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[Enhancement] Create ML Models (#173)

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## 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>
This commit is contained in:
liambrydon
2025-10-23 06:20:22 +13:00
committed by jebus
parent 8fda2577ee
commit 551cfc7768
3 changed files with 36 additions and 14 deletions
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36
epdb/management/commands/create_ml_models.py
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@ -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]`
For example, to train both EnviFormer and MLRelativeReasoning on BBD and SOIL and evaluate them on SLUDGE
the below command would be used:
`python manage.py create_ml_models enviformer mlrr -d bbd soil -e sludge
`python manage.py create_ml_models [model_names] -d [data_packages] FOR MLRR ONLY: -r [rule_packages]
OPTIONAL: -e [eval_packages] -t threshold`
For example, to train both EnviFormer and MLRelativeReasoning on BBD and SOIL and evaluate them on SLUDGE with a
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,
name="EnviFormer - T0.5",
description="EnviFormer transformer",
threshold=options['threshold'],
name=f"EnviFormer - {', '.join(options['data_packages'])} - T{options['threshold']:.2f}",
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,
name="ECC - BBD - T0.5",
description="ML Relative Reasoning",
threshold=options['threshold'],
name=f"ECC - {', '.join(options['data_packages'])} - T{options['threshold']:.2f}",
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")