from __future__ import annotations
from collections.abc import Mapping
from typing import Iterable, Optional, Union
from pharmpy.basic import Expr
from pharmpy.model import Assignment, Model, Parameter, Parameters, RandomVariables
[docs]
def get_thetas(model: Model):
"""Get all thetas (structural parameters) of a model
Parameters
----------
model : Model
Pharmpy model object
Returns
-------
Parameters
A copy of all theta parameters
Example
-------
>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> get_thetas(model)
value lower upper fix
POP_CL 0.004693 0.00 ∞ False
POP_VC 1.009160 0.00 ∞ False
COVAPGR 0.100000 -0.99 ∞ False
See also
--------
get_omegas : Get omega parameters
get_sigmas : Get sigma parameters
"""
rvs_fs = model.random_variables.free_symbols
thetas = [p for p in model.parameters if p.symbol not in rvs_fs]
return Parameters(tuple(thetas))
[docs]
def get_omegas(model: Model):
"""Get all omegas (variability parameters) of a model
Parameters
----------
model : Model
Pharmpy model object
Returns
-------
Parameters
A copy of all omega parameters
Example
-------
>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> get_omegas(model)
value lower upper fix
IIV_CL 0.030963 0.0 ∞ False
IIV_VC 0.031128 0.0 ∞ False
See also
--------
get_thetas : Get theta parameters
get_sigmas : Get sigma parameters
"""
omegas = [p for p in model.parameters if p.symbol in model.random_variables.etas.free_symbols]
return Parameters(tuple(omegas))
[docs]
def get_sigmas(model: Model):
"""Get all sigmas (residual error variability parameters) of a model
Parameters
----------
model : Model
Pharmpy model object
Returns
-------
Parameters
A copy of all sigma parameters
Example
-------
>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> get_sigmas(model)
value lower upper fix
SIGMA 0.013086 0.0 ∞ False
See also
--------
get_thetas : Get theta parameters
get_omegas : Get omega parameters
"""
sigmas = [
p for p in model.parameters if p.symbol in model.random_variables.epsilons.free_symbols
]
return Parameters(tuple(sigmas))
[docs]
def set_initial_estimates(
model: Model,
inits: Mapping[str, float],
move_est_close_to_bounds: bool = False,
strict: bool = True,
):
"""Update initial parameter estimate for a model
Updates initial estimates of population parameters for a model.
If the new initial estimates are out of bounds or NaN this function will raise.
Parameters
----------
model : Model
Pharmpy model to update initial estimates
inits : pd.Series or dict
Initial parameter estimates to update
move_est_close_to_bounds : bool
Move estimates that are close to bounds. If correlation >0.99 the correlation will
be set to 0.9, if variance is <0.001 the variance will be set to 0.01.
