def test_shape_validation2():
m = utils.load_model('PhenoGrass')()
uneven_data = predictor_vars.copy()
# Drop a single timestep of evapotranspiration data
uneven_data['evap'] = uneven_data['evap'][:-1]
with pytest.raises(ValueError):
m.fit(GCC, uneven_data, optimizer_params=quick_testing_params)
def test_shape_validation1():
m = utils.load_model('PhenoGrass')()
uneven_data = predictor_vars.copy()
# Drop a single site of mean avg precip data
uneven_data['MAP'] = uneven_data['MAP'][:-1]
with pytest.raises(ValueError):
m.fit(GCC, uneven_data, optimizer_params=quick_testing_params)
def test_shape_validation3():
m = utils.load_model('PhenoGrass')()
# Drop a single site of gcc data
with pytest.raises(ValueError):
m.fit(GCC[:, :-1],
predictor_vars,
optimizer_params=quick_testing_params)
def test_predictor_validation1():
# Raise error when not all predictors are passed
m = utils.load_model('PhenoGrass')()
data_with_missing_var = predictor_vars.copy()
_ = data_with_missing_var.pop('precip')
with pytest.raises(ValueError):
m.fit(GCC,
data_with_missing_var,
optimizer_params=quick_testing_params)
def test_predictor_validation2():
# Raise error when unknown predictors are pass
# CholerPR does not need everything
m = utils.load_model('CholerPR1')()
with pytest.raises(ValueError):
m.fit(GCC, predictor_vars, optimizer_params=quick_testing_params)
]
GCC, predictor_vars = utils.load_test_data()
quick_testing_params = {
'maxiter': 3,
'popsize': 2,
'mutation': (0.5, 1),
'recombination': 0.25,
'disp': False
}
# Setup a list of test cases where each one = (model_name, fitted_model object)
fitted_models = []
for name in core_model_names:
m = utils.load_model(name)()
models.validation.validate_model(m)
this_model_data = {
k: predictor_vars[k]
for k in m._required_predictors.keys()
}
m.fit(GCC, this_model_data, optimizer_params=quick_testing_params)
fitted_models.append(m)
model_test_cases = list(zip(core_model_names, fitted_models))
#######################################################################
@pytest.mark.parametrize('model_name, fitted_model', model_test_cases)
def test_state_variables_match1(model_name, fitted_model):