forked from enviPath/enviPy
liambrydon
d2f4fdc58a
## Changes - I have finished the backend integration of EnviFormer (#19), this includes, dataset building, model finetuning, model evaluation and model prediction with the finetuned model. - `PackageBasedModel` has been adjusted to be more abstract, this includes making the `_save_model` method and making `compute_averages` a static class function. - I had to bump the python-version in `pyproject.toml` to >=3.12 from >=3.11 otherwise uv failed to install EnviFormer. - The default EnviFormer loading during `settings.py` has been removed. ## Future Fix I noticed you have a little bit of code in `PackageBasedModel` -> `evaluate_model` for using the `eval_packages` during evaluation instead of train/test splits on `data_packages`. It doesn't seem finished, I presume we want this for all models, so I will take care of that in a new branch/pullrequest after this request is merged. Also, I haven't done anything for a POST request to finetune the model, I'm not sure if that is something we want now. Co-authored-by: Liam Brydon <62733830+MyCreativityOutlet@users.noreply.github.com> Reviewed-on: enviPath/enviPy#141 Reviewed-by: jebus <lorsbach@envipath.com> Co-authored-by: liambrydon <lbry121@aucklanduni.ac.nz> Co-committed-by: liambrydon <lbry121@aucklanduni.ac.nz>
75 lines
3.9 KiB
Python
75 lines
3.9 KiB
Python
from django.conf import settings as s
|
|
from django.core.management.base import BaseCommand
|
|
from django.db import transaction
|
|
|
|
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
|
|
"""
|
|
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=[])
|
|
|
|
@transaction.atomic
|
|
def handle(self, *args, **options):
|
|
# Find Public Prediction Models package to add new models to
|
|
try:
|
|
pack = Package.objects.filter(name="Public Prediction Models")[0]
|
|
bbd = Package.objects.filter(name="EAWAG-BBD")[0]
|
|
soil = Package.objects.filter(name="EAWAG-SOIL")[0]
|
|
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")
|
|
|
|
def decode_packages(package_list):
|
|
"""Decode package strings into their respective packages"""
|
|
packages = []
|
|
for p in package_list:
|
|
p = p.lower()
|
|
if p == "bbd":
|
|
packages.append(bbd)
|
|
elif p == "soil":
|
|
packages.append(soil)
|
|
elif p == "sludge":
|
|
packages.append(sludge)
|
|
elif p == "sediment":
|
|
packages.append(sediment)
|
|
else:
|
|
raise ValueError(f"Unknown package {p}")
|
|
return packages
|
|
|
|
# Iteratively create models in options["model_names"]
|
|
print(f"Creating models: {options['model_names']}")
|
|
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']:
|
|
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")
|
|
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')
|
|
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
|
|
print(f"Building dataset for {model_name}")
|
|
model.build_dataset()
|
|
print(f"Training {model_name}")
|
|
model.build_model()
|
|
print(f"Evaluating {model_name}")
|
|
model.evaluate_model()
|
|
print(f"Saving {model_name}")
|
|
model.save()
|