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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 hyperopt |
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from .backend import AnnifBackend |
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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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@abc.abstractmethod |
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def get_hp_space(self): |
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"""Get the hyperparameter space definition of this backend""" |
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pass # pragma: no cover |
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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 _test(self, hps): |
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"""Evaluate a set of hyperparameters""" |
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pass # pragma: no cover |
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@abc.abstractmethod |
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def _postprocess(self, best, trials): |
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"""Convert the trial results into hyperparameter recommendations""" |
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pass # pragma: no cover |
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def optimize(self, n_trials): |
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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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space = self.get_hp_space() |
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trials = hyperopt.Trials() |
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best = hyperopt.fmin( |
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fn=self._test, |
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space=space, |
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algo=hyperopt.tpe.suggest, |
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max_evals=n_trials, |
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trials=trials) |
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return self._postprocess(best, trials) |
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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): |
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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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pass # pragma: no cover |
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NatLibFi /
Annif
