forked from enviPath/enviPy
87 lines
3.5 KiB
Python
87 lines
3.5 KiB
Python
from collections import defaultdict
|
|
from datetime import datetime
|
|
from tempfile import TemporaryDirectory
|
|
from django.test import TestCase, tag
|
|
from epdb.logic import PackageManager
|
|
from epdb.models import User, EnviFormer, Package, Setting, Pathway
|
|
from epdb.tasks import predict_simple, predict
|
|
|
|
|
|
def measure_predict(mod, pathway_pk=None):
|
|
# Measure and return the prediction time
|
|
start = datetime.now()
|
|
if pathway_pk:
|
|
s = Setting()
|
|
s.model = mod
|
|
s.model_threshold = 0.2
|
|
s.max_depth = 4
|
|
s.max_nodes = 20
|
|
s.save()
|
|
pred_result = predict.delay(pathway_pk, s.pk, limit=s.max_depth)
|
|
else:
|
|
pred_result = predict_simple.delay(mod.pk, "C1=CC=C(CSCC2=CC=CC=C2)C=C1")
|
|
_ = pred_result.get()
|
|
return round((datetime.now() - start).total_seconds(), 2)
|
|
|
|
|
|
@tag("slow")
|
|
class EnviFormerTest(TestCase):
|
|
fixtures = ["test_fixtures.jsonl.gz"]
|
|
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
super(EnviFormerTest, cls).setUpClass()
|
|
cls.user = User.objects.get(username="anonymous")
|
|
cls.package = PackageManager.create_package(cls.user, "Anon Test Package", "No Desc")
|
|
cls.BBD_SUBSET = Package.objects.get(name="Fixtures")
|
|
|
|
def test_model_flow(self):
|
|
"""Test the full flow of EnviFormer, dataset build -> model finetune -> model evaluate -> model inference"""
|
|
with TemporaryDirectory() as tmpdir:
|
|
with self.settings(MODEL_DIR=tmpdir):
|
|
threshold = float(0.5)
|
|
data_package_objs = [self.BBD_SUBSET]
|
|
eval_packages_objs = [self.BBD_SUBSET]
|
|
mod = EnviFormer.create(
|
|
self.package, data_package_objs, eval_packages_objs, threshold=threshold
|
|
)
|
|
|
|
mod.build_dataset()
|
|
mod.build_model()
|
|
mod.multigen_eval = True
|
|
mod.save()
|
|
mod.evaluate_model(n_splits=2)
|
|
|
|
mod.predict("CCN(CC)C(=O)C1=CC(=CC=C1)C")
|
|
|
|
def test_predict_runtime(self):
|
|
with TemporaryDirectory() as tmpdir:
|
|
with self.settings(MODEL_DIR=tmpdir):
|
|
threshold = float(0.5)
|
|
data_package_objs = [self.BBD_SUBSET]
|
|
eval_packages_objs = [self.BBD_SUBSET]
|
|
mods = []
|
|
for _ in range(4):
|
|
mod = EnviFormer.create(
|
|
self.package, data_package_objs, eval_packages_objs, threshold=threshold
|
|
)
|
|
mod.build_dataset()
|
|
mod.build_model()
|
|
mods.append(mod)
|
|
|
|
# Test prediction time drops after first prediction
|
|
times = [measure_predict(mods[0]) for _ in range(5)]
|
|
print(f"First prediction took {times[0]} seconds, subsequent ones took {times[1:]}")
|
|
|
|
# Test pathway prediction
|
|
times = [measure_predict(mods[1], self.BBD_SUBSET.pathways[0].pk) for _ in range(5)]
|
|
print(f"First pathway prediction took {times[0]} seconds, subsequent ones took {times[1:]}")
|
|
|
|
# Test eviction by performing three prediction with every model, twice.
|
|
times = defaultdict(list)
|
|
for _ in range(2): # Eviction should cause the second iteration here to have to reload the models
|
|
for mod in mods:
|
|
for _ in range(3):
|
|
times[mod.pk].append(measure_predict(mod))
|
|
print(times)
|