###############################################################################
# WaterTAP Copyright (c) 2021, The Regents of the University of California,
# through Lawrence Berkeley National Laboratory, Oak Ridge National
# Laboratory, National Renewable Energy Laboratory, and National Energy
# Technology Laboratory (subject to receipt of any required approvals from
# the U.S. Dept. of Energy). All rights reserved.
#
# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
# information, respectively. These files are also available online at the URL
# "https://github.com/watertap-org/watertap/"
#
###############################################################################
from watertap.tools.parameter_sweep.parameter_sweep import (
ParameterSweep,
RecursiveParameterSweep,
)
from watertap.tools.parameter_sweep.parameter_sweep_differential import (
DifferentialParameterSweep,
)
[docs]def parameter_sweep(
model,
sweep_params,
outputs=None,
csv_results_file_name=None,
h5_results_file_name=None,
optimize_function=None,
optimize_kwargs=None,
reinitialize_function=None,
reinitialize_kwargs=None,
reinitialize_before_sweep=False,
probe_function=None,
mpi_comm=None,
debugging_data_dir=None,
interpolate_nan_outputs=False,
num_samples=None,
seed=None,
):
"""
This function offers a general way to perform repeated optimizations
of a model for the purposes of exploring a parameter space while
monitoring multiple outputs.
If provided, writes single CSV file to ``results_file`` with all inputs and resulting outputs.
Arguments:
model : A Pyomo ConcreteModel containing a watertap flowsheet, for best
results it should be initialized before being passed to this
function.
sweep_params: A dictionary containing the values to vary with the format
``sweep_params['Short/Pretty-print Name'] =
(model.fs.variable_or_param[index], lower_limit, upper_limit, num_samples)``.
A uniform number of samples ``num_samples`` will be take between
the ``lower_limit`` and ``upper_limit``.
outputs (optional) : An optional dictionary containing "short names" as keys and and Pyomo objects
on ``model`` whose values to report as values. E.g.,
``outputs['Short/Pretty-print Name'] = model.fs.variable_or_expression_to_report``.
If not provided, i.e., outputs = None, the default behavior is to save all model
variables, parameters, and expressions which provides very thorough results
at the cost of large file sizes.
csv_results_file_name (optional) : The path and file name to write a csv file. The default `None`
does not write a csv file.
h5_results_file_name (optional) : The path and file name to write a h5 file. The default `None`
does not write a file.
Writing an h5 file will also create a companion text file `{h5_results_file_name}.txt`
which contains the variable names contained within the H5 file.
optimize_function (optional) : A user-defined function to perform the optimization of flowsheet
``model`` and loads the results back into ``model``. The first
argument of this function is ``model``. The default uses the
default IDAES solver, raising an exception if the termination
condition is not optimal.
optimize_kwargs (optional) : Dictionary of kwargs to pass into every call to
``optimize_function``. The first arg will always be ``model``,
e.g., ``optimize_function(model, **optimize_kwargs)``. The default
uses no kwargs.
reinitialize_function (optional) : A user-defined function to perform the re-initialize the
flowsheet ``model`` if the first call to ``optimize_function``
fails for any reason. After ``reinitialize_function``, the
parameter sweep tool will immediately call
``optimize_function`` again.
reinitialize_kwargs (optional) : Dictionary or kwargs to pass into every call to
``reinitialize_function``. The first arg will always be
``model``, e.g.,
``reinitialize_function(model, **reinitialize_kwargs)``.
The default uses no kwargs.
reinitialize_before_sweep (optional): Boolean option to reinitialize the flow sheet model before
every parameter sweep realization. The default is False.
Note the parameter sweep model will try to reinitialize the
solve regardless of the option if the run fails.
probe_function (optional): A user-defined function that can cheaply check if a current model
configuration is solvable without actually reinitializing or solving.
mpi_comm (optional) : User-provided MPI communicator for parallel parameter sweeps.
If None COMM_WORLD will be used. The default is sufficient for most
users.
debugging_data_dir (optional) : Save results on a per-process basis for parallel debugging
purposes. If None no `debugging` data will be saved.
interpolate_nan_outputs (optional) : When the parameter sweep has finished, interior values
of np.nan will be replaced with a value obtained via
a linear interpolation of their surrounding valid neighbors.
