[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/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(
@@ -3106,7 +3107,8 @@ class EnviFormer(PackageBasedModel):
                     for smile in reaction.products.all()
                 ]
             )
-            ds.append(f"{educts}>>{products}")
+            if products not in co2:
+                ds.append(f"{educts}>>{products}")
 
         end = datetime.now()
         logger.debug(f"build_dataset took {(end - start).total_seconds()} seconds")
@@ -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]