from __future__ import annotations
from itertools import product
from typing import Any, Iterable, Literal, Mapping, Optional, Sequence, Union
from pharmpy.basic import Expr
from pharmpy.model import EstimationStep, ExecutionSteps, Model, SimulationStep
from pharmpy.modeling.help_functions import _as_integer
MethodType = Literal['FO', 'FOCE', 'ITS', 'LAPLACE', 'IMPMAP', 'IMP', 'SAEM', 'BAYES']
[docs]
def set_estimation_step(model: Model, method: MethodType, idx: int = 0, **kwargs):
"""Set estimation step
Sets estimation step for a model. Methods currently supported are:
FO, FOCE, ITS, LAPLACE, IMPMAP, IMP, SAEM, BAYES
Parameters
----------
model : Model
Pharmpy model
method : MethodType
estimation method to change to
idx : int
index of estimation step, default is 0 (first estimation step)
kwargs
Arguments to pass to EstimationStep (such as interaction, evaluation)
Returns
-------
Model
Pharmpy model object
Examples
--------
>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> opts = {'NITER': 1000, 'ISAMPLE': 100}
>>> model = set_estimation_step(model, 'IMP', evaluation=True, tool_options=opts)
>>> model.execution_steps[0] # doctest: +ELLIPSIS
EstimationStep('IMP', interaction=True, parameter_uncertainty_method='SANDWICH', evaluation=True, ...,
tool_options=...
See also
--------
add_estimation_step
remove_estimation_step
append_estimation_step_options
add_parameter_uncertainty_step
remove_parameter_uncertainty_step
set_evaluation_step
"""
try:
idx = _as_integer(idx)
except TypeError:
raise TypeError(f'Index must be integer: {idx}')
d = kwargs
d['method'] = method
if 'evaluation' in d:
d['maximum_evaluations'] = None
steps = model.execution_steps
newstep = steps[idx].replace(**d)
newsteps = steps[0:idx] + newstep + steps[idx + 1 :]
model = model.replace(execution_steps=newsteps)
return model.update_source()
[docs]
def add_estimation_step(model: Model, method: MethodType, idx: Optional[int] = None, **kwargs):
"""Add estimation step
Adds estimation step for a model in a given index. Methods currently supported are:
FO, FOCE, ITS, LAPLACE, IMPMAP, IMP, SAEM
Parameters
----------
model : Model
Pharmpy model
method : MethodType
estimation method to change to
idx : int
index of estimation step (starting from 0), default is None (adds step at the end)
kwargs
Arguments to pass to EstimationStep (such as interaction, evaluation)
Returns
-------
Model
Pharmpy model object
Examples
--------
>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> opts = {'NITER': 1000, 'ISAMPLE': 100}
>>> model = add_estimation_step(model, 'IMP', tool_options=opts)
>>> ests = model.execution_steps
>>> len(ests)
2
>>> ests[1] # doctest: +ELLIPSIS
EstimationStep('IMP', interaction=False, parameter_uncertainty_method=None, ..., tool_options={'NITER': 1000,...