If true, a second output file with the extension "_clean"
will be saved alongside the raw (un-interpolated) values.
num_samples (optional) : If the user is using sampling techniques rather than a linear grid
of values, they need to set the number of samples
seed (optional) : If the user is using a random sampling technique, this sets the seed
Returns:
save_data : A list were the first N columns are the values of the parameters passed
by ``sweep_params`` and the remaining columns are the values of the
simulation identified by the ``outputs`` argument.
"""
kwargs = {}
if mpi_comm is not None:
kwargs["comm"] = mpi_comm
if csv_results_file_name is not None:
kwargs["csv_results_file_name"] = csv_results_file_name
if h5_results_file_name is not None:
kwargs["h5_results_file_name"] = h5_results_file_name
if optimize_function is not None:
kwargs["optimize_function"] = optimize_function
if optimize_kwargs is not None:
kwargs["optimize_kwargs"] = optimize_kwargs
if reinitialize_function is not None:
kwargs["reinitialize_function"] = reinitialize_function
if reinitialize_kwargs is not None:
kwargs["reinitialize_kwargs"] = reinitialize_kwargs
kwargs["reinitialize_before_sweep"] = reinitialize_before_sweep
if probe_function is not None:
kwargs["probe_function"] = probe_function
if debugging_data_dir is not None:
kwargs["debugging_data_dir"] = debugging_data_dir
if interpolate_nan_outputs is not None:
kwargs["interpolate_nan_outputs"] = interpolate_nan_outputs
ps = ParameterSweep(**kwargs)
return ps.parameter_sweep(
model, sweep_params, outputs=outputs, num_samples=num_samples, seed=seed
)
[docs]def recursive_parameter_sweep(
model,
sweep_params,
outputs=None,
csv_results_file_name=None,
h5_results_file_name=None,
optimize_function=None,
optimize_kwargs=None,
reinitialize_function=None,
reinitialize_kwargs=None,
reinitialize_before_sweep=False,
probe_function=None,
mpi_comm=None,
debugging_data_dir=None,
interpolate_nan_outputs=False,
req_num_samples=None,
seed=None,
):
"""
This function is similar to the `parameter_sweep` function for exploring the parameter space while guranteeing a required number of solves.
If provided, writes single CSV file to ``results_file`` with all inputs and resulting outputs.
Arguments:
model : A Pyomo ConcreteModel containing a watertap flowsheet, for best
results it should be initialized before being passed to this
function.
sweep_params: A dictionary containing the values to vary with the format
``sweep_params['Short/Pretty-print Name'] =
(model.fs.variable_or_param[index], lower_limit, upper_limit, num_samples)``.
A uniform number of samples ``num_samples`` will be take between
the ``lower_limit`` and ``upper_limit``.
outputs : An optional dictionary containing "short names" as keys and and Pyomo objects
on ``model`` whose values to report as values. E.g.,
``outputs['Short/Pretty-print Name'] = model.fs.variable_or_expression_to_report``.
If not provided, i.e., outputs = None, the default behavior is to save all model
variables, parameters, and expressions which provides very thorough results
at the cost of large file sizes.
csv_results_file_name (optional) : The path and file name to write a csv file. The default `None`
does not write a csv file.
h5_results_file_name (optional) : The path and file name to write a h5 file. The default `None`
does not write a file.
Writing an h5 file will also create a companion text file `{h5_results_file_name}.txt`
which contains the variable names contained within the H5 file.
optimize_function (optional) : A user-defined function to perform the optimization of flowsheet
``model`` and loads the results back into ``model``. The first
argument of this function is ``model``. The default uses the
default IDAES solver, raising an exception if the termination
condition is not optimal.
optimize_kwargs (optional) : Dictionary of kwargs to pass into every call to
``optimize_function``. The first arg will always be ``model``,
e.g., ``optimize_function(model, **optimize_kwargs)``. The default
uses no kwargs.
reinitialize_function (optional) : A user-defined function to perform the re-initialize the
flowsheet ``model`` if the first call to ``optimize_function``
fails for any reason. After ``reinitialize_function``, the
parameter sweep tool will immediately call
``optimize_function`` again.
reinitialize_kwargs (optional) : Dictionary or kwargs to pass into every call to
``reinitialize_function``. The first arg will always be
``model``, e.g.,
``reinitialize_function(model, **reinitialize_kwargs)``.
The default uses no kwargs.
reinitialize_before_sweep (optional): Boolean option to reinitialize the flow sheet model before
every parameter sweep realization. The default is False.
