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Hyperparameter optimization for NN ensemble #569

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Hyperparameter optimization for NN ensemble #569

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59b45f5
refactor: avoid self.params; assign self._model outside _create_model
osma Feb 11, 2022
86f05b6
use proper documented value for verbose option of Keras Model.fit
osma Feb 11, 2022
e785835
Initial rough version of hyperparameter optimization for NN ensemble.…
osma Feb 11, 2022
7447ede
Add very basic unit test for nn_ensemble hyperopt
osma Feb 11, 2022
6d67fc3
Merge branch 'master' into issue435-hyperopt-nn-ensemble
osma Mar 16, 2022
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Add very basic unit test for nn_ensemble hyperopt
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@osma
osma committed Feb 11, 2022
commit 7447ede152c63b9757d110eb7ff4273c56c3bccd
11 changes: 11 additions & 0 deletions tests/test_backend_nn_ensemble.py
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Expand Up @@ -170,6 +170,17 @@ def test_nn_ensemble_is_trained(app_project):
assert nn_ensemble.is_trained


def test_nn_ensemble_hyperopt(app_project, fulltext_corpus):
nn_ensemble_type = annif.backend.get_backend('nn_ensemble')
nn_ensemble = nn_ensemble_type(
backend_id='nn_ensemble',
config_params={'sources': 'dummy-en'},
project=app_project)

optimizer = nn_ensemble.get_hp_optimizer(fulltext_corpus, metric='NDCG')
optimizer.optimize(n_trials=2, n_jobs=1, results_file=None)


def test_nn_ensemble_modification_time(app_project):
nn_ensemble_type = annif.backend.get_backend("nn_ensemble")
nn_ensemble = nn_ensemble_type(
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