ner_file.py

"""Module responsible for Morphosyntactic dict that uses a tsv file with NER tags."""

import random
from collections import defaultdict
from typing import List, Optional, Type

from src.detections import DETECTION_CLASSES_MAP, Detection, MorphosyntacticInfoMixin
from src.dictionaries.morphosyntactic.interface import MorphosyntacticDictionary


class NERFileMorphosyntacticDictionary(MorphosyntacticDictionary):
    """Morphosyntactic dictionary that uses a tsv file with NER tags as a source.

    Example of a tsv file:
    name	Aaronom	Aaron	subst:pl:dat:m1
    name	Aaronami	Aaron	subst:pl:inst:m1
    name	Aaronach	Aaron	subst:pl:loc:m1
    country	Apolonie	Apolonia	subst:pl:voc:f
    country	Apolonii	Apolonia	subst:sg:dat:f
    country	Apolonii	Apolonia	subst:pl:gen:f
    country	Apolonii	Apolonia	subst:sg:loc:f
    city	Araba	Arab	subst:sg:gen:m2
    city	Arabie	Arab	subst:sg:voc:m2
    city	Arabem	Arab	subst:sg:inst:m2
    """

    def __init__(
        self,
        dictionary_path: Optional[str] = None,
        always_replace=True,
    ) -> None:
        """Initializes NERFileMorphosyntacticDictionary.

        Args:
            dictionary_path (Optional[str], optional): Path to dictionary tsv file.
                    Defaults to None.
            always_replace (bool, optional): Wheter to replace detection even if no
                    word with matching morpho tag is found. Defaults to True.

        """
        super().__init__()
        self._dictionary = None
        self._always_replace = always_replace

        self._from_file(dictionary_path)

    def _from_file(self, path_to_dictionary: str) -> None:
        replacement_dictionary = defaultdict(lambda: defaultdict(dict))
        with open(path_to_dictionary, "r", encoding="utf-8") as file:
            for line in file:
                line = line.strip()
                ner_tag, word, lemma, morpho_tag = line.split("\t")
                replacement_dictionary[ner_tag][morpho_tag][lemma] = word

        self._dictionary = replacement_dictionary

    def get_supported_detection_classes(self) -> List[Type[Detection]]:
        """Returns a list of supported detection classess.

        Returns:
            List[Type[Detection]]: List of detection classes that are supported

        """
        return [DETECTION_CLASSES_MAP[name] for name in self._dictionary.keys()]

    def get_random_replacement(self, original_entry: Detection) -> Optional[str]:
        """Returns a random replacement of original entry.

        Args:
            original_entry (Detection): Detection that should be replaced. Class
                should have MorphosyntacticInfoMixin


        Returns:
            Optional[str]: Text that should replace the original entry

        """
        original_entry_type = type(original_entry)
        original_entry_type_name = original_entry_type.TYPE_NAME

        result = None

        if issubclass(original_entry_type, MorphosyntacticInfoMixin):
            morpho_tag = original_entry.morpho_tag

            if original_entry_type_name in self._dictionary:
                if morpho_tag in self._dictionary[original_entry_type_name]:
                    result = random.choice(
                        list(
                            self._dictionary[original_entry_type_name][
                                morpho_tag
                            ].values()
                        )
                    )
                else:
                    morpho_tag = result = random.choice(
                        list(self._dictionary[original_entry_type_name].keys())
                    )
                    result = random.choice(
                        list(
                            self._dictionary[original_entry_type_name][
                                morpho_tag
                            ].keys()
                        )
                    )

        if result is None and self._always_replace:
            random_type = random.choice(list(self._dictionary.keys()))
            random_tag = random.choice(list(self._dictionary[random_type].keys()))

            result = random.choice(
                list(self._dictionary[random_type][random_tag].values())
            )

        return result