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"""PAV ensemble backend that combines results from multiple projects and |
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learns which concept suggestions from each backend are trustworthy using the |
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PAV algorithm, a.k.a. isotonic regression, to turn raw scores returned by |
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individual backends into probabilities.""" |
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import os.path |
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import joblib |
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from scipy.sparse import coo_matrix, csc_matrix |
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from sklearn.isotonic import IsotonicRegression |
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import numpy as np |
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import annif.corpus |
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import annif.suggestion |
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import annif.util |
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from annif.exception import NotInitializedException, NotSupportedException |
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from . import ensemble |
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class PAVBackend(ensemble.BaseEnsembleBackend): |
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"""PAV ensemble backend that combines results from multiple projects""" |
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name = "pav" |
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MODEL_FILE_PREFIX = "pav-model-" |
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# defaults for uninitialized instances |
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_models = None |
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DEFAULT_PARAMETERS = {'min-docs': 10} |
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def initialize(self): |
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super().initialize() |
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if self._models is not None: |
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return # already initialized |
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self._models = {} |
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sources = annif.util.parse_sources(self.params['sources']) |
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for source_project_id, _ in sources: |
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model_filename = self.MODEL_FILE_PREFIX + source_project_id |
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path = os.path.join(self.datadir, model_filename) |
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if os.path.exists(path): |
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self.debug('loading PAV model from {}'.format(path)) |
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self._models[source_project_id] = joblib.load(path) |
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else: |
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raise NotInitializedException( |
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"PAV model file '{}' not found".format(path), |
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backend_id=self.backend_id) |
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def _get_model(self, source_project_id): |
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self.initialize() |
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return self._models[source_project_id] |
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def _normalize_hits(self, hits, source_project): |
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reg_models = self._get_model(source_project.project_id) |
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pav_result = [] |
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for hit in hits.as_list(source_project.subjects): |
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if hit.uri in reg_models: |
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score = reg_models[hit.uri].predict([hit.score])[0] |
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else: # default to raw score |
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score = hit.score |
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pav_result.append( |
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annif.suggestion.SubjectSuggestion( |
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uri=hit.uri, |
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label=hit.label, |
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notation=hit.notation, |
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score=score)) |
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pav_result.sort(key=lambda hit: hit.score, reverse=True) |
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return annif.suggestion.ListSuggestionResult(pav_result) |
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@staticmethod |
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def _suggest_train_corpus(source_project, corpus): |
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# lists for constructing score matrix |
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data, row, col = [], [], [] |
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# lists for constructing true label matrix |
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trow, tcol = [], [] |
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ndocs = 0 |
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for docid, doc in enumerate(corpus.documents): |
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hits = source_project.suggest(doc.text) |
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vector = hits.as_vector(source_project.subjects) |
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for cid in np.flatnonzero(vector): |
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data.append(vector[cid]) |
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row.append(docid) |
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col.append(cid) |
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subjects = annif.corpus.SubjectSet((doc.uris, doc.labels)) |
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for cid in np.flatnonzero( |
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subjects.as_vector(source_project.subjects)): |
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trow.append(docid) |
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tcol.append(cid) |
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ndocs += 1 |
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scores = coo_matrix((data, (row, col)), |
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shape=(ndocs, len(source_project.subjects)), |
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dtype=np.float32) |
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true = coo_matrix((np.ones(len(trow), dtype=np.bool), (trow, tcol)), |
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shape=(ndocs, len(source_project.subjects)), |
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dtype=np.bool) |
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return csc_matrix(scores), csc_matrix(true) |
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def _create_pav_model(self, source_project_id, min_docs, corpus): |
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self.info("creating PAV model for source {}, min_docs={}".format( |
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source_project_id, min_docs)) |
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source_project = self.project.registry.get_project(source_project_id) |
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# suggest subjects for the training corpus |
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scores, true = self._suggest_train_corpus(source_project, corpus) |
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# create the concept-specific PAV regression models |
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pav_regressions = {} |
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for cid in range(len(source_project.subjects)): |
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if true[:, cid].sum() < min_docs: |
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continue # don't create model b/c of too few examples |
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reg = IsotonicRegression(out_of_bounds='clip') |
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cid_scores = scores[:, cid].toarray().flatten().astype(np.float64) |
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reg.fit(cid_scores, true[:, cid].toarray().flatten()) |
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pav_regressions[source_project.subjects[cid][0]] = reg |
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self.info("created PAV model for {} concepts".format( |
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len(pav_regressions))) |
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model_filename = self.MODEL_FILE_PREFIX + source_project_id |
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annif.util.atomic_save( |
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pav_regressions, |
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self.datadir, |
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model_filename, |
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method=joblib.dump) |
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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 pav project from cached data not supported.') |
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if corpus.is_empty(): |
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raise NotSupportedException('training backend {} with no documents' |
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.format(self.backend_id)) |
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self.info("creating PAV models") |
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sources = annif.util.parse_sources(self.params['sources']) |
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min_docs = int(params['min-docs']) |
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for source_project_id, _ in sources: |
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self._create_pav_model(source_project_id, min_docs, corpus) |
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NatLibFi /
Annif
