forked from enviPath/enviPy
Merge remote-tracking branch 'origin/develop' into fix/xss
# Conflicts: # templates/modals/collections/new_model_modal.html
This commit is contained in:
Liam Brydon
@ -16,3 +16,5 @@ POSTGRES_PORT=
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# MAIL
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EMAIL_HOST_USER=
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EMAIL_HOST_PASSWORD=
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# MATOMO
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MATOMO_SITE_ID
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@ -137,6 +137,7 @@ USE_TZ = True
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DEFAULT_AUTO_FIELD = "django.db.models.BigAutoField"
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EMAIL_SUBJECT_PREFIX = "[enviPath] "
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if DEBUG:
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EMAIL_BACKEND = "django.core.mail.backends.console.EmailBackend"
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else:
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@ -146,6 +147,8 @@ else:
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EMAIL_HOST_USER = os.environ["EMAIL_HOST_USER"]
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EMAIL_HOST_PASSWORD = os.environ["EMAIL_HOST_PASSWORD"]
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EMAIL_PORT = 587
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DEFAULT_FROM_EMAIL = os.environ["DEFAULT_FROM_EMAIL"]
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SERVER_EMAIL = os.environ["SERVER_EMAIL"]
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AUTH_USER_MODEL = "epdb.User"
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ADMIN_APPROVAL_REQUIRED = os.environ.get("ADMIN_APPROVAL_REQUIRED", "False") == "True"
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@ -356,3 +359,6 @@ if MS_ENTRA_ENABLED:
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MS_ENTRA_AUTHORITY = f"https://login.microsoftonline.com/{MS_ENTRA_TENANT_ID}"
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MS_ENTRA_REDIRECT_URI = os.environ["MS_REDIRECT_URI"]
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MS_ENTRA_SCOPES = os.environ.get("MS_SCOPES", "").split(",")
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# Site ID 10 -> beta.envipath.org
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MATOMO_SITE_ID = os.environ.get("MATOMO_SITE_ID", "10")
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@ -7,6 +7,7 @@ from .models import (
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GroupPackagePermission,
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Package,
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MLRelativeReasoning,
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EnviFormer,
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Compound,
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CompoundStructure,
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SimpleAmbitRule,
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@ -19,11 +20,12 @@ from .models import (
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Setting,
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ExternalDatabase,
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ExternalIdentifier,
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JobLog,
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)
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class UserAdmin(admin.ModelAdmin):
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pass
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list_display = ["username", "email", "is_active"]
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class UserPackagePermissionAdmin(admin.ModelAdmin):
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@ -38,8 +40,14 @@ class GroupPackagePermissionAdmin(admin.ModelAdmin):
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pass
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class JobLogAdmin(admin.ModelAdmin):
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pass
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class EPAdmin(admin.ModelAdmin):
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search_fields = ["name", "description"]
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list_display = ["name", "url", "created"]
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ordering = ["-created"]
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class PackageAdmin(EPAdmin):
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@ -50,6 +58,10 @@ class MLRelativeReasoningAdmin(EPAdmin):
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pass
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class EnviFormerAdmin(EPAdmin):
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pass
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class CompoundAdmin(EPAdmin):
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pass
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@ -102,8 +114,10 @@ admin.site.register(User, UserAdmin)
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admin.site.register(UserPackagePermission, UserPackagePermissionAdmin)
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admin.site.register(Group, GroupAdmin)
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admin.site.register(GroupPackagePermission, GroupPackagePermissionAdmin)
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admin.site.register(JobLog, JobLogAdmin)
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admin.site.register(Package, PackageAdmin)
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admin.site.register(MLRelativeReasoning, MLRelativeReasoningAdmin)
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admin.site.register(EnviFormer, EnviFormerAdmin)
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admin.site.register(Compound, CompoundAdmin)
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admin.site.register(CompoundStructure, CompoundStructureAdmin)
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admin.site.register(SimpleAmbitRule, SimpleAmbitRuleAdmin)
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@ -27,6 +27,7 @@ from epdb.models import (
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Compound,
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Reaction,
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CompoundStructure,
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EnzymeLink,
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)
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from utilities.chem import FormatConverter
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from utilities.misc import PackageImporter, PackageExporter
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@ -626,6 +627,8 @@ class PackageManager(object):
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parent_mapping = {}
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# Mapping old scen_id to old_obj_id
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scen_mapping = defaultdict(list)
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# Enzymelink Mapping rule_id to enzymelink objects
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enzyme_mapping = defaultdict(list)
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# Store Scenarios
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for scenario in data["scenarios"]:
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@ -657,9 +660,7 @@ class PackageManager(object):
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# Broken eP Data
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if name == "initialmasssediment" and addinf_data == "missing data":
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continue
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# TODO Enzymes arent ready yet
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if name == "enzyme":
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if name == "columnheight" and addinf_data == "(2)-(2.5);(6)-(8)":
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continue
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try:
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@ -749,6 +750,9 @@ class PackageManager(object):
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for scen in rule["scenarios"]:
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scen_mapping[scen["id"]].append(r)
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for enzyme_link in rule.get("enzymeLinks", []):
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enzyme_mapping[r.uuid].append(enzyme_link)
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print("Par: ", len(par_rules))
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print("Seq: ", len(seq_rules))
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@ -766,6 +770,9 @@ class PackageManager(object):
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for scen in par_rule["scenarios"]:
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scen_mapping[scen["id"]].append(r)
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for enzyme_link in par_rule.get("enzymeLinks", []):
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enzyme_mapping[r.uuid].append(enzyme_link)
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for simple_rule in par_rule["simpleRules"]:
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if simple_rule["id"] in mapping:
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r.simple_rules.add(SimpleRule.objects.get(uuid=mapping[simple_rule["id"]]))
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@ -786,6 +793,9 @@ class PackageManager(object):
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for scen in seq_rule["scenarios"]:
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scen_mapping[scen["id"]].append(r)
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for enzyme_link in seq_rule.get("enzymeLinks", []):
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enzyme_mapping[r.uuid].append(enzyme_link)
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for i, simple_rule in enumerate(seq_rule["simpleRules"]):
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sro = SequentialRuleOrdering()
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sro.simple_rule = simple_rule
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@ -919,6 +929,39 @@ class PackageManager(object):
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print("Scenarios linked...")
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# Import Enzyme Links
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for rule_uuid, enzyme_links in enzyme_mapping.items():
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r = Rule.objects.get(uuid=rule_uuid)
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for enzyme in enzyme_links:
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e = EnzymeLink()
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e.uuid = UUID(enzyme["id"].split("/")[-1]) if keep_ids else uuid4()
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e.rule = r
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e.name = enzyme["name"]
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e.ec_number = enzyme["ecNumber"]
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e.classification_level = enzyme["classificationLevel"]
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e.linking_method = enzyme["linkingMethod"]
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e.save()
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for reaction in enzyme["reactionLinkEvidence"]:
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reaction = Reaction.objects.get(uuid=mapping[reaction["id"]])
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e.reaction_evidence.add(reaction)
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for edge in enzyme["edgeLinkEvidence"]:
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edge = Edge.objects.get(uuid=mapping[edge["id"]])
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e.reaction_evidence.add(edge)
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for evidence in enzyme["linkEvidence"]:
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matches = re.findall(r">(R[0-9]+)<", evidence["evidence"])
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if not matches or len(matches) != 1:
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logger.warning(f"Could not find reaction id in {evidence['evidence']}")
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continue
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e.add_kegg_reaction_id(matches[0])
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e.save()
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print("Enzyme links imported...")
