[Enhancement] Create ML Models (#173)

## Changes

- Ability to change the threshold from a command line argument.
- Names of data packages included in model name
- Names of data, rule and eval packages included in the model description
- EnviFormer models are now viewable on the admin site
- Ignore CO2 for training and evaluating EnviFormer

Co-authored-by: Liam Brydon <62733830+MyCreativityOutlet@users.noreply.github.com>
Reviewed-on: enviPath/enviPy#173
Reviewed-by: jebus <lorsbach@envipath.com>
Co-authored-by: liambrydon <lbry121@aucklanduni.ac.nz>
Co-committed-by: liambrydon <lbry121@aucklanduni.ac.nz>

epdb/admin.py

@@ -7,6 +7,7 @@ from .models import (
     GroupPackagePermission,
     Package,
     MLRelativeReasoning,
+    EnviFormer,
     Compound,
     CompoundStructure,
     SimpleAmbitRule,
@@ -50,6 +51,10 @@ class MLRelativeReasoningAdmin(EPAdmin):
     pass
 
 
+class EnviFormerAdmin(EPAdmin):
+    pass
+
+
 class CompoundAdmin(EPAdmin):
     pass
 
@@ -104,6 +109,7 @@ admin.site.register(Group, GroupAdmin)
 admin.site.register(GroupPackagePermission, GroupPackagePermissionAdmin)
 admin.site.register(Package, PackageAdmin)
 admin.site.register(MLRelativeReasoning, MLRelativeReasoningAdmin)
+admin.site.register(EnviFormer, EnviFormerAdmin)
 admin.site.register(Compound, CompoundAdmin)
 admin.site.register(CompoundStructure, CompoundStructureAdmin)
 admin.site.register(SimpleAmbitRule, SimpleAmbitRuleAdmin)

epdb/management/commands/create_ml_models.py

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

epdb/models.py

@@ -3092,6 +3092,7 @@ 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(
@@ -3106,6 +3107,7 @@ class EnviFormer(PackageBasedModel):
                     for smile in reaction.products.all()
                 ]
             )
+            if products not in co2:
                 ds.append(f"{educts}>>{products}")
 
         end = datetime.now()
@@ -3302,7 +3304,7 @@ 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.
@@ -3370,7 +3372,7 @@ 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]