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"""Ensemble backend that combines results from multiple projects""" |
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import annif.eval |
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import annif.parallel |
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import annif.suggestion |
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import annif.util |
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from annif.exception import NotSupportedException |
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from annif.suggestion import SuggestionBatch |
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from . import backend, hyperopt |
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class BaseEnsembleBackend(backend.AnnifBackend): |
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"""Base class for ensemble backends""" |
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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 [ |
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getattr(self.project.registry.get_project(project_id), attr) |
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for project_id, _ in sources |
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] |
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def initialize(self, parallel=False): |
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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(parallel) |
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def _suggest_with_sources(self, texts, sources): |
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return { |
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project_id: self.project.registry.get_project(project_id).suggest(texts) |
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for project_id, _ in sources |
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} |
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def _merge_source_batches(self, batch_by_source, sources, params): |
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"""Merge the given SuggestionBatches from each source into a single |
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SuggestionBatch. The default implementation computes a weighted |
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average based on the weights given in the sources tuple. Intended |
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to be overridden in subclasses.""" |
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batches = [batch_by_source[project_id] for project_id, _ in sources] |
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weights = [weight for _, weight in sources] |
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return SuggestionBatch.from_averaged(batches, weights) |
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def _suggest_batch(self, texts, params): |
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sources = annif.util.parse_sources(params["sources"]) |
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batch_by_source = self._suggest_with_sources(texts, sources) |
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merged = self._merge_source_batches(batch_by_source, sources, params) |
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return merged.filter(limit=int(params["limit"])) |
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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 = [ |
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project_id |
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for project_id, _ in annif.util.parse_sources( |
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backend.config_params["sources"] |
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) |
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] |
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def _prepare(self, n_jobs=1): |
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self._gold_batches = [] |
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self._source_batches = [] |
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for project_id in self._sources: |
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project = self._backend.project.registry.get_project(project_id) |
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project.initialize() |
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psmap = annif.parallel.ProjectSuggestMap( |
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self._backend.project.registry, |
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self._sources, |
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backend_params=None, |
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limit=int(self._backend.params["limit"]), |
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threshold=0.0, |
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) |
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jobs, pool_class = annif.parallel.get_pool(n_jobs) |
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with pool_class(jobs) as pool: |
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for suggestions, gold_batch in pool.imap_unordered( |
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psmap.suggest_batch, self._corpus.doc_batches |
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): |
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self._source_batches.append(suggestions) |
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self._gold_batches.append(gold_batch) |
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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( |
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[f"{src}:{weight:.4f}" for src, weight in hps.items()] |
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) |
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def _objective(self, trial): |
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eval_batch = annif.eval.EvaluationBatch(self._backend.project.subjects) |
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proj_weights = { |
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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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} |
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for gold_batch, src_batches in zip(self._gold_batches, self._source_batches): |
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batches = [src_batches[project_id] for project_id in self._sources] |
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weights = [proj_weights[project_id] for project_id in self._sources] |
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avg_batch = SuggestionBatch.from_averaged(batches, weights).filter( |
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limit=int(self._backend.params["limit"]) |
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) |
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eval_batch.evaluate_many(avg_batch, gold_batch) |
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results = eval_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(BaseEnsembleBackend, hyperopt.AnnifHyperoptBackend): |
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"""Ensemble backend that combines results from multiple projects""" |
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name = "ensemble" |
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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_hp_optimizer(self, corpus, metric): |
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return EnsembleOptimizer(self, corpus, metric) |
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def _train(self, corpus, params, jobs=0): |
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raise NotSupportedException("Training ensemble backend is not possible.") |
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NatLibFi /
Annif