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print("Import statistics:")
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print("Package {} stored".format(pack.url))
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print("Imported {} compounds".format(Compound.objects.filter(package=pack).count()))
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@ -7,10 +7,11 @@ from epdb.models import MLRelativeReasoning, EnviFormer, Package
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class Command(BaseCommand):
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"""This command can be run with
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`python manage.py create_ml_models [model_names] -d [data_packages] OPTIONAL: -e [eval_packages]`
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For example, to train both EnviFormer and MLRelativeReasoning on BBD and SOIL and evaluate them on SLUDGE
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the below command would be used:
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`python manage.py create_ml_models enviformer mlrr -d bbd soil -e sludge
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`python manage.py create_ml_models [model_names] -d [data_packages] FOR MLRR ONLY: -r [rule_packages]
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OPTIONAL: -e [eval_packages] -t threshold`
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For example, to train both EnviFormer and MLRelativeReasoning on BBD and SOIL and evaluate them on SLUDGE with a
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threshold of 0.6, the below command would be used:
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`python manage.py create_ml_models enviformer mlrr -d bbd soil -e sludge -t 0.6
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"""
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def add_arguments(self, parser):
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@ -34,6 +35,13 @@ class Command(BaseCommand):
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help="Rule Packages mandatory for MLRR",
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default=[],
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)
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parser.add_argument(
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"-t",
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"--threshold",
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type=float,
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help="Model prediction threshold",
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default=0.5,
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)
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@transaction.atomic
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def handle(self, *args, **options):
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@ -67,7 +75,11 @@ class Command(BaseCommand):
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return packages
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# Iteratively create models in options["model_names"]
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print(f"Creating models: {options['model_names']}")
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print(f"Creating models: {options['model_names']}\n"
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f"Data packages: {options['data_packages']}\n"
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f"Rule Packages (only for MLRR): {options['rule_packages']}\n"
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f"Eval Packages: {options['eval_packages']}\n"
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f"Threshold: {options['threshold']:.2f}")
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data_packages = decode_packages(options["data_packages"])
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eval_packages = decode_packages(options["eval_packages"])
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rule_packages = decode_packages(options["rule_packages"])
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@ -78,9 +90,10 @@ class Command(BaseCommand):
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pack,
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data_packages=data_packages,
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eval_packages=eval_packages,
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threshold=0.5,
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name="EnviFormer - T0.5",
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description="EnviFormer transformer",
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threshold=options['threshold'],
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name=f"EnviFormer - {', '.join(options['data_packages'])} - T{options['threshold']:.2f}",
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description=f"EnviFormer transformer trained on {options['data_packages']} "
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f"evaluated on {options['eval_packages']}.",
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)
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elif model_name == "mlrr":
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model = MLRelativeReasoning.create(
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@ -88,9 +101,10 @@ class Command(BaseCommand):
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rule_packages=rule_packages,
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data_packages=data_packages,
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eval_packages=eval_packages,
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threshold=0.5,
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name="ECC - BBD - T0.5",
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description="ML Relative Reasoning",
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threshold=options['threshold'],
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name=f"ECC - {', '.join(options['data_packages'])} - T{options['threshold']:.2f}",
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description=f"ML Relative Reasoning trained on {options['data_packages']} with rules from "
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f"{options['rule_packages']} and evaluated on {options['eval_packages']}.",
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)
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else:
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raise ValueError(f"Cannot create model of type {model_name}, unknown model type")
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@ -100,6 +114,6 @@ class Command(BaseCommand):
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print(f"Training {model_name}")
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model.build_model()
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print(f"Evaluating {model_name}")
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model.evaluate_model()
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model.evaluate_model(False, eval_packages=eval_packages)
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print(f"Saving {model_name}")
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model.save()
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59
epdb/management/commands/dump_enviformer.py
Normal file
59
epdb/management/commands/dump_enviformer.py
Normal file
@ -0,0 +1,59 @@
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import json
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import os
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import tarfile
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from tempfile import TemporaryDirectory
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from django.conf import settings as s
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from django.core.management.base import BaseCommand
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||||
from django.db import transaction
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from epdb.models import EnviFormer
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||||
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|
||||
class Command(BaseCommand):
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def add_arguments(self, parser):
|
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parser.add_argument(
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"model",
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type=str,
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||||
help="Model UUID of the Model to Dump",
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||||
)
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parser.add_argument("--output", type=str)
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||||
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def package_dict_and_folder(self, dict_data, folder_path, output_path):
|
||||
with TemporaryDirectory() as tmpdir:
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||||
dict_filename = os.path.join(tmpdir, "data.json")
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||||
|
||||
with open(dict_filename, "w", encoding="utf-8") as f:
|
||||
json.dump(dict_data, f, indent=2)
|
||||
|
||||
with tarfile.open(output_path, "w:gz") as tar:
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tar.add(dict_filename, arcname="data.json")
|
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tar.add(folder_path, arcname=os.path.basename(folder_path))
|
||||
|
||||
os.remove(dict_filename)
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||||
|
||||
@transaction.atomic
|
||||
def handle(self, *args, **options):
|
||||
output = options["output"]
|
||||
|
||||
if os.path.exists(output):
|
||||
raise ValueError(f"Output file {output} already exists")
|
||||
|
||||
model = EnviFormer.objects.get(uuid=options["model"])
|
||||
|
||||
data = {
|
||||
"uuid": str(model.uuid),
|
||||
"name": model.name,
|
||||
"description": model.description,
|
||||
"kv": model.kv,
|
||||
"data_packages_uuids": [str(p.uuid) for p in model.data_packages.all()],
|
||||
"eval_packages_uuids": [str(p.uuid) for p in model.data_packages.all()],
|
||||
"threshold": model.threshold,
|
||||
"eval_results": model.eval_results,
|
||||
"multigen_eval": model.multigen_eval,
|
||||
"model_status": model.model_status,
|
||||
}
|
||||
|
||||
model_folder = os.path.join(s.MODEL_DIR, "enviformer", str(model.uuid))
|
||||
|
||||
self.package_dict_and_folder(data, model_folder, output)
|
||||
81
epdb/management/commands/load_enviformer.py
Normal file
81
epdb/management/commands/load_enviformer.py
Normal file
@ -0,0 +1,81 @@
|
||||
import json
|
||||
import os
|
||||
import shutil
|
||||
import tarfile
|
||||
from tempfile import TemporaryDirectory
|
||||
|
||||
from django.conf import settings as s
|
||||
from django.core.management.base import BaseCommand
|
||||
from django.db import transaction
|
||||
|
||||
from epdb.models import EnviFormer, Package
|
||||
|
||||
|
||||
class Command(BaseCommand):
|
||||
def add_arguments(self, parser):
|
||||
parser.add_argument(
|
||||
"input",
|
||||
type=str,
|
||||
help=".tar.gz file containing the Model dump.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"package",
|
||||
type=str,
|
||||
help="Package UUID where the Model should be loaded to.",
|
||||
)
|
||||
|
||||
def read_dict_and_folder_from_archive(self, archive_path, extract_to="extracted_folder"):
|
||||
with tarfile.open(archive_path, "r:gz") as tar:
|
||||
tar.extractall(extract_to)
|
||||
|
||||
dict_path = os.path.join(extract_to, "data.json")
|
||||
|
||||
if not os.path.exists(dict_path):
|
||||
raise FileNotFoundError("data.json not found in the archive.")
|
||||
|
||||
with open(dict_path, "r", encoding="utf-8") as f:
|
||||
data_dict = json.load(f)
|
||||
|
||||
extracted_items = os.listdir(extract_to)
|
||||
folders = [item for item in extracted_items if item != "data.json"]
|
||||
folder_path = os.path.join(extract_to, folders[0]) if folders else None
