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 test_extract_from_block():
etas = JointNormalSeparate(['ETA(1)', 'ETA(2)'], [0, 0],
[[3, 0.25], [0.25, 1]])
for rv in etas:
rv.variability_level = VariabilityLevel.IIV
rvs = RandomVariables(etas)
eta3 = stats.Normal('ETA(3)', 0.5, 2)
eta3.variability_level = VariabilityLevel.IIV
rvs.add(eta3)
dists = rvs.distributions()
assert len(dists) == 2
rvs.extract_from_block(etas[0])
dists = rvs.distributions()
assert len(dists) == 3
assert rvs[0].name == 'ETA(1)'
assert rvs[2].name == 'ETA(3)'
def create_rv_block(model, list_of_rvs=None):
"""
Creates a full or partial block structure of etas. The etas must be IIVs and cannot
be fixed. Initial estimates for covariance between the etas is dependent on whether
the model has results from a previous results. In that case, the correlation will
be calculated from individual estimates, otherwise correlation will be set to 10%.
Parameters
----------
model : Model
Pharmpy model to create block effect on.
list_of_rvs : list
List of etas to create a block structure from. If None, all etas that are IIVs and
non-fixed will be used (full block). None is default.
"""
rvs_full = model.random_variables
rvs_block = _get_rvs(model, list_of_rvs)
if list_of_rvs is not None:
for rv in rvs_block:
if isinstance(rv.pspace.distribution, MultivariateNormalDistribution):
if rvs_full.get_rvs_from_same_dist(rv):
rv_extracted = rvs_full.extract_from_block(rv)
rvs_block.discard(rv)
rvs_block.add(rv_extracted)
pset = _merge_rvs(model, rvs_block)
rvs_new = RandomVariables()
are_consecutive = rvs_full.are_consecutive(rvs_block)
for rv in rvs_full:
if rv.name not in [rv.name for rv in rvs_block.etas]:
rvs_new.add(rv)
elif are_consecutive:
rvs_new.add(rvs_block[rv.name])
if not are_consecutive:
{rvs_new.add(rv) for rv in rvs_block} # Add new block last
model.random_variables = rvs_new
model.parameters = pset
return model
def random_variables(self, start_omega, previous_cov=None):
"""Get a RandomVariableSet for this omega record
start_omega - the first omega in this record
previous_sigma - the matrix of the previous omega block
"""
next_cov = None # The cov matrix if a block
block = self.root.find('block')
bare_block = self.root.find('bare_block')
zero_fix = []
if not (block or bare_block):
rvs = RandomVariables()
i = start_omega
numetas = len(self.root.all('diag_item'))
for node in self.root.all('diag_item'):
init = node.init.NUMERIC
fixed = bool(node.find('FIX'))
name = self._rv_name(i)
if not (init == 0 and fixed): # 0 FIX are not RVs
eta = sympy.stats.Normal(name, 0, sympy.sqrt(
sympy.Symbol(f'{self.name}({i},{i})')))
rvs.add(eta)
else:
zero_fix.append(name)
i += 1
else:
if bare_block:
numetas = previous_cov.rows
else:
numetas = self.root.block.size.INT
same = bool(self.root.find('same'))
params, _, _ = self.parameters(start_omega, previous_cov.rows if
hasattr(previous_cov, 'rows') else None)
all_zero_fix = True
for param in params:
if not (param.init == 0 and param.fix):
all_zero_fix = False
if all_zero_fix and len(params) > 0 or (previous_cov == 'ZERO' and same):
names = [self._rv_name(i) for i in range(start_omega, start_omega + numetas)]
return RandomVariables(), start_omega + numetas, 'ZERO', names
if numetas > 1:
names = [self._rv_name(i) for i in range(start_omega, start_omega + numetas)]
means = [0] * numetas
if same:
rvs = JointNormalSeparate(names, means, previous_cov)
next_cov = previous_cov
else:
cov = sympy.zeros(numetas)
for row in range(numetas):
