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new RuleBasedDataset and EnviFormer dataset working for respective models #120

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This commit is contained in:
Liam Brydon
2025-11-04 10:58:16 +13:00
parent ff51e48f90
commit ac5d370b18
5 changed files with 126 additions and 101 deletions
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47
tests/test_model.py
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@ -4,7 +4,7 @@ import numpy as np
from django.test import TestCase
from epdb.logic import PackageManager
from epdb.models import User, MLRelativeReasoning, Package
from epdb.models import User, MLRelativeReasoning, Package, RuleBasedRelativeReasoning
class ModelTest(TestCase):
@ -17,7 +17,7 @@ class ModelTest(TestCase):
cls.package = PackageManager.create_package(cls.user, "Anon Test Package", "No Desc")
cls.BBD_SUBSET = Package.objects.get(name="Fixtures")
def test_smoke(self):
def test_mlrr(self):
with TemporaryDirectory() as tmpdir:
with self.settings(MODEL_DIR=tmpdir):
threshold = float(0.5)
@ -36,23 +36,11 @@ class ModelTest(TestCase):
description="Created MLRelativeReasoning in Testcase",
)
# mod = RuleBasedRelativeReasoning.create(
# self.package,
# rule_package_objs,
# data_package_objs,
# eval_packages_objs,
# threshold=threshold,
# min_count=5,
# max_count=0,
# name='ECC - BBD - 0.5',
# description='Created MLRelativeReasoning in Testcase',
# )
mod.build_dataset()
mod.build_model()
mod.multigen_eval = True
mod.save()
mod.evaluate_model()
mod.evaluate_model(n_splits=2)
results = mod.predict("CCN(CC)C(=O)C1=CC(=CC=C1)C")
@ -73,3 +61,32 @@ class ModelTest(TestCase):
# from pprint import pprint
# pprint(mod.eval_results)
def test_rbrr(self):
with TemporaryDirectory() as tmpdir:
with self.settings(MODEL_DIR=tmpdir):
threshold = float(0.5)
rule_package_objs = [self.BBD_SUBSET]
data_package_objs = [self.BBD_SUBSET]
eval_packages_objs = [self.BBD_SUBSET]
mod = RuleBasedRelativeReasoning.create(
self.package,
rule_package_objs,
data_package_objs,
eval_packages_objs,
threshold=threshold,
min_count=5,
max_count=0,
name='ECC - BBD - 0.5',
description='Created MLRelativeReasoning in Testcase',
)
mod.build_dataset()
mod.build_model()
mod.multigen_eval = True
mod.save()
mod.evaluate_model(n_splits=2)
results = mod.predict("CCN(CC)C(=O)C1=CC(=CC=C1)C")