See also
--------
set_estimation_step
remove_estimation_step
append_estimation_step_options
add_parameter_uncertainty_step
remove_parameter_uncertainty_step
set_evaluation_step
"""
meth = EstimationStep.create(method, **kwargs)
if idx is not None:
try:
idx = _as_integer(idx)
except TypeError:
raise TypeError(f'Index must be integer: {idx}')
steps = model.execution_steps
newsteps = steps[0:idx] + meth + steps[idx:]
else:
newsteps = model.execution_steps + meth
model = model.replace(execution_steps=newsteps)
return model.update_source()
[docs]
def set_simulation(model: Model, n: int = 1, seed: int = 64206):
"""Change model into simulation model
Parameters
----------
model : Model
Pharmpy model
n : int
Number of replicates
seed : int
Random seed for the simulation
Returns
-------
Model
Pharmpy model object
Examples
--------
>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> model = set_simulation(model, n=10, seed=1234)
>>> steps = model.execution_steps
>>> steps[0]
SimulationStep(n=10, seed=1234, solver=None, solver_rtol=None, solver_atol=None, tool_options={})
"""
final_est = None
for step in model.execution_steps:
if isinstance(step, EstimationStep):
final_est = step
if final_est is not None:
step = SimulationStep.create(
n=n,
seed=seed,
solver=final_est.solver,
solver_atol=final_est.solver_atol,
solver_rtol=final_est.solver_rtol,
)
else:
step = SimulationStep.create(n=n, seed=seed)
steps = ExecutionSteps((step,))
model = model.replace(execution_steps=steps)
return model.update_source()
[docs]
def remove_estimation_step(model: Model, idx: int):
"""Remove estimation step
Parameters
----------
model : Model
Pharmpy model
idx : int
index of estimation step to remove (starting from 0)
Returns
-------
Model
Pharmpy model object
Examples
--------
>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> model = remove_estimation_step(model, 0)
>>> ests = model.execution_steps
>>> len(ests)
0
See also
--------
add_estimation_step
set_estimation_step
append_estimation_step_options
add_parameter_uncertainty_step
remove_parameter_uncertainty_step
set_evaluation_step
"""
try:
idx = _as_integer(idx)
except TypeError:
raise TypeError(f'Index must be integer: {idx}')
steps = model.execution_steps
newsteps = steps[0:idx] + steps[idx + 1 :]
model = model.replace(execution_steps=newsteps)
return model.update_source()
[docs]
def append_estimation_step_options(model: Model, tool_options: Mapping[str, Any], idx: int):
"""Append estimation step options
Appends options to an existing estimation step.
Parameters
----------
model : Model
Pharmpy model
tool_options : dict
any additional tool specific options
idx : int
index of estimation step (starting from 0)
Returns
-------
Model
Pharmpy model object
Examples
--------
>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> opts = {'NITER': 1000, 'ISAMPLE': 100}
>>> model = append_estimation_step_options(model, tool_options=opts, idx=0)
>>> est = model.execution_steps[0]
>>> len(est.tool_options)
2
See also
--------
add_estimation_step
set_estimation_step
remove_estimation_step
add_parameter_uncertainty_step
remove_parameter_uncertainty_step
set_evaluation_step
"""
try:
idx = _as_integer(idx)
except TypeError:
raise TypeError(f'Index must be integer: {idx}')
steps = model.execution_steps
toolopts = dict(steps[idx].tool_options)
toolopts.update(tool_options)
newstep = steps[idx].replace(tool_options=toolopts)
newsteps = steps[0:idx] + newstep + steps[idx + 1 :]
model = model.replace(execution_steps=newsteps)
return model.update_source()
[docs]
def add_parameter_uncertainty_step(
model: Model, parameter_uncertainty_method: Literal['SANDWICH', 'SMAT', 'RMAT', 'EFIM']
):
"""Adds parameter uncertainty step to the final estimation step
Parameters
----------
model : Model
Pharmpy model
parameter_uncertainty_method : str
Parameter uncertainty method to use
Returns
-------
Model
Pharmpy model object
Examples
--------
>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> model = set_estimation_step(model, 'FOCE', parameter_uncertainty_method=None)
>>> model = add_parameter_uncertainty_step(model, 'SANDWICH')
>>> ests = model.execution_steps
>>> ests[0] # doctest: +ELLIPSIS
EstimationStep('FOCE', interaction=True, parameter_uncertainty_method='SANDWICH', ...)