Note the parameter sweep model will try to reinitialize the
solve regardless of the option if the run fails.
probe_function (optional): A user-defined function that can cheaply check if a current model
configuration is solvable without actually reinitializing or solving.
mpi_comm (optional) : User-provided MPI communicator for parallel parameter sweeps.
If None COMM_WORLD will be used. The default is sufficient for most
users.
debugging_data_dir (optional) : Save results on a per-process basis for parallel debugging
purposes. If None no `debugging` data will be saved.
interpolate_nan_outputs (optional) : When the parameter sweep has finished, interior values
of np.nan will be replaced with a value obtained via
a linear interpolation of their surrounding valid neighbors.
If true, a second output file with the extension "_clean"
will be saved alongside the raw (un-interpolated) values.
req_num_samples (optional) : If the user is using sampling techniques rather than a linear grid
of values, they need to set the required number of samples. This is the
guaranteed number of solves that the user requires.
seed (optional) : If the user is using a random sampling technique, this sets the seed
Returns:
save_data : A list were the first N columns are the values of the parameters passed
by ``sweep_params`` and the remaining columns are the values of the
simulation identified by the ``outputs`` argument.
"""
kwargs = {}
if mpi_comm is not None:
kwargs["comm"] = mpi_comm
if csv_results_file_name is not None:
kwargs["csv_results_file_name"] = csv_results_file_name
if h5_results_file_name is not None:
kwargs["h5_results_file_name"] = h5_results_file_name
if optimize_function is not None:
kwargs["optimize_function"] = optimize_function
if optimize_kwargs is not None:
kwargs["optimize_kwargs"] = optimize_kwargs
if reinitialize_function is not None:
kwargs["reinitialize_function"] = reinitialize_function
if reinitialize_kwargs is not None:
kwargs["reinitialize_kwargs"] = reinitialize_kwargs
kwargs["reinitialize_before_sweep"] = reinitialize_before_sweep
if probe_function is not None:
kwargs["probe_function"] = probe_function
if debugging_data_dir is not None:
kwargs["debugging_data_dir"] = debugging_data_dir
if interpolate_nan_outputs is not None:
kwargs["interpolate_nan_outputs"] = interpolate_nan_outputs
rps = RecursiveParameterSweep(**kwargs)
return rps.parameter_sweep(
model, sweep_params, outputs=outputs, req_num_samples=req_num_samples, seed=seed
)
[docs]def differential_parameter_sweep(
model,
sweep_params,
differential_sweep_specs,
outputs=None,
csv_results_file_name=None,
h5_results_file_name=None,
optimize_function=None,
optimize_kwargs=None,
reinitialize_function=None,
reinitialize_kwargs=None,
reinitialize_before_sweep=False,
probe_function=None,
mpi_comm=None,
debugging_data_dir=None,
interpolate_nan_outputs=False,
num_samples=None,
num_diff_samples=1,
seed=None,
guarantee_solves=False,
):
"""
This function is similar to the `parameter_sweep` function for exploring the parameter space while guranteeing a required number of solves.
If provided, writes single CSV file to ``results_file`` with all inputs and resulting outputs.
Arguments:
model : A Pyomo ConcreteModel containing a watertap flowsheet, for best
results it should be initialized before being passed to this
function.
sweep_params: A dictionary containing the values to vary with the format
``sweep_params['Short/Pretty-print Name'] =
(model.fs.variable_or_param[index], lower_limit, upper_limit, num_samples)``.
A uniform number of samples ``num_samples`` will be take between
the ``lower_limit`` and ``upper_limit``.
differential_sweep_specs: A specification dictionary that contains details for how to construct the parameter sweep dictionary for differential sweep.
This is a nested dictionary where the first level denotes the variable names for which the differential sweep needs to be carried out.
The second level denotes various options to be used for wach variable.
The number of samples for each differential sweep is specified while initializing the DifferentialParameterSweep object wsing the keyword `num_diff_samples`
e.g.
.. highlight::
{"fs.a": {"diff_mode": "sum", \
"diff_sample_type": NormalSample, \
"std_dev": 0.01, \
"pyomo_object": m.fs.input["a"]}, \
"fs.b": {"diff_mode": "product", \
"diff_sample_type": UniformSample, \
"relative_lb": 0.01, \
"relative_ub": 0.01, \
"pyomo_object": m.fs.input["b"]}, \
"fs.c": {"diff_mode": "sum", \
"diff_sample_type": GeomSample, \
"relative_lb": 0.01, \
"relative_ub": 10.0, \
"pyomo_object": m.fs.input["c"]}} \
outputs (optional) : An optional dictionary containing "short names" as keys and and Pyomo objects
on ``model`` whose values to report as values. E.g.,
``outputs['Short/Pretty-print Name'] = model.fs.variable_or_expression_to_report``.
