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"""Hyperparameter optimization functionality for backends""" |
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import abc |
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import collections |
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import optuna |
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from .backend import AnnifBackend |
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from annif import logger |
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9
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HPRecommendation = collections.namedtuple('HPRecommendation', 'lines score') |
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class HyperparameterOptimizer: |
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"""Base class for hyperparameter optimizers""" |
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def __init__(self, backend, corpus, metric): |
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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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def _prepare(self): |
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"""Prepare the optimizer for hyperparameter evaluation""" |
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pass # pragma: no cover |
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@abc.abstractmethod |
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def _objective(self, trial): |
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"""Objective function to optimize""" |
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pass # pragma: no cover |
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@abc.abstractmethod |
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def _postprocess(self, study): |
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"""Convert the study results into hyperparameter recommendations""" |
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pass # pragma: no cover |
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def optimize(self, n_trials, n_jobs): |
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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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self._prepare() |
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study = optuna.create_study(direction='maximize') |
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study.optimize(self._objective, |
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n_trials=n_trials, |
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n_jobs=n_jobs, |
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gc_after_trial=False, |
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show_progress_bar=True) |
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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, metric): |
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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