See also
--------
add_estimation_step
set_estimation_step
remove_estimation_step
append_estimation_step_options
remove_parameter_uncertainty_step
set_evaluation_step
"""
steps = model.execution_steps
newstep = steps[-1].replace(parameter_uncertainty_method=f'{parameter_uncertainty_method}')
newsteps = steps[0:-1] + newstep
model = model.replace(execution_steps=newsteps)
return model.update_source()
[docs]
def remove_parameter_uncertainty_step(model: Model):
"""Removes parameter uncertainty step from the final estimation step
Parameters
----------
model : Model
Pharmpy model
Returns
-------
Model
Pharmpy model object
Examples
--------
>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> model = remove_parameter_uncertainty_step(model)
>>> ests = model.execution_steps
>>> ests[0] # doctest: +ELLIPSIS
EstimationStep('FOCE', interaction=True, parameter_uncertainty_method=None, ...)
See also
--------
add_estimation_step
set_estimation_step
remove_estimation_step
append_estimation_step_options
add_parameter_uncertainty_step
set_evaluation_step
"""
steps = model.execution_steps
newstep = steps[-1].replace(parameter_uncertainty_method=None)
newsteps = steps[:-1] + newstep
model = model.replace(execution_steps=newsteps)
return model.update_source()
[docs]
def set_evaluation_step(model: Model, idx: int = -1):
"""Set evaluation step
Change the final or the estimation step with a specific index to do evaulation.
Parameters
----------
model : Model
Pharmpy model
idx : int
Index of estimation step, default is -1 (last estimation step)
Returns
-------
Model
Pharmpy model object
Examples
--------
>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> model = set_evaluation_step(model)
>>> model.execution_steps[0] # doctest: +ELLIPSIS
EstimationStep('FOCE', interaction=True, parameter_uncertainty_method='SANDWICH', evaluation=True, ...
See also
--------
set_estimation_step
add_estimation_step
remove_estimation_step
append_estimation_step_options
add_parameter_uncertainty_step
remove_parameter_uncertainty_step
"""
try:
idx = _as_integer(idx)
except TypeError:
raise TypeError(f'Index must be integer: {idx}')
steps = model.execution_steps
newstep = steps[idx].replace(evaluation=True, maximum_evaluations=None)
if idx != -1:
newsteps = steps[0:idx] + newstep + steps[idx + 1 :]
else:
newsteps = steps[0:-1] + newstep
model = model.replace(execution_steps=newsteps)
return model.update_source()
[docs]
def add_derivative(
model: Model, with_respect_to: Optional[Union[Sequence[Union[Sequence[str], str]], str]] = None
):
"""
Add a derivative to be calculcated when running the model. Currently, only
derivatives with respect to the prediction is supported. Default is to add all possible
ETA and EPS derivatives.
First order derivates are specied either by single string or single-element tuple.
For instance with_respect_to = "ETA_1" or with_respect_to = ("ETA_1",)
Second order derivatives are specified by giving the two independent varibles in a tuple
of tuples. For instance with_respect_to ((ETA_1, EPS_1),)
Multiple derivatives can be specified within a tuple. For instance ((ETA_1, EPS_1), "ETA_1")
Currently, only ETAs and EPSILONs are supported
Parameters
----------
model : Model
Pharmpy modeas.
with_respect_to : Union[str, Sequence[str, Sequence[str]]]
Parameter name(s) to use as independent variables. Default is None.
Returns
-------
Pharmpy model.