|
||||
|
||||
return data_dict, folder_path
|
||||
|
||||
@transaction.atomic
|
||||
def handle(self, *args, **options):
|
||||
if not os.path.exists(options["input"]):
|
||||
raise ValueError(f"Input file {options['input']} does not exist.")
|
||||
|
||||
target_package = Package.objects.get(uuid=options["package"])
|
||||
|
||||
with TemporaryDirectory() as tmpdir:
|
||||
data, folder = self.read_dict_and_folder_from_archive(options["input"], tmpdir)
|
||||
|
||||
model = EnviFormer()
|
||||
model.package = target_package
|
||||
# model.uuid = data["uuid"]
|
||||
model.name = data["name"]
|
||||
model.description = data["description"]
|
||||
model.kv = data["kv"]
|
||||
model.threshold = float(data["threshold"])
|
||||
model.eval_results = data["eval_results"]
|
||||
model.multigen_eval = data["multigen_eval"]
|
||||
model.model_status = data["model_status"]
|
||||
model.save()
|
||||
|
||||
for p_uuid in data["data_packages_uuids"]:
|
||||
p = Package.objects.get(uuid=p_uuid)
|
||||
model.data_packages.add(p)
|
||||
|
||||
for p_uuid in data["eval_packages_uuids"]:
|
||||
p = Package.objects.get(uuid=p_uuid)
|
||||
model.eval_packages.add(p)
|
||||
|
||||
target_folder = os.path.join(s.MODEL_DIR, "enviformer", str(model.uuid))
|
||||
|
||||
shutil.copytree(folder, target_folder)
|
||||
os.rename(
|
||||
os.path.join(s.MODEL_DIR, "enviformer", str(model.uuid), f"{data['uuid']}.ckpt"),
|
||||
os.path.join(s.MODEL_DIR, "enviformer", str(model.uuid), f"{model.uuid}.ckpt"),
|
||||
)
|
||||
@ -1,8 +1,10 @@
|
||||
from django.apps import apps
|
||||
from django.core.management.base import BaseCommand
|
||||
|
||||
from django.db.models import F, Value
|
||||
from django.db.models.functions import Replace
|
||||
from django.db.models import F, Value, TextField, JSONField
|
||||
from django.db.models.functions import Replace, Cast
|
||||
|
||||
from epdb.models import EnviPathModel
|
||||
|
||||
|
||||
class Command(BaseCommand):
|
||||
@ -41,6 +43,7 @@ class Command(BaseCommand):
|
||||
"RuleBasedRelativeReasoning",
|
||||
"EnviFormer",
|
||||
"ApplicabilityDomain",
|
||||
"EnzymeLink",
|
||||
]
|
||||
for model in MODELS:
|
||||
obj_cls = apps.get_model("epdb", model)
|
||||
@ -48,3 +51,14 @@ class Command(BaseCommand):
|
||||
obj_cls.objects.update(
|
||||
url=Replace(F("url"), Value(options["old"]), Value(options["new"]))
|
||||
)
|
||||
if issubclass(obj_cls, EnviPathModel):
|
||||
obj_cls.objects.update(
|
||||
kv=Cast(
|
||||
Replace(
|
||||
Cast(F("kv"), output_field=TextField()),
|
||||
Value(options["old"]),
|
||||
Value(options["new"]),
|
||||
),
|
||||
output_field=JSONField(),
|
||||
)
|
||||
)
|
||||
|
||||
38
epdb/management/commands/update_job_logs.py
Normal file
38
epdb/management/commands/update_job_logs.py
Normal file
@ -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"])
|
||||
64
epdb/migrations/0008_enzymelink.py
Normal file
64
epdb/migrations/0008_enzymelink.py
Normal file
@ -0,0 +1,64 @@
|
||||
# Generated by Django 5.2.7 on 2025-10-10 06:58
|
||||
|
||||
import django.db.models.deletion
|
||||
import django.utils.timezone
|
||||
import model_utils.fields
|
||||
import uuid
|
||||
from django.db import migrations, models
|
||||
|
||||
|
||||
class Migration(migrations.Migration):
|
||||
dependencies = [
|
||||
("epdb", "0007_alter_enviformer_options_enviformer_app_domain_and_more"),
|
||||
]
|
||||
|
||||
operations = [
|
||||
migrations.CreateModel(
|
||||
name="EnzymeLink",
|
||||
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"
|
||||
),
|
||||
),
|
||||
(
|
||||
"uuid",
|
||||
models.UUIDField(
|
||||
default=uuid.uuid4, unique=True, verbose_name="UUID of this object"
|
||||
),
|
||||
),
|
||||
("name", models.TextField(default="no name", verbose_name="Name")),
|
||||
(
|
||||
"description",
|
||||
models.TextField(default="no description", verbose_name="Descriptions"),
|
||||
),
|
||||
("url", models.TextField(null=True, unique=True, verbose_name="URL")),
|
||||
("kv", models.JSONField(blank=True, default=dict, null=True)),
|
||||
("ec_number", models.TextField(verbose_name="EC Number")),
|
||||
("classification_level", models.IntegerField(verbose_name="Classification Level")),
|
||||
("linking_method", models.TextField(verbose_name="Linking Method")),
|
||||
("edge_evidence", models.ManyToManyField(to="epdb.edge")),
|
||||
("reaction_evidence", models.ManyToManyField(to="epdb.reaction")),
|
||||
(
|
||||
"rule",
|
||||
models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to="epdb.rule"),
|
||||
),
|
||||
],
|
||||
options={
|
||||
"abstract": False,
|
||||
},
|
||||
),
|
||||
]
|
||||
66
epdb/migrations/0009_joblog.py
Normal file
66
epdb/migrations/0009_joblog.py
Normal file
@ -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,
|
||||
},
|
||||
),
|
||||
]
|
||||
164
epdb/models.py
164
epdb/models.py
@ -311,7 +311,7 @@ class ExternalDatabase(TimeStampedModel):
|
||||
},
|
||||
{
|
||||
"database": ExternalDatabase.objects.get(name="ChEBI"),
|
||||
"placeholder": "ChEBI ID without prefix e.g. 12345",
|
||||
"placeholder": "ChEBI ID without prefix e.g. 10576",
|
||||
},
|
||||
],
|
||||
"structure": [
|
||||
@ -329,7 +329,7 @@ class ExternalDatabase(TimeStampedModel):
|
||||
},
|
||||
{
|
||||
"database": ExternalDatabase.objects.get(name="ChEBI"),
|
||||
"placeholder": "ChEBI ID without prefix e.g. 12345",
|
||||
"placeholder": "ChEBI ID without prefix e.g. 10576",
|
||||
},
|
||||
],
|
||||
"reaction": [
|
||||
@ -343,7 +343,7 @@ class ExternalDatabase(TimeStampedModel):
|
||||
},
|
||||
{
|
||||
"database": ExternalDatabase.objects.get(name="UniProt"),
|
||||
"placeholder": "Query ID for UniPro e.g. rhea:12345",
|
||||
"placeholder": "Query ID for UniProt e.g. rhea:12345",
|
||||
},
|
||||
],
|
||||
}
|
||||
@ -478,7 +478,7 @@ class ChemicalIdentifierMixin(ExternalIdentifierMixin):
|
||||
return self.add_external_identifier("CAS", cas_number)
|
||||
|
||||
def get_pubchem_identifiers(self):
|
||||
return self.get_external_identifier("PubChem Compound") or self.get_external_identifier(
|
||||
return self.get_external_identifier("PubChem Compound") | self.get_external_identifier(
|
||||
"PubChem Substance"
|
||||
)
|
||||
|
||||
@ -495,6 +495,20 @@ class ChemicalIdentifierMixin(ExternalIdentifierMixin):
|
||||
return self.get_external_identifier("CAS")
|
||||
|
||||
|
||||
class KEGGIdentifierMixin(ExternalIdentifierMixin):
|
||||
@property
|
||||
def kegg_reaction_links(self):
|
||||
return self.get_external_identifier("KEGG Reaction")
|
||||
|
||||
def add_kegg_reaction_id(self, kegg_id):
|
||||
return self.add_external_identifier(
|
||||
"KEGG Reaction", kegg_id, f"https://www.genome.jp/entry/{kegg_id}"
|
||||
)
|
||||
|
||||
class Meta:
|
||||
abstract = True
|
||||
|
||||
|
||||
class ReactionIdentifierMixin(ExternalIdentifierMixin):
|
||||
class Meta:
|
||||
abstract = True
|
||||
@ -1014,6 +1028,26 @@ class CompoundStructure(EnviPathModel, AliasMixin, ScenarioMixin, ChemicalIdenti
|
||||
return self.compound.default_structure == self
|
||||
|
||||
|
||||
class EnzymeLink(EnviPathModel, KEGGIdentifierMixin):
|
||||
rule = models.ForeignKey("Rule", on_delete=models.CASCADE, db_index=True)
|
||||
ec_number = models.TextField(blank=False, null=False, verbose_name="EC Number")
|
||||
classification_level = models.IntegerField(
|
||||
blank=False, null=False, verbose_name="Classification Level"
|
||||
)
|
||||
linking_method = models.TextField(blank=False, null=False, verbose_name="Linking Method")
|
||||
|
||||
reaction_evidence = models.ManyToManyField("epdb.Reaction")
|
||||
edge_evidence = models.ManyToManyField("epdb.Edge")
|
||||
|
||||
external_identifiers = GenericRelation("ExternalIdentifier")
|
||||
|
||||
def _url(self):
|
||||
return "{}/enzymelink/{}".format(self.rule.url, self.uuid)
|
||||
|
||||
def get_group(self) -> str:
|
||||
return ".".join(self.ec_number.split(".")[:3]) + ".-"
|
||||
|
||||
|
||||
class Rule(PolymorphicModel, EnviPathModel, AliasMixin, ScenarioMixin):
|
||||
package = models.ForeignKey(
|
||||
"epdb.Package", verbose_name="Package", on_delete=models.CASCADE, db_index=True
|
||||
@ -1095,6 +1129,18 @@ class Rule(PolymorphicModel, EnviPathModel, AliasMixin, ScenarioMixin):
|
||||
|
||||
return new_rule
|
||||
|
||||
def enzymelinks(self):
|
||||
return self.enzymelink_set.all()
|
||||
|
||||
def get_grouped_enzymelinks(self):
|
||||
res = defaultdict(list)
|
||||
|
||||
for el in self.enzymelinks():
|
||||
key = ".".join(el.ec_number.split(".")[:3]) + ".-"
|
||||
res[key].append(el)
|
||||
|
||||
return dict(res)
|
||||
|
||||
|
||||
class SimpleRule(Rule):
|
||||
pass
|
||||
@ -1436,6 +1482,16 @@ class Reaction(EnviPathModel, AliasMixin, ScenarioMixin, ReactionIdentifierMixin
|
||||
id__in=Edge.objects.filter(edge_label=self).values("pathway_id")
|
||||
).order_by("name")
|
||||
|
||||
def get_related_enzymes(self):
|
||||
res = []
|
||||
edges = Edge.objects.filter(edge_label=self)
|
||||
for e in edges:
|
||||
for scen in e.scenarios.all():
|
||||
for ai in scen.additional_information.keys():
|
||||
if ai == "Enzyme":
|
||||
res.extend(scen.additional_information[ai])
|
||||
return res
|
||||
|
||||
|
||||
class Pathway(EnviPathModel, AliasMixin, ScenarioMixin):
|
||||
package = models.ForeignKey(
|
||||
@ -2172,10 +2228,18 @@ class PackageBasedModel(EPModel):
|
||||
self.model_status = self.BUILT_NOT_EVALUATED
|
||||
self.save()
|
||||
|
||||
def evaluate_model(self):
|
||||
def evaluate_model(self, multigen: bool, eval_packages: List["Package"] = None):
|
||||
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()
|
||||
|
||||
@ -2472,7 +2536,6 @@ 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,
|
||||
@ -2521,10 +2584,6 @@ 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
|
||||
@ -2579,7 +2638,6 @@ 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,
|
||||
@ -2619,10 +2677,6 @@ 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,
|
||||
@ -2942,7 +2996,6 @@ 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,
|
||||
@ -2975,10 +3028,6 @@ 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)
|
||||
@ -2992,7 +3041,8 @@ class EnviFormer(PackageBasedModel):
|
||||
from enviformer import load
|
||||
|
||||
ckpt = os.path.join(s.MODEL_DIR, "enviformer", str(self.uuid), f"{self.uuid}.ckpt")
|
||||
return load(device=s.ENVIFORMER_DEVICE, ckpt_path=ckpt)
|
||||
mod = load(device=s.ENVIFORMER_DEVICE, ckpt_path=ckpt)
|
||||
return mod
|
||||
|
||||
def predict(self, smiles) -> List["PredictionResult"]:
|
||||
return self.predict_batch([smiles])[0]
|
||||
@ -3006,8 +3056,12 @@ class EnviFormer(PackageBasedModel):
|
||||
for smiles in smiles_list
|
||||
]
|
||||
logger.info(f"Submitting {canon_smiles} to {self.name}")
|
||||
start = datetime.now()
|
||||
products_list = self.model.predict_batch(canon_smiles)
|
||||
logger.info(f"Got results {products_list}")
|
||||
end = datetime.now()
|
||||
logger.info(
|
||||
f"Prediction took {(end - start).total_seconds():.2f} seconds. Got results {products_list}"
|
||||
)
|
||||
|
||||
results = []
|
||||
for products in products_list:
|
||||
@ -3034,6 +3088,7 @@ class EnviFormer(PackageBasedModel):
|
||||
|
||||
start = datetime.now()
|
||||
# Standardise reactions for the training data, EnviFormer ignores stereochemistry currently
|
||||
co2 = {"C(=O)=O", "O=C=O"}
|
||||
ds = []
|
||||
for reaction in self._get_reactions():
|
||||
educts = ".".join(
|
||||
@ -3048,6 +3103,7 @@ class EnviFormer(PackageBasedModel):
|
||||
for smile in reaction.products.all()
|
||||
]
|
||||
)
|
||||
if products not in co2:
|
||||
ds.append(f"{educts}>>{products}")
|
||||
|
||||
end = datetime.now()
|
||||
@ -3084,10 +3140,18 @@ class EnviFormer(PackageBasedModel):
|
||||
args = {"clz": "EnviFormer"}
|
||||
return args
|
||||
|
||||
def evaluate_model(self):
|
||||
def evaluate_model(self, multigen: bool, eval_packages: List["Package"] = None):
|
||||
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()
|
||||
|
||||
@ -3244,7 +3308,7 @@ class EnviFormer(PackageBasedModel):
|
||||
|
||||
ds = self.load_dataset()
|
||||
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.
