[Feature] Basic logging of Jobs, Model Evaluation (#169)

Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: enviPath/enviPy#169

tests/test_enviformer.py

@@ -3,7 +3,7 @@ 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.models import User, EnviFormer, Package, Setting
 from epdb.tasks import predict_simple, predict
 
 
@@ -48,9 +48,7 @@ class EnviFormerTest(TestCase):
 
                 mod.build_dataset()
                 mod.build_model()
-                mod.multigen_eval = True
-                mod.save()
-                mod.evaluate_model()
+                mod.evaluate_model(True, eval_packages_objs)
 
                 mod.predict("CCN(CC)C(=O)C1=CC(=CC=C1)C")
 
@@ -75,11 +73,15 @@ class EnviFormerTest(TestCase):
 
                 # 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:]}")
+                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 _ 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))