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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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from __future__ import annotations |
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import os.path |
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from typing import TYPE_CHECKING, Any |
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import joblib |
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import numpy as np |
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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 annif.corpus |
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import annif.util |
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from annif.exception import NotInitializedException, NotSupportedException |
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from annif.suggestion import SubjectSuggestion, SuggestionBatch |
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from . import ensemble |
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if TYPE_CHECKING: |
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from annif.corpus.document import DocumentCorpus |
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from annif.project import AnnifProject |
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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, parallel: bool = False) -> None: |
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super().initialize(parallel) |
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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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) |
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def _get_model(self, source_project_id: str) -> dict[int, IsotonicRegression]: |
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self.initialize() |
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return self._models[source_project_id] |
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def _merge_source_batches( |
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self, |
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batch_by_source: dict[str, SuggestionBatch], |
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sources: list[tuple[str, float]], |
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params: dict[str, Any], |
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) -> SuggestionBatch: |
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reg_batch_by_source = {} |
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for project_id, batch in batch_by_source.items(): |
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reg_models = self._get_model(project_id) |
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pav_batch = [ |
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[ |
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SubjectSuggestion( |
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subject_id=sugg.subject_id, |
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score=reg_models[sugg.subject_id].predict([sugg.score])[0], |
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) |
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if sugg.subject_id in reg_models |
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else SubjectSuggestion( |
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subject_id=sugg.subject_id, score=sugg.score |
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) # default to raw score |
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for sugg in result |
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] |
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for result in batch |
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] |
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reg_batch_by_source[project_id] = SuggestionBatch.from_sequence( |
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pav_batch, self.project.subjects |
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) |
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return super()._merge_source_batches(reg_batch_by_source, sources, params) |
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@staticmethod |
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def _suggest_train_corpus( |
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source_project: AnnifProject, corpus: DocumentCorpus |
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) -> tuple[csc_matrix, csc_matrix]: |
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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])[0] |
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vector = hits.as_vector() |
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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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for cid in np.flatnonzero( |
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doc.subject_set.as_vector(len(source_project.subjects)) |
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): |
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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( |
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(data, (row, col)), |
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shape=(ndocs, len(source_project.subjects)), |
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dtype=np.float32, |
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) |
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true = coo_matrix( |
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(np.ones(len(trow), dtype=bool), (trow, tcol)), |
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shape=(ndocs, len(source_project.subjects)), |
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dtype=bool, |
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) |
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return csc_matrix(scores), csc_matrix(true) |
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def _create_pav_model( |
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self, source_project_id: str, min_docs: int, corpus: DocumentCorpus |
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) -> None: |
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self.info( |
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"creating PAV model for source {}, min_docs={}".format( |
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source_project_id, min_docs |
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) |
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) |
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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[cid] = reg |
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self.info("created PAV model for {} concepts".format(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, self.datadir, model_filename, method=joblib.dump |
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) |
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def _train( |
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self, |
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corpus: DocumentCorpus, |
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params: dict[str, Any], |
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jobs: int = 0, |
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) -> None: |
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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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) |
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if corpus.is_empty(): |
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raise NotSupportedException( |
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"training backend {} with no documents".format(self.backend_id) |
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) |
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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
