Spaces:
Running
Running
File size: 7,594 Bytes
224a33f |
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 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 |
from functools import cached_property
from pathlib import Path
from typing import Any
import pandas as pd
import torch
import torch.nn.functional as F
from esm.tokenization.tokenizer_base import EsmTokenizerBase
from esm.utils.constants import esm3 as C
Sample = dict[str, Any]
class ResidueAnnotationsTokenizer(EsmTokenizerBase):
def __init__(
self,
csv_path: str | None = None,
max_annotations: int = 16,
):
if csv_path is None:
csv_path = str(C.data_root() / C.RESID_CSV)
self.csv_path = csv_path
self.max_annotations = max_annotations
@cached_property
def _description2label(self) -> dict[str, str]:
with Path(self.csv_path).open() as f: # type: ignore
df = pd.read_csv(f)
return dict(zip(df.label, df.label_clean))
@cached_property
def _labels(self) -> list[str]:
with Path(self.csv_path).open() as f: # type: ignore
df = pd.read_csv(f)
labels = (
df.groupby("label_clean")["count"]
.sum()
.sort_values(ascending=False, kind="stable") # type: ignore
.index.tolist()
)
assert isinstance(labels, list)
return labels # type: ignore
def _description2id(self, description: str) -> int | None:
label = self._description2label.get(description)
return self._label2id.get(label) # type: ignore
@cached_property
def _label2id(self) -> dict[str, int]:
offset = len(self.special_tokens) + 1 # +1 for "<none>"
return {label: offset + i for i, label in enumerate(self._labels)}
@cached_property
def special_tokens(self) -> list[str]:
"""List of special tokens which come before cluster toknes in vocab."""
return ["<pad>", "<motif>", "<unk>"]
@cached_property
def vocab(self):
annotation_tokens = [f"<ra:{id}>" for _, id in self._label2id.items()]
return self.special_tokens + ["<none>"] + annotation_tokens
@cached_property
def vocab_to_index(self) -> dict[str, int]:
return {token: token_id for token_id, token in enumerate(self.vocab)}
@cached_property
def vocabulary(self) -> list[str]:
"""Full vocabulary."""
return [*self.special_tokens, "<none>", *self._labels]
def get_special_tokens_mask(self, encoded: torch.Tensor) -> torch.Tensor:
"""Determines where in the sequence are special tokens."""
return encoded[:, 0] < len(self.special_tokens)
def tokenize(
self, sample: Sample | None, sequence: str, fail_on_mismatch: bool = False
) -> list[str]:
"""
# interpro_site_starts
# interpro_site_ends # should always == interpro_site_starts. but I haven't checked overall.
# interpro_site_residues # the residue identity of the specfic residue that is annotated. good for a sanity check that parsing occurred correctly.
# interpro_site_descriptions
# ASSERT (i.e. drop if bad)
# interpro_site_residues matches the residue at that position
# all these lists ^ above are the same length
"""
seqlen = len(sequence)
assert seqlen >= 0
# None mean sequence is *not annotated* - so use full <pad>
if sample is None:
return ["<pad>"] * seqlen
if any(
sample.get(field) is None
for field in [
"interpro_site_descriptions",
"interpro_site_starts",
"interpro_site_ends",
"interpro_site_residues",
]
):
return ["<pad>"] * seqlen
num_annotations = len(sample["interpro_site_descriptions"])
if any(
len(sample[field]) != num_annotations
for field in [
"interpro_site_starts",
"interpro_site_ends",
"interpro_site_residues",
]
):
# mismatched length.
return ["<pad>"] * seqlen
positional_ids = [set() for _ in range(seqlen)]
for description, start, end, residues in zip(
sample["interpro_site_descriptions"],
sample["interpro_site_starts"],
sample["interpro_site_ends"],
sample["interpro_site_residues"],
):
try:
start = int(start)
end = int(end)
except (TypeError, ValueError):
continue
# Start / End are 1-indexed [inclusive, inclusive].
if start <= 0 or end > seqlen or start > end:
print(f"invalid start/end: ({start}, {end}), len: {seqlen}")
continue
if len(residues) != (end - start) + 1:
print(f"bad reference residue: {residues}")
continue
token_id = self._description2id(description)
if token_id is None:
token_id = self.vocab_to_index["<unk>"]
for i, residue in zip(range(start - 1, end), residues):
# If there are any mismatching residues, skip the entire sample.
if sequence[i] != residue:
if fail_on_mismatch:
raise ValueError(
f"Residue mismatch at position {i} (1-indexed): {sequence[i]} != {residue}"
)
return ["<pad>"] * seqlen
positional_ids[i].add(token_id)
tokens = []
for token_ids in positional_ids:
if token_ids:
token = "<ra:" + ",".join(str(token_id) for token_id in token_ids) + ">"
else:
token = "<none>"
tokens.append(token)
return tokens
def _token2ids(self, token: str) -> list[int]:
if token.startswith("<ra:") and token.endswith(">"):
return [int(token_id) for token_id in token[4:-1].split(",")]
else:
token_id = self.vocab_to_index[token]
return [token_id]
def encode(
self, tokens: list[str], add_special_tokens: bool = True
) -> torch.Tensor:
token_ids = torch.full(
size=(len(tokens), self.max_annotations),
dtype=torch.int64,
fill_value=self.vocab_to_index["<pad>"],
)
for i, token in enumerate(tokens):
ids = self._token2ids(token)[: self.max_annotations]
token_ids[i, : len(ids)] = torch.tensor(ids)
if add_special_tokens:
token_ids = F.pad(
token_ids, (0, 0, 1, 1), value=self.vocab_to_index["<pad>"]
)
return token_ids
def decode(self, encoded: torch.Tensor) -> list[str]:
raise NotImplementedError(
"Residue annotation decoding should be handled with util.decoding.decode_residue_annotations"
)
@property
def mask_token(self) -> str:
return "<pad>"
@property
def mask_token_id(self) -> int:
return self.vocab_to_index[self.mask_token]
@property
def bos_token(self) -> str:
return "<pad>"
@property
def bos_token_id(self) -> int:
return self.vocab_to_index[self.bos_token]
@property
def eos_token(self) -> str:
return "<pad>"
@property
def eos_token_id(self) -> int:
return self.vocab_to_index[self.eos_token]
@property
def pad_token(self) -> str:
return "<pad>"
@property
def pad_token_id(self) -> int:
return self.vocab_to_index[self.pad_token]
|