Merge remote-tracking branch 'origin/develop' into enhancement/dataset

# Conflicts:
#	epdb/models.py
#	tests/test_enviformer.py
#	tests/test_model.py

.env.prod.example

@@ -16,3 +16,5 @@ POSTGRES_PORT=
 # MAIL
 EMAIL_HOST_USER=
 EMAIL_HOST_PASSWORD=
+# MATOMO
+MATOMO_SITE_ID

envipath/settings.py

@@ -357,3 +357,6 @@ if MS_ENTRA_ENABLED:
     MS_ENTRA_AUTHORITY = f"https://login.microsoftonline.com/{MS_ENTRA_TENANT_ID}"
     MS_ENTRA_REDIRECT_URI = os.environ["MS_REDIRECT_URI"]
     MS_ENTRA_SCOPES = os.environ.get("MS_SCOPES", "").split(",")
+
+# Site ID 10 -> beta.envipath.org
+MATOMO_SITE_ID = os.environ.get("MATOMO_SITE_ID", "10")

epdb/admin.py

@@ -7,6 +7,7 @@ from .models import (
     GroupPackagePermission,
     Package,
     MLRelativeReasoning,
+    EnviFormer,
     Compound,
     CompoundStructure,
     SimpleAmbitRule,
@@ -19,11 +20,12 @@ from .models import (
     Setting,
     ExternalDatabase,
     ExternalIdentifier,
+    JobLog,
 )
 
 
 class UserAdmin(admin.ModelAdmin):
-    pass
+    list_display = ["username", "email", "is_active"]
 
 
 class UserPackagePermissionAdmin(admin.ModelAdmin):
@@ -38,8 +40,14 @@ class GroupPackagePermissionAdmin(admin.ModelAdmin):
     pass
 
 
+class JobLogAdmin(admin.ModelAdmin):
+    pass
+
+
 class EPAdmin(admin.ModelAdmin):
     search_fields = ["name", "description"]
+    list_display = ["name", "url", "created"]
+    ordering = ["-created"]
 
 
 class PackageAdmin(EPAdmin):
@@ -50,6 +58,10 @@ class MLRelativeReasoningAdmin(EPAdmin):
     pass
 
 
+class EnviFormerAdmin(EPAdmin):
+    pass
+
+
 class CompoundAdmin(EPAdmin):
     pass
 
@@ -102,8 +114,10 @@ admin.site.register(User, UserAdmin)
 admin.site.register(UserPackagePermission, UserPackagePermissionAdmin)
 admin.site.register(Group, GroupAdmin)
 admin.site.register(GroupPackagePermission, GroupPackagePermissionAdmin)
+admin.site.register(JobLog, JobLogAdmin)
 admin.site.register(Package, PackageAdmin)
 admin.site.register(MLRelativeReasoning, MLRelativeReasoningAdmin)
+admin.site.register(EnviFormer, EnviFormerAdmin)
 admin.site.register(Compound, CompoundAdmin)
 admin.site.register(CompoundStructure, CompoundStructureAdmin)
 admin.site.register(SimpleAmbitRule, SimpleAmbitRuleAdmin)

epdb/management/commands/create_ml_models.py

@@ -7,10 +7,11 @@ from epdb.models import MLRelativeReasoning, EnviFormer, Package
 
 class Command(BaseCommand):
     """This command can be run with
-    `python manage.py create_ml_models [model_names] -d [data_packages] OPTIONAL: -e [eval_packages]`
-    For example, to train both EnviFormer and MLRelativeReasoning on BBD and SOIL and evaluate them on SLUDGE
-    the below command would be used:
-    `python manage.py create_ml_models enviformer mlrr -d bbd soil -e sludge
+    `python manage.py create_ml_models [model_names] -d [data_packages] FOR MLRR ONLY: -r [rule_packages]
+                                        OPTIONAL: -e [eval_packages] -t threshold`
+    For example, to train both EnviFormer and MLRelativeReasoning on BBD and SOIL and evaluate them on SLUDGE with a
+    threshold of 0.6, the below command would be used:
+    `python manage.py create_ml_models enviformer mlrr -d bbd soil -e sludge -t 0.6
     """
 
     def add_arguments(self, parser):
@@ -34,6 +35,13 @@ class Command(BaseCommand):
             help="Rule Packages mandatory for MLRR",
             default=[],
         )
+        parser.add_argument(
+            "-t",
+            "--threshold",
+            type=float,
+            help="Model prediction threshold",
+            default=0.5,
+        )
 
     @transaction.atomic
     def handle(self, *args, **options):
@@ -67,7 +75,11 @@ class Command(BaseCommand):
             return packages
 
         # Iteratively create models in options["model_names"]
-        print(f"Creating models: {options['model_names']}")
+        print(f"Creating models: {options['model_names']}\n"
+              f"Data packages: {options['data_packages']}\n"
+              f"Rule Packages (only for MLRR): {options['rule_packages']}\n"
+              f"Eval Packages: {options['eval_packages']}\n"
+              f"Threshold: {options['threshold']:.2f}")
         data_packages = decode_packages(options["data_packages"])
         eval_packages = decode_packages(options["eval_packages"])
         rule_packages = decode_packages(options["rule_packages"])
@@ -78,9 +90,10 @@ class Command(BaseCommand):
                     pack,
                     data_packages=data_packages,
                     eval_packages=eval_packages,
-                    threshold=0.5,
-                    name="EnviFormer - T0.5",
-                    description="EnviFormer transformer",
+                    threshold=options['threshold'],
+                    name=f"EnviFormer - {', '.join(options['data_packages'])} - T{options['threshold']:.2f}",
+                    description=f"EnviFormer transformer trained on {options['data_packages']} "
+                                f"evaluated on {options['eval_packages']}.",
                 )
             elif model_name == "mlrr":
                 model = MLRelativeReasoning.create(
@@ -88,9 +101,10 @@ class Command(BaseCommand):
                     rule_packages=rule_packages,
                     data_packages=data_packages,
                     eval_packages=eval_packages,
-                    threshold=0.5,
-                    name="ECC - BBD - T0.5",
-                    description="ML Relative Reasoning",
+                    threshold=options['threshold'],
+                    name=f"ECC - {', '.join(options['data_packages'])} - T{options['threshold']:.2f}",
+                    description=f"ML Relative Reasoning trained on {options['data_packages']} with rules from "
+                                f"{options['rule_packages']} and evaluated on {options['eval_packages']}.",
                 )
             else:
                 raise ValueError(f"Cannot create model of type {model_name}, unknown model type")
@@ -100,6 +114,6 @@ class Command(BaseCommand):
             print(f"Training {model_name}")
             model.build_model()
             print(f"Evaluating {model_name}")
-            model.evaluate_model()
+            model.evaluate_model(False, eval_packages=eval_packages)
             print(f"Saving {model_name}")
             model.save()

epdb/management/commands/update_job_logs.py

@@ -0,0 +1,38 @@
+from datetime import date, timedelta
+
+from django.core.management.base import BaseCommand
+from django.db import transaction
+
+from epdb.models import JobLog
+
+
+class Command(BaseCommand):
+    def add_arguments(self, parser):
+        parser.add_argument(
+            "--cleanup",
+            type=int,
+            default=None,
+            help="Remove all logs older than this number of days. Default is None, which does not remove any logs.",
+        )
+
+    @transaction.atomic
+    def handle(self, *args, **options):
+        if options["cleanup"] is not None:
+            cleanup_dt = date.today() - timedelta(days=options["cleanup"])
+            print(JobLog.objects.filter(created__lt=cleanup_dt).delete())
+
+        logs = JobLog.objects.filter(status="INITIAL")
+        print(f"Found {logs.count()} logs to update")
+        updated = 0
+        for log in logs:
+            res = log.check_for_update()
+            if res:
+                updated += 1
+
+        print(f"Updated {updated} logs")
+
+        from django.db.models import Count
+
+        qs = JobLog.objects.values("status").annotate(total=Count("status"))
+        for r in qs:
+            print(r["status"], r["total"])

