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from __future__ import annotations |
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import os |
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from typing import TYPE_CHECKING, Any |
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from stwfsapy.predictor import StwfsapyPredictor |
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from annif.exception import NotInitializedException, NotSupportedException |
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from annif.suggestion import SubjectSuggestion |
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from annif.util import atomic_save, boolean |
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from . import backend |
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if TYPE_CHECKING: |
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from annif.corpus.document import DocumentCorpus |
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_KEY_CONCEPT_TYPE_URI = "concept_type_uri" |
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_KEY_SUBTHESAURUS_TYPE_URI = "sub_thesaurus_type_uri" |
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_KEY_THESAURUS_RELATION_TYPE_URI = "thesaurus_relation_type_uri" |
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_KEY_THESAURUS_RELATION_IS_SPECIALISATION = "thesaurus_relation_is_specialisation" |
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_KEY_REMOVE_DEPRECATED = "remove_deprecated" |
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_KEY_HANDLE_TITLE_CASE = "handle_title_case" |
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_KEY_EXTRACT_UPPER_CASE_FROM_BRACES = "extract_upper_case_from_braces" |
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_KEY_EXTRACT_ANY_CASE_FROM_BRACES = "extract_any_case_from_braces" |
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_KEY_EXPAND_AMPERSAND_WITH_SPACES = "expand_ampersand_with_spaces" |
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_KEY_EXPAND_ABBREVIATION_WITH_PUNCTUATION = "expand_abbreviation_with_punctuation" |
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_KEY_SIMPLE_ENGLISH_PLURAL_RULES = "simple_english_plural_rules" |
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_KEY_USE_TXT_VEC = "use_txt_vec" |
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class StwfsaBackend(backend.AnnifBackend): |
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name = "stwfsa" |
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STWFSA_PARAMETERS = { |
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_KEY_CONCEPT_TYPE_URI: str, |
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_KEY_SUBTHESAURUS_TYPE_URI: str, |
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_KEY_THESAURUS_RELATION_TYPE_URI: str, |
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_KEY_THESAURUS_RELATION_IS_SPECIALISATION: boolean, |
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_KEY_REMOVE_DEPRECATED: boolean, |
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_KEY_HANDLE_TITLE_CASE: boolean, |
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_KEY_EXTRACT_UPPER_CASE_FROM_BRACES: boolean, |
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_KEY_EXTRACT_ANY_CASE_FROM_BRACES: boolean, |
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_KEY_EXPAND_AMPERSAND_WITH_SPACES: boolean, |
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_KEY_EXPAND_ABBREVIATION_WITH_PUNCTUATION: boolean, |
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_KEY_SIMPLE_ENGLISH_PLURAL_RULES: boolean, |
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_KEY_USE_TXT_VEC: bool, |
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} |
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DEFAULT_PARAMETERS = { |
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_KEY_CONCEPT_TYPE_URI: "http://www.w3.org/2004/02/skos/core#Concept", |
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_KEY_SUBTHESAURUS_TYPE_URI: "http://www.w3.org/2004/02/skos/core#Collection", |
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_KEY_THESAURUS_RELATION_TYPE_URI: "http://www.w3.org/2004/02/skos/core#member", |
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_KEY_THESAURUS_RELATION_IS_SPECIALISATION: True, |
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_KEY_REMOVE_DEPRECATED: True, |
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_KEY_HANDLE_TITLE_CASE: True, |
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_KEY_EXTRACT_UPPER_CASE_FROM_BRACES: True, |
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_KEY_EXTRACT_ANY_CASE_FROM_BRACES: False, |
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_KEY_EXPAND_AMPERSAND_WITH_SPACES: True, |
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_KEY_EXPAND_ABBREVIATION_WITH_PUNCTUATION: True, |
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_KEY_SIMPLE_ENGLISH_PLURAL_RULES: False, |
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_KEY_USE_TXT_VEC: False, |
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} |
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MODEL_FILE = "stwfsa_predictor.zip" |
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_model = None |
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def initialize(self, parallel: bool = False) -> None: |
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if self._model is None: |
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path = os.path.join(self.datadir, self.MODEL_FILE) |
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self.debug(f"Loading STWFSA model from {path}.") |
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if os.path.exists(path): |
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self._model = StwfsapyPredictor.load(path) |
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self.debug("Loaded model.") |
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else: |
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raise NotInitializedException( |
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f"Model not found at {path}", backend_id=self.backend_id |
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) |
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def _load_data(self, corpus: DocumentCorpus) -> tuple[list[str], list[list[str]]]: |
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if corpus == "cached": |
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raise NotSupportedException( |
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"Training stwfsa project from cached data not supported." |
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) |
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if corpus.is_empty(): |
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raise NotSupportedException( |
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"Cannot train stwfsa project with no documents." |
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) |
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self.debug("Transforming training data.") |
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X = [] |
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y = [] |
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for doc in corpus.documents: |
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X.append(doc.text) |
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y.append( |
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[ |
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self.project.subjects[subject_id].uri |
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for subject_id in doc.subject_set |
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] |
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) |
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return X, y |
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def _train( |
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self, |
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corpus: DocumentCorpus, |
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params: dict[str, Any], |
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jobs: int = 0, |
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) -> None: |
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X, y = self._load_data(corpus) |
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new_params = { |
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key: self.STWFSA_PARAMETERS[key](val) |
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for key, val in params.items() |
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if key in self.STWFSA_PARAMETERS |
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} |
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p = StwfsapyPredictor( |
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graph=self.project.vocab.as_graph(), |
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langs=frozenset([params["language"]]), |
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**new_params, |
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) |
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p.fit(X, y) |
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self._model = p |
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atomic_save( |
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p, |
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self.datadir, |
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self.MODEL_FILE, |
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lambda model, store_path: model.store(store_path), |
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) |
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def _suggest(self, text: str, params: dict[str, Any]) -> list[SubjectSuggestion]: |
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self.debug(f'Suggesting subjects for text "{text[:20]}..." (len={len(text)})') |
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result = self._model.suggest_proba([text])[0] |
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suggestions = [] |
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for uri, score in result: |
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subject_id = self.project.subjects.by_uri(uri) |
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if subject_id is not None: |
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suggestions.append( |
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SubjectSuggestion(subject_id=subject_id, score=score) |
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) |
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return suggestions |
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NatLibFi /
Annif