If not provided, i.e., outputs = None, the default behavior is to save all model
variables, parameters, and expressions which provides very thorough results
at the cost of large file sizes.
csv_results_file_name (optional) : The path and file name to write a csv file. The default `None`
does not write a csv file.
h5_results_file_name (optional) : The path and file name to write a h5 file. The default `None`
does not write a file.
Writing an h5 file will also create a companion text file `{h5_results_file_name}.txt`
which contains the variable names contained within the H5 file.
optimize_function (optional) : A user-defined function to perform the optimization of flowsheet
``model`` and loads the results back into ``model``. The first
argument of this function is ``model``. The default uses the
default IDAES solver, raising an exception if the termination
condition is not optimal.
optimize_kwargs (optional) : Dictionary of kwargs to pass into every call to
``optimize_function``. The first arg will always be ``model``,
e.g., ``optimize_function(model, **optimize_kwargs)``. The default
uses no kwargs.
reinitialize_function (optional) : A user-defined function to perform the re-initialize the
flowsheet ``model`` if the first call to ``optimize_function``
fails for any reason. After ``reinitialize_function``, the
parameter sweep tool will immediately call
``optimize_function`` again.
reinitialize_kwargs (optional) : Dictionary or kwargs to pass into every call to
``reinitialize_function``. The first arg will always be
``model``, e.g.,
``reinitialize_function(model, **reinitialize_kwargs)``.
The default uses no kwargs.
reinitialize_before_sweep (optional): Boolean option to reinitialize the flow sheet model before
every parameter sweep realization. The default is False.
Note the parameter sweep model will try to reinitialize the
solve regardless of the option if the run fails.
probe_function (optional): A user-defined function that can cheaply check if a current model
configuration is solvable without actually reinitializing or solving.
mpi_comm (optional) : User-provided MPI communicator for parallel parameter sweeps.
If None COMM_WORLD will be used. The default is sufficient for most
users.
debugging_data_dir (optional) : Save results on a per-process basis for parallel debugging
purposes. If None no `debugging` data will be saved.
interpolate_nan_outputs (optional) : When the parameter sweep has finished, interior values
of np.nan will be replaced with a value obtained via
a linear interpolation of their surrounding valid neighbors.
If true, a second output file with the extension "_clean"
will be saved alongside the raw (un-interpolated) values.
num_samples (optional) : If the user is using sampling techniques rather than a grid
of values, they need to set the required number of samples. This is the
number of base solves that the user desires. The differential sweep will
be performed on these base solves.
num_diff_samples (optional) : Number of samples for the differential sweep at a given base value.
seed (optional) : If the user is using a random sampling technique, this sets the seed
Returns:
save_data : A list were the first N columns are the values of the parameters passed
by ``sweep_params`` and the remaining columns are the values of the
simulation identified by the ``outputs`` argument.
"""
kwargs = {}
kwargs["differential_sweep_specs"] = differential_sweep_specs
if mpi_comm is not None:
kwargs["comm"] = mpi_comm
if csv_results_file_name is not None:
kwargs["csv_results_file_name"] = csv_results_file_name
if h5_results_file_name is not None:
kwargs["h5_results_file_name"] = h5_results_file_name
if optimize_function is not None:
kwargs["optimize_function"] = optimize_function
if optimize_kwargs is not None:
kwargs["optimize_kwargs"] = optimize_kwargs
if reinitialize_function is not None:
kwargs["reinitialize_function"] = reinitialize_function
if reinitialize_kwargs is not None:
kwargs["reinitialize_kwargs"] = reinitialize_kwargs
kwargs["reinitialize_before_sweep"] = reinitialize_before_sweep
if probe_function is not None:
kwargs["probe_function"] = probe_function
if debugging_data_dir is not None:
kwargs["debugging_data_dir"] = debugging_data_dir
if interpolate_nan_outputs is not None:
kwargs["interpolate_nan_outputs"] = interpolate_nan_outputs
kwargs["num_diff_samples"] = num_diff_samples
kwargs["guarantee_solves"] = guarantee_solves
dps = DifferentialParameterSweep(**kwargs)
return dps.parameter_sweep(
model, sweep_params, outputs=outputs, seed=seed, num_samples=num_samples
)