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"""Common functionality for backends.""" |
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from __future__ import annotations |
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import abc |
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import os.path |
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from datetime import datetime, timezone |
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from glob import glob |
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from typing import TYPE_CHECKING, Any |
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from annif import logger |
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from annif.suggestion import SuggestionBatch |
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if TYPE_CHECKING: |
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from configparser import SectionProxy |
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from annif.corpus.document import DocumentCorpus |
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from annif.project import AnnifProject |
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class AnnifBackend(metaclass=abc.ABCMeta): |
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"""Base class for Annif backends that perform analysis. The |
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non-implemented methods should be overridden in subclasses.""" |
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name = None |
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DEFAULT_PARAMETERS = {"limit": 100} |
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def __init__( |
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self, |
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backend_id: str, |
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config_params: dict[str, Any] | SectionProxy, |
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project: AnnifProject, |
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) -> None: |
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"""Initialize backend with specific parameters. The |
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parameters are a dict. Keys and values depend on the specific |
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backend type.""" |
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self.backend_id = backend_id |
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self.config_params = config_params |
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self.project = project |
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self.datadir = project.datadir |
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def default_params(self) -> dict[str, Any]: |
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params = AnnifBackend.DEFAULT_PARAMETERS.copy() |
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params.update(self.DEFAULT_PARAMETERS) # Optional backend specific parameters |
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return params |
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@property |
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def params(self) -> dict[str, Any]: |
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params = {} |
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params.update(self.default_params()) |
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params.update(self.config_params) |
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return params |
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@property |
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def _model_file_paths(self) -> list: |
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all_paths = glob(os.path.join(self.datadir, "**"), recursive=True) |
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file_paths = [p for p in all_paths if os.path.isfile(p)] |
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ignore_patterns = ("*-train*", "tmp-*", "vectorizer") |
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ignore_paths = [ |
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path |
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for igp in ignore_patterns |
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for path in glob(os.path.join(self.datadir, igp)) |
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] |
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return list(set(file_paths) - set(ignore_paths)) |
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@property |
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def is_trained(self) -> bool: |
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return bool(self._model_file_paths) |
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@property |
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def modification_time(self) -> datetime | None: |
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mtimes = [ |
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datetime.utcfromtimestamp(os.path.getmtime(p)) |
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for p in self._model_file_paths |
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] |
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most_recent = max(mtimes, default=None) |
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if most_recent is None: |
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return None |
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return most_recent.replace(tzinfo=timezone.utc) |
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def _get_backend_params( |
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self, |
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params: dict[str, Any] | None, |
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) -> dict[str, Any]: |
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backend_params = dict(self.params) |
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if params is not None: |
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backend_params.update(params) |
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return backend_params |
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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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"""This method can be overridden by backends. It implements |
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the train functionality, with pre-processed parameters.""" |
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pass # default is to do nothing, subclasses may override |
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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] | None = None, |
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jobs: int = 0, |
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) -> None: |
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"""Train the model on the given document or subject corpus.""" |
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beparams = self._get_backend_params(params) |
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return self._train(corpus, params=beparams, jobs=jobs) |
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def initialize(self, parallel: bool = False) -> None: |
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"""This method can be overridden by backends. It should cause the |
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backend to pre-load all data it needs during operation. |
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If parallel is True, the backend should expect to be used for |
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parallel operation.""" |
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pass |
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def _suggest(self, text, params): |
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"""Either this method or _suggest_batch should be implemented by by |
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backends. It implements the suggest functionality for a single |
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document, with pre-processed parameters.""" |
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pass # pragma: no cover |
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def _suggest_batch( |
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self, texts: list[str], params: dict[str, Any] |
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) -> SuggestionBatch: |
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"""This method can be implemented by backends to use batching of documents in |
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their operations. This default implementation uses the regular suggest |
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functionality.""" |
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return SuggestionBatch.from_sequence( |
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[self._suggest(text, params) for text in texts], |
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self.project.subjects, |
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limit=int(params.get("limit")), |
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) |
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def suggest( |
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self, |
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texts: list[str], |
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params: dict[str, Any] | None = None, |
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) -> SuggestionBatch: |
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"""Suggest subjects for the input documents and return a list of subject sets |
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represented as a list of SubjectSuggestion objects.""" |
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beparams = self._get_backend_params(params) |
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self.initialize() |
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return self._suggest_batch(texts, params=beparams) |
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def debug(self, message: str) -> None: |
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"""Log a debug message from this backend""" |
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logger.debug("Backend {}: {}".format(self.backend_id, message)) |
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def info(self, message: str) -> None: |
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"""Log an info message from this backend""" |
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logger.info("Backend {}: {}".format(self.backend_id, message)) |
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def warning(self, message: str) -> None: |
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"""Log a warning message from this backend""" |
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logger.warning("Backend {}: {}".format(self.backend_id, message)) |
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class AnnifLearningBackend(AnnifBackend): |
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"""Base class for Annif backends that can perform online learning""" |
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@abc.abstractmethod |
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def _learn(self, corpus, params): |
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"""This method should implemented by backends. It implements the learn |
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functionality, with pre-processed parameters.""" |
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pass # pragma: no cover |
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def learn( |
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self, |
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corpus: DocumentCorpus, |
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params: dict[str, Any] | None = None, |
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) -> None: |
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"""Further train the model on the given document or subject corpus.""" |
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beparams = self._get_backend_params(params) |
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return self._learn(corpus, params=beparams) |
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NatLibFi /
Annif
