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"""Annif backend using the fastText classifier""" |
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import collections |
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import os.path |
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import annif.util |
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from annif.suggestion import SubjectSuggestion, ListSuggestionResult |
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from annif.exception import NotInitializedException, NotSupportedException |
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import fasttext |
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from . import backend |
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from . import mixins |
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class FastTextBackend(mixins.ChunkingBackend, backend.AnnifBackend): |
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"""fastText backend for Annif""" |
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name = "fasttext" |
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needs_subject_index = True |
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FASTTEXT_PARAMS = { |
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'lr': float, |
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'lrUpdateRate': int, |
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'dim': int, |
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'ws': int, |
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'epoch': int, |
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'minCount': int, |
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'neg': int, |
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'wordNgrams': int, |
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'loss': str, |
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'bucket': int, |
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'minn': int, |
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'maxn': int, |
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'thread': int, |
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't': float, |
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'pretrainedVectors': str |
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} |
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DEFAULT_PARAMETERS = { |
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'dim': 100, |
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'lr': 0.25, |
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'epoch': 5, |
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'loss': 'hs', |
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} |
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MODEL_FILE = 'fasttext-model' |
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TRAIN_FILE = 'fasttext-train.txt' |
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# defaults for uninitialized instances |
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_model = None |
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def default_params(self): |
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params = backend.AnnifBackend.DEFAULT_PARAMETERS.copy() |
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params.update(mixins.ChunkingBackend.DEFAULT_PARAMETERS) |
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params.update(self.DEFAULT_PARAMETERS) |
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return params |
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@staticmethod |
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def _load_model(path): |
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# monkey patch fasttext.FastText.eprint to avoid spurious warning |
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# see https://github.com/facebookresearch/fastText/issues/1067 |
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orig_eprint = fasttext.FastText.eprint |
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fasttext.FastText.eprint = lambda x:None |
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model = fasttext.load_model(path) |
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# restore the original eprint |
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fasttext.FastText.eprint = orig_eprint |
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return model |
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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('loading fastText model from {}'.format(path)) |
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if os.path.exists(path): |
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self._model = self._load_model(path) |
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self.debug('loaded model {}'.format(str(self._model))) |
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self.debug('dim: {}'.format(self._model.get_dimension())) |
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else: |
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raise NotInitializedException( |
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'model {} not found'.format(path), |
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backend_id=self.backend_id) |
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@staticmethod |
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def _id_to_label(subject_id): |
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return "__label__{:d}".format(subject_id) |
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def _label_to_subject(self, label): |
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labelnum = label.replace('__label__', '') |
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subject_id = int(labelnum) |
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return self.project.subjects[subject_id] |
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def _write_train_file(self, doc_subjects, filename): |
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with open(filename, 'w', encoding='utf-8') as trainfile: |
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for doc, subject_ids in doc_subjects.items(): |
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labels = [self._id_to_label(sid) for sid in subject_ids |
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if sid is not None] |
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if labels: |
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print(' '.join(labels), doc, file=trainfile) |
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else: |
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self.warning('no labels for document "{}"'.format(doc)) |
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def _normalize_text(self, text): |
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return ' '.join(self.project.analyzer.tokenize_words(text)) |
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def _create_train_file(self, corpus): |
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self.info('creating fastText training file') |
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doc_subjects = collections.defaultdict(set) |
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for doc in corpus.documents: |
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text = self._normalize_text(doc.text) |
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if text == '': |
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continue |
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doc_subjects[text] = [self.project.subjects.by_uri(uri) |
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for uri in doc.uris] |
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annif.util.atomic_save(doc_subjects, |
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self.datadir, |
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self.TRAIN_FILE, |
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method=self._write_train_file) |
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def _create_model(self, params): |
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self.info('creating fastText model') |
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trainpath = os.path.join(self.datadir, self.TRAIN_FILE) |
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modelpath = os.path.join(self.datadir, self.MODEL_FILE) |
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params = {param: self.FASTTEXT_PARAMS[param](val) |
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for param, val in params.items() |
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if param in self.FASTTEXT_PARAMS} |
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self.debug('Model parameters: {}'.format(params)) |
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self._model = fasttext.train_supervised(trainpath, **params) |
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self._model.save_model(modelpath) |
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def _train(self, corpus, params): |
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if corpus != 'cached': |
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if corpus.is_empty(): |
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raise NotSupportedException( |
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'training backend {} with no documents' .format( |
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self.backend_id)) |
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self._create_train_file(corpus) |
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else: |
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self.info("Reusing cached training data from previous run.") |
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self._create_model(params) |
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def _predict_chunks(self, chunktexts, limit): |
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return self._model.predict(list( |
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filter(None, [self._normalize_text(chunktext) |
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for chunktext in chunktexts])), limit) |
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def _suggest_chunks(self, chunktexts, params): |
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limit = int(params['limit']) |
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chunklabels, chunkscores = self._predict_chunks( |
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chunktexts, limit) |
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label_scores = collections.defaultdict(float) |
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for labels, scores in zip(chunklabels, chunkscores): |
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for label, score in zip(labels, scores): |
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label_scores[label] += score |
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best_labels = sorted([(score, label) |
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for label, score in label_scores.items()], |
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reverse=True) |
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results = [] |
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for score, label in best_labels[:limit]: |
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subject = self._label_to_subject(label) |
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results.append(SubjectSuggestion( |
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uri=subject[0], |
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label=subject[1], |
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notation=subject[2], |
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score=score / len(chunktexts))) |
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return ListSuggestionResult(results, self.project.subjects) |
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NatLibFi /
Annif