|
||||
@ -3312,7 +3376,7 @@ 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]
|
||||
@ -3614,3 +3678,53 @@ 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))
|
||||
|
||||
220
epdb/tasks.py
220
epdb/tasks.py
@ -1,12 +1,58 @@
|
||||
import csv
|
||||
import io
|
||||
import logging
|
||||
from typing import Optional
|
||||
from datetime import datetime
|
||||
from typing import Any, Callable, List, Optional
|
||||
from uuid import uuid4
|
||||
|
||||
from celery import shared_task
|
||||
from epdb.models import Pathway, Node, EPModel, Setting
|
||||
from epdb.logic import SPathway
|
||||
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.
|
||||
|
||||
|
||||
def get_ml_model(model_pk: int):
|
||||
if model_pk not in ML_CACHE:
|
||||
ML_CACHE[model_pk] = EPModel.objects.get(id=model_pk)
|
||||
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")
|
||||
@ -16,7 +62,7 @@ def mul(a, b):
|
||||
|
||||
@shared_task(queue="predict")
|
||||
def predict_simple(model_pk: int, smiles: str):
|
||||
mod = EPModel.objects.get(id=model_pk)
|
||||
mod = get_ml_model(model_pk)
|
||||
res = mod.predict(smiles)
|
||||
return res
|
||||
|
||||
@ -26,17 +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")
|
||||
@ -45,16 +129,26 @@ 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)
|
||||
# If the setting has a model add/restore it from the cache
|
||||
if setting.model is not None:
|
||||
setting.model = get_ml_model(setting.model.pk)
|
||||
|
||||
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:
|
||||
@ -79,7 +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()
|
||||
|
||||
@ -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
|
||||
|
||||
35
epdb/urls.py
35
epdb/urls.py
@ -1,5 +1,5 @@
|
||||
from django.urls import path, re_path
|
||||
from django.contrib.auth import views as auth_views
|
||||
from django.urls import path, re_path
|
||||
|
||||
from . import views as v
|
||||
|
||||
@ -88,20 +88,36 @@ urlpatterns = [
|
||||
v.package_rule,
|
||||
name="package rule detail",
|
||||
),
|
||||
re_path(
|
||||
rf"^package/(?P<package_uuid>{UUID})/simple-rdkit-rule/(?P<rule_uuid>{UUID})$",
|
||||
v.package_rule,
|
||||
name="package rule detail",
|
||||
),
|
||||
# re_path(
|
||||
# rf"^package/(?P<package_uuid>{UUID})/simple-rdkit-rule/(?P<rule_uuid>{UUID})$",
|
||||
# v.package_rule,
|
||||
# name="package rule detail",
|
||||
# ),
|
||||
re_path(
|
||||
rf"^package/(?P<package_uuid>{UUID})/parallel-rule/(?P<rule_uuid>{UUID})$",
|
||||
v.package_rule,
|
||||
name="package rule detail",
|
||||
),
|
||||
# re_path(
|
||||
# rf"^package/(?P<package_uuid>{UUID})/sequential-rule/(?P<rule_uuid>{UUID})$",
|
||||
# v.package_rule,
|
||||
# name="package rule detail",
|
||||
# ),
|
||||
# EnzymeLinks
|
||||
re_path(
|
||||
rf"^package/(?P<package_uuid>{UUID})/sequential-rule/(?P<rule_uuid>{UUID})$",
|
||||
v.package_rule,
|
||||
name="package rule detail",
|
||||
rf"^package/(?P<package_uuid>{UUID})/rule/(?P<rule_uuid>{UUID})/enzymelink/(?P<enzymelink_uuid>{UUID})$",
|
||||
v.package_rule_enzymelink,
|
||||
name="package rule enzymelink detail",
|
||||
),
|
||||
re_path(
|
||||
rf"^package/(?P<package_uuid>{UUID})/simple-ambit-rule/(?P<rule_uuid>{UUID})/enzymelink/(?P<enzymelink_uuid>{UUID})$",
|
||||
v.package_rule_enzymelink,
|
||||
name="package rule enzymelink detail",
|
||||
),
|
||||
re_path(
|
||||
rf"^package/(?P<package_uuid>{UUID})/parallel-rule/(?P<rule_uuid>{UUID})/enzymelink/(?P<enzymelink_uuid>{UUID})$",
|
||||
v.package_rule_enzymelink,
|
||||
name="package rule enzymelink detail",
|
||||
),
|
||||
# Reaction
|
||||
re_path(
|
||||
@ -174,6 +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"),
|
||||
]
|
||||
|
||||
222
epdb/views.py
222
epdb/views.py
@ -47,6 +47,8 @@ from .models import (
|
||||
Edge,
|
||||
ExternalDatabase,
|
||||
ExternalIdentifier,
|
||||
EnzymeLink,
|
||||
JobLog,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@ -239,6 +241,7 @@ 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,
|
||||
},
|
||||
}
|
||||
|
||||
@ -756,8 +759,8 @@ 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):
|
||||
@ -776,48 +779,40 @@ 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
|
||||
@ -827,18 +822,24 @@ 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"])
|
||||
|
||||
@ -866,6 +867,10 @@ 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 = []
|
||||
|
||||
@ -910,9 +915,25 @@ 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()
|
||||
@ -1255,7 +1276,16 @@ def package_compound_structures(request, package_uuid, compound_uuid):
|
||||
structure_smiles = request.POST.get("structure-smiles").strip()
|
||||
structure_description = request.POST.get("structure-description")
|
||||
|
||||
cs = current_compound.add_structure(structure_smiles, structure_name, structure_description)
|
||||
try:
|
||||
cs = current_compound.add_structure(
|
||||
structure_smiles, structure_name, structure_description
|
||||
)
|
||||
except ValueError:
|
||||
return error(
|
||||
request,
|
||||
"Adding structure failed!",
|
||||
"The structure could not be added as normalized structures don't match!",
|
||||
)
|
||||
|
||||
return redirect(cs.url)
|
||||
|
||||
@ -1460,12 +1490,20 @@ def package_rule(request, package_uuid, rule_uuid):
|
||||
logger.info(
|
||||
f"Rule {current_rule.uuid} returned multiple product sets on {smiles}, picking the first one."
