ner-study/read_data.py

import enum
from io import TextIOWrapper
from typing import Iterable, NamedTuple, List, Sequence, TypeVar
import json

KoreanBase="[Ko, En] NER, POStag data/국문 NER, POS"
EnglishBase="[Ko, En] NER, POStag data/영문 NER, POS"

class Sentence(NamedTuple):
    word: List[str]
    pos: List[str]
    """
    POS
    """
    namedEntity: List[str]
    """
    Named Entity
    """
    detail: List[str]
    """
    Named Entity Detail
    """
    def append(self,word,pos,namedEntity,detail):
        self.word.append(word)
        self.pos.append(pos)
        self.namedEntity.append(namedEntity)
        self.detail.append(detail)
T = TypeVar('T')
def readDataList(lst: Iterable[str], sep="\t"):
    ret:List[str] = []
    for l in lst:
        l = l.strip()
        if l == "":
            yield ret
            ret.clear()
        else:
            ret.append(l.split(sep))

def readKoreanData(fp: TextIOWrapper) -> List[Sentence]:
    ret = []
    # NOTE(monoid): Do not use csv reader.
    for lines in readDataList(fp):
        sentence = Sentence([],[],[],[])
        for line in lines:
            word_pos:str = line[0]
            words = word_pos.split("/")
            sentence.append(words[0],line[1],line[2],line[3])
        ret.append(sentence)
    
    fp.close()
    return ret

def readEnglishData(fp: TextIOWrapper) -> List[Sentence]:
    ret = []
    for lines in readDataList(fp,sep=" "):
        if len(lines) == 1 and lines[0][0] == "-DOCSTART-":
            continue
        sentence = Sentence([],[],[],[])
        for line in lines:
            sentence.append(line[0],line[1],line[2],line[3])
        ret.append(sentence)
    return ret

def readKoreanDataAll():
    """
        Each entry is structured as follows:
            POS, 
        Return: train, dev, test tuple
    """
    with open(f"{KoreanBase}/dev.txt", encoding="utf-8") as fp:
        dev = readKoreanData(fp)
    with open(f"{KoreanBase}/test.txt", encoding="utf-8") as fp:
        test = readKoreanData(fp)
    with open(f"{KoreanBase}/train.txt", encoding="utf-8") as fp:
        train = readKoreanData(fp)
    return train, dev, test

def readEnglishDataAll():
    with open(f"{EnglishBase}/valid.txt", encoding="utf-8") as fp:
        print("a")
        dev = readEnglishData(fp)
        print("b")
    with open(f"{EnglishBase}/test.txt", encoding="utf-8") as fp:
        test = readEnglishData(fp)
    with open(f"{EnglishBase}/train.txt", encoding="utf-8") as fp:
        train = readEnglishData(fp)
    return train, dev, test

class TagIdConverter:
    def __init__(self, dict_path = "tags.json") -> None:
        with open(dict_path,"r+",encoding="utf-8") as fp:
            data = json.load(fp)
        self.vocab = {}
        for item in data:
            self.vocab[item["name"]] = item["index"]
        self.ids_to_token = {}
        for item in data:
            self.ids_to_token[item["index"]] = item["name"]

    @property
    def O_id(self):
        return self.vocab["O"]
    @property
    def pad_id(self):
        return self.vocab["[PAD]"]
    
    def convert_ids_to_tokens(self,ids: List[int]):
        return [self.ids_to_token[id] for id in ids]
    def convert_tokens_to_ids(self, tokens: List[str]):
        return [self.vocab[tt] for tt in tokens]

class MatchState(enum.IntEnum):
    MATCH = 0
    BEGIN = 1
    INTER = 2


def match_indexes(a,b) -> Sequence[MatchState]:
    s = iter(b)
    v = ""
    try:
        v = next(s)
    except StopIteration:
        return
    for k in a:
        try:
            if k == v:
                yield MatchState.MATCH, k, v
                v = next(s)
            else:
                yield MatchState.BEGIN, k, v
                cum = v
                while True:
                    v: str = next(s)
                    cum += v.strip("#")
                    yield MatchState.INTER, k, v
                    if k == cum:
                        v = next(s)
                        break
        except StopIteration:
            break

def make_long_namedEntity(a,b,c):
    it = iter(c)
    ret = []
    entityType = ""
    o = False
    for s,_,_ in match_indexes(a,b):
        try:
            if s == MatchState.MATCH:
                v = next(it)
                ret.append(v)
            elif s == MatchState.BEGIN:
                v = next(it)
                ret.append(v)
                if v == "O":
                    o = True
                else:
                    vv = v.split("-")
                    entityType = vv[1]
                    o = False
            elif s == MatchState.INTER:
                if o:
                    ret.append("O")
                else:
                    ret.append(f"I-{entityType}")
        except StopIteration:
            break
    return ret

"""
extracts and stores tags set from the given data. 
"""
if __name__ == "__main__":
    from tqdm import tqdm
    t = TagIdConverter()
    
    train, dev, test = readEnglishDataAll()
    vocab = set()
    def getTags(lst: List[Sentence]):
        for s in tqdm(lst):
            for e in s.detail:
                vocab.add(e)
    print("get tags from train...")
    getTags(train)
    print("get tags from dev...")
    getTags(dev)
    print("get tags from test...")
    getTags(test)
    print(vocab)
    for v in vocab:
        if v == "O":
            continue
        s = v.split("-")
        s[0] = {"B":"I","I":"B"}[(s[0])]
        v:str = "-".join(s)
        if not v in vocab:
            print("could not found pair " ,v)
            vocab.add(v)
    tags = [{"name":"[PAD]","index":0}]
    i = 1
    vocab_list = [*vocab]
    vocab_list.sort()
    for v in vocab_list:
        tags.append({"name":v,"index":i})
        i += 1
    print(tags)
    #with open("tags.json","w",encoding="utf-8") as fp:
    #    json.dump(tags,fp,ensure_ascii=False, indent=2)