jebus/enviPy-bayer
1
0
Fork 0
forked from enviPath/enviPy
Code Issues Pull Requests Actions Releases Activity

start towards #120

Browse Source
This commit is contained in:
Liam Brydon
2025-10-22 08:22:29 +13:00
parent 376fd65785
commit 2980a75daa
3 changed files with 53 additions and 24 deletions
Download Patch File Download Diff File Expand all files Collapse all files

20
epdb/models.py
View File

@ -28,7 +28,7 @@ from sklearn.metrics import precision_score, recall_score, jaccard_score
from sklearn.model_selection import ShuffleSplit
from utilities.chem import FormatConverter, ProductSet, PredictionResult, IndigoUtils
from utilities.ml import Dataset, ApplicabilityDomainPCA, EnsembleClassifierChain, RelativeReasoning
from utilities.ml import RuleBasedDataset, ApplicabilityDomainPCA, EnsembleClassifierChain, RelativeReasoning
logger = logging.getLogger(__name__)
@ -2175,7 +2175,7 @@ class PackageBasedModel(EPModel):
applicable_rules = self.applicable_rules
reactions = list(self._get_reactions())
ds = Dataset.generate_dataset(reactions, applicable_rules, educts_only=True)
ds = RuleBasedDataset.generate_dataset(reactions, applicable_rules, educts_only=True)
end = datetime.now()
logger.debug(f"build_dataset took {(end - start).total_seconds()} seconds")
@ -2184,9 +2184,9 @@ class PackageBasedModel(EPModel):
ds.save(f)
return ds
def load_dataset(self) -> "Dataset":
def load_dataset(self) -> "RuleBasedDataset":
ds_path = os.path.join(s.MODEL_DIR, f"{self.uuid}_ds.pkl")
return Dataset.load(ds_path)
return RuleBasedDataset.load(ds_path)
def retrain(self):
self.build_dataset()
@ -2196,7 +2196,7 @@ class PackageBasedModel(EPModel):
self.build_model()
@abstractmethod
def _fit_model(self, ds: Dataset):
def _fit_model(self, ds: RuleBasedDataset):
pass
@abstractmethod
@ -2335,7 +2335,7 @@ class PackageBasedModel(EPModel):
eval_reactions = list(
Reaction.objects.filter(package__in=self.eval_packages.all()).distinct()
)
ds = Dataset.generate_dataset(eval_reactions, self.applicable_rules, educts_only=True)
ds = RuleBasedDataset.generate_dataset(eval_reactions, self.applicable_rules, educts_only=True)
if isinstance(self, RuleBasedRelativeReasoning):
X = np.array(ds.X(exclude_id_col=False, na_replacement=None))
y = np.array(ds.y(na_replacement=np.nan))
@ -2582,7 +2582,7 @@ class RuleBasedRelativeReasoning(PackageBasedModel):
return rbrr
def _fit_model(self, ds: Dataset):
def _fit_model(self, ds: RuleBasedDataset):
X, y = ds.X(exclude_id_col=False, na_replacement=None), ds.y(na_replacement=None)
model = RelativeReasoning(
start_index=ds.triggered()[0],
@ -2689,7 +2689,7 @@ class MLRelativeReasoning(PackageBasedModel):
return mlrr
def _fit_model(self, ds: Dataset):
def _fit_model(self, ds: RuleBasedDataset):
X, y = ds.X(na_replacement=np.nan), ds.y(na_replacement=np.nan)
model = EnsembleClassifierChain(**s.DEFAULT_MODEL_PARAMS)
@ -2967,7 +2967,7 @@ class ApplicabilityDomain(EnviPathModel):
return distances
@staticmethod
def _compute_compatibility(rule_idx: int, preds, neighbours: List[Tuple[int, "Dataset"]]):
def _compute_compatibility(rule_idx: int, preds, neighbours: List[Tuple[int, "RuleBasedDataset"]]):
tp, tn, fp, fn = 0.0, 0.0, 0.0, 0.0
accuracy = 0.0
@ -3112,7 +3112,7 @@ class EnviFormer(PackageBasedModel):
json.dump(ds, d_file)
return ds
def load_dataset(self) -> "Dataset":
def load_dataset(self) -> "RuleBasedDataset":
ds_path = os.path.join(s.MODEL_DIR, f"{self.uuid}_ds.json")
with open(ds_path) as d_file:
ds = json.load(d_file)