|
||||
)
|
||||
|
||||
smirks = f"{stand_smiles}>>{'.'.join(sorted(res[0]))}"
|
||||
# Some Rules are touching unrelated areas which might result in ~ indicating
|
||||
# any bond (-, =, #). For drawing we need a concrete bond. -> use single bond
|
||||
product_smiles = [x.replace("~", "-") for x in res[0]]
|
||||
smirks = f"{stand_smiles}>>{'.'.join(sorted(product_smiles))}"
|
||||
# Usually the functional groups are a mapping of fg -> count
|
||||
# As we are doing it on the fly here fake a high count to ensure that its properly highlighted
|
||||
|
||||
if isinstance(current_rule, SimpleAmbitRule):
|
||||
educt_functional_groups = {current_rule.reactants_smarts: 1000}
|
||||
product_functional_groups = {current_rule.products_smarts: 1000}
|
||||
else:
|
||||
educt_functional_groups = {x: 1000 for x in current_rule.reactants_smarts}
|
||||
product_functional_groups = {x: 1000 for x in current_rule.products_smarts}
|
||||
|
||||
return HttpResponse(
|
||||
IndigoUtils.smirks_to_svg(
|
||||
smirks,
|
||||
@ -1536,6 +1574,32 @@ def package_rule(request, package_uuid, rule_uuid):
|
||||
return HttpResponseNotAllowed(["GET", "POST"])
|
||||
|
||||
|
||||
@package_permission_required()
|
||||
def package_rule_enzymelink(request, package_uuid, rule_uuid, enzymelink_uuid):
|
||||
current_user = _anonymous_or_real(request)
|
||||
current_package = PackageManager.get_package_by_id(current_user, package_uuid)
|
||||
current_rule = Rule.objects.get(package=current_package, uuid=rule_uuid)
|
||||
current_enzymelink = EnzymeLink.objects.get(rule=current_rule, uuid=enzymelink_uuid)
|
||||
|
||||
if request.method == "GET":
|
||||
context = get_base_context(request)
|
||||
|
||||
context["title"] = f"enviPath - {current_package.name} - {current_rule.name}"
|
||||
|
||||
context["meta"]["current_package"] = current_package
|
||||
context["object_type"] = "enzyme"
|
||||
context["breadcrumbs"] = breadcrumbs(
|
||||
current_package, "rule", current_rule, "enzymelink", current_enzymelink
|
||||
)
|
||||
|
||||
context["enzymelink"] = current_enzymelink
|
||||
context["current_object"] = current_enzymelink
|
||||
|
||||
return render(request, "objects/enzymelink.html", context)
|
||||
|
||||
return HttpResponseNotAllowed(["GET"])
|
||||
|
||||
|
||||
@package_permission_required()
|
||||
def package_reactions(request, package_uuid):
|
||||
current_user = _anonymous_or_real(request)
|
||||
@ -1774,9 +1838,9 @@ 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)
|
||||
|
||||
@ -1812,6 +1876,25 @@ 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
|
||||
@ -1896,10 +1979,16 @@ 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()
|
||||
@ -1976,9 +2065,42 @@ def package_pathway_node(request, package_uuid, pathway_uuid, node_uuid):
|
||||
|
||||
if request.method == "GET":
|
||||
is_image_request = request.GET.get("image")
|
||||
is_highlight_request = request.GET.get("highlight", False)
|
||||
is_highlight_reactivity = request.GET.get("highlightReactivity", False)
|
||||
if is_image_request:
|
||||
if is_image_request == "svg":
|
||||
# TODO optimize this chain
|
||||
if is_highlight_request:
|
||||
# User functional groups covered by the model training data
|
||||
fgs = {}
|
||||
if current_pathway.setting:
|
||||
if current_pathway.setting.model:
|
||||
if current_pathway.setting.model.app_domain:
|
||||
fgs = current_pathway.setting.model.app_domain.functional_groups
|
||||
|
||||
svg_data = IndigoUtils.mol_to_svg(
|
||||
current_node.default_node_label.smiles, functional_groups=fgs
|
||||
)
|
||||
elif is_highlight_reactivity:
|
||||
# Use reactant smarts to show all reaction sites
|
||||
# set a high count to obtain a strong color
|
||||
ad_data = current_node.get_app_domain_assessment_data()
|
||||
fgs = {}
|
||||
for t in ad_data.get("assessment", {}).get("transformations", []):
|
||||
r = Rule.objects.get(url=t["rule"]["url"])
|
||||
|
||||
if isinstance(r, SimpleAmbitRule):
|
||||
fgs[r.reactants_smarts] = 1000
|
||||
else:
|
||||
for sr in r.srs:
|
||||
fgs[sr.reactants_smarts] = 1000
|
||||
|
||||
svg_data = IndigoUtils.mol_to_svg(
|
||||
current_node.default_node_label.smiles, functional_groups=fgs
|
||||
)
|
||||
else:
|
||||
svg_data = current_node.as_svg
|
||||
|
||||
return HttpResponse(svg_data, content_type="image/svg+xml")
|
||||
|
||||
context = get_base_context(request)
|
||||
@ -2640,6 +2762,24 @@ 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 #
|
||||
###########
|
||||
|
||||
@ -12,7 +12,6 @@ dependencies = [
|
||||
"django-ninja>=1.4.1",
|
||||
"django-oauth-toolkit>=3.0.1",
|
||||
"django-polymorphic>=4.1.0",
|
||||
"django-stubs>=5.2.4",
|
||||
"enviformer",
|
||||
"envipy-additional-information",
|
||||
"envipy-ambit>=0.1.0",
|
||||
@ -34,12 +33,14 @@ dependencies = [
|
||||
[tool.uv.sources]
|
||||
enviformer = { git = "ssh://git@git.envipath.com/enviPath/enviformer.git", rev = "v0.1.2" }
|
||||
envipy-plugins = { git = "ssh://git@git.envipath.com/enviPath/enviPy-plugins.git", rev = "v0.1.0" }
|
||||
envipy-additional-information = { git = "ssh://git@git.envipath.com/enviPath/enviPy-additional-information.git", rev = "v0.1.4"}
|
||||
envipy-additional-information = { git = "ssh://git@git.envipath.com/enviPath/enviPy-additional-information.git", rev = "v0.1.7"}
|
||||
envipy-ambit = { git = "ssh://git@git.envipath.com/enviPath/enviPy-ambit.git" }
|
||||
|
||||
[project.optional-dependencies]
|
||||
ms-login = ["msal>=1.33.0"]
|
||||
dev = [
|
||||
"celery-stubs==0.1.3",
|
||||
"django-stubs>=5.2.4",
|
||||
"poethepoet>=0.37.0",
|
||||
"pre-commit>=4.3.0",
|
||||
"ruff>=0.13.3",
|
||||
|
||||
BIN
static/images/UoA-Logo-Primary-RGB-Large.png
Normal file
BIN
static/images/UoA-Logo-Primary-RGB-Large.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 114 KiB |
BIN
static/images/UoA-Logo-Primary-RGB-Reversed-Large.png
Normal file
BIN
static/images/UoA-Logo-Primary-RGB-Reversed-Large.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 114 KiB |
BIN
static/images/UoA-Logo-Primary-RGB-Reversed-Small.png
Normal file
BIN
static/images/UoA-Logo-Primary-RGB-Reversed-Small.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 18 KiB |
BIN
static/images/UoA-Logo-Primary-RGB-Small.png
Normal file
BIN
static/images/UoA-Logo-Primary-RGB-Small.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 17 KiB |
488
static/images/uzh-logo.svg
Normal file
488
static/images/uzh-logo.svg
Normal file
@ -0,0 +1,488 @@
|
||||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<!-- Generator: Adobe Illustrator 16.0.0, SVG Export Plug-In . SVG Version: 6.00 Build 0) -->
|
||||
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1 Tiny//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11-tiny.dtd">
|
||||
<svg version="1.1" baseProfile="tiny" id="Universität_Zürich"
|
||||
xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
|
||||
viewBox="-0.499 -0.501 142.73 49.19" xml:space="preserve">
|
||||
<path d="M6.818,32.721C6.296,32.944,6.2,32.986,6.093,33.04c-0.113,0.058-0.156,0.108-0.133,0.231
|
||||
c0.005,0.029,0.026,0.091,0.047,0.139c0.017,0.038,0.019,0.062-0.006,0.071c-0.023,0.011-0.041-0.01-0.062-0.062
|
||||
c-0.078-0.182-0.161-0.401-0.205-0.506c-0.035-0.082-0.138-0.295-0.191-0.418c-0.021-0.052-0.024-0.079-0.001-0.089
|
||||
c0.024-0.011,0.04,0.007,0.056,0.042c0.016,0.037,0.027,0.057,0.053,0.095c0.057,0.086,0.127,0.088,0.25,0.043
|
||||
c0.113-0.04,0.209-0.081,0.731-0.305l0.481-0.206c0.499-0.215,0.666-0.404,0.729-0.631c0.062-0.21,0.008-0.373-0.043-0.49
|
||||
c-0.063-0.151-0.191-0.315-0.389-0.395c-0.27-0.108-0.584,0.014-0.938,0.166l-0.431,0.184c-0.521,0.225-0.619,0.266-0.726,0.319
|
||||
c-0.114,0.058-0.157,0.107-0.132,0.23c0.005,0.031,0.025,0.091,0.042,0.129c0.016,0.037,0.018,0.062-0.006,0.071
|
||||
c-0.024,0.011-0.041-0.011-0.062-0.06C5.083,31.425,5.001,31.204,5,31.201c-0.018-0.042-0.121-0.254-0.178-0.387
|
||||
c-0.021-0.048-0.024-0.075,0-0.085c0.024-0.012,0.04,0.007,0.057,0.048c0.017,0.038,0.029,0.058,0.054,0.096
|
||||