"""
if with_respect_to is None:
etas = model.random_variables.etas.symbols
eps = model.random_variables.epsilons.symbols
wrt = tuple(
tuple((e,) for e in etas) + tuple((e,) for e in eps) + tuple(product(eps, etas))
)
elif isinstance(with_respect_to, str):
wrt = ((Expr.symbol(with_respect_to),),)
elif isinstance(with_respect_to, Sequence):
wrt = tuple(
(Expr.symbol(w),) if isinstance(w, str) else tuple(map(Expr.symbol, w))
for w in with_respect_to
)
else:
raise ValueError(
f"with_respect_to argument need to be tuple, str or None. Recieved {type(with_respect_to)}"
)
for derivative in wrt:
for parameter in derivative:
if parameter not in model.random_variables.symbols:
raise ValueError(f"Parameter '{parameter}' not part of ETAs or EPS")
steps = model.execution_steps
new_step = steps[-1]
for new_derivative in wrt:
derivatives = tuple(set(d) for d in new_step.derivatives)
if set(new_derivative) not in derivatives:
new_step = new_step.replace(derivatives=derivatives + (new_derivative,))
new_steps = steps[:-1] + new_step
model = model.replace(execution_steps=new_steps)
return model.update_source()
[docs]
def remove_derivative(
model: Model, with_respect_to: Optional[Union[Sequence[Union[Sequence[str], str]], str]] = None
):
"""
Remove a derivative currently being calculcate when running model. Currently, only
derivatives with respect to the prediction is supported. Default is to remove all
that are present,
First order derivates are specied either by single string or single-element tuple.
For instance with_respect_to = "ETA_1" or with_respect_to = ("ETA_1",)
Second order derivatives are specified by giving the two independent varibles in a tuple
of tuples. For instance with_respect_to ((ETA_1, EPS_1),)
Multiple derivatives can be specified within a tuple. For instance ((ETA_1, EPS_1), "ETA_1")
Currently, only ETAs and EPSILONs are supported
Parameters
----------
model : Model
Pharmpy modeas.
with_respect_to : Union[str, Sequence[str, Sequence[str]]]
Parameter name(s) to use as independent variables. Default is None.
Returns
-------
Pharmpy model.
"""
if with_respect_to is None:
etas = model.random_variables.etas.symbols
eps = model.random_variables.epsilons.symbols
wrt = tuple(
tuple((e,) for e in etas) + tuple((e,) for e in eps) + tuple(product(eps, etas))
)
elif isinstance(with_respect_to, str):
wrt = ((Expr.symbol(with_respect_to),),)
elif isinstance(with_respect_to, Sequence):
wrt = tuple(
(Expr.symbol(w),) if isinstance(w, str) else tuple(map(Expr.symbol, w))
for w in with_respect_to
)
else:
raise ValueError(
f"with_respect_to argument need to be tuple, str or None. Recieved {type(with_respect_to)}"
)
wrt = tuple(set(d) for d in wrt)
new_steps = model.execution_steps
derivatives = new_steps[-1].derivatives
derivatives = tuple(d for d in derivatives if set(d) not in wrt)
new_step = new_steps[-1].replace(derivatives=derivatives)
new_steps = new_steps[:-1] + new_step
model = model.replace(execution_steps=new_steps)
return model.update_source()
[docs]
def add_predictions(model: Model, pred: list[str]):
"""Add predictions and/or residuals
Add predictions to estimation step.
Parameters
----------
model : Model
Pharmpy model
pred : list
List of predictions (e.g. ['IPRED', 'PRED'])
Returns
-------
Model
Pharmpy model object
Examples
--------
>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> model.execution_steps[-1].predictions
('CIPREDI', 'PRED')
>>> model = add_predictions(model, ['IPRED'])
>>> model.execution_steps[-1].predictions
('CIPREDI', 'IPRED', 'PRED')
See also
--------
remove_predictions
remove_residuals
set_estimation_step
add_estimation_step
remove_estimation_step
append_estimation_step_options
add_parameter_uncertainty_step
remove_parameter_uncertainty_step
"""
allowed_prediction_variables = ['PRED', 'IPRED', 'CIPREDI']
if any(p not in allowed_prediction_variables for p in pred):
raise ValueError(
f'Prediction variables need to be one of the following:'
f' {allowed_prediction_variables}'
)
steps = model.execution_steps
old_predictions = steps[-1].predictions
new_predictions = tuple(sorted(set(old_predictions) | set(pred)))
newstep = steps[-1].replace(predictions=new_predictions)
newsteps = steps[0:-1] + newstep
model = model.replace(execution_steps=newsteps)
return model.update_source()
[docs]
def add_residuals(model: Model, res: list[str]):
"""Add predictions and/or residuals
Add residuals to estimation step.
