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"""Ensemble backend that combines results from multiple projects""" |
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import annif.suggestion |
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import annif.util |
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import annif.eval |
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from . import hyperopt |
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from annif.exception import NotSupportedException |
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class EnsembleOptimizer(hyperopt.HyperparameterOptimizer): |
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"""Hyperparameter optimizer for the ensemble backend""" |
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def __init__(self, backend, corpus, metric): |
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super().__init__(backend, corpus, metric) |
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self._sources = [project_id for project_id, _ |
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in annif.util.parse_sources( |
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backend.config_params['sources'])] |
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def _prepare(self): |
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self._gold_subjects = [] |
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self._source_hits = [] |
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for doc in self._corpus.documents: |
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self._gold_subjects.append( |
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annif.corpus.SubjectSet((doc.uris, doc.labels))) |
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srchits = {} |
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for project_id in self._sources: |
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registry = self._backend.project.registry |
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source_project = registry.get_project(project_id) |
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hits = source_project.suggest(doc.text) |
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srchits[project_id] = hits |
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self._source_hits.append(srchits) |
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def _normalize(self, hps): |
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total = sum(hps.values()) |
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return {source: hps[source] / total for source in hps} |
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def _format_cfg_line(self, hps): |
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return 'sources=' + ','.join([f"{src}:{weight:.4f}" |
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for src, weight in hps.items()]) |
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def _objective(self, trial): |
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batch = annif.eval.EvaluationBatch(self._backend.project.subjects) |
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weights = {project_id: trial.suggest_uniform(project_id, 0.0, 1.0) |
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for project_id in self._sources} |
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for goldsubj, srchits in zip(self._gold_subjects, self._source_hits): |
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weighted_hits = [] |
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for project_id, hits in srchits.items(): |
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weighted_hits.append(annif.suggestion.WeightedSuggestion( |
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hits=hits, |
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weight=weights[project_id], |
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subjects=self._backend.project.subjects)) |
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batch.evaluate( |
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annif.util.merge_hits( |
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weighted_hits, |
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self._backend.project.subjects), |
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goldsubj) |
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results = batch.results(metrics=[self._metric]) |
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return results[self._metric] |
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def _postprocess(self, study): |
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line = self._format_cfg_line(self._normalize(study.best_params)) |
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return hyperopt.HPRecommendation(lines=[line], score=study.best_value) |
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class EnsembleBackend(hyperopt.AnnifHyperoptBackend): |
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"""Ensemble backend that combines results from multiple projects""" |
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name = "ensemble" |
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def get_hp_optimizer(self, corpus, metric): |
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return EnsembleOptimizer(self, corpus, metric) |
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@property |
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def is_trained(self): |
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sources_trained = self._get_sources_attribute('is_trained') |
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return all(sources_trained) |
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@property |
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def modification_time(self): |
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mtimes = self._get_sources_attribute('modification_time') |
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return max(filter(None, mtimes), default=None) |
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def _get_sources_attribute(self, attr): |
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params = self._get_backend_params(None) |
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sources = annif.util.parse_sources(params['sources']) |
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return [getattr(self.project.registry.get_project(project_id), attr) |
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for project_id, _ in sources] |
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def initialize(self): |
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# initialize all the source projects |
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params = self._get_backend_params(None) |
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for project_id, _ in annif.util.parse_sources(params['sources']): |
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project = self.project.registry.get_project(project_id) |
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project.initialize() |
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def _normalize_hits(self, hits, source_project): |
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"""Hook for processing hits from backends. Intended to be overridden |
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by subclasses.""" |
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return hits |
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def _suggest_with_sources(self, text, sources): |
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hits_from_sources = [] |
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for project_id, weight in sources: |
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source_project = self.project.registry.get_project(project_id) |
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hits = source_project.suggest(text) |
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self.debug( |
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'Got {} hits from project {}, weight {}'.format( |
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len(hits), source_project.project_id, weight)) |
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norm_hits = self._normalize_hits(hits, source_project) |
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hits_from_sources.append( |
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annif.suggestion.WeightedSuggestion( |
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hits=norm_hits, |
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weight=weight, |
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subjects=source_project.subjects)) |
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return hits_from_sources |
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def _merge_hits_from_sources(self, hits_from_sources, params): |
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"""Hook for merging hits from sources. Can be overridden by |
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subclasses.""" |
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return annif.util.merge_hits(hits_from_sources, self.project.subjects) |
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def _suggest(self, text, params): |
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sources = annif.util.parse_sources(params['sources']) |
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hits_from_sources = self._suggest_with_sources(text, sources) |
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merged_hits = self._merge_hits_from_sources(hits_from_sources, params) |
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self.debug('{} hits after merging'.format(len(merged_hits))) |
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return merged_hits |
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def _train(self, corpus, params): |
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raise NotSupportedException('Training ensemble model is not possible.') |
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NatLibFi /
Annif