c0.057,0.085,0.127,0.088,0.25,0.043c0.112-0.04,0.209-0.082,0.73-0.306l0.368-0.157c0.382-0.164,0.803-0.3,1.175-0.117
|
||||
c0.314,0.154,0.458,0.387,0.554,0.608c0.078,0.183,0.21,0.517,0.091,0.865c-0.083,0.242-0.281,0.481-0.78,0.695L6.818,32.721z
|
||||
M6.676,28.899c0.283-0.069,0.368-0.139,0.378-0.217c0.008-0.066-0.004-0.138-0.017-0.195c-0.009-0.04-0.007-0.063,0.017-0.068
|
||||
c0.028-0.006,0.043,0.025,0.055,0.076c0.052,0.237,0.073,0.386,0.088,0.455c0.007,0.033,0.056,0.202,0.098,0.393
|
||||
c0.011,0.048,0.015,0.081-0.019,0.089c-0.022,0.005-0.034-0.017-0.042-0.053c-0.01-0.048-0.028-0.111-0.054-0.16
|
||||
c-0.051-0.089-0.159-0.079-0.479-0.015l-2.169,0.442c-0.073,0.016-0.127,0.016-0.135-0.021c-0.009-0.04,0.031-0.083,0.083-0.16
|
||||
c0.038-0.054,0.511-0.739,0.924-1.39c0.194-0.303,0.612-0.911,0.657-0.986l-0.004-0.018l-1.633,0.396
|
||||
c-0.222,0.053-0.283,0.102-0.301,0.196c-0.01,0.061,0.009,0.146,0.021,0.198c0.01,0.044,0.002,0.061-0.021,0.064
|
||||
c-0.028,0.008-0.042-0.032-0.054-0.088C4.028,27.651,4,27.474,3.984,27.397c-0.009-0.041-0.05-0.178-0.09-0.356
|
||||
c-0.011-0.048-0.016-0.085,0.015-0.091c0.022-0.005,0.038,0.015,0.047,0.059c0.008,0.036,0.015,0.065,0.035,0.106
|
||||
c0.051,0.097,0.135,0.108,0.34,0.067l2.313-0.467c0.08-0.018,0.115-0.01,0.124,0.022c0.009,0.041-0.021,0.094-0.054,0.143
|
||||
c-0.168,0.277-0.544,0.852-0.837,1.312c-0.308,0.484-0.675,0.99-0.729,1.071l0.002,0.011L6.676,28.899z M4.871,25.556
|
||||
c-0.566,0.035-0.672,0.042-0.791,0.058c-0.126,0.016-0.188,0.057-0.201,0.14C3.87,25.796,3.87,25.844,3.873,25.89
|
||||
c0.002,0.036-0.004,0.06-0.034,0.062c-0.021,0.002-0.031-0.027-0.035-0.087c-0.01-0.142-0.014-0.378-0.021-0.486
|
||||
c-0.006-0.093-0.031-0.312-0.04-0.455c-0.003-0.048,0.003-0.079,0.025-0.081c0.029-0.002,0.039,0.021,0.041,0.059
|
||||
c0.002,0.037,0.008,0.066,0.018,0.111c0.025,0.1,0.09,0.126,0.222,0.123c0.12,0,0.225-0.006,0.792-0.042l0.657-0.042
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
v-3.103H21.164z M21.164,29.375v5.247h-3.941v-5.247H21.164z M24.634,16.538v1.416l-0.542-0.542l-0.541,0.541v-1.415H24.634z
|
||||
M23.566,16.13l0.396-1.694l0.163-2.835l0.098,2.833l0.396,1.696H23.566z M24.092,17.988l2.511,2.51h-5.021L24.092,17.988z
|
||||
M26.603,20.838v1.921h-5.037v-1.921H26.603z M26.603,23.354v2.238h-1.575v-0.561c0-0.516-0.419-0.938-0.938-0.938
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||||
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|
||||
H23.155z M26.603,29.375v5.247h-5.037v-5.247H26.603z M30.067,7.089v0.187c-0.005,0.038-0.017,0.093-0.035,0.145
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
c0.003-0.171,0.033-0.409,0.125-0.632C30.025,8.17,30.088,8.07,30.146,7.988z M29.473,8.994l-0.001,2.476H28.5
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||||
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||||
c0,0,0.219,0.144,0.342,0.386C29.455,8.618,29.466,8.828,29.473,8.994z M28.036,8.343c0.091,0.224,0.106,0.465,0.109,0.636
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||||
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||||
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||||
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||||
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|
||||
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|
||||
v3.995H30.08v-1.995c0-0.468-0.176-0.875-0.47-1.155c-0.157-0.15-0.344-0.263-0.55-0.333c-0.01-0.005-0.074-0.024-0.074-0.024
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||||
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|
||||
L30.956,16.503L30.956,16.503z M29.74,18.506v1.992h-1.506v-1.995c0-0.384,0.141-0.694,0.364-0.909
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||||
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|
||||
M30.956,20.838v4.755h-3.941v-4.755H30.956z M30.956,25.933v3.103h-3.941v-3.103H30.956z M30.956,29.375v5.247h-3.941v-5.247
|
||||
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||||
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||||
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|
||||
c-0.058,0.084-0.138,0.211-0.226,0.398c-0.132,0.324-0.141,0.624-0.146,0.804v2.465h-0.5v8.452l-1.368-1.367v-2.413l-0.464-1.558
|
||||
l-0.68-5.341l0,0l-0.501,5.117l-0.466,1.782v2.234l-1.354,1.355v-8.265h-0.497V8.917c-0.005-0.317-0.051-0.519-0.192-0.844
|
||||
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|
||||
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|
||||
c-0.43,0.593-0.444,1.354-0.448,1.563c0,0.034,0,0.054,0,0.057v0.851c-0.058,0.084-0.138,0.211-0.226,0.398
|
||||
c-0.131,0.324-0.141,0.624-0.145,0.804v2.465h-0.5v23.151H4.646c-1.702-3.133-2.672-6.721-2.672-10.538
|
||||
c0-6.106,2.475-11.635,6.477-15.639c4.003-4.002,9.531-6.479,15.639-6.479c6.108,0,11.637,2.476,15.64,6.479
|
||||
c4.002,4.003,6.478,9.531,6.478,15.639c0,3.817-0.97,7.405-2.672,10.538L31.562,34.622L31.562,34.622z"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 53 KiB |
@ -646,8 +646,8 @@ function handleAssessmentResponse(depict_url, data) {
|
||||
var reactivityCentersImgSrc = null;
|
||||
|
||||
if (data['assessment']['node'] !== undefined) {
|
||||
functionalGroupsImgSrc = "<img width='400' src='" + data['assessment']['node']['image'] + "'>";
|
||||
reactivityCentersImgSrc = "<img width='400' src='" + data['assessment']['node']['image'] + "'>"
|
||||
functionalGroupsImgSrc = "<img width='400' src='" + data['assessment']['node']['image'] + "&highlight=true'>";
|
||||
reactivityCentersImgSrc = "<img width='400' src='" + data['assessment']['node']['image'] + "&highlightReactivity=true'>"
|
||||
} else {
|
||||
functionalGroupsImgSrc = "<img width='400' src=\"" + depict_url + "?smiles=" + encodeURIComponent(data['assessment']['smiles']) + "\">";
|
||||
reactivityCentersImgSrc = "<img width='400' src=\"" + depict_url + "?smiles=" + encodeURIComponent(data['assessment']['smiles']) + "\">"
|
||||
|
||||
@ -444,6 +444,13 @@ function serializeSVG(svgElement) {
|
||||
line.setAttribute("fill", style.fill);
|
||||
});
|
||||
|
||||
svgElement.querySelectorAll("line.link_no_arrow").forEach(line => {
|
||||
const style = getComputedStyle(line);
|
||||
line.setAttribute("stroke", style.stroke);
|
||||
line.setAttribute("stroke-width", style.strokeWidth);
|
||||
line.setAttribute("fill", style.fill);
|
||||
});
|
||||
|
||||
const serializer = new XMLSerializer();
|
||||
let svgString = serializer.serializeToString(svgElement);
|
||||
|
||||
@ -455,7 +462,26 @@ function serializeSVG(svgElement) {
|
||||
return svgString;
|
||||
}
|
||||
|
||||
function shrinkSVG(svgSelector) {
|
||||
|
||||
const svg = d3.select(svgSelector);
|
||||
const node = svg.node();
|
||||
|
||||
// Compute bounding box of everything inside the SVG
|
||||
const bbox = node.getBBox();
|
||||
|
||||
const padding = 10;
|
||||
svg.attr("viewBox",
|
||||
`${bbox.x - padding} ${bbox.y - padding} ${bbox.width + 2 * padding} ${bbox.height + 2 * padding}`
|
||||
)
|
||||
.attr("width", bbox.width + 2 * padding)
|
||||
.attr("height", bbox.height + 2 * padding);
|
||||
|
||||
return bbox;
|
||||
}
|
||||
|
||||
function downloadSVG(svgElement, filename = 'chart.svg') {
|
||||
shrinkSVG("#" + svgElement.id);
|
||||
const svgString = serializeSVG(svgElement);
|
||||
const blob = new Blob([svgString], {type: 'image/svg+xml;charset=utf-8'});
|
||||
const url = URL.createObjectURL(blob);
|
||||
|
||||
@ -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">
|
||||
|
||||
71
templates/collections/joblog.html
Normal file
71
templates/collections/joblog.html
Normal file
@ -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 %}
|
||||
@ -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';
|
||||
@ -227,19 +227,21 @@
|
||||
<div class="row">
|
||||
<div class="col-lg-12">
|
||||
<ul class="nav nav-pills nav-justified">
|
||||
<li><a href="http://eawag.ch" target="_blank">
|
||||
<img id="image-ealogo"
|
||||
height="60"
|
||||
src='{% static "/images/ealogo.gif" %}'
|
||||
alt="Eawag"/>
|
||||
<li>
|
||||
<a href="http://ml.auckland.ac.nz" target="_blank">
