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[Chore] Linted Files (#150)

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Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: enviPath/enviPy#150
This commit is contained in:
jebus
2025-10-09 07:25:13 +13:00
parent 22f0bbe10b
commit afeb56622c
50 changed files with 5616 additions and 4408 deletions
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53
epdb/management/commands/create_ml_models.py
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@ -12,11 +12,28 @@ class Command(BaseCommand):
the below command would be used:
`python manage.py create_ml_models enviformer mlrr -d bbd soil -e sludge
"""
def add_arguments(self, parser):
parser.add_argument("model_names", nargs="+", type=str, help="The names of models to train. Options are: enviformer, mlrr")
parser.add_argument("-d", "--data-packages", nargs="+", type=str, help="Packages for training")
parser.add_argument("-e", "--eval-packages", nargs="*", type=str, help="Packages for evaluation", default=[])
parser.add_argument("-r", "--rule-packages", nargs="*", type=str, help="Rule Packages mandatory for MLRR", default=[])
parser.add_argument(
"model_names",
nargs="+",
type=str,
help="The names of models to train. Options are: enviformer, mlrr",
)
parser.add_argument(
"-d", "--data-packages", nargs="+", type=str, help="Packages for training"
)
parser.add_argument(
"-e", "--eval-packages", nargs="*", type=str, help="Packages for evaluation", default=[]
)
parser.add_argument(
"-r",
"--rule-packages",
nargs="*",
type=str,
help="Rule Packages mandatory for MLRR",
default=[],
)
@transaction.atomic
def handle(self, *args, **options):
@ -28,7 +45,9 @@ class Command(BaseCommand):
sludge = Package.objects.filter(name="EAWAG-SLUDGE")[0]
sediment = Package.objects.filter(name="EAWAG-SEDIMENT")[0]
except IndexError:
raise IndexError("Can't find correct packages. They should be created with the bootstrap command")
raise IndexError(
"Can't find correct packages. They should be created with the bootstrap command"
)
def decode_packages(package_list):
"""Decode package strings into their respective packages"""
@ -52,15 +71,27 @@ class Command(BaseCommand):
data_packages = decode_packages(options["data_packages"])
eval_packages = decode_packages(options["eval_packages"])
rule_packages = decode_packages(options["rule_packages"])
for model_name in options['model_names']:
for model_name in options["model_names"]:
model_name = model_name.lower()
if model_name == "enviformer" and s.ENVIFORMER_PRESENT:
model = EnviFormer.create(pack, data_packages=data_packages, eval_packages=eval_packages, threshold=0.5,
name="EnviFormer - T0.5", description="EnviFormer transformer")
model = EnviFormer.create(
pack,
data_packages=data_packages,
eval_packages=eval_packages,
threshold=0.5,
name="EnviFormer - T0.5",
description="EnviFormer transformer",
)
elif model_name == "mlrr":
model = MLRelativeReasoning.create(package=pack, rule_packages=rule_packages,
data_packages=data_packages, eval_packages=eval_packages, threshold=0.5,
name='ECC - BBD - T0.5', description='ML Relative Reasoning')
model = MLRelativeReasoning.create(
package=pack,
rule_packages=rule_packages,
data_packages=data_packages,
eval_packages=eval_packages,
threshold=0.5,
name="ECC - BBD - T0.5",
description="ML Relative Reasoning",
)
else:
raise ValueError(f"Cannot create model of type {model_name}, unknown model type")
# Build the dataset for the model, train it, evaluate it and save it