def test_choose_param_init(pheno_path, testdata):
model = Model(pheno_path)
params = (model.parameters['OMEGA(1,1)'], model.parameters['OMEGA(2,2)'])
rvs = RandomVariables(model.random_variables.etas)
init = _choose_param_init(model, rvs, params)
assert init == 0.0118179
model = Model(pheno_path)
model.source.path = testdata # Path where there is no .ext-file
init = _choose_param_init(model, rvs, params)
assert init == 0.0031045
model = Model(pheno_path)
omega1 = S('OMEGA(3,3)')
x = stats.Normal('ETA(3)', 0, sympy.sqrt(omega1))
x.variability_level = VariabilityLevel.IIV
rvs.add(x)
ie = model.modelfit_results.individual_estimates
ie['ETA(3)'] = ie['ETA(1)']
model.modelfit_results = ModelfitResults(individual_estimates=ie)
init = _choose_param_init(model, rvs, params)
assert init == 0.0118179
def psn_frem_results(path,
force_posdef_covmatrix=False,
force_posdef_samples=500,
method=None):
"""Create frem results from a PsN FREM run
:param path: Path to PsN frem run directory
:return: A :class:`FREMResults` object
"""
path = Path(path)
model_4_path = path / 'final_models' / 'model_4.mod'
if not model_4_path.is_file():
raise IOError(f'Could not find FREM model 4: {str(model_4_path)}')
model_4 = Model(model_4_path)
if model_4.modelfit_results is None:
raise ValueError('Model 4 has no results')
cov_model = None
if method == 'cov_sampling':
try:
model_4.modelfit_results.covariance_matrix
except Exception:
model_4b_path = path / 'final_models' / 'model_4b.mod'
try:
model_4b = Model(model_4b_path)
except FileNotFoundError:
pass
else:
cov_model = model_4b
with open(path / 'covariates_summary.csv') as covsum:
covsum.readline()
raw_cov_list = covsum.readline()
all_covariates = raw_cov_list[1:].rstrip().split(',')
# FIXME: Not introducing yaml parser in pharmpy just yet. Options should be collected
# differently. Perhaps using json
logtransformed_covariates = []
with open(path / 'meta.yaml') as meta:
for row in meta:
row = row.strip()
if row.startswith('rescale: 1'):
rescale = True
elif row.startswith('rescale: 0'):
rescale = False
if row.startswith("log: ''"):
logtransformed_covariates = []
elif row.startswith('log: '):
logtransformed_covariates = row[5:].split(',')
# add log transformed columns for the -log option. Should be done when creating dataset
df = model_4.dataset
if logtransformed_covariates:
for lncov in logtransformed_covariates:
df[f'LN{lncov}'] = np.log(df[lncov])
model_4.dataset = df
nunique = model_4.dataset.pharmpy.baselines[all_covariates].nunique()
continuous = list(nunique.index[nunique != 2])
categorical = list(nunique.index[nunique == 2])
intmod_names = [
'model_1.mod', 'model_2.mod', 'model_3.mod', 'model_3b.mod'
]
intmods = []
for m in intmod_names:
intmod_path = path / 'm1' / m
if intmod_path.is_file():
intmod = Model(intmod_path)
intmods.append(intmod)
model1b = Model(path / 'm1' / 'model_1b.mod')
model1 = intmods[0]
model1b.modelfit_results = model1.modelfit_results
model1b.modelfit_results.parameter_estimates = model1b.parameters.nonfixed_inits
psn_reorder_base_model_inits(model1b, path)
res = calculate_results(
model_4,
continuous,
categorical,
method=method,
force_posdef_covmatrix=force_posdef_covmatrix,
force_posdef_samples=force_posdef_samples,
cov_model=cov_model,
rescale=rescale,
intermediate_models=intmods,
seed=np.random.default_rng(9843),
)
return res
