(preds).flatten().tolist()
trues = np.array(labels).flatten().tolist()
return sklearn.metrics.f1_score(trues, preds, labels=label_indices, average="macro", zero_division=True) * 100.0
return skl_f1_score(trues, preds, labels=label_indices, average="macro", zero_division=True) * 100.0


def klue_re_micro_f1(preds: np.ndarray, labels: np.ndarray, label_list: List[str]) -> Any:
"""KLUE-RE micro f1 (except no_relation)"""
no_relation_label_idx = label_list.index("no_relation")
label_indices = list(range(len(label_list)))
label_indices.remove(no_relation_label_idx)
return sklearn.metrics.f1_score(labels, preds, average="micro", labels=label_indices) * 100.0
return skl_f1_score(labels, preds, average="micro", labels=label_indices) * 100.0


def klue_re_auprc(probs: np.ndarray, labels: np.ndarray) -> Any:
Expand All @@ -95,7 +96,7 @@ def klue_dp_uas_macro_f1(preds: List[List[DPResult]], labels: List[List[DPResult
index = [i for i, label in enumerate(head_labels) if label == -1]
head_preds = np.delete(head_preds, index)
head_labels = np.delete(head_labels, index)
return sklearn.metrics.f1_score(head_labels.tolist(), head_preds.tolist(), average="macro") * 100.0
return skl_f1_score(head_labels.tolist(), head_preds.tolist(), average="macro") * 100.0


def klue_dp_uas_micro_f1(preds: List[List[DPResult]], labels: List[List[DPResult]]) -> Any:
Expand All @@ -110,7 +111,7 @@ def klue_dp_uas_micro_f1(preds: List[List[DPResult]], labels: List[List[DPResult
index = [i for i, label in enumerate(head_labels) if label == -1]
head_preds = np.delete(head_preds, index)
head_labels = np.delete(head_labels, index)
return sklearn.metrics.f1_score(head_labels.tolist(), head_preds.tolist(), average="micro") * 100.0
return skl_f1_score(head_labels.tolist(), head_preds.tolist(), average="micro") * 100.0


def klue_dp_las_macro_f1(preds: List[List[DPResult]], labels: List[List[DPResult]]) -> Any:
Expand Down Expand Up @@ -151,7 +152,7 @@ def klue_dp_las_macro_f1(preds: List[List[DPResult]], labels: List[List[DPResult
uas_incorrect = np.nonzero(np.invert(uas_correct))
for idx in uas_incorrect:
type_preds[idx] = PAD
return sklearn.metrics.f1_score(type_labels.tolist(), type_preds.tolist(), average="macro") * 100.0
return skl_f1_score(type_labels.tolist(), type_preds.tolist(), average="macro") * 100.0


def klue_dp_las_micro_f1(preds: List[List[DPResult]], labels: List[List[DPResult]]) -> Any:
Expand Down Expand Up @@ -191,7 +192,7 @@ def klue_dp_las_micro_f1(preds: List[List[DPResult]], labels: List[List[DPResult
uas_incorrect = np.nonzero(np.invert(uas_correct))
for idx in uas_incorrect:
type_preds[idx] = PAD
return sklearn.metrics.f1_score(type_labels.tolist(), type_preds.tolist(), average="micro") * 100.0
return skl_f1_score(type_labels.tolist(), type_preds.tolist(), average="micro") * 100.0


def klue_mrc_em(preds: List[Dict[str, str]], examples: List[List[KlueMRCExample]]) -> Any:
Expand Down