strict : bool
Whether all parameters in input need to exist in the model. Default is True
Returns
-------
Model
Pharmpy model object
Example
-------
>>> from pharmpy.modeling import load_example_model, set_initial_estimates
>>> from pharmpy.tools import load_example_modelfit_results
>>> model = load_example_model("pheno")
>>> results = load_example_modelfit_results("pheno")
>>> model.parameters.inits # doctest:+ELLIPSIS
{'POP_CL': 0.00469307, 'POP_VC': 1.00916, 'COVAPGR': 0.1, 'IIV_CL': 0.0309626, 'IIV_VC': ...}
>>> model = set_initial_estimates(model, results.parameter_estimates)
>>> model.parameters.inits # doctest:+ELLIPSIS
{'POP_CL': 0.00469555, 'POP_VC': 0.984258, 'COVAPGR': 0.15892, 'IIV_CL': 0.0293508, ...}
>>> model = load_example_model("pheno")
>>> model = set_initial_estimates(model, {'POP_CL': 2.0})
>>> model.parameters['POP_CL']
Parameter("POP_CL", 2.0, lower=0.0, upper=∞, fix=False)
See also
--------
fix_parameters_to : Fixing and setting parameter initial estimates in the same function
unfix_paramaters_to : Unfixing parameters and setting a new initial estimate in the same
"""
if strict:
_check_input_params(model, inits.keys())
if move_est_close_to_bounds:
inits = _move_est_close_to_bounds(model, inits)
new = model.parameters.set_initial_estimates(inits)
model = model.replace(parameters=new)
return model.update_source()
def _get_nonfixed_rvs(model):
fixed_omegas = get_omegas(model).fixed.names
rvs = model.random_variables
nonfixed_rvs = [rv for rv in rvs if str(list(rv.variance.free_symbols)[0]) not in fixed_omegas]
return RandomVariables.create(nonfixed_rvs)
def _move_est_close_to_bounds(model: Model, est_new):
rvs, pset = _get_nonfixed_rvs(model), model.parameters
parameter_estimates = model.parameters.inits
parameter_estimates.update(est_new) # Need all numerical values for sdcorr
sdcorr = rvs.parameters_sdcorr(parameter_estimates)
newdict = est_new.copy()
for dist in rvs:
rvs = dist.names
if len(rvs) > 1:
sigma_sym = dist.variance
for i in range(sigma_sym.rows):
for j in range(sigma_sym.cols):
param_name = sigma_sym[i, j].name
if i != j:
if sdcorr[param_name] > 0.99:
name_i, name_j = sigma_sym[i, i].name, sigma_sym[j, j].name
# From correlation to covariance
corr_new = 0.9
sd_i, sd_j = sdcorr[name_i], sdcorr[name_j]
newdict[param_name] = corr_new * sd_i * sd_j
else:
if param_name in est_new.keys():
if not _is_zero_fix(pset[param_name]) and est_new[param_name] < 0.001:
newdict[param_name] = 0.01
else:
param_name = dist.variance.name
if not pset[param_name].fix:
if param_name in est_new.keys():
if not _is_zero_fix(pset[param_name]) and est_new[param_name] < 0.001:
newdict[param_name] = 0.01
return newdict
def _is_zero_fix(param):
return param.init == 0 and param.fix
[docs]
def set_upper_bounds(model: Model, bounds: Mapping[str, float], strict: bool = True):
"""Set parameter upper bounds
Parameters
----------
model : Model
Pharmpy model
bounds : dict
A dictionary of parameter bounds for parameters to change
strict : bool
Whether all parameters in input need to exist in the model. Default is True
Returns
-------
Model
Pharmpy model object
Examples
--------
>>> from pharmpy.modeling import load_example_model, set_upper_bounds
>>> model = load_example_model("pheno")
>>> model = set_upper_bounds(model, {'POP_CL': 10})
>>> model.parameters['POP_CL']
Parameter("POP_CL", 0.00469307, lower=0.0, upper=10, fix=False)
See also
--------
set_lower_bounds : Set parameter lower bounds
unconstrain_parameters : Remove all constraints of parameters
"""
if strict:
_check_input_params(model, bounds.keys())
new = []
for p in model.parameters:
if p.name in bounds:
newparam = Parameter(
name=p.name, init=p.init, lower=p.lower, upper=bounds[p.name], fix=p.fix
)
else:
newparam = p
new.append(newparam)
model = model.replace(parameters=Parameters(tuple(new)))
