File size: 2,356 Bytes
8e04e6f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | from typing import List, Literal
def extract_cath_code_from_batch(batch):
cath_code = batch.get("cath_code", None)
if cath_code:
# Remove the additional tuple layer introduced during collate
_cath_code = []
for codes in cath_code:
_cath_code.append(
[code[0] if isinstance(code, tuple) else code for code in codes]
)
cath_code = _cath_code
return cath_code
def extract_cath_code_by_level(
cath_code: str, level: Literal["C", "A", "T", "H"]
) -> str:
"""Extract cath_code at certain level.
Args:
cath_code: CATH code.
level: Level to be extracted
Returns:
CATH code at the corresponding level.
"""
mapping = {"H": 0, "T": 1, "A": 2, "C": 3}
return cath_code.rsplit(".", mapping[level])[0]
def mask_cath_code_by_level(
cath_code: List[str], level: Literal["C", "A", "T", "H"]
) -> str:
"""Mask cath_code at certain level.
Args:
cath_code: List of CATH code.
level: Level to be extracted
Returns:
List of CATH code. Replace the corresponding level with unknown token 'x'.
"""
mapping = {"H": 3, "T": 2, "A": 1, "C": 0}
_cath_code = []
for code in cath_code:
code = code.split(".")
code[mapping[level]] = "x"
_cath_code.append(".".join(code))
return _cath_code
def transform_global_percentage_to_mask_dropout(fold_label_sample_ratio):
assert (
len(fold_label_sample_ratio) == 4
), "Length of fold_label_sample_ratio should be 4"
assert (
sum(fold_label_sample_ratio) == 1.0
), "Sum of fold_label_sample_ratio should be 1.0"
mask_T_prob = sum(fold_label_sample_ratio[:3]) / sum(
fold_label_sample_ratio
) # Among all samples, how many T-level labels are dropped? (null + C + CA) / (null + C + CA + CAT)
mask_A_prob = sum(fold_label_sample_ratio[:2]) / (
sum(fold_label_sample_ratio[:3]) + 1e-10
) # Among samples with T labels dropped, how many A-level labels are dropped? (null + C) / (null + C + CA)
mask_C_prob = sum(fold_label_sample_ratio[:1]) / (
sum(fold_label_sample_ratio[:2]) + 1e-10
) # Among samples with A and T labels dropped, how many C-level labels are dropped? null / (null + C)
return mask_T_prob, mask_A_prob, mask_C_prob
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