|
||||
<img id="image-uoalogo" height="60" src='{% static "/images/UoA-Logo-Primary-RGB-Small.png" %}'
|
||||
alt="The Univserity of Auckland"/>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="http://ml.auckland.ac.nz" target="_blank">
|
||||
<img id="image-uoalogo"
|
||||
height="60"
|
||||
src='{% static "/images/uoa.png" %}'
|
||||
alt="The Univserity of Auckland"/>
|
||||
<a href="https://eawag.ch" target="_blank">
|
||||
<img id="image-ealogo" height="60" src='{% static "/images/ealogo.gif" %}' alt="Eawag"/>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.uzh.ch/" target="_blank">
|
||||
<img id="image-ufzlogo" height="60" src='{% static "/images/uzh-logo.svg" %}'
|
||||
alt="University of Zurich"/>
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
@ -19,13 +19,19 @@
|
||||
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 >></a>
|
||||
<a target="_blank" href="https://wiki.envipath.org/index.php/relative-reasoning" role="button">wiki
|
||||
>></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>
|
||||
@ -33,12 +39,12 @@
|
||||
<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 %}
|
||||
@ -53,10 +59,13 @@
|
||||
{% 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 %}
|
||||
@ -71,32 +80,31 @@
|
||||
{% 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">
|
||||
@ -108,11 +116,13 @@
|
||||
<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>
|
||||
@ -121,12 +131,6 @@
|
||||
</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">
|
||||
@ -139,19 +143,22 @@
|
||||
|
||||
<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")) {
|
||||
@ -163,18 +170,12 @@ $(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
|
||||
}
|
||||
});
|
||||
|
||||
@ -184,7 +185,4 @@ $(function() {
|
||||
});
|
||||
|
||||
});
|
||||
|
||||
|
||||
|
||||
</script>
|
||||
|
||||
@ -18,10 +18,10 @@
|
||||
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 %}
|
||||
@ -36,6 +36,15 @@
|
||||
{% 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>
|
||||
@ -51,7 +60,7 @@
|
||||
|
||||
$(function () {
|
||||
|
||||
$("#relative-reasoning-evaluation-packages").selectpicker();
|
||||
$("#model-evaluation-packages").selectpicker();
|
||||
|
||||
$('#evaluate_model_form_submit').on('click', function (e) {
|
||||
e.preventDefault();
|
||||
|
||||
@ -19,6 +19,7 @@
|
||||
<select id="scenario-select" name="selected-scenarios" data-actions-box='true' class="form-control"
|
||||
multiple data-width='100%'>
|
||||
<option disabled>Select Scenarios</option>
|
||||
<option value="" hidden></option>
|
||||
</select>
|
||||
</form>
|
||||
</div>
|
||||
@ -66,7 +67,7 @@
|
||||
$('#set_scenario_modal_form_submit').on('click', function (e) {
|
||||
e.preventDefault();
|
||||
if ($('#scenario-select').val().length == 0) {
|
||||
$('#scenario-select').val([''])
|
||||
$('#scenario-select').val("")
|
||||
}
|
||||
$('#set_scenario_modal_form').submit();
|
||||
});
|
||||
|
||||
54
templates/modals/objects/identify_missing_rules_modal.html
Normal file
54
templates/modals/objects/identify_missing_rules_modal.html
Normal file
@ -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">×</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>
|
||||
@ -88,6 +88,7 @@
|
||||
</div>
|
||||
{% endif %}
|
||||
|
||||
{% if rule.enzymelinks %}
|
||||
<!-- EC Numbers -->
|
||||
<div class="panel panel-default panel-heading list-group-item" style="background-color:silver">
|
||||
<h4 class="panel-title">
|
||||
@ -95,12 +96,33 @@
|
||||
href="#rule-ec-numbers">EC Numbers</a>
|
||||
</h4>
|
||||
</div>
|
||||
<div id="rule-ec-numbers" class="panel-collapse collapse">
|
||||
<div id="rule-ec-numbers" class="panel-collapse collapse in">
|
||||
<div class="panel-body list-group-item">
|
||||
|
||||
{% for k, v in rule.get_grouped_enzymelinks.items %}
|
||||
<div class="panel panel-default panel-heading list-group-item" style="background-color:silver">
|
||||
<h4 class="panel-title">
|
||||
<a id="{{ k|slugify }}_Link" data-toggle="collapse"
|
||||
data-parent="#{{ k|slugify }}_Accordion"
|
||||
href="#{{ k|slugify }}">
|
||||
{{ k }}
|
||||
</a>
|
||||
</h4>
|
||||
</div>
|
||||
<div id="{{ k|slugify }}" class="panel-collapse collapse in">
|
||||
<div class="panel-body list-group-item">
|
||||
{% for enzyme in v %}
|
||||
<a class="list-group-item" href="{{ enzyme.url }}">
|
||||
{{ enzyme.ec_number }}
|
||||
<div style="position:absolute;bottom:10px;left:100px;">{{ enzyme.name }}</div>
|
||||
<div style="float:right;">{{ enzyme.linking_method }}</div>
|
||||
</a>
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
{% endif %}
|
||||
</div>
|
||||
</div>
|
||||
{% endblock content %}
|
||||
|
||||
@ -184,7 +184,7 @@
|
||||
</div>
|
||||
<div id="compound-external-identifier" class="panel-collapse collapse in">
|
||||
<div class="panel-body list-group-item">
|
||||
{% if compound.get_pubchem_identifiers %}
|
||||
{% if compound.get_pubchem_compound_identifiers %}
|
||||
<div class="panel panel-default panel-heading list-group-item"
|
||||
style="background-color:silver">
|
||||
<h4 class="panel-title">
|
||||
@ -194,12 +194,28 @@
|
||||
</h4>
|
||||
</div>
|
||||
<div id="compound-pubchem-identifier" class="panel-collapse collapse in">
|
||||
{% for eid in compound.get_pubchem_identifiers %}
|
||||
{% for eid in compound.get_pubchem_compound_identifiers %}
|
||||
<a class="list-group-item"
|
||||
href="{{ eid.external_url }}">CID{{ eid.identifier_value }}</a>
|
||||
{% endfor %}
|
||||
</div>
|
||||
{% endif %}
|
||||
{% if compound.get_pubchem_substance_identifiers %}
|
||||
<div class="panel panel-default panel-heading list-group-item"
|
||||
style="background-color:silver">
|
||||
<h4 class="panel-title">
|
||||
<a id="compound-pubchem-identifier-link" data-toggle="collapse"
|
||||
data-parent="#compound-external-identifier"
|
||||
href="#compound-pubchem-identifier">PubChem Substance Identifier</a>
|
||||
</h4>
|
||||
</div>
|
||||
<div id="compound-pubchem-identifier" class="panel-collapse collapse in">
|
||||
{% for eid in compound.get_pubchem_substance_identifiers %}
|
||||
<a class="list-group-item"
|
||||
href="{{ eid.external_url }}">SID{{ eid.identifier_value }}</a>
|
||||
{% endfor %}
|
||||
</div>
|
||||
{% endif %}
|
||||
{% if compound.get_chebi_identifiers %}
|
||||
<div class="panel panel-default panel-heading list-group-item"
|
||||
style="background-color:silver">
|
||||
|
||||
105
templates/objects/enzymelink.html
Normal file
105
templates/objects/enzymelink.html
Normal file
@ -0,0 +1,105 @@
|
||||
{% extends "framework.html" %}
|
||||
|
||||
{% block content %}
|
||||
|
||||
<div class="panel-group" id="enzyme-detail">
|
||||
<div class="panel panel-default">
|
||||
<div class="panel-heading" id="headingPanel" style="font-size:2rem;height: 46px">
|
||||
{{ enzymelink.ec_number }}
|
||||
</div>
|
||||
|
||||
<!-- Name -->
|
||||
<div class="panel panel-default panel-heading list-group-item" style="background-color:silver">
|
||||
<h4 class="panel-title">
|
||||
<a id="enzyme-name-link" data-toggle="collapse" data-parent="#enzyme-detail"
|
||||
href="#enzyme-name">Enzyme Name</a>
|
||||
</h4>
|
||||
</div>
|
||||
<div id="enzyme-name" class="panel-collapse collapse in">
|
||||
<div class="panel-body list-group-item">
|
||||
{{ enzymelink.name }}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Linking Method -->
|
||||
<div class="panel panel-default panel-heading list-group-item" style="background-color:silver">
|
||||
<h4 class="panel-title">
|
||||
<a id="enzyme-linking-link" data-toggle="collapse" data-parent="#enzyme-detail"
|
||||
href="#enzyme-linking">Linking Method</a>
|
||||
</h4>
|
||||
</div>
|
||||
<div id="enzyme-linking" class="panel-collapse collapse in">
|
||||
<div class="panel-body list-group-item">
|
||||
{{ enzymelink.linking_method }}. <a
|
||||
href="https://wiki.envipath.org/index.php/Rules#EnzymeLinks" target="#">Learn more >></a>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{% if enzymelink.kegg_reaction_links %}
