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"""Hyperparameter optimization functionality for backends""" |
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from __future__ import annotations |
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import abc |
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import collections |
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import tempfile |
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from typing import TYPE_CHECKING, Any, Callable |
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import optuna |
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import optuna.exceptions |
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import annif.parallel |
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from .backend import AnnifBackend |
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if TYPE_CHECKING: |
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from click.utils import LazyFile |
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from optuna.study.study import Study |
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from optuna.trial import Trial |
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from annif.corpus.document import DocumentCorpus |
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HPRecommendation = collections.namedtuple("HPRecommendation", "lines score") |
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class TrialWriter: |
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"""Object that writes hyperparameter optimization trial results into a |
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TSV file.""" |
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def __init__(self, results_file: LazyFile, normalize_func: Callable) -> None: |
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self.results_file = results_file |
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self.normalize_func = normalize_func |
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self.header_written = False |
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def write(self, trial_data: dict[str, Any]) -> None: |
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"""Write the results of one trial into the results file. On the |
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first run, write the header line first.""" |
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if not self.header_written: |
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param_names = list(trial_data["params"].keys()) |
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print("\t".join(["trial", "value"] + param_names), file=self.results_file) |
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self.header_written = True |
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print( |
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"\t".join( |
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( |
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str(e) |
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for e in [trial_data["number"], trial_data["value"]] |
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+ list(self.normalize_func(trial_data["params"]).values()) |
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) |
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), |
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file=self.results_file, |
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) |
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class HPObjective(annif.parallel.BaseWorker): |
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"""Base class for hyperparameter optimizer objective functions""" |
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@classmethod |
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def objective(cls, trial: Trial, args) -> float: |
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"""Objective function to optimize. To be implemented by subclasses.""" |
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pass # pragma: no cover |
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@classmethod |
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def _objective_wrapper(cls, trial: Trial) -> float: |
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return cls.objective(trial, cls.args) |
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@classmethod |
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def run_trial( |
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cls, trial_id: int, storage_url: str, study_name: str |
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) -> dict[str, Any]: |
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# use a callback to set the completed trial, to avoid race conditions |
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completed_trial = [] |
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def set_trial_callback(study: Study, trial: Trial) -> None: |
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completed_trial.append(trial) |
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study = optuna.load_study(storage=storage_url, study_name=study_name) |
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study.optimize( |
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cls._objective_wrapper, |
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n_trials=1, |
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callbacks=[set_trial_callback], |
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) |
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return { |
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"number": completed_trial[0].number, |
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"value": completed_trial[0].value, |
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"params": completed_trial[0].params, |
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} |
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class HyperparameterOptimizer: |
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"""Base class for hyperparameter optimizers""" |
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def __init__( |
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self, |
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backend: AnnifBackend, |
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corpus: DocumentCorpus, |
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metric: str, |
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objective: HPObjective, |
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) -> None: |
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self._backend = backend |
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self._corpus = corpus |
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self._metric = metric |
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self._objective = objective |
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def _prepare(self, n_jobs: int = 1): |
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"""Prepare the optimizer for hyperparameter evaluation. Up to |
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n_jobs parallel threads or processes may be used during the |
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operation. The return value will be passed to the objective function.""" |
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pass # pragma: no cover |
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@abc.abstractmethod |
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def _postprocess(self, study: Study) -> HPRecommendation: |
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"""Convert the study results into hyperparameter recommendations""" |
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pass # pragma: no cover |
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def _normalize(self, hps: dict[str, float]) -> dict[str, float]: |
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"""Normalize the given raw hyperparameters. Intended to be overridden |
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by subclasses when necessary. The default is to keep them as-is.""" |
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return hps |
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def optimize( |
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self, n_trials: int, n_jobs: int, results_file: LazyFile | None |
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) -> HPRecommendation: |
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"""Find the optimal hyperparameters by testing up to the given number |
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of hyperparameter combinations""" |
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objective_args = self._prepare(n_jobs) |
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self._objective.init(objective_args) |
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writer = TrialWriter(results_file, self._normalize) if results_file else None |
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write_callback = writer.write if writer else None |
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temp_db = tempfile.NamedTemporaryFile(suffix=".db", delete=False) |
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storage_url = f"sqlite:///{temp_db.name}" |
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study = optuna.create_study(direction="maximize", storage=storage_url) |
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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 i in range(n_trials): |
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pool.apply_async( |
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self._objective.run_trial, |
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args=(i, storage_url, study.study_name), |
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callback=write_callback, |
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) |
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pool.close() |
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pool.join() |
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return self._postprocess(study) |
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class AnnifHyperoptBackend(AnnifBackend): |
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"""Base class for Annif backends that can perform hyperparameter |
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optimization""" |
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@abc.abstractmethod |
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def get_hp_optimizer(self, corpus: DocumentCorpus, metric: str): |
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"""Get a HyperparameterOptimizer object that can look for |
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optimal hyperparameter combinations for the given corpus, |
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measured using the given metric""" |
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pass # pragma: no cover |
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NatLibFi /
Annif