epdb/migrations/0009_joblog.py

@@ -0,0 +1,66 @@
+# Generated by Django 5.2.7 on 2025-10-27 09:39
+
+import django.db.models.deletion
+import django.utils.timezone
+import model_utils.fields
+from django.conf import settings
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+    dependencies = [
+        ("epdb", "0008_enzymelink"),
+    ]
+
+    operations = [
+        migrations.CreateModel(
+            name="JobLog",
+            fields=[
+                (
+                    "id",
+                    models.BigAutoField(
+                        auto_created=True, primary_key=True, serialize=False, verbose_name="ID"
+                    ),
+                ),
+                (
+                    "created",
+                    model_utils.fields.AutoCreatedField(
+                        default=django.utils.timezone.now, editable=False, verbose_name="created"
+                    ),
+                ),
+                (
+                    "modified",
+                    model_utils.fields.AutoLastModifiedField(
+                        default=django.utils.timezone.now, editable=False, verbose_name="modified"
+                    ),
+                ),
+                ("task_id", models.UUIDField(unique=True)),
+                ("job_name", models.TextField()),
+                (
+                    "status",
+                    models.CharField(
+                        choices=[
+                            ("INITIAL", "Initial"),
+                            ("SUCCESS", "Success"),
+                            ("FAILURE", "Failure"),
+                            ("REVOKED", "Revoked"),
+                            ("IGNORED", "Ignored"),
+                        ],
+                        default="INITIAL",
+                        max_length=20,
+                    ),
+                ),
+                ("done_at", models.DateTimeField(blank=True, default=None, null=True)),
+                ("task_result", models.TextField(blank=True, default=None, null=True)),
+                (
+                    "user",
+                    models.ForeignKey(
+                        on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL
+                    ),
+                ),
+            ],
+            options={
+                "abstract": False,
+            },
+        ),
+    ]

epdb/models.py

@@ -2226,10 +2226,18 @@ class PackageBasedModel(EPModel):
         self.model_status = self.BUILT_NOT_EVALUATED
         self.save()
 
-    def evaluate_model(self, **kwargs):
+    def evaluate_model(self, multigen: bool, eval_packages: List["Package"] = None, **kwargs):
         if self.model_status != self.BUILT_NOT_EVALUATED:
             raise ValueError(f"Can't evaluate a model in state {self.model_status}!")
 
+        if multigen:
+            self.multigen_eval = multigen
+            self.save()
+
+        if eval_packages is not None:
+            for p in eval_packages:
+                self.eval_packages.add(p)
+
         self.model_status = self.EVALUATING
         self.save()
 
@@ -2526,7 +2534,6 @@ class RuleBasedRelativeReasoning(PackageBasedModel):
         package: "Package",
         rule_packages: List["Package"],
         data_packages: List["Package"],
-        eval_packages: List["Package"],
         threshold: float = 0.5,
         min_count: int = 10,
         max_count: int = 0,
@@ -2575,10 +2582,6 @@ class RuleBasedRelativeReasoning(PackageBasedModel):
             for p in rule_packages:
                 rbrr.data_packages.add(p)
 
-        if eval_packages:
-            for p in eval_packages:
-                rbrr.eval_packages.add(p)
-
         rbrr.save()
 
         return rbrr
@@ -2633,7 +2636,6 @@ class MLRelativeReasoning(PackageBasedModel):
         package: "Package",
         rule_packages: List["Package"],
         data_packages: List["Package"],
-        eval_packages: List["Package"],
         threshold: float = 0.5,
         name: "str" = None,
         description: str = None,
@@ -2673,10 +2675,6 @@ class MLRelativeReasoning(PackageBasedModel):
             for p in rule_packages:
                 mlrr.data_packages.add(p)
 
-        if eval_packages:
-            for p in eval_packages:
-                mlrr.eval_packages.add(p)
-
         if build_app_domain:
             ad = ApplicabilityDomain.create(
                 mlrr,
@@ -2953,7 +2951,6 @@ class EnviFormer(PackageBasedModel):
     def create(
         package: "Package",
         data_packages: List["Package"],
-        eval_packages: List["Package"],
         threshold: float = 0.5,
         name: "str" = None,
         description: str = None,
@@ -2986,10 +2983,6 @@ class EnviFormer(PackageBasedModel):
         for p in data_packages:
             mod.data_packages.add(p)
 
-        if eval_packages:
-            for p in eval_packages:
-                mod.eval_packages.add(p)
-
         # if build_app_domain:
         #     ad = ApplicabilityDomain.create(mod, app_domain_num_neighbours, app_domain_reliability_threshold,
         #                                     app_domain_local_compatibility_threshold)
@@ -3082,10 +3075,18 @@ class EnviFormer(PackageBasedModel):
         args = {"clz": "EnviFormer"}
         return args
 
-    def evaluate_model(self, **kwargs):
+    def evaluate_model(self, multigen: bool, eval_packages: List["Package"] = None, **kwargs):
         if self.model_status != self.BUILT_NOT_EVALUATED:
             raise ValueError(f"Can't evaluate a model in state {self.model_status}!")
 
+        if multigen:
+            self.multigen_eval = multigen
+            self.save()
+
+        if eval_packages is not None:
+            for p in eval_packages:
+                self.eval_packages.add(p)
+
         self.model_status = self.EVALUATING
         self.save()
 
@@ -3226,7 +3227,7 @@ class EnviFormer(PackageBasedModel):
 
             ds = self.load_dataset()
             n_splits = kwargs.get("n_splits", 20)
-            shuff = ShuffleSplit(n_splits=n_splits, test_size=0.25, random_state=42)
+            shuff = ShuffleSplit(n_splits=n_splits, test_size=0.1, random_state=42)
 
             # Single gen eval is done in one loop of train then evaluate rather than storing all n_splits trained models
             # this helps reduce the memory footprint.
@@ -3294,7 +3295,7 @@ class EnviFormer(PackageBasedModel):
                 # Compute splits of the collected pathway and evaluate. Like single gen we train and evaluate in each
                 # iteration instead of storing all trained models.
                 for split_id, (train, test) in enumerate(
-                    ShuffleSplit(n_splits=n_splits, test_size=0.25, random_state=42).split(pathways)
+                    ShuffleSplit(n_splits=n_splits, test_size=0.1, random_state=42).split(pathways)
                 ):
                     train_pathways = [pathways[i] for i in train]
                     test_pathways = [pathways[i] for i in test]
@@ -3577,3 +3578,53 @@ class Setting(EnviPathModel):
             self.public = True
         self.global_default = True
         self.save()
+
+
+class JobLogStatus(models.TextChoices):
+    INITIAL = "INITIAL", "Initial"
+    SUCCESS = "SUCCESS", "Success"
+    FAILURE = "FAILURE", "Failure"
+    REVOKED = "REVOKED", "Revoked"
+    IGNORED = "IGNORED", "Ignored"
+
+
+class JobLog(TimeStampedModel):
+    user = models.ForeignKey("epdb.User", models.CASCADE)
+    task_id = models.UUIDField(unique=True)
+    job_name = models.TextField(null=False, blank=False)
+    status = models.CharField(
+        max_length=20,
+        choices=JobLogStatus.choices,
+        default=JobLogStatus.INITIAL,
+    )
+
+    done_at = models.DateTimeField(null=True, blank=True, default=None)
+    task_result = models.TextField(null=True, blank=True, default=None)
+
+    def check_for_update(self):
+        async_res = self.get_result()
+        new_status = async_res.state
+
+        TERMINAL_STATES = [
+            "SUCCESS",
+            "FAILURE",
+            "REVOKED",
+            "IGNORED",
+        ]
+
+        if new_status != self.status and new_status in TERMINAL_STATES:
+            self.status = new_status
+            self.done_at = async_res.date_done
+
+            if new_status == "SUCCESS":
+                self.task_result = async_res.result
+
+            self.save()
+
+            return True
+        return False
+
+    def get_result(self):
+        from celery.result import AsyncResult
+
+        return AsyncResult(str(self.task_id))

epdb/tasks.py

@@ -1,10 +1,15 @@
+import csv
+import io
 import logging
-from typing import Optional
-from celery.utils.functional import LRUCache
-from celery import shared_task
-from epdb.models import Pathway, Node, EPModel, Setting
-from epdb.logic import SPathway
+from datetime import datetime
+from typing import Any, Callable, List, Optional
+from uuid import uuid4
 
