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import os |
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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 ListSuggestionResult, SubjectSuggestion |
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from . import backend |
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from annif.util import boolean |
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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 = ( |
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'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 = ( |
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'expand_abbreviation_with_punctuation') |
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_KEY_SIMPLE_ENGLISH_PLURAL_RULES = 'simple_english_plural_rules' |
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class StwfsapyBackend(backend.AnnifBackend): |
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name = "stwfsapy" |
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needs_subject_index = True |
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STWFSAPY_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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} |
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DEFAULT_PARAMETERS = { |
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_KEY_THESAURUS_RELATION_IS_SPECIALISATION: False, |
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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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} |
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MODEL_FILE = 'stwfsapy_predictor.zip' |
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_model = None |
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def initialize(self): |
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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 STWFSAPY 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}', |
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backend_id=self.backend_id) |
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def _train(self, corpus, params): |
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if corpus == 'cached': |
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raise NotSupportedException( |
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'Training stwfsapy project from cached data not supported.') |
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if corpus.is_empty(): |
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raise NotSupportedException( |
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'Cannot train stwfsapy project with no documents.') |
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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(doc.uris) |
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graph = self.project.vocab.as_graph() |
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new_params = { |
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key: self.STWFSAPY_PARAMETERS[key](val) |
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for key, val |
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in params.items() |
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if key in self.STWFSAPY_PARAMETERS |
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} |
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p = StwfsapyPredictor( |
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graph=graph, |
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langs=frozenset([params['language']]), |
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**new_params) |
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p.fit(X, y) |
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self._model = p |
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p.store(os.path.join(self.datadir, self.MODEL_FILE)) |
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def _suggest(self, text, params): |
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self.debug( |
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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: |
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label = self.project.subjects[subject_id][1] |
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else: |
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label = None |
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suggestion = SubjectSuggestion( |
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uri, |
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label, |
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None, |
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score) |
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suggestions.append(suggestion) |
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return ListSuggestionResult(suggestions) |
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NatLibFi /
Annif
