forked from enviPath/enviPy
liambrydon
d2f4fdc58a
## Changes - I have finished the backend integration of EnviFormer (#19), this includes, dataset building, model finetuning, model evaluation and model prediction with the finetuned model. - `PackageBasedModel` has been adjusted to be more abstract, this includes making the `_save_model` method and making `compute_averages` a static class function. - I had to bump the python-version in `pyproject.toml` to >=3.12 from >=3.11 otherwise uv failed to install EnviFormer. - The default EnviFormer loading during `settings.py` has been removed. ## Future Fix I noticed you have a little bit of code in `PackageBasedModel` -> `evaluate_model` for using the `eval_packages` during evaluation instead of train/test splits on `data_packages`. It doesn't seem finished, I presume we want this for all models, so I will take care of that in a new branch/pullrequest after this request is merged. Also, I haven't done anything for a POST request to finetune the model, I'm not sure if that is something we want now. Co-authored-by: Liam Brydon <62733830+MyCreativityOutlet@users.noreply.github.com> Reviewed-on: enviPath/enviPy#141 Reviewed-by: jebus <lorsbach@envipath.com> Co-authored-by: liambrydon <lbry121@aucklanduni.ac.nz> Co-committed-by: liambrydon <lbry121@aucklanduni.ac.nz>
32 lines
1.2 KiB
Python
32 lines
1.2 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 = []
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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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