+from celery import shared_task
+from celery.utils.functional import LRUCache
+
+from epdb.logic import SPathway
+from epdb.models import EPModel, JobLog, Node, Package, Pathway, Rule, Setting, User, Edge
 
 logger = logging.getLogger(__name__)
 ML_CACHE = LRUCache(3)  # Cache the three most recent ML models to reduce load times.
@@ -16,6 +21,40 @@ def get_ml_model(model_pk: int):
     return ML_CACHE[model_pk]
 
 
+def dispatch_eager(user: "User", job: Callable, *args, **kwargs):
+    try:
+        x = job(*args, **kwargs)
+        log = JobLog()
+        log.user = user
+        log.task_id = uuid4()
+        log.job_name = job.__name__
+        log.status = "SUCCESS"
+        log.done_at = datetime.now()
+        log.task_result = str(x) if x else None
+        log.save()
+
+        return x
+    except Exception as e:
+        logger.exception(e)
+        raise e
+
+
+def dispatch(user: "User", job: Callable, *args, **kwargs):
+    try:
+        x = job.delay(*args, **kwargs)
+        log = JobLog()
+        log.user = user
+        log.task_id = x.task_id
+        log.job_name = job.__name__
+        log.status = "INITIAL"
+        log.save()
+
+        return x.result
+    except Exception as e:
+        logger.exception(e)
+        raise e
+
+
 @shared_task(queue="background")
 def mul(a, b):
     return a * b
@@ -33,17 +72,55 @@ def send_registration_mail(user_pk: int):
     pass
 
 
-@shared_task(queue="model")
-def build_model(model_pk: int):
+@shared_task(bind=True, queue="model")
+def build_model(self, model_pk: int):
     mod = EPModel.objects.get(id=model_pk)
+
+    if JobLog.objects.filter(task_id=self.request.id).exists():
+        JobLog.objects.filter(task_id=self.request.id).update(status="RUNNING", task_result=mod.url)
+
+    try:
         mod.build_dataset()
         mod.build_model()
+    except Exception as e:
+        if JobLog.objects.filter(task_id=self.request.id).exists():
+            JobLog.objects.filter(task_id=self.request.id).update(
+                status="FAILED", task_result=mod.url
+            )
+
+        raise e
+
+    if JobLog.objects.filter(task_id=self.request.id).exists():
+        JobLog.objects.filter(task_id=self.request.id).update(status="SUCCESS", task_result=mod.url)
+
+    return mod.url
 
 
-@shared_task(queue="model")
-def evaluate_model(model_pk: int):
+@shared_task(bind=True, queue="model")
+def evaluate_model(self, model_pk: int, multigen: bool, package_pks: Optional[list] = None):
+    packages = None
+
+    if package_pks:
+        packages = Package.objects.filter(pk__in=package_pks)
+
     mod = EPModel.objects.get(id=model_pk)
-    mod.evaluate_model()
+    if JobLog.objects.filter(task_id=self.request.id).exists():
+        JobLog.objects.filter(task_id=self.request.id).update(status="RUNNING", task_result=mod.url)
+
+    try:
+        mod.evaluate_model(multigen, eval_packages=packages)
+    except Exception as e:
+        if JobLog.objects.filter(task_id=self.request.id).exists():
+            JobLog.objects.filter(task_id=self.request.id).update(
+                status="FAILED", task_result=mod.url
+            )
+
+        raise e
+
+    if JobLog.objects.filter(task_id=self.request.id).exists():
+        JobLog.objects.filter(task_id=self.request.id).update(status="SUCCESS", task_result=mod.url)
+
+    return mod.url
 
 
 @shared_task(queue="model")
@@ -52,9 +129,13 @@ def retrain(model_pk: int):
     mod.retrain()
 
 
-@shared_task(queue="predict")
+@shared_task(bind=True, queue="predict")
 def predict(
-    pw_pk: int, pred_setting_pk: int, limit: Optional[int] = None, node_pk: Optional[int] = None
+    self,
+    pw_pk: int,
+    pred_setting_pk: int,
+    limit: Optional[int] = None,
+    node_pk: Optional[int] = None,
 ) -> Pathway:
     pw = Pathway.objects.get(id=pw_pk)
     setting = Setting.objects.get(id=pred_setting_pk)
@@ -65,6 +146,9 @@ def predict(
     pw.kv.update(**{"status": "running"})
     pw.save()
 
+    if JobLog.objects.filter(task_id=self.request.id).exists():
+        JobLog.objects.filter(task_id=self.request.id).update(status="RUNNING", task_result=pw.url)
+
     try:
         # regular prediction
         if limit is not None:
@@ -89,7 +173,111 @@ def predict(
     except Exception as e:
         pw.kv.update({"status": "failed"})
         pw.save()
+
+        if JobLog.objects.filter(task_id=self.request.id).exists():
+            JobLog.objects.filter(task_id=self.request.id).update(
+                status="FAILED", task_result=pw.url
+            )
+
         raise e
 
     pw.kv.update(**{"status": "completed"})
     pw.save()
+
+    if JobLog.objects.filter(task_id=self.request.id).exists():
+        JobLog.objects.filter(task_id=self.request.id).update(status="SUCCESS", task_result=pw.url)
+
+    return pw.url
+
+
+@shared_task(bind=True, queue="background")
+def identify_missing_rules(
+    self,
+    pw_pks: List[int],
+    rule_package_pk: int,
+):
+    from utilities.misc import PathwayUtils
+
+    rules = Package.objects.get(pk=rule_package_pk).get_applicable_rules()
+
+    rows: List[Any] = []
+    header = [
+        "Package Name",
+        "Pathway Name",
+        "Educt Name",
+        "Educt SMILES",
+        "Reaction Name",
+        "Reaction SMIRKS",
+        "Triggered Rules",
+        "Reactant SMARTS",
+        "Product SMARTS",
+        "Product Names",
+        "Product SMILES",
+    ]
+
+    rows.append(header)
+
+    for pw in Pathway.objects.filter(pk__in=pw_pks):
+        pu = PathwayUtils(pw)
+
+        missing_rules = pu.find_missing_rules(rules)
+
+        package_name = pw.package.name
+        pathway_name = pw.name
+
+        for edge_url, rule_chain in missing_rules.items():
+            row: List[Any] = [package_name, pathway_name]
+            edge = Edge.objects.get(url=edge_url)
+            educts = edge.start_nodes.all()
+
+            for educt in educts:
+                row.append(educt.default_node_label.name)
+                row.append(educt.default_node_label.smiles)
+
+            row.append(edge.edge_label.name)
+            row.append(edge.edge_label.smirks())
+
+            rule_names = []
+            reactant_smarts = []
+            product_smarts = []
+
+            for r in rule_chain:
+                r = Rule.objects.get(url=r[0])
+                rule_names.append(r.name)
+
+                rs = r.reactants_smarts
+                if isinstance(rs, set):
+                    rs = list(rs)
+
+                ps = r.products_smarts
+                if isinstance(ps, set):
+                    ps = list(ps)
+
+                reactant_smarts.append(rs)
+                product_smarts.append(ps)
+
+            row.append(rule_names)
+            row.append(reactant_smarts)
+            row.append(product_smarts)
+
+            products = edge.end_nodes.all()
+            product_names = []
+            product_smiles = []
+
+            for product in products:
+                product_names.append(product.default_node_label.name)
+                product_smiles.append(product.default_node_label.smiles)
+
+            row.append(product_names)
+            row.append(product_smiles)
+
+            rows.append(row)
+
+    buffer = io.StringIO()
+
+    writer = csv.writer(buffer)
+    writer.writerows(rows)
+
+    buffer.seek(0)
+
+    return buffer.getvalue()

epdb/templatetags/envipytags.py

@@ -1,8 +1,21 @@
 from django import template
+from pydantic import AnyHttpUrl, ValidationError
+from pydantic.type_adapter import TypeAdapter
 
 register = template.Library()
 