|
||||
<div class="panel panel-default panel-heading list-group-item" style="background-color:silver">
|
||||
<h4 class="panel-title">
|
||||
<a id="enzyme-evidence-link" data-toggle="collapse" data-parent="#enzyme-detail"
|
||||
href="#enzyme-evidence">Linking Evidence</a>
|
||||
</h4>
|
||||
</div>
|
||||
<div id="enzyme-evidence" class="panel-collapse collapse in">
|
||||
<div class="panel-body list-group-item">
|
||||
{% for kl in enzymelink.kegg_reaction_links %}
|
||||
<a class="list-group-item"
|
||||
href="{{ kl.external_url }}">{{ kl.identifier_value }}</a>
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
{% endif %}
|
||||
|
||||
{% if enzymelink.reaction_evidence.all %}
|
||||
<div class="panel panel-default panel-heading list-group-item" style="background-color:silver">
|
||||
<h4 class="panel-title">
|
||||
<a id="enzyme-reaction-evidence-link" data-toggle="collapse" data-parent="#enzyme-detail"
|
||||
href="#enzyme-reaction-evidence">Linking Evidence - enviPath Reactions</a>
|
||||
</h4>
|
||||
</div>
|
||||
<div id="enzyme-reaction-evidence" class="panel-collapse collapse in">
|
||||
<div class="panel-body list-group-item">
|
||||
{% for r in enzymelink.reaction_evidence.all %}
|
||||
<a class="list-group-item" href="{{ r.url }}">{{ r.name }} <i>({{ r.package.name }})</i></a>
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
{% endif %}
|
||||
|
||||
{% if enzymelink.edge_evidence.all %}
|
||||
<div class="panel panel-default panel-heading list-group-item" style="background-color:silver">
|
||||
<h4 class="panel-title">
|
||||
<a id="enzyme-edge-evidence-link" data-toggle="collapse" data-parent="#enzyme-detail"
|
||||
href="#enzyme-edge-evidence">Linking Evidence - enviPath Pathways</a>
|
||||
</h4>
|
||||
</div>
|
||||
<div id="enzyme-edge-evidence" class="panel-collapse collapse in">
|
||||
<div class="panel-body list-group-item">
|
||||
{% for e in enzymelink.edge_evidence.all %}
|
||||
<a class="list-group-item" href="{{ e.pathway.url }}">{{ e.pathway.name }}
|
||||
<i>({{ r.package.name }})</i></a>
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
{% endif %}
|
||||
|
||||
<!-- External DB Reference -->
|
||||
<div class="panel panel-default panel-heading list-group-item" style="background-color:silver">
|
||||
<h4 class="panel-title">
|
||||
<a id="enzyme-external-identifier-link" data-toggle="collapse" data-parent="#enzyme-detail"
|
||||
href="#enzyme-external-identifier">External DB References</a>
|
||||
</h4>
|
||||
</div>
|
||||
<div id="enzyme-external-identifier" class="panel-collapse collapse in">
|
||||
<div class="panel-body list-group-item">
|
||||
<a class="list-group-item"
|
||||
href="http://www.brenda-enzymes.org/enzyme.php?ecno={{ enzymelink.ec_number }}"
|
||||
target="_blank"> Brenda entry for {{ enzymelink.ec_number }}</a>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
{% endblock content %}
|
||||
@ -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">
|
||||
|
||||
@ -84,6 +84,7 @@
|
||||
{% 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" %}
|
||||
@ -178,9 +179,6 @@
|
||||
</nav>
|
||||
<div id="vizdiv" >
|
||||
<svg id="pwsvg">
|
||||
{% if debug %}
|
||||
<rect width="100%" height="100%" fill="aliceblue"/>
|
||||
{% endif %}
|
||||
<defs>
|
||||
<marker id="arrow" viewBox="0 0 10 10" refX="43" refY="5" markerWidth="6" markerHeight="6"
|
||||
orient="auto-start-reverse" markerUnits="userSpaceOnUse">
|
||||
|
||||
@ -125,6 +125,23 @@
|
||||
</div>
|
||||
{% endif %}
|
||||
|
||||
{% if reaction.get_related_enzymes %}
|
||||
<!-- EC Numbers -->
|
||||
<div class="panel panel-default panel-heading list-group-item" style="background-color:silver">
|
||||
<h4 class="panel-title">
|
||||
<a id="rule-ec-numbers-link" data-toggle="collapse" data-parent="#rule-detail"
|
||||
href="#rule-ec-numbers">EC Numbers</a>
|
||||
</h4>
|
||||
</div>
|
||||
<div id="rule-ec-numbers" class="panel-collapse collapse in">
|
||||
<div class="panel-body list-group-item">
|
||||
{% for e in reaction.get_related_enzymes %}
|
||||
<a class="list-group-item" href="http://www.brenda-enzymes.org/enzyme.php?ecno={{ e.ec_number }}">{{ e.name }}</a>
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
{% endif %}
|
||||
|
||||
{% if reaction.related_pathways %}
|
||||
<!-- Pathways -->
|
||||
<div class="panel panel-default panel-heading list-group-item" style="background-color:silver">
|
||||
|
||||
@ -202,6 +202,43 @@
|
||||
</div>
|
||||
</div>
|
||||
{% endif %}
|
||||
|
||||
{% if rule.enzymelinks %}
|
||||
<!-- EC Numbers -->
|
||||
<div class="panel panel-default panel-heading list-group-item" style="background-color:silver">
|
||||
<h4 class="panel-title">
|
||||
<a id="rule-ec-numbers-link" data-toggle="collapse" data-parent="#rule-detail"
|
||||
href="#rule-ec-numbers">EC Numbers</a>
|
||||
</h4>
|
||||
</div>
|
||||
<div id="rule-ec-numbers" class="panel-collapse collapse in">
|
||||
<div class="panel-body list-group-item">
|
||||
{% for k, v in rule.get_grouped_enzymelinks.items %}
|
||||
<div class="panel panel-default panel-heading list-group-item"
|
||||
style="background-color:silver">
|
||||
<h4 class="panel-title">
|
||||
<a id="{{ k|slugify }}_Link" data-toggle="collapse"
|
||||
data-parent="#{{ k|slugify }}_Accordion"
|
||||
href="#{{ k|slugify }}">
|
||||
{{ k }}
|
||||
</a>
|
||||
</h4>
|
||||
</div>
|
||||
<div id="{{ k|slugify }}" class="panel-collapse collapse in">
|
||||
<div class="panel-body list-group-item">
|
||||
{% for enzyme in v %}
|
||||
<a class="list-group-item" href="{{ enzyme.url }}">
|
||||
{{ enzyme.ec_number }}
|
||||
<div style="position:absolute;bottom:10px;left:100px;">{{ enzyme.name }}</div>
|
||||
<div style="float:right;">{{ enzyme.linking_method }}</div>
|
||||
</a>
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
{% endfor %}
|
||||
</div>
|
||||
</div>
|
||||
{% endif %}
|
||||
</div>
|
||||
</div>
|
||||
{% endblock content %}
|
||||
|
||||
@ -1,7 +1,27 @@
|
||||
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
|
||||
from epdb.models import User, EnviFormer, Package, Setting
|
||||
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")
|
||||
@ -28,8 +48,41 @@ 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")
|
||||
|
||||
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)
|
||||
|
||||
@ -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",
|
||||
@ -50,9 +49,7 @@ class ModelTest(TestCase):
|
||||
|
||||
mod.build_dataset()
|
||||
mod.build_model()
|
||||
mod.multigen_eval = True
|
||||
mod.save()
|
||||
mod.evaluate_model()
|
||||
mod.evaluate_model(True, eval_packages_objs)
|
||||
|
||||
results = mod.predict("CCN(CC)C(=O)C1=CC(=CC=C1)C")
|
||||
|
||||
|
||||
@ -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"]
|
||||
|
||||
|
||||
@ -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"]
|
||||
|
||||
|
||||
@ -185,7 +185,7 @@ 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
|
||||
@ -203,19 +203,21 @@ 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):
|
||||
@ -363,6 +365,76 @@ 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):
|
||||
@ -729,6 +801,7 @@ class IndigoUtils(object):
|
||||
height: int = 0,
|
||||
educt_functional_groups: Dict[str, int] = None,
|
||||
product_functional_groups: Dict[str, int] = None,
|
||||
debug: bool = False,
|
||||
):
|
||||
if educt_functional_groups is None:
|
||||
educt_functional_groups = {}
|
||||
@ -739,6 +812,11 @@ class IndigoUtils(object):
|
||||
i = Indigo()
|
||||
renderer = IndigoRenderer(i)
|
||||
|
||||
if debug:
|
||||
i.setOption("render-atom-ids-visible", True)
|
||||
i.setOption("render-bond-ids-visible", False)
|
||||
i.setOption("render-atom-bond-ids-from-one", True)
|
||||
|
||||
i.setOption("render-output-format", "svg")
|
||||
i.setOption("render-coloring", True)
|
||||
i.setOption("render-image-size", width, height)
|
||||
|
||||
@ -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
|
||||
|
||||
Reference in New Issue
Block a user