return model.update_source()
[docs]
def set_lower_bounds(model: Model, bounds: Mapping[str, float], strict: bool = True):
"""Set parameter lower bounds
Parameters
----------
model : Model
Pharmpy model
bounds : dict
A dictionary of parameter bounds for parameters to change
strict : bool
Whether all parameters in input need to exist in the model. Default is True
Returns
-------
Model
Pharmpy model object
Examples
--------
>>> from pharmpy.modeling import load_example_model, set_lower_bounds
>>> model = load_example_model("pheno")
>>> model = set_lower_bounds(model, {'POP_CL': -10})
>>> model.parameters['POP_CL']
Parameter("POP_CL", 0.00469307, lower=-10, upper=∞, fix=False)
See also
--------
set_upper_bounds : Set parameter upper bounds
unconstrain_parameters : Remove all constraints of parameters
"""
if strict:
_check_input_params(model, bounds.keys())
new = []
for p in model.parameters:
if p.name in bounds:
newparam = Parameter(
name=p.name, init=p.init, lower=bounds[p.name], upper=p.upper, fix=p.fix
)
else:
newparam = p
new.append(newparam)
model = model.replace(parameters=Parameters(tuple(new)))
model = model.update_source()
return model
[docs]
def fix_parameters(model: Model, parameter_names: Union[Iterable[str], str], strict: bool = True):
"""Fix parameters
Fix all listed parameters
Parameters
----------
model : Model
Pharmpy model
parameter_names : list or str
one parameter name or a list of parameter names
strict : bool
Whether all parameters in input need to exist in the model. Default is True
Returns
-------
Model
Pharmpy model object
Example
-------
>>> from pharmpy.modeling import fix_parameters, load_example_model
>>> model = load_example_model("pheno")
>>> model.parameters['POP_CL']
Parameter("POP_CL", 0.00469307, lower=0.0, upper=∞, fix=False)
>>> model = fix_parameters(model, 'POP_CL')
>>> model.parameters['POP_CL']
Parameter("POP_CL", 0.00469307, lower=0.0, upper=∞, fix=True)
See also
--------
fix_or_unfix_parameters : Fix or unfix parameters (given boolean)
fix_parameters_to : Fixing and setting parameter initial estimates in the same function
unfix_paramaters : Unfixing parameters
unfix_paramaters_to : Unfixing parameters and setting a new initial estimate in the same
function
"""
if isinstance(parameter_names, str):
parameter_names = [parameter_names]
if strict:
_check_input_params(model, parameter_names)
params = model.parameters
new = []
for p in params:
if p.name in parameter_names:
new_param = p.replace(fix=True)
else:
new_param = p
new.append(new_param)
model = model.replace(parameters=Parameters(tuple(new)))
return model.update_source()
def _check_input_params(model, parameter_names):
params_not_in_model = [
p_name for p_name in parameter_names if p_name not in model.parameters.names
]
if params_not_in_model:
raise ValueError(f'Parameters not found in model: {params_not_in_model}')
[docs]
def unfix_parameters(model: Model, parameter_names: Union[Iterable[str], str], strict: bool = True):
"""Unfix parameters
Unfix all listed parameters
Parameters
----------
model : Model
Pharmpy model
parameter_names : list or str
one parameter name or a list of parameter names
strict : bool
Whether all parameters in input need to exist in the model. Default is True
Returns
-------
Model
Pharmpy model object
Examples
--------
>>> from pharmpy.modeling import fix_parameters, unfix_parameters, load_example_model
>>> model = load_example_model("pheno")
>>> model = fix_parameters(model, ['POP_CL', 'POP_VC'])
>>> model.parameters.fix # doctest: +ELLIPSIS
{'POP_CL': True, 'POP_VC': True, ...}
>>> model = unfix_parameters(model, 'POP_CL')
>>> model.parameters.fix # doctest: +ELLIPSIS
{'POP_CL': False, 'POP_VC': True, ...}
See also
--------
unfix_paramaters_to : Unfixing parameters and setting a new initial estimate in the same