+url_adapter = TypeAdapter(AnyHttpUrl)
+
 
 @register.filter
 def classname(obj):
     return obj.__class__.__name__
+
+
+@register.filter
+def is_url(value):
+    try:
+        url_adapter.validate_python(value)
+        return True
+    except ValidationError:
+        return False

epdb/urls.py

@@ -190,6 +190,7 @@ urlpatterns = [
     re_path(r"^indigo/dearomatize$", v.dearomatize, name="indigo_dearomatize"),
     re_path(r"^indigo/layout$", v.layout, name="indigo_layout"),
     re_path(r"^depict$", v.depict, name="depict"),
+    re_path(r"^jobs", v.jobs, name="jobs"),
     # OAuth Stuff
     path("o/userinfo/", v.userinfo, name="oauth_userinfo"),
 ]

epdb/views.py

@@ -47,6 +47,7 @@ from .models import (
     ExternalDatabase,
     ExternalIdentifier,
     EnzymeLink,
+    JobLog,
 )
 
 logger = logging.getLogger(__name__)
@@ -236,6 +237,7 @@ def get_base_context(request, for_user=None) -> Dict[str, Any]:
             "enabled_features": s.FLAGS,
             "debug": s.DEBUG,
             "external_databases": ExternalDatabase.get_databases(),
+            "site_id": s.MATOMO_SITE_ID,
         },
     }
 
@@ -754,8 +756,8 @@ def package_models(request, package_uuid):
         context["unreviewed_objects"] = unreviewed_model_qs
 
         context["model_types"] = {
-            "ML Relative Reasoning": "ml-relative-reasoning",
-            "Rule Based Relative Reasoning": "rule-based-relative-reasoning",
+            "ML Relative Reasoning": "mlrr",
+            "Rule Based Relative Reasoning": "rbrr",
         }
 
         if s.FLAGS.get("ENVIFORMER", False):
@@ -775,48 +777,40 @@ def package_models(request, package_uuid):
 
         model_type = request.POST.get("model-type")
 
-        if model_type == "enviformer":
-            threshold = float(request.POST.get(f"{model_type}-threshold", 0.5))
-
-            mod = EnviFormer.create(current_package, name, description, threshold)
-
-        elif model_type == "ml-relative-reasoning" or model_type == "rule-based-relative-reasoning":
         # Generic fields for ML and Rule Based
-            rule_packages = request.POST.getlist("package-based-relative-reasoning-rule-packages")
-            data_packages = request.POST.getlist("package-based-relative-reasoning-data-packages")
-            eval_packages = request.POST.getlist(
-                "package-based-relative-reasoning-evaluation-packages", []
-            )
+        rule_packages = request.POST.getlist("model-rule-packages")
+        data_packages = request.POST.getlist("model-data-packages")
 
         # Generic params
         params = {
             "package": current_package,
             "name": name,
             "description": description,
-                "rule_packages": [
-                    PackageManager.get_package_by_url(current_user, p) for p in rule_packages
-                ],
             "data_packages": [
                 PackageManager.get_package_by_url(current_user, p) for p in data_packages
             ],
-                "eval_packages": [
-                    PackageManager.get_package_by_url(current_user, p) for p in eval_packages
-                ],
         }
 
-            if model_type == "ml-relative-reasoning":
+        if model_type == "enviformer":
+            threshold = float(request.POST.get("model-threshold", 0.5))
+            params["threshold"] = threshold
+
+            mod = EnviFormer.create(**params)
+        elif model_type == "mlrr":
             # ML Specific
-                threshold = float(request.POST.get(f"{model_type}-threshold", 0.5))
+            threshold = float(request.POST.get("model-threshold", 0.5))
             # TODO handle additional fingerprinter
-                # fingerprinter = request.POST.get(f"{model_type}-fingerprinter")
+            # fingerprinter = request.POST.get("model-fingerprinter")
+
+            params["rule_packages"] = [
+                PackageManager.get_package_by_url(current_user, p) for p in rule_packages
+            ]
 
             # App Domain related parameters
             build_ad = request.POST.get("build-app-domain", False) == "on"
             num_neighbors = request.POST.get("num-neighbors", 5)
             reliability_threshold = request.POST.get("reliability-threshold", 0.5)
-                local_compatibility_threshold = request.POST.get(
-                    "local-compatibility-threshold", 0.5
-                )
+            local_compatibility_threshold = request.POST.get("local-compatibility-threshold", 0.5)
 
             params["threshold"] = threshold
             # params['fingerprinter'] = fingerprinter
@@ -826,18 +820,24 @@ def package_models(request, package_uuid):
             params["app_domain_local_compatibility_threshold"] = local_compatibility_threshold
 
             mod = MLRelativeReasoning.create(**params)
-            else:
+        elif model_type == "rbrr":
+            params["rule_packages"] = [
+                PackageManager.get_package_by_url(current_user, p) for p in rule_packages
+            ]
+
             mod = RuleBasedRelativeReasoning.create(**params)
-
-            from .tasks import build_model
-
-            build_model.delay(mod.pk)
+        elif s.FLAGS.get("PLUGINS", False) and model_type in s.CLASSIFIER_PLUGINS.values():
+            pass
         else:
             return error(
                 request, "Invalid model type.", f'Model type "{model_type}" is not supported."'
             )
-        return redirect(mod.url)
 
+        from .tasks import dispatch, build_model
+
+        dispatch(current_user, build_model, mod.pk)
+
+        return redirect(mod.url)
     else:
         return HttpResponseNotAllowed(["GET", "POST"])
 
@@ -865,6 +865,10 @@ def package_model(request, package_uuid, model_uuid):
                 return JsonResponse({"error": f'"{smiles}" is not a valid SMILES'}, status=400)
 
             if classify:
+                from epdb.tasks import dispatch_eager, predict_simple
+
+                res = dispatch_eager(current_user, predict_simple, current_model.pk, stand_smiles)
+
                 pred_res = current_model.predict(stand_smiles)
                 res = []
 
@@ -909,9 +913,25 @@ def package_model(request, package_uuid, model_uuid):
                 current_model.delete()
                 return redirect(current_package.url + "/model")
             elif hidden == "evaluate":
-                from .tasks import evaluate_model
+                from .tasks import dispatch, evaluate_model
+
+                eval_type = request.POST.get("model-evaluation-type")
+
+                if eval_type not in ["sg", "mg"]:
+                    return error(
+                        request,
+                        "Invalid evaluation type",
+                        f'Evaluation type "{eval_type}" is not supported. Only "sg" and "mg" are supported.',
+                    )
+
+                multigen = eval_type == "mg"
+
+                eval_packages = request.POST.getlist("model-evaluation-packages")
+                eval_package_ids = [
+                    PackageManager.get_package_by_url(current_user, p).id for p in eval_packages
+                ]
+                dispatch(current_user, evaluate_model, current_model.pk, multigen, eval_package_ids)
 
-                evaluate_model.delay(current_model.pk)
                 return redirect(current_model.url)
             else:
                 return HttpResponseBadRequest()
@@ -1809,9 +1829,9 @@ def package_pathways(request, package_uuid):
             pw.setting = prediction_setting
             pw.save()
 
-            from .tasks import predict
+            from .tasks import dispatch, predict
 
