annif.backend.hyperopt.HyperparameterOptimizer.optimize() - Code Metrics - Inspection of "Initial implementation: hyperparameter optimizatio..." - NatLibFi/Annif - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#414)

by Osma

created 2020-05-15 14:10 UTC

HyperparameterOptimizer.optimize() A

↳ Parent: annif.backend.hyperopt

Complexity

Conditions

Size

Total Lines	15
Code Lines	12

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	12
dl	0
loc	15
rs	9.8
c	0
b	0
f	0
cc	1
nop	2

"""Hyperparameter optimization functionality for backends"""

import abc
import hyperopt
from .backend import AnnifBackend


class HyperparameterOptimizer:
    """Base class for hyperparameter optimizers"""

    def __init__(self, backend, corpus):
        self._backend = backend
        self._corpus = corpus

    @abc.abstractmethod
    def get_hp_space(self):
        """Get the hyperparameter space definition of this backend"""
        pass  # pragma: no cover

    def _prepare(self):
        """Prepare the optimizer for hyperparameter evaluation"""
        pass  # pragma: no cover

    @abc.abstractmethod
    def _test(self, hps):
        """Evaluate a set of hyperparameters"""
        pass  # pragma: no cover

    def optimize(self, n_trials):
        """Find the optimal hyperparameters by testing up to the given number of
        hyperparameter combinations"""

        self._prepare()
        space = self.get_hp_space()
        trials = hyperopt.Trials()
        best = hyperopt.fmin(
            show_progressbar=False,
            fn=self._test,
            space=space,
            algo=hyperopt.tpe.suggest,
            max_evals=n_trials,
            trials=trials)
        return (best, 1 - trials.best_trial['result']['loss'])


class AnnifHyperoptBackend(AnnifBackend):
    """Base class for Annif backends that can perform hyperparameter
    optimization"""

    @abc.abstractmethod
    def get_hp_optimizer(self, corpus):
        """Get a HyperparameterOptimizer object that can look for
        optimal hyperparameter combinations for the given corpus"""

        pass  # pragma: no cover


1			"""Hyperparameter optimization functionality for backends"""
2
3			import abc
4			import hyperopt
5			from .backend import AnnifBackend
6
7
8			class HyperparameterOptimizer:
9			"""Base class for hyperparameter optimizers"""
10
11			def __init__(self, backend, corpus):
12			self._backend = backend
13			self._corpus = corpus
14
15			@abc.abstractmethod
16			def get_hp_space(self):
17			"""Get the hyperparameter space definition of this backend"""
18			pass # pragma: no cover
19
20			def _prepare(self):
21			"""Prepare the optimizer for hyperparameter evaluation"""
22			pass # pragma: no cover
23
24			@abc.abstractmethod
25			def _test(self, hps):
26			"""Evaluate a set of hyperparameters"""
27			pass # pragma: no cover
28
29			def optimize(self, n_trials):
30			"""Find the optimal hyperparameters by testing up to the given number of
31			hyperparameter combinations"""
32
33			self._prepare()
34			space = self.get_hp_space()
35			trials = hyperopt.Trials()
36			best = hyperopt.fmin(
37			show_progressbar=False,
38			fn=self._test,
39			space=space,
40			algo=hyperopt.tpe.suggest,
41			max_evals=n_trials,
42			trials=trials)
43			return (best, 1 - trials.best_trial['result']['loss'])
44
45
46			class AnnifHyperoptBackend(AnnifBackend):
47			"""Base class for Annif backends that can perform hyperparameter
48			optimization"""
49
50			@abc.abstractmethod
51			def get_hp_optimizer(self, corpus):
52			"""Get a HyperparameterOptimizer object that can look for
53			optimal hyperparameter combinations for the given corpus"""
54
55			pass # pragma: no cover
56

NatLibFi / Annif

Pull Request — master (#414)

HyperparameterOptimizer.optimize() A

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