function
fix_parameters : Fix parameters
fix_or_unfix_parameters : Fix or unfix parameters (given boolean)
fix_parameters_to : Fixing and setting parameter initial estimates in the same function
unconstrain_parameters : Remove all constraints of parameters
"""
if isinstance(parameter_names, str):
parameter_names = [parameter_names]
if strict:
_check_input_params(model, parameter_names)
params = model.parameters
new = []
for p in params:
if p.name in parameter_names:
new_param = p.replace(fix=False)
else:
new_param = p
new.append(new_param)
model = model.replace(parameters=Parameters(tuple(new)))
model = model.update_source()
return model
[docs]
def fix_parameters_to(model: Model, inits: Mapping[str, float], strict: bool = True):
"""Fix parameters to
Fix all listed parameters to specified value/values
Parameters
----------
model : Model
Pharmpy model
inits : dict
Inits for all parameters to fix and set init
strict : bool
Whether all parameters in input need to exist in the model. Default is True
Returns
-------
Model
Pharmpy model object
Examples
--------
>>> from pharmpy.modeling import fix_parameters_to, load_example_model
>>> model = load_example_model("pheno")
>>> model.parameters['POP_CL']
Parameter("POP_CL", 0.00469307, lower=0.0, upper=∞, fix=False)
>>> model = fix_parameters_to(model, {'POP_CL': 0.5})
>>> model.parameters['POP_CL']
Parameter("POP_CL", 0.5, lower=0.0, upper=∞, fix=True)
See also
--------
fix_parameters : Fix parameters
fix_or_unfix_parameters : Fix or unfix parameters (given boolean)
unfix_paramaters : Unfixing parameters
unfix_paramaters_to : Unfixing parameters and setting a new initial estimate in the same
function
"""
model = fix_parameters(model, inits.keys(), strict=strict)
model = set_initial_estimates(model, inits, strict=strict)
return model
[docs]
def unfix_parameters_to(model: Model, inits: Mapping[str, float], strict: bool = True):
"""Unfix parameters to
Unfix all listed parameters to specified value/values
Parameters
----------
model : Model
Pharmpy model
inits : dict
Inits for all parameters to unfix and change init
strict : bool
Whether all parameters in input need to exist in the model. Default is True
Examples
--------
>>> from pharmpy.modeling import fix_parameters, unfix_parameters_to, load_example_model
>>> model = load_example_model("pheno")
>>> model = fix_parameters(model, ['POP_CL', 'POP_VC'])
>>> model.parameters.fix # doctest: +ELLIPSIS
{'POP_CL': True, 'POP_VC': True, 'COVAPGR': False, 'IIV_CL': False, 'IIV_VC': False, 'SIGMA': False}
>>> model = unfix_parameters_to(model, {'POP_CL': 0.5})
>>> model.parameters.fix # doctest: +ELLIPSIS
{'POP_CL': False, 'POP_VC': True, 'COVAPGR': False, ...}
>>> model.parameters['POP_CL']
Parameter("POP_CL", 0.5, lower=0.0, upper=∞, fix=False)
Returns
-------
Model
Pharmpy model object
See also
--------
fix_parameters : Fix parameters
fix_or_unfix_parameters : Fix or unfix parameters (given boolean)
unfix_paramaters : Unfixing parameters
fix_paramaters_to : Fixing parameters and setting a new initial estimate in the same
function
"""
model = unfix_parameters(model, inits.keys(), strict=strict)
model = set_initial_estimates(model, inits, strict=strict)
return model
[docs]
def fix_or_unfix_parameters(model: Model, parameters: Mapping[str, bool], strict: bool = True):
"""Fix or unfix parameters
Set fixedness of parameters to specified values
Parameters
----------
model : Model
Pharmpy model
parameters : dict
Set fix/unfix for these parameters
strict : bool
Whether all parameters in input need to exist in the model. Default is True
Examples
--------
>>> from pharmpy.modeling import fix_or_unfix_parameters, load_example_model
>>> model = load_example_model("pheno")
>>> model.parameters['POP_CL']
Parameter("POP_CL", 0.00469307, lower=0.0, upper=∞, fix=False)