-            predict.delay(pw.pk, prediction_setting.pk, limit=limit)
+            dispatch(current_user, predict, pw.pk, prediction_setting.pk, limit=limit)
 
         return redirect(pw.url)
 
@@ -1847,6 +1867,25 @@ def package_pathway(request, package_uuid, pathway_uuid):
 
             return response
 
+        if (
+            request.GET.get("identify-missing-rules", False) == "true"
+            and request.GET.get("rule-package") is not None
+        ):
+            from .tasks import dispatch_eager, identify_missing_rules
+
+            rule_package = PackageManager.get_package_by_url(
+                current_user, request.GET.get("rule-package")
+            )
+            res = dispatch_eager(
+                current_user, identify_missing_rules, [current_pathway.pk], rule_package.pk
+            )
+
+            filename = f"{current_pathway.name.replace(' ', '_')}_{current_pathway.uuid}.csv"
+            response = HttpResponse(res, content_type="text/csv")
+            response["Content-Disposition"] = f'attachment; filename="{filename}"'
+
+            return response
+
         # Pathway d3_json() relies on a lot of related objects (Nodes, Structures, Edges, Reaction, Rules, ...)
         # we will again fetch the current pathway identified by this url, but this time together with nearly all
         # related objects
@@ -1930,10 +1969,16 @@ def package_pathway(request, package_uuid, pathway_uuid):
         if node_url:
             n = current_pathway.get_node(node_url)
 
-            from .tasks import predict
+            from .tasks import dispatch, predict
+
+            dispatch(
+                current_user,
+                predict,
+                current_pathway.pk,
+                current_pathway.setting.pk,
+                node_pk=n.pk,
+            )
 
-            # Dont delay?
-            predict(current_pathway.pk, current_pathway.setting.pk, node_pk=n.pk)
             return JsonResponse({"success": current_pathway.url})
 
         return HttpResponseBadRequest()
@@ -2705,6 +2750,24 @@ def setting(request, setting_uuid):
     pass
 
 
+def jobs(request):
+    current_user = _anonymous_or_real(request)
+    context = get_base_context(request)
+
+    if request.method == "GET":
+        context["object_type"] = "joblog"
+        context["breadcrumbs"] = [
+            {"Home": s.SERVER_URL},
+            {"Jobs": s.SERVER_URL + "/jobs"},
+        ]
+        if current_user.is_superuser:
+            context["jobs"] = JobLog.objects.all().order_by("-created")
+        else:
+            context["jobs"] = JobLog.objects.filter(user=current_user).order_by("-created")
+
+        return render(request, "collections/joblog.html", context)
+
+
 ###########
 # KETCHER #
 ###########

templates/actions/objects/pathway.html

@@ -22,6 +22,10 @@
             <i class="glyphicon glyphicon-floppy-save"></i> Download Pathway as Image</a>
     </li>
 {% if meta.can_edit %}
+    <li>
+        <a class="button" data-toggle="modal" data-target="#identify_missing_rules_modal">
+            <i class="glyphicon glyphicon-question-sign"></i> Identify Missing Rules</a>
+    </li>
     <li role="separator" class="divider"></li>
     <li>
         <a class="button" data-toggle="modal" data-target="#edit_pathway_modal">

templates/collections/joblog.html

@@ -0,0 +1,71 @@
+{% extends "framework.html" %}
+{% load static %}
+{% load envipytags %}
+{% block content %}
+
+    <div class="panel-group" id="reviewListAccordion">
+        <div class="panel panel-default">
+            <div class="panel-heading" id="headingPanel" style="font-size:2rem;height: 46px">
+                Jobs
+            </div>
+            <div class="panel-body">
+                <p>
+                    Job Logs Desc
+                </p>
+
+            </div>
+
+            <div class="panel panel-default panel-heading list-group-item" style="background-color:silver">
+                <h4 class="panel-title">
+                    <a id="job-accordion-link" data-toggle="collapse" data-parent="#job-accordion" href="#jobs">
+                        Jobs
+                    </a>
+                </h4>
+            </div>
+            <div id="jobs"
+                 class="panel-collapse collapse in">
+                <div class="panel-body list-group-item" id="job-content">
+                    <table class="table table-bordered table-hover">
+                        <tr style="background-color: rgba(0, 0, 0, 0.08);">
+                            <th scope="col">ID</th>
+                            <th scope="col">Name</th>
+                            <th scope="col">Status</th>
+                            <th scope="col">Queued</th>
+                            <th scope="col">Done</th>
+                            <th scope="col">Result</th>
+                        </tr>
+                        <tbody>
+                        {% for job in jobs %}
+                            <tr>
+                                <td>{{ job.task_id }}</td>
+                                <td>{{ job.job_name }}</td>
+                                <td>{{ job.status }}</td>
+                                <td>{{ job.created }}</td>
+                                <td>{{ job.done_at }}</td>
+                                {% if job.task_result and job.task_result|is_url == True %}
+                                    <td><a href="{{ job.task_result }}">Result</a></td>
+                                {% elif job.task_result %}
+                                    <td>{{ job.task_result|slice:"40" }}...</td>
+                                {% else %}
+                                    <td>Empty</td>
+                                {% endif %}
+                            </tr>
+                        {% endfor %}
+                        </tbody>
+                    </table>
+                </div>
+            </div>
+
+            <!-- Unreviewable objects such as User / Group / Setting -->
+            <ul class='list-group'>
+                {% for obj in objects %}
+                    {% if object_type == 'user' %}
+                        <a class="list-group-item" href="{{ obj.url }}">{{ obj.username }}</a>
+                    {% else %}
+                        <a class="list-group-item" href="{{ obj.url }}">{{ obj.name }}</a>
+                    {% endif %}
+                {% endfor %}
+            </ul>
+        </div>
+    </div>
+{% endblock content %}

templates/framework.html

@@ -56,7 +56,7 @@
             (function () {
                 var u = "//matomo.envipath.com/";
                 _paq.push(['setTrackerUrl', u + 'matomo.php']);
-                _paq.push(['setSiteId', '10']);
+                _paq.push(['setSiteId', '{{ meta.site_id }}']);
                 var d = document, g = d.createElement('script'), s = d.getElementsByTagName('script')[0];
                 g.async = true;
                 g.src = u + 'matomo.js';

templates/modals/collections/new_model_modal.html

@@ -18,13 +18,19 @@
                         prediction. You just need to set a name and the packages
                         you want the object to be based on. There are multiple types of models available.
                         For additional information have a look at our
-                        <a target="_blank" href="https://wiki.envipath.org/index.php/relative-reasoning" role="button">wiki &gt;&gt;</a>
+                        <a target="_blank" href="https://wiki.envipath.org/index.php/relative-reasoning" role="button">wiki
+                            &gt;&gt;</a>
                     </div>
+                    <!-- Name -->
                     <label for="model-name">Name</label>
                     <input id="model-name" name="model-name" class="form-control" placeholder="Name"/>
+
+                    <!-- Description -->
                     <label for="model-description">Description</label>
                     <input id="model-description" name="model-description" class="form-control"
                            placeholder="Description"/>
+
+                    <!-- Model Type -->
                     <label for="model-type">Model Type</label>
                     <select id="model-type" name="model-type" class="form-control" data-width='100%'>
                         <option disabled selected>Select Model Type</option>
@@ -32,12 +38,12 @@
                             <option value="{{ v }}">{{ k }}</option>
                         {% endfor %}
                     </select>
-                    <!-- ML and Rule Based Based Form-->
-                    <div id="package-based-relative-reasoning-specific-form">
+
                     <!-- Rule Packages -->
-                        <label for="package-based-relative-reasoning-rule-packages">Rule Packages</label>
-                        <select id="package-based-relative-reasoning-rule-packages" name="package-based-relative-reasoning-rule-packages"
-                                data-actions-box='true' class="form-control" multiple data-width='100%'>
+                    <div id="rule-packages" class="ep-model-param mlrr rbrr">
+                        <label for="model-rule-packages">Rule Packages</label>
+                        <select id="model-rule-packages" name="model-rule-packages" data-actions-box='true'
+                                class="form-control" multiple data-width='100%'>
                             <option disabled>Reviewed Packages</option>
                             {% for obj in meta.readable_packages %}
                                 {% if obj.reviewed %}
@@ -52,10 +58,13 @@
                                 {% endif %}
                             {% endfor %}
                         </select>
+                    </div>
+
                     <!-- Data Packages -->
-                        <label for="package-based-relative-reasoning-data-packages" >Data Packages</label>
-                        <select id="package-based-relative-reasoning-data-packages" name="package-based-relative-reasoning-data-packages"
-                                data-actions-box='true' class="form-control" multiple data-width='100%'>
+                    <div id="data-packages" class="ep-model-param mlrr rbrr enviformer">
+                        <label for="model-data-packages">Data Packages</label>
+                        <select id="model-data-packages" name="model-data-packages" data-actions-box='true'
+                                class="form-control" multiple data-width='100%'>
                             <option disabled>Reviewed Packages</option>
                             {% for obj in meta.readable_packages %}
                                 {% if obj.reviewed %}
@@ -70,32 +79,31 @@
                                 {% endif %}
                             {% endfor %}
                         </select>
+                    </div>
 
