forked from enviPath/enviPy
Liam Brydon
901de4640c
**This pull request will need a separate migration pull-request**
I have added an alert box in two places when the user tries to predict with stereo chemistry.
When a user predicts a pathway with stereo chemistry an alert box is shown in that node's hover.
To do this I added two new fields. Pathway now has a "predicted" BooleanField indicating whether it was predicted or not. It is set to True if the pathway mode for prediction is "predict" or "incremental" and False if it is "build". I think it is a flag that could be useful in the future, perhaps for analysing how many predicted pathways are in enviPath?
Node now has a `stereo_removed` BooleanField which is set to True if the Node's parent Pathways has "predicted" as true and the node SMILES has stereochemistry.
<img width="500" alt="{927AC9FF-DBC9-4A19-9E6E-0EDD3B08C7AC}.png" src="attachments/69ea29bc-c2d2-4cd2-8e98-aae5c5737f69">
When a user does a prediction on a model's page it shows at the top of the list. This did not require any new fields as the entered SMILES does not get saved anywhere.
<img width="500" alt="{BED66F12-5F07-419E-AAA6-FE1FE5B4F266}.png" src="attachments/5fcc3a9b-4d1a-4e48-acac-76b7571f6507">
I think the alert box is an alright solution but if you have a great idea for something that looks/fits better please change it or let me know.
Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: enviPath/enviPy#250
Co-authored-by: Liam Brydon <lbry121@aucklanduni.ac.nz>
Co-committed-by: Liam Brydon <lbry121@aucklanduni.ac.nz>
119 lines
4.3 KiB
Python
119 lines
4.3 KiB
Python
from tempfile import TemporaryDirectory
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import numpy as np
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from django.conf import settings as s
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from django.test import TestCase, override_settings
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from epdb.logic import PackageManager
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from epdb.models import MLRelativeReasoning, RuleBasedRelativeReasoning, User
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Package = s.GET_PACKAGE_MODEL()
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@override_settings(MODEL_DIR=s.FIXTURE_DIRS[0] / "models", CELERY_TASK_ALWAYS_EAGER=True)
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class ModelTest(TestCase):
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fixtures = ["test_fixtures_incl_model.jsonl.gz"]
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@classmethod
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def setUpClass(cls):
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super(ModelTest, 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_mlrr(self):
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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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rule_package_objs = [self.BBD_SUBSET]
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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 = MLRelativeReasoning.create(
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self.package,
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rule_package_objs,
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data_package_objs,
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threshold=threshold,
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name="ECC - BBD - 0.5",
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description="Created MLRelativeReasoning in Testcase",
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)
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mod.build_dataset()
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mod.build_model()
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mod.evaluate_model(True, eval_packages_objs, n_splits=2)
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results = mod.predict("CCN(CC)C(=O)C1=CC(=CC=C1)C")
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products = dict()
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for r in results:
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for ps in r.product_sets:
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products[tuple(sorted(ps.product_set))] = (r.rule.name, r.probability)
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expected = {
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("CC=O", "CCNC(=O)C1=CC(C)=CC=C1"): (
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"bt0243-4301",
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np.float64(0.5),
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),
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("CC1=CC=CC(C(=O)O)=C1", "CCNCC"): ("bt0430-4011", np.float64(0.25)),
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}
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self.assertEqual(products, expected)
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# from pprint import pprint
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# pprint(mod.eval_results)
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def test_applicability(self):
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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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rule_package_objs = [self.BBD_SUBSET]
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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 = MLRelativeReasoning.create(
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self.package,
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rule_package_objs,
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data_package_objs,
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threshold=threshold,
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name="ECC - BBD - 0.5",
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description="Created MLRelativeReasoning in Testcase",
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build_app_domain=True, # To test the applicability domain this must be True
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app_domain_num_neighbours=5,
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app_domain_local_compatibility_threshold=0.5,
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app_domain_reliability_threshold=0.5,
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)
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mod.build_dataset()
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mod.build_model()
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mod.evaluate_model(True, eval_packages_objs, n_splits=2)
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_ = mod.predict("CCN(CC)C(=O)C1=CC(=CC=C1)C")
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def test_rbrr(self):
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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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rule_package_objs = [self.BBD_SUBSET]
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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 = RuleBasedRelativeReasoning.create(
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self.package,
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rule_package_objs,
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data_package_objs,
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threshold=threshold,
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min_count=5,
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max_count=0,
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name="ECC - BBD - 0.5",
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description="Created MLRelativeReasoning in Testcase",
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)
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mod.build_dataset()
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mod.build_model()
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mod.evaluate_model(True, eval_packages_objs, n_splits=2)
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_ = mod.predict("CCN(CC)C(=O)C1=CC(=CC=C1)C")
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