def test_metrics_and_loss_caluclation(self, metric):
if metric == 'bad_metric':
with pytest.raises(NotImplementedError):
StaticDatatypes.measurement_wo_errs.get_loss(metric=metric, predictions=[StaticDatatypes.modelstate])
# Only for metric SS (sum-of-squares) the loss can be calculated from Measurement objects without having errors
elif metric == 'SS':
assert not np.isnan(StaticDatatypes.measurement_wo_errs.get_loss(metric=metric, predictions=[StaticDatatypes.modelstate]))
assert not np.isnan(StaticDatatypes.measurement_w_errs.get_loss(metric=metric, predictions=[StaticDatatypes.modelstate]))
else:
with pytest.raises(AttributeError):
StaticDatatypes.measurement_wo_errs.get_loss(metric=metric, predictions=[StaticDatatypes.modelstate])
assert not np.isnan(StaticDatatypes.measurement_w_errs.get_loss(metric=metric, predictions=[StaticDatatypes.modelstate]))
# Fail fast for ambiguous, non-unique predictions: list of prediction with the same 'name' and 'replicate_id'
with pytest.raises(ValueError):
StaticDatatypes.measurement_w_errs.get_loss(metric=metric, predictions=[StaticDatatypes.modelstate]*2)
modelstate = ModelState(
name=StaticHelpers.name,
timepoints=StaticHelpers.timepoints[::2], # Use less timepoints
values=StaticHelpers.values[::2], # Use less values, corresponding to using less timepoints
)
# Using predictions that have not matching replicate_ids returns nan loss
assert np.isnan(StaticDatatypes.measurement_wo_errs.get_loss(metric='SS', predictions=[modelstate]))
# When adding at least one matching prediction, a loss can be calculated
assert not np.isnan(StaticDatatypes.measurement_w_errs.get_loss(metric=metric, predictions=[modelstate, StaticDatatypes.modelstate]))
class StaticDatatypes():
timeseries = TimeSeries(
name=StaticHelpers.name,
timepoints=StaticHelpers.timepoints,
values=StaticHelpers.values,
replicate_id=StaticHelpers.replicate_id
)
modelstate = ModelState(
name=StaticHelpers.name,
timepoints=StaticHelpers.timepoints,
values=StaticHelpers.values,
replicate_id=StaticHelpers.replicate_id
)
measurement_wo_errs = Measurement(
name=StaticHelpers.name,
timepoints=StaticHelpers.timepoints,
values=StaticHelpers.values,
replicate_id=StaticHelpers.replicate_id
)
measurement_w_errs = Measurement(
name=StaticHelpers.name,
timepoints=StaticHelpers.timepoints,
values=StaticHelpers.values,
errors=StaticHelpers.errors,
replicate_id=StaticHelpers.replicate_id
)
observation = Observation(
name=StaticHelpers.name,
timepoints=StaticHelpers.timepoints,
values=StaticHelpers.values,
observed_state=StaticHelpers.state,
replicate_id=StaticHelpers.replicate_id
)
sensitivity = Sensitivity(
timepoints=StaticHelpers.timepoints,
values=StaticHelpers.values,
response=StaticHelpers.state,
parameter=StaticHelpers.parameter,
replicate_id=StaticHelpers.replicate_id
)
def test_init_datatypes(self, values, errors, info, replicate_id):
"""
To test typical instantiations of datatypes.
"""
# Standard instatiations
TimeSeries(name=StaticHelpers.name, timepoints=StaticHelpers.timepoints, values=values, info=info, replicate_id=replicate_id)
ModelState(name=StaticHelpers.name, timepoints=StaticHelpers.timepoints, values=values, info=info, replicate_id=replicate_id)
Measurement(name=StaticHelpers.name, timepoints=StaticHelpers.timepoints, values=values, errors=errors, info=info, replicate_id=replicate_id)
Observation(name=StaticHelpers.name, timepoints=StaticHelpers.timepoints, values=values, observed_state='y1', replicate_id=replicate_id)
Sensitivity(timepoints=StaticHelpers.timepoints, values=values, response='y1', parameter='p1', replicate_id=replicate_id)
Sensitivity(timepoints=StaticHelpers.timepoints, values=values, response='y1', parameter='p1', h=1e-8, replicate_id=replicate_id)
# Must provide timepoints
with pytest.raises(ValueError):
TimeSeries(name=StaticHelpers.name, timepoints=None, values=values)
# Must provide values
with pytest.raises(ValueError):
TimeSeries(name=StaticHelpers.name, timepoints=StaticHelpers.timepoints, values=None)
# Measurements can be created with error_models
Measurement(
name=StaticHelpers.name,
timepoints=StaticHelpers.timepoints,
values=values,
error_model=StaticErrorModelHelpers.constant_error_model,
error_model_parameters=StaticErrorModelHelpers.constant_error_model_parameters,
)
# Must provide a subclass of rvs.continous as p.d.f.
with pytest.raises(ValueError):
Measurement(name=StaticHelpers.name, timepoints=StaticHelpers.timepoints, values=values, error_distribution=scipy.stats.bernoulli)
# Error values must be >0
with pytest.raises(ValueError):
Measurement(name=StaticHelpers.name, timepoints=StaticHelpers.timepoints, values=values, errors=[0]*len(StaticHelpers.values))
with pytest.raises(ValueError):
Measurement(name=StaticHelpers.name, timepoints=StaticHelpers.timepoints, values=values, errors=[-1]*len(StaticHelpers.values))
def test_init_observe(self, simulator, obsfun_name):
obsfun = ObservationFunctionLibrary.observation_functions[obsfun_name]
obsfun_parameters = ObservationFunctionLibrary.observation_function_parameters[
obsfun_name]
observed_state = ObservationFunctionLibrary.observed_states[
obsfun_name]
obsfuns_params = [(obsfun, {
**obsfun_parameters, 'observed_state':
observed_state
})]
observer = ModelObserver(
observation_functions_parameters=obsfuns_params)
# The observed_state must be indicated in the dictionary with observation function parameters
with pytest.raises(KeyError):
ModelObserver(
observation_functions_parameters=[(obsfun, obsfun_parameters)])
# Create and observe a Modelstate
modelstate = ModelState(name=observed_state,
timepoints=[1, 2, 3],
values=[10, 20, 30])
observer.get_observations(model_states=[modelstate])
# There is also a str method
print(observer)