-                        <div id="ml-relative-reasoning-specific-form">
                     <!-- Fingerprinter -->
-                            <label for="ml-relative-reasoning-fingerprinter">Fingerprinter</label>
-                            <select id="ml-relative-reasoning-fingerprinter" name="ml-relative-reasoning-fingerprinter"
-                                    class="form-control">
+                    <div id="fingerprinter" class="ep-model-param mlrr">
+                        <label for="model-fingerprinter">Fingerprinter</label>
+                        <select id="model-fingerprinter" name="model-fingerprinter" data-actions-box='true'
+                                class="form-control" multiple data-width='100%'>
                             <option value="MACCS" selected>MACCS Fingerprinter</option>
-                            </select>
                             {% if meta.enabled_features.PLUGINS and additional_descriptors %}
-                                <!-- Property Plugins go here -->
-                                <label for="ml-relative-reasoning-additional-fingerprinter">Additional Fingerprinter /
-                                    Descriptors</label>
-                                <select id="ml-relative-reasoning-additional-fingerprinter"
-                                        name="ml-relative-reasoning-additional-fingerprinter" class="form-control">
                                 <option disabled selected>Select Additional Fingerprinter / Descriptor</option>
                                 {% for k, v in additional_descriptors.items %}
                                     <option value="{{ v }}">{{ k }}</option>
                                 {% endfor %}
-                                </select>
                             {% endif %}
-
-                            <label for="ml-relative-reasoning-threshold">Threshold</label>
-                            <input type="number" min="0" max="1" step="0.05" value="0.5"
-                                   id="ml-relative-reasoning-threshold"
-                                   name="ml-relative-reasoning-threshold" class="form-control">
+                        </select>
                     </div>
+
+                    <!-- Threshold -->
+                    <div id="threshold" class="ep-model-param mlrr enviformer">
+                        <label for="model-threshold">Threshold</label>
+                        <input type="number" min="0" max="1" step="0.05" value="0.5" id="model-threshold"
+                               name="model-threshold" class="form-control">
+                    </div>
+
+                    <div id="appdomain" class="ep-model-param mlrr">
                         {% if meta.enabled_features.APPLICABILITY_DOMAIN %}
                             <!-- Build AD? -->
                             <div class="checkbox">
@@ -107,11 +115,13 @@
                             <div id="ad-params" style="display:none">
                                 <!-- Num Neighbors -->
                                 <label for="num-neighbors">Number of Neighbors</label>
-                            <input id="num-neighbors" name="num-neighbors" type="number" class="form-control" value="5"
+                                <input id="num-neighbors" name="num-neighbors" type="number" class="form-control"
+                                       value="5"
                                        step="1" min="0" max="10">
                                 <!-- Local Compatibility -->
                                 <label for="local-compatibility-threshold">Local Compatibility Threshold</label>
-                            <input id="local-compatibility-threshold" name="local-compatibility-threshold" type="number"
+                                <input id="local-compatibility-threshold" name="local-compatibility-threshold"
+                                       type="number"
                                        class="form-control" value="0.5" step="0.01" min="0" max="1">
                                 <!-- Reliability -->
                                 <label for="reliability-threshold">Reliability Threshold</label>
@@ -120,12 +130,6 @@
                             </div>
                         {% endif %}
                     </div>
-                    <!-- EnviFormer-->
-                    <div id="enviformer-specific-form">
-                        <label for="enviformer-threshold">Threshold</label>
-                        <input type="number" min="0" max="1" step="0.05" value="0.5" id="enviformer-threshold"
-                               name="enviformer-threshold" class="form-control">
-                    </div>
                 </form>
             </div>
             <div class="modal-footer">
@@ -138,19 +142,22 @@
 
 <script>
     $(function () {
+        // Built in Model Types
+        var nativeModelTypes = [
+            "mlrr",
+            "rbrr",
+            "enviformer",
+        ]
+
         // Initially hide all "specific" forms
-    $("div[id$='-specific-form']").each( function() {
+        $(".ep-model-param").each(function () {
             $(this).hide();
         });
 
         $('#model-type').selectpicker();
-    $("#ml-relative-reasoning-fingerprinter").selectpicker();
-    $("#package-based-relative-reasoning-rule-packages").selectpicker();
-    $("#package-based-relative-reasoning-data-packages").selectpicker();
-    $("#package-based-relative-reasoning-evaluation-packages").selectpicker();
-    if ($('#ml-relative-reasoning-additional-fingerprinter').length > 0) {
-        $("#ml-relative-reasoning-additional-fingerprinter").selectpicker();
-    }
+        $("#model-fingerprinter").selectpicker();
+        $("#model-rule-packages").selectpicker();
+        $("#model-data-packages").selectpicker();
 
         $("#build-app-domain").change(function () {
             if ($(this).is(":checked")) {
@@ -162,18 +169,12 @@ $(function() {
 
         // On change hide all and show only selected
         $("#model-type").change(function () {
-        $("div[id$='-specific-form']").each( function() {
-            $(this).hide();
-        });
-        val = $('option:selected', this).val();
-
-        if (val === 'ml-relative-reasoning' || val === 'rule-based-relative-reasoning') {
-            $("#package-based-relative-reasoning-specific-form").show();
-            if (val === 'ml-relative-reasoning') {
-                 $("#ml-relative-reasoning-specific-form").show();
-            }
+            $('.ep-model-param').hide();
+            var modelType = $('#model-type').val();
+            if (nativeModelTypes.indexOf(modelType) !== -1) {
+                $('.' + modelType).show();
             } else {
-            $("#" + val + "-specific-form").show();
+                // do nothing
             }
         });
 
@@ -183,7 +184,4 @@ $(function() {
         });
 
     });
-
-
-
 </script>

templates/modals/objects/evaluate_model_modal.html

@@ -17,10 +17,10 @@
                         For evaluation, you need to select the packages you want to use.
                         While the model is evaluating, you can use the model for predictions.
                     </div>
-                    <!-- Evaluation -->
-                    <label for="relative-reasoning-evaluation-packages">Evaluation Packages</label>
-                    <select id="relative-reasoning-evaluation-packages" name=relative-reasoning-evaluation-packages"
-                            data-actions-box='true' class="form-control" multiple data-width='100%'>
+                    <!-- Evaluation Packages -->
+                    <label for="model-evaluation-packages">Evaluation Packages</label>
+                    <select id="model-evaluation-packages" name="model-evaluation-packages" data-actions-box='true'
+                            class="form-control" multiple data-width='100%'>
                         <option disabled>Reviewed Packages</option>
                         {% for obj in meta.readable_packages %}
                             {% if obj.reviewed %}
@@ -35,6 +35,15 @@
                             {% endif %}
                         {% endfor %}
                     </select>
+
+                    <!-- Eval Type -->
+                    <label for="model-evaluation-type">Evaluation Type</label>
+                    <select id="model-evaluation-type" name="model-evaluation-type" class="form-control">
+                        <option disabled selected>Select evaluation type</option>
+                        <option value="sg">Single Generation</option>
+                        <option value="mg">Multiple Generations</option>
+                    </select>
+
                     <input type="hidden" name="hidden" value="evaluate">
                 </form>
             </div>
@@ -50,7 +59,7 @@
 