Added redidual variable(s) need to be one of the following :
['RES', 'IRES', 'WRES', 'IWRES', 'CWRES']
Parameters
----------
model : Model
Pharmpy model
res : list
List of residuals (e.g. ['CWRES'])
Returns
-------
Model
Pharmpy model object
Examples
--------
>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> model.execution_steps[-1].residuals
('CWRES', 'RES')
>>> model = add_residuals(model, ['WRES'])
>>> model.execution_steps[-1].residuals
('CWRES', 'RES', 'WRES')
See also
--------
remove_predictions
remove_residuals
set_estimation_step
add_estimation_step
remove_estimation_step
append_estimation_step_options
add_parameter_uncertainty_step
remove_parameter_uncertainty_step
"""
allowed_residual_variables = ['RES', 'IRES', 'WRES', 'IWRES', 'CWRES']
if any(p not in allowed_residual_variables for p in res):
raise ValueError(
f'Residual variables need to be one of the following:' f' {allowed_residual_variables}'
)
steps = model.execution_steps
old_residuals = steps[-1].residuals
new_residuals = tuple(sorted(set(old_residuals) | set(res)))
newstep = steps[-1].replace(residuals=new_residuals)
newsteps = steps[0:-1] + newstep
model = model.replace(execution_steps=newsteps)
return model.update_source()
[docs]
def remove_predictions(model: Model, to_remove: Optional[Iterable[str]] = None):
"""Remove predictions and/or residuals
Remove predictions from estimation step.
Parameters
----------
model : Model
Pharmpy model
to_remove : Optional[Iterable[str]]
Predictions to remove
Returns
-------
Model
Pharmpy model object
Examples
--------
>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> model = remove_predictions(model)
>>> model.execution_steps[-1].predictions
()
See also
--------
add_predictions
add_residuals
set_estimation_step
add_estimation_step
remove_estimation_step
append_estimation_step_options
add_parameter_uncertainty_step
remove_parameter_uncertainty_step
"""
steps = model.execution_steps
if to_remove is not None:
old_predictions = steps[-1].predictions
for value in to_remove:
if value not in old_predictions:
raise ValueError(f"{value} not in predictions")
new_predictions = tuple(sorted(set(old_predictions) - set(to_remove)))
newstep = steps[-1].replace(predictions=new_predictions)
else:
newstep = steps[-1].replace(predictions=())
newsteps = steps[0:-1] + newstep
model = model.replace(execution_steps=newsteps)
model = model.update_source()
return model
[docs]
def remove_residuals(model: Model, to_remove: Optional[Iterable[str]] = None):
"""Remove residuals
Remove residuals from estimation step.
Parameters
----------
model : Model
Pharmpy model
to_remove : Optional[Iterable[str]]
Residuals to remove
Returns
-------
Model
Pharmpy model object
Examples
--------
>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> model = remove_residuals(model)
>>> model.execution_steps[-1].residuals
()
See also
--------
add_predictions
add_residuals
set_estimation_step
add_estimation_step
remove_estimation_step
append_estimation_step_options
add_parameter_uncertainty_step
remove_parameter_uncertainty_step
"""
steps = model.execution_steps
if to_remove is not None:
old_residuals = steps[-1].residuals
for value in to_remove:
if value not in old_residuals:
raise ValueError(f"{value} not in residuals")
new_residuals = tuple(sorted(set(old_residuals) - set(to_remove)))
newstep = steps[-1].replace(residuals=new_residuals)
newsteps = steps[0:-1] + newstep
else:
newstep = steps[-1].replace(residuals=())
newsteps = steps[0:-1] + newstep
model = model.replace(execution_steps=newsteps)
model = model.update_source()
return model
