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start towards #120

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Liam Brydon
2025-10-22 08:22:29 +13:00
parent 376fd65785
commit 2980a75daa
3 changed files with 53 additions and 24 deletions
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20
epdb/models.py
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@ -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)

4
tests/test_dataset.py
View File

@ -2,7 +2,7 @@ from django.test import TestCase
from epdb.logic import PackageManager
from epdb.models import Reaction, Compound, User, Rule
from utilities.ml import Dataset
from utilities.ml import RuleBasedDataset
class DatasetTest(TestCase):
@ -46,7 +46,7 @@ class DatasetTest(TestCase):
reactions = [r for r in Reaction.objects.filter(package=self.package)]
applicable_rules = [self.rule1]
ds = Dataset.generate_dataset(reactions, applicable_rules)
ds = RuleBasedDataset.generate_dataset(reactions, applicable_rules)
self.assertEqual(len(ds.y()), 1)
self.assertEqual(sum(ds.y()[0]), 1)

53
utilities/ml.py
View File

@ -6,6 +6,7 @@ from collections import defaultdict
from datetime import datetime
from pathlib import Path
from typing import List, Dict, Set, Tuple, TYPE_CHECKING
from abc import ABC, abstractmethod
import networkx as nx
import numpy as np
@ -26,7 +27,21 @@ if TYPE_CHECKING:
from epdb.models import Rule, CompoundStructure, Reaction
class Dataset:
class Dataset(ABC):
@abstractmethod
def X(self):
pass
@abstractmethod
def y(self):
pass
@abstractmethod
def __getitem__(self, item):
pass
class RuleBasedDataset(Dataset):
def __init__(
self, columns: List[str], num_labels: int, data: List[List[str | int | float]] = None
):
@ -78,18 +93,18 @@ class Dataset:
def observed(self) -> Tuple[int, int]:
return self._observed
def at(self, position: int) -> Dataset:
return Dataset(self.columns, self.num_labels, [self.data[position]])
def at(self, position: int) -> RuleBasedDataset:
return RuleBasedDataset(self.columns, self.num_labels, [self.data[position]])
def limit(self, limit: int) -> Dataset:
return Dataset(self.columns, self.num_labels, self.data[:limit])
def limit(self, limit: int) -> RuleBasedDataset:
return RuleBasedDataset(self.columns, self.num_labels, self.data[:limit])
def __iter__(self):
return (self.at(i) for i, _ in enumerate(self.data))
def classification_dataset(
self, structures: List[str | "CompoundStructure"], applicable_rules: List["Rule"]
) -> Tuple[Dataset, List[List[PredictionResult]]]:
) -> Tuple[RuleBasedDataset, List[List[PredictionResult]]]:
classify_data = []
classify_products = []
for struct in structures:
@ -117,14 +132,14 @@ class Dataset:
classify_data.append([struct_id] + features + trig + ([-1] * len(trig)))
classify_products.append(prods)
return Dataset(
return RuleBasedDataset(
columns=self.columns, num_labels=self.num_labels, data=classify_data
), classify_products
@staticmethod
def generate_dataset(
reactions: List["Reaction"], applicable_rules: List["Rule"], educts_only: bool = True
) -> Dataset:
) -> RuleBasedDataset:
_structures = set()
for r in reactions:
@ -231,7 +246,7 @@ class Dataset:
+ [f"trig_{r.uuid}" for r in applicable_rules]
+ [f"obs_{r.uuid}" for r in applicable_rules]
)
ds = Dataset(header, len(applicable_rules))
ds = RuleBasedDataset(header, len(applicable_rules))
ds.add_row([str(comp.uuid)] + feat + trig + obs)
@ -289,7 +304,7 @@ class Dataset:
pickle.dump(self, fh)
@staticmethod
def load(path: "Path") -> "Dataset":
def load(path: "Path") -> "RuleBasedDataset":
import pickle
return pickle.load(open(path, "rb"))
@ -319,6 +334,20 @@ class Dataset:
)
class EnviFormerDataset(Dataset):
def __init__(self):
pass
def X(self):
pass
def y(self):
pass
def __getitem__(self, item):
pass
class SparseLabelECC(BaseEstimator, ClassifierMixin):
"""
Ensemble of Classifier Chains with sparse label removal.
@ -598,7 +627,7 @@ class ApplicabilityDomainPCA(PCA):
self.min_vals = None
self.max_vals = None
def build(self, train_dataset: "Dataset"):
def build(self, train_dataset: "RuleBasedDataset"):
# transform
X_scaled = self.scaler.fit_transform(train_dataset.X())
# fit pca
@ -612,7 +641,7 @@ class ApplicabilityDomainPCA(PCA):
instances_pca = self.transform(instances_scaled)
return instances_pca
def is_applicable(self, classify_instances: "Dataset"):
def is_applicable(self, classify_instances: "RuleBasedDataset"):
instances_pca = self.__transform(classify_instances.X())
is_applicable = []