Data Fits