forked from enviPath/enviPy
liambrydon
22f0bbe10b
`evaluate_model` in `PackageBasedModel` and `EnviFormer` now use evaluation packages if any are present instead of the random splits. Co-authored-by: Liam Brydon <62733830+MyCreativityOutlet@users.noreply.github.com> Reviewed-on: enviPath/enviPy#148 Co-authored-by: liambrydon <lbry121@aucklanduni.ac.nz> Co-committed-by: liambrydon <lbry121@aucklanduni.ac.nz>
32 lines
1.3 KiB
Python
32 lines
1.3 KiB
Python
from tempfile import TemporaryDirectory
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from django.test import TestCase
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from epdb.logic import PackageManager
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from epdb.models import User, EnviFormer, Package
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class EnviFormerTest(TestCase):
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fixtures = ["test_fixtures.jsonl.gz"]
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@classmethod
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def setUpClass(cls):
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super(EnviFormerTest, cls).setUpClass()
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cls.user = User.objects.get(username='anonymous')
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cls.package = PackageManager.create_package(cls.user, 'Anon Test Package', 'No Desc')
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cls.BBD_SUBSET = Package.objects.get(name='Fixtures')
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def test_model_flow(self):
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"""Test the full flow of EnviFormer, dataset build -> model finetune -> model evaluate -> model inference"""
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with TemporaryDirectory() as tmpdir:
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with self.settings(MODEL_DIR=tmpdir):
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threshold = float(0.5)
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data_package_objs = [self.BBD_SUBSET]
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eval_packages_objs = [self.BBD_SUBSET]
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mod = EnviFormer.create(self.package, data_package_objs, eval_packages_objs, threshold=threshold)
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mod.build_dataset()
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mod.build_model()
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mod.multigen_eval = True
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mod.save()
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mod.evaluate_model()
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results = mod.predict('CCN(CC)C(=O)C1=CC(=CC=C1)C')
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