>>> model = fix_or_unfix_parameters(model, {'POP_CL': True})
>>> model.parameters['POP_CL']
Parameter("POP_CL", 0.00469307, lower=0.0, upper=∞, fix=True)
Returns
-------
Model
Pharmpy model object
See also
--------
fix_parameters : Fix parameters
unfix_paramaters : Unfixing parameters
fix_paramaters_to : Fixing parameters and setting a new initial estimate in the same
function
unfix_paramaters_to : Unfixing parameters and setting a new initial estimate in the same
function
"""
if strict:
_check_input_params(model, parameters.keys())
params = model.parameters
new = []
for p in params:
if p.name in parameters:
new_param = p.replace(fix=parameters[p.name])
else:
new_param = p
new.append(new_param)
model = model.replace(parameters=Parameters(tuple(new)))
model = model.update_source()
return model
[docs]
def unconstrain_parameters(model: Model, parameter_names: Iterable[str], strict: bool = True):
"""Remove all constraints from parameters
Parameters
----------
model : Model
Pharmpy model
parameter_names : list
Remove all constraints for the listed parameters
strict : bool
Whether all parameters in input need to exist in the model. Default is True
Examples
--------
>>> from pharmpy.modeling import unconstrain_parameters, load_example_model
>>> model = load_example_model("pheno")
>>> model.parameters['POP_CL']
Parameter("POP_CL", 0.00469307, lower=0.0, upper=∞, fix=False)
>>> model = unconstrain_parameters(model, ['POP_CL'])
>>> model.parameters['POP_CL']
Parameter("POP_CL", 0.00469307, lower=-∞, upper=∞, fix=False)
Returns
-------
Model
Pharmpy model object
See also
--------
set_lower_bounds : Set parameter lower bounds
set_upper_bounds : Set parameter upper bounds
unfix_parameters : Unfix parameters
"""
if isinstance(parameter_names, str):
parameter_names = [parameter_names]
if strict:
_check_input_params(model, parameter_names)
new = []
for p in model.parameters:
if p.name in parameter_names:
newparam = Parameter(name=p.name, init=p.init)
else:
newparam = p
new.append(newparam)
model = model.replace(parameters=Parameters(tuple(new)))
model = model.update_source()
return model
[docs]
def add_population_parameter(
model: Model,
name: str,
init: float,
lower: Optional[float] = None,
upper: Optional[float] = None,
fix: bool = False,
):
"""Add a new population parameter to the model
Parameters
----------
model : Model
Pharmpy model
name : str
Name of the new parameter
init : float
Initial estimate of the new parameter
lower : float
Lower bound of the new parameter
upper : float
Upper bound of the new parameter
fix : bool
Should the new parameter be fixed?
Returns
-------
Model
Pharmpy model object
Examples
--------
>>> from pharmpy.modeling import add_population_parameter, load_example_model
>>> model = load_example_model("pheno")
>>> model = add_population_parameter(model, 'POP_KA', 2)
>>> model.parameters
value lower upper fix
POP_CL 0.004693 0.0 ∞ False
POP_VC 1.009160 0.0 ∞ False
COVAPGR 0.100000 -0.99 ∞ False
IIV_CL 0.030963 0.0 ∞ False
IIV_VC 0.031128 0.0 ∞ False
SIGMA 0.013086 0.0 ∞ False
POP_KA 2.000000 -∞ ∞ False
"""
param = Parameter.create(name, init, lower=lower, upper=upper, fix=fix)
params = model.parameters + param
model = model.replace(parameters=params)
return model.update_source()
[docs]
def replace_fixed_thetas(model: Model):
"""Replace all fixed thetas with constants in the model statements
Parameters
----------
model : Model
Pharmpy model
Returns
-------
Model
A new model
"""
keep = []
new_assignments = []
for p in model.parameters:
if p.fix:
ass = Assignment(p.symbol, Expr.float(p.init))
new_assignments.append(ass)
else:
keep.append(p)
model = model.replace(
parameters=Parameters(tuple(keep)), statements=new_assignments + model.statements
)
model = model.update_source()
return model