for col in range(row + 1):
cov[row, col] = sympy.Symbol(
f'{self.name}({start_omega + row},{start_omega + col})')
if row != col:
cov[col, row] = cov[row, col]
next_cov = cov
rvs = JointNormalSeparate(names, means, cov)
else:
rvs = RandomVariables()
name = self._rv_name(start_omega)
if same:
symbol = previous_cov
else:
symbol = sympy.Symbol(f'{self.name}({start_omega},{start_omega})')
eta = sympy.stats.Normal(name, 0, sympy.sqrt(symbol))
next_cov = symbol
rvs.add(eta)
if self.name == 'OMEGA':
level = VariabilityLevel.IIV
else:
level = VariabilityLevel.RUV
for rv in rvs:
rv.variability_level = level
return rvs, start_omega + numetas, next_cov, zero_fix
def random_variables(self, start_omega, previous_cov=None):
"""Get a RandomVariableSet for this omega record
start_omega - the first omega in this record
previous_cov - the matrix of the previous omega block
"""
same = bool(self.root.find('same'))
if not hasattr(self, 'name_map') and not same:
if isinstance(previous_cov, sympy.Symbol):
prev_size = 1
elif previous_cov is not None:
prev_size = len(previous_cov)
else:
prev_size = None
self.parameters(start_omega, prev_size)
if hasattr(self, 'name_map'):
rev_map = {value: key for key, value in self.name_map.items()}
next_cov = None # The cov matrix if a block
block = self.root.find('block')
bare_block = self.root.find('bare_block')
zero_fix = []
etas = []
if not (block or bare_block):
rvs = RandomVariables()
i = start_omega
numetas = len(self.root.all('diag_item'))
for node in self.root.all('diag_item'):
init = node.init.NUMERIC
fixed = bool(node.find('FIX'))
name = self._rv_name(i)
if not (init == 0 and fixed): # 0 FIX are not RVs
eta = sympy.stats.Normal(
name, 0, sympy.sqrt(symbol(rev_map[(i, i)])))
rvs.add(eta)
etas.append(eta.name)
else:
zero_fix.append(name)
etas.append(name)
i += 1
else:
if bare_block:
numetas = previous_cov.rows
else:
numetas = self.root.block.size.INT
params, _, _ = self.parameters(
start_omega,
previous_cov.rows if hasattr(previous_cov, 'rows') else None)
all_zero_fix = True
for param in params:
if not (param.init == 0 and param.fix):
all_zero_fix = False
if all_zero_fix and len(params) > 0 or (previous_cov == 'ZERO'
and same):
names = [
self._rv_name(i)
for i in range(start_omega, start_omega + numetas)
]
self.eta_map = {
eta: start_omega + i
for i, eta in enumerate(names)
}
return RandomVariables(), start_omega + numetas, 'ZERO', names
if numetas > 1:
names = [
self._rv_name(i)
for i in range(start_omega, start_omega + numetas)
]
means = [0] * numetas
if same:
rvs = JointNormalSeparate(names, means, previous_cov)
etas = [rv.name for rv in rvs]
next_cov = previous_cov
else:
cov = sympy.zeros(numetas)
for row in range(numetas):
for col in range(row + 1):
cov[row,
col] = symbol(rev_map[(start_omega + row,
start_omega + col)])
if row != col:
cov[col, row] = cov[row, col]
next_cov = cov
rvs = JointNormalSeparate(names, means, cov)
etas = [rv.name for rv in rvs]
else:
rvs = RandomVariables()
name = self._rv_name(start_omega)
if same:
sym = previous_cov
else:
sym = symbol(rev_map[(start_omega, start_omega)])
eta = sympy.stats.Normal(name, 0, sympy.sqrt(sym))
next_cov = sym
rvs.add(eta)
etas.append(eta.name)
if self.name == 'OMEGA':
if same:
level = VariabilityLevel.IOV
else:
level = VariabilityLevel.IIV
else:
level = VariabilityLevel.RUV
for rv in rvs:
rv.variability_level = level
self.eta_map = {eta: start_omega + i for i, eta in enumerate(etas)}
return rvs, start_omega + numetas, next_cov, zero_fix