def test_dataset_validation():
# make dummy datasets
dummy3 = [1, 2, 3]
dummy4 = [1, 2, 3, 4]
# these should be valid
bmds.DichotomousDataset(doses=dummy3, ns=dummy3, incidences=dummy3)
bmds.ContinuousDataset(doses=dummy3,
ns=dummy3,
responses=dummy3,
stdevs=dummy3)
# these should raise errors
with pytest.raises(ValueError):
# different sized lists
bmds.DichotomousDataset(doses=dummy4, ns=dummy3, incidences=dummy3)
bmds.ContinuousDataset(doses=dummy4,
ns=dummy3,
responses=dummy3,
stdevs=dummy3)
# 2 remaining after dropping-doses
bmds.DichotomousDataset(doses=dummy3,
ns=dummy3,
incidences=dummy3,
doses_dropped=1)
bmds.ContinuousDataset(doses=dummy3,
ns=dummy3,
responses=dummy3,
stdevs=dummy3,
doses_dropped=1)
def test_doses_used():
ds5 = [1, 2, 3, 4, 5]
ds = bmds.DichotomousDataset(ds5, ds5, ds5)
assert ds.doses_used == 5
ds = bmds.DichotomousDataset(ds5, ds5, ds5, doses_dropped=2)
assert ds.doses_used == 3
ds = bmds.ContinuousDataset(ds5, ds5, ds5, ds5)
assert ds.doses_used == 5
ds = bmds.ContinuousDataset(ds5, ds5, ds5, ds5, doses_dropped=2)
assert ds.doses_used == 3
def build_session(inputs, dataset):
bmds_version = inputs['bmds_version']
dataset_type = inputs['dataset_type']
models = inputs.get('models')
bmr = inputs.get('bmr')
# build dataset
if dataset_type == bmds.constants.CONTINUOUS:
dataset = bmds.ContinuousDataset(doses=dataset['doses'],
ns=dataset['ns'],
means=dataset['means'],
stdevs=dataset['stdevs'])
elif dataset_type == bmds.constants.CONTINUOUS_INDIVIDUAL:
dataset = bmds.ContinuousIndividualDataset(
doses=dataset['doses'], responses=dataset['responses'])
else:
dataset = bmds.DichotomousDataset(
doses=dataset['doses'],
ns=dataset['ns'],
incidences=dataset['incidences'],
)
# build session
session = bmds.BMDS.versions[bmds_version](dataset_type,
dataset=dataset)
# get BMR
global_overrides = {}
if bmr is not None:
global_overrides = {
'bmr':
bmr['value'],
'bmr_type':
bmds.constants.BMR_CROSSWALK[dataset_type][bmr['type']],
}
# Add models to session
if models is None:
session.add_default_models(global_overrides=global_overrides)
else:
for model in models:
overrides = deepcopy(global_overrides)
if 'settings' in model:
overrides.update(model['settings'])
session.add_model(model['name'], overrides=overrides)
return session
def build_session(inputs, dataset):
bmds_version = inputs["bmds_version"]
dataset_type = inputs["dataset_type"]
models = inputs.get("models")
bmr = inputs.get("bmr")
# build dataset
if dataset_type == bmds.constants.CONTINUOUS:
dataset = bmds.ContinuousDataset(
doses=dataset["doses"],
ns=dataset["ns"],
means=dataset["means"],
stdevs=dataset["stdevs"],
)
elif dataset_type == bmds.constants.CONTINUOUS_INDIVIDUAL:
dataset = bmds.ContinuousIndividualDataset(
doses=dataset["doses"], responses=dataset["responses"])
else:
dataset = bmds.DichotomousDataset(doses=dataset["doses"],
ns=dataset["ns"],
incidences=dataset["incidences"])
# build session
session = bmds.BMDS.versions[bmds_version](dataset_type,
dataset=dataset)
# get BMR
global_overrides = {}
if bmr is not None:
global_overrides = {
"bmr":
bmr["value"],
"bmr_type":
bmds.constants.BMR_CROSSWALK[dataset_type][bmr["type"]],
}
# Add models to session
if models is None:
session.add_default_models(global_overrides=global_overrides)
else:
for model in models:
overrides = deepcopy(global_overrides)
if "settings" in model:
overrides.update(model["settings"])
session.add_model(model["name"], overrides=overrides)
return session
def test_dfile_outputs():
dummy4 = [1, 2, 3, 4]
# check dichotomous
ds = bmds.DichotomousDataset(doses=dummy4,
ns=[5, 5, 5, 5],
incidences=[0, 1, 2, 3],
doses_dropped=1)
dfile = ds.as_dfile()
expected = 'Dose Incidence NEGATIVE_RESPONSE\n1.000000 0 5\n2.000000 1 4\n3.000000 2 3' # noqa
assert dfile == expected
# check continuous
ds = bmds.ContinuousDataset(doses=dummy4,
ns=dummy4,
responses=dummy4,
stdevs=dummy4,
doses_dropped=1)
dfile = ds.as_dfile()
expected = 'Dose NumAnimals Response Stdev\n1.000000 1 1.000000 1.000000\n2.000000 2 2.000000 2.000000\n3.000000 3 3.000000 3.000000' # noqa
assert dfile == expected
def get_endpoint_dataset(self):
ds = self.endpoint.get_json(json_encode=False)
doses = [
dose['dose']
for dose in ds['animal_group']['dosing_regime']['doses']
if dose['dose_units']['id'] == self.dose_units_id
]
grps = ds['groups']
if self.endpoint.data_type == 'C':
return bmds.ContinuousDataset(
doses=doses,
ns=[d['n'] for d in grps],
means=[d['response'] for d in grps],
stdevs=[d['stdev'] for d in grps],
)
else:
return bmds.DichotomousDataset(
doses=doses,
ns=[d['n'] for d in grps],
incidences=[d['incidence'] for d in grps],
)
def dataset():
return bmds.DichotomousDataset(doses=[0, 1.96, 5.69, 29.75],
ns=[75, 49, 50, 49],
incidences=[5, 1, 3, 14])
import bmds
from bmds.bmds3.models.dichotomous import Logistic
dataset = bmds.DichotomousDataset(doses=[0, 50, 100, 150, 200],
ns=[100, 100, 100, 100, 100],
incidences=[0, 5, 30, 65, 90])
model = Logistic(dataset=dataset)
result = model.execute()
print(result.dict())