     $(function () {
 
-        $("#relative-reasoning-evaluation-packages").selectpicker();
+        $("#model-evaluation-packages").selectpicker();
 
         $('#evaluate_model_form_submit').on('click', function (e) {
             e.preventDefault();

templates/modals/objects/identify_missing_rules_modal.html

@@ -0,0 +1,54 @@
+{% load static %}
+<!-- Identify Missing Rules -->
+<div id="identify_missing_rules_modal" class="modal" tabindex="-1">
+    <div class="modal-dialog">
+        <div class="modal-content">
+            <div class="modal-header">
+                <h3 class="modal-title">Identify Missing Rules</h3>
+                <button type="button" class="close" data-dismiss="modal" aria-label="Close">
+                    <span aria-hidden="true">&times;</span>
+                </button>
+            </div>
+            <div class="modal-body">
+                By clicking on Download we'll search the Pathway for Reactions that are not backed by
+                a Rule or which can be assembled by chaining two rules.
+                <form id="identify-missing-rules-modal-form" accept-charset="UTF-8" action="{{ pathway.url }}"
+                      data-remote="true" method="GET">
+                    <label for="rule-package">Select the Rule Package</label>
+                    <select id="rule-package" name="rule-package" data-actions-box='true' class="form-control"
+                            data-width='100%'>
+                        <option disabled>Reviewed Packages</option>
+                        {% for obj in meta.readable_packages %}
+                            {% if obj.reviewed %}
+                                <option value="{{ obj.url }}">{{ obj.name }}</option>
+                            {% endif %}
+                        {% endfor %}
+
+                        <option disabled>Unreviewed Packages</option>
+                        {% for obj in meta.readable_packages %}
+                            {% if not obj.reviewed %}
+                                <option value="{{ obj.url }}">{{ obj.name }}</option>
+                            {% endif %}
+                        {% endfor %}
+                    </select>
+                    <input type="hidden" name="identify-missing-rules" value="true"/>
+                </form>
+            </div>
+            <div class="modal-footer">
+                <button type="button" class="btn btn-secondary" data-dismiss="modal">Close</button>
+                <button type="button" class="btn btn-primary" id="identify-missing-rules-modal-submit">Download</button>
+            </div>
+        </div>
+    </div>
+</div>
+<script>
+    $(function () {
+
+        $('#identify-missing-rules-modal-submit').click(function (e) {
+            e.preventDefault();
+            $('#identify-missing-rules-modal-form').submit();
+            $('#identify_missing_rules_modal').modal('hide');
+        });
+
+    })
+</script>

templates/objects/model.html

@@ -117,7 +117,7 @@
                 <!-- End Predict Panel       -->
             {% endif %}
 
-            {% if model.app_domain %}
+            {% if model.ready_for_prediction and model.app_domain %}
                 <!-- App Domain -->
                 <div class="panel panel-default panel-heading list-group-item" style="background-color:silver">
                     <h4 class="panel-title">

templates/objects/pathway.html

@@ -83,6 +83,7 @@
         {% include "modals/objects/add_pathway_edge_modal.html" %}
         {% include "modals/objects/download_pathway_csv_modal.html" %}
         {% include "modals/objects/download_pathway_image_modal.html" %}
+        {% include "modals/objects/identify_missing_rules_modal.html" %}
         {% include "modals/objects/generic_copy_object_modal.html" %}
         {% include "modals/objects/edit_pathway_modal.html" %}
         {% include "modals/objects/generic_set_aliases_modal.html" %}

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(n_splits=2)
+                mod.evaluate_model(True, eval_packages_objs, n_splits=2)
 
                 mod.predict("CCN(CC)C(=O)C1=CC(=CC=C1)C")
 

tests/test_model.py

@@ -30,7 +30,6 @@ class ModelTest(TestCase):
                     self.package,
                     rule_package_objs,
                     data_package_objs,
-                    eval_packages_objs,
                     threshold=threshold,
                     name="ECC - BBD - 0.5",
                     description="Created MLRelativeReasoning in Testcase",
@@ -38,9 +37,7 @@ class ModelTest(TestCase):
 
                 mod.build_dataset()
                 mod.build_model()
-                mod.multigen_eval = True
-                mod.save()
-                mod.evaluate_model(n_splits=2)
+                mod.evaluate_model(True, eval_packages_objs, n_splits=2)
 
                 results = mod.predict("CCN(CC)C(=O)C1=CC(=CC=C1)C")
 

tests/views/test_model_views.py

@@ -6,7 +6,7 @@ from epdb.logic import UserManager
 from epdb.models import Package, User
 
 
-@override_settings(MODEL_DIR=s.FIXTURE_DIRS[0] / "models")
+@override_settings(MODEL_DIR=s.FIXTURE_DIRS[0] / "models", CELERY_TASK_ALWAYS_EAGER=True)
 class PathwayViewTest(TestCase):
     fixtures = ["test_fixtures_incl_model.jsonl.gz"]
 

tests/views/test_pathway_views.py

@@ -6,7 +6,7 @@ from epdb.logic import UserManager, PackageManager
 from epdb.models import Pathway, Edge
 
 
-@override_settings(MODEL_DIR=s.FIXTURE_DIRS[0] / "models")
+@override_settings(MODEL_DIR=s.FIXTURE_DIRS[0] / "models", CELERY_TASK_ALWAYS_EAGER=True)
 class PathwayViewTest(TestCase):
     fixtures = ["test_fixtures_incl_model.jsonl.gz"]
 

utilities/chem.py

@@ -192,7 +192,7 @@ class FormatConverter(object):
         return smiles
 
     @staticmethod
-    def standardize(smiles, remove_stereo=False):
+    def standardize(smiles, remove_stereo=False, canonicalize_tautomers=False):
         # Taken from https://bitsilla.com/blog/2021/06/standardizing-a-molecule-using-rdkit/
         # follows the steps in
         # https://github.com/greglandrum/RSC_OpenScience_Standardization_202104/blob/main/MolStandardize%20pieces.ipynb
@@ -210,19 +210,21 @@ class FormatConverter(object):
         uncharger = (
             rdMolStandardize.Uncharger()
         )  # annoying, but necessary as no convenience method exists
-        uncharged_parent_clean_mol = uncharger.uncharge(parent_clean_mol)
+        res_mol = uncharger.uncharge(parent_clean_mol)
 
         # note that no attempt is made at reionization at this step
         # nor at ionization at some pH (rdkit has no pKa caculator)
         # the main aim to to represent all molecules from different sources
         # in a (single) standard way, for use in ML, catalogue, etc.
-        # te = rdMolStandardize.TautomerEnumerator()  # idem
-        # taut_uncharged_parent_clean_mol = te.Canonicalize(uncharged_parent_clean_mol)
 
         if remove_stereo:
-            Chem.RemoveStereochemistry(uncharged_parent_clean_mol)
+            Chem.RemoveStereochemistry(res_mol)
 
-        return Chem.MolToSmiles(uncharged_parent_clean_mol, kekuleSmiles=True)
+        if canonicalize_tautomers:
+            te = rdMolStandardize.TautomerEnumerator()  # idem
+            res_mol = te.Canonicalize(res_mol)
+
+        return Chem.MolToSmiles(res_mol, kekuleSmiles=True)
 
     @staticmethod
     def neutralize_smiles(smiles):
@@ -370,6 +372,76 @@ class FormatConverter(object):
 
         return parsed_smiles, errors
 
+    @staticmethod
+    def smiles_covered_by(
+        l_smiles: List[str],
+        r_smiles: List[str],
+        standardize: bool = True,
+        canonicalize_tautomers: bool = True,
+    ) -> bool:
+        """
+        Check if all SMILES in the left list are covered by (contained in) the right list.
+
+        This function performs a subset check to determine if every chemical structure
+        represented in l_smiles has a corresponding representation in r_smiles.
+
+        Args:
+            l_smiles (List[str]): List of SMILES strings to check for coverage.
+            r_smiles (List[str]): List of SMILES strings that should contain all l_smiles.
+            standardize (bool, optional): Whether to standardize SMILES before comparison.
+                Defaults to True. When True, applies FormatConverter.standardize() to
+                normalize representations for accurate comparison.
+            canonicalize_tautomers (bool, optional): Whether to canonicalize tautomers
+                Defaults to False. When True, applies rdMolStandardize.TautomerEnumerator().Canonicalize(res_mol)
+                to the compounds before comparison.
+        Returns:
+            bool: True if all SMILES in l_smiles are found in r_smiles (i.e., l_smiles
+                  is a subset of r_smiles), False otherwise.
+
+        Note:
+            - Comparison treats lists as sets, ignoring duplicates and order
+            - Failed standardization attempts are silently ignored (original SMILES used)
+            - This is a one-directional check: l_smiles ⊆ r_smiles
+            - For bidirectional equality, both directions must be checked separately
+
+        Example:
+            >>> FormatConverter.smiles_covered_by(["CCO", "CC"], ["CCO", "CC", "CCC"])
+            True
+            >>> FormatConverter.smiles_covered_by(["CCO", "CCCC"], ["CCO", "CC", "CCC"])
+            False
+        """
+
+        standardized_l_smiles = []
+
+        if standardize:
+            for smi in l_smiles:
+                try:
+                    smi = FormatConverter.standardize(
+                        smi, canonicalize_tautomers=canonicalize_tautomers
+                    )
+                except Exception:
+                    # :shrug:
+                    # logger.debug(f'Standardizing SMILES failed for {smi}')
+                    pass
+                standardized_l_smiles.append(smi)
+        else:
+            standardized_l_smiles = l_smiles
+
+        standardized_r_smiles = []
+        if standardize:
+            for smi in r_smiles:
+                try:
+                    smi = FormatConverter.standardize(smi)
+                except Exception:
+                    # :shrug:
+                    # logger.debug(f'Standardizing SMILES failed for {smi}')
+                    pass
+                standardized_r_smiles.append(smi)
+        else:
+            standardized_r_smiles = r_smiles
+
+        return len(set(standardized_l_smiles).difference(set(standardized_r_smiles))) == 0
+
 
 class Standardizer(ABC):
     def __init__(self, name):

utilities/misc.py

@@ -9,36 +9,37 @@ from collections import defaultdict
 from datetime import datetime
 from enum import Enum
 from types import NoneType
-from typing import Dict, Any, List
+from typing import Any, Dict, List
 
 from django.db import transaction
-from envipy_additional_information import Interval, EnviPyModel
-from envipy_additional_information import NAME_MAPPING
+from envipy_additional_information import NAME_MAPPING, EnviPyModel, Interval
 from pydantic import BaseModel, HttpUrl
 
 from epdb.models import (
-    Package,
     Compound,
     CompoundStructure,
-    SimpleRule,
+    Edge,
+    EnviFormer,
+    EPModel,
+    ExternalDatabase,
+    ExternalIdentifier,
+    License,
+    MLRelativeReasoning,
+    Node,
+    Package,
+    ParallelRule,
+    Pathway,
+    PluginModel,
+    Reaction,
+    Rule,
+    RuleBasedRelativeReasoning,
+    Scenario,
+    SequentialRule,
     SimpleAmbitRule,
     SimpleRDKitRule,
-    ParallelRule,
-    SequentialRule,
-    Reaction,
-    Pathway,
-    Node,
-    Edge,
-    Scenario,
-    EPModel,
-    MLRelativeReasoning,
-    RuleBasedRelativeReasoning,
-    EnviFormer,
-    PluginModel,
-    ExternalIdentifier,
-    ExternalDatabase,
-    License,
+    SimpleRule,
 )
+from utilities.chem import FormatConverter
 
 logger = logging.getLogger(__name__)
 
@@ -48,7 +49,7 @@ class HTMLGenerator:
 
     @staticmethod
     def generate_html(additional_information: "EnviPyModel", prefix="") -> str:
-        from typing import get_origin, get_args, Union
+        from typing import Union, get_args, get_origin
 
         if isinstance(additional_information, type):
             clz_name = additional_information.__name__
@@ -1171,3 +1172,89 @@ class PackageImporter:
                 url=identifier_data.get("url", ""),
                 is_primary=identifier_data.get("is_primary", False),
             )
+
+
+class PathwayUtils:
+    def __init__(self, pathway: "Pathway"):
+        self.pathway = pathway
+
+    @staticmethod
+    def _get_products(smiles: str, rules: List["Rule"]):
+        educt_rule_products: Dict[str, Dict[str, List[str]]] = defaultdict(
+            lambda: defaultdict(list)
+        )
+
+        for r in rules:
+            product_sets = r.apply(smiles)
+            for product_set in product_sets:
+                for product in product_set:
+                    educt_rule_products[smiles][r.url].append(product)
+
+        return educt_rule_products
+
+    def find_missing_rules(self, rules: List["Rule"]):
+        print(f"Processing {self.pathway.name}")
+        # compute products for each node / rule combination in the pathway
+        educt_rule_products = defaultdict(lambda: defaultdict(list))
+
+        for node in self.pathway.nodes:
+            educt_rule_products.update(**self._get_products(node.default_node_label.smiles, rules))
+
+        # loop through edges and determine reactions that can't be constructed by
+        # any of the rules or a combination of two rules in a chained fashion
+
+        res: Dict[str, List["Rule"]] = dict()
+
+        for edge in self.pathway.edges:
+            found = False
+            reaction = edge.edge_label
+
+            educts = [cs for cs in reaction.educts.all()]
+            products = [cs.smiles for cs in reaction.products.all()]
+            rule_chain = []
+
+            for educt in educts:
+                educt = educt.smiles
+                triggered_rules = list(educt_rule_products.get(educt, {}).keys())
+                for triggered_rule in triggered_rules:
+                    if rule_products := educt_rule_products[educt][triggered_rule]:
+                        # check if this rule covers the reaction
+                        if FormatConverter.smiles_covered_by(
+                            products, rule_products, standardize=True, canonicalize_tautomers=True
+                        ):
+                            found = True
+                        else:
+                            # Check if another prediction step would cover the reaction
+                            for product in rule_products:
+                                prod_rule_products = self._get_products(product, rules)
+                                prod_triggered_rules = list(
+                                    prod_rule_products.get(product, {}).keys()
+                                )
+                                for prod_triggered_rule in prod_triggered_rules:
+                                    if second_step_products := prod_rule_products[product][
+                                        prod_triggered_rule
+                                    ]:
+                                        if FormatConverter.smiles_covered_by(
+                                            products,
+                                            second_step_products,
+                                            standardize=True,
+                                            canonicalize_tautomers=True,
+                                        ):
+                                            rule_chain.append(
+                                                (
+                                                    triggered_rule,
+                                                    Rule.objects.get(url=triggered_rule).name,
+                                                )
+                                            )
+                                            rule_chain.append(
+                                                (
+                                                    prod_triggered_rule,
+                                                    Rule.objects.get(url=prod_triggered_rule).name,
+                                                )
+                                            )
+                                            res[edge.url] = rule_chain
+
+            if not found:
+                res[edge.url] = rule_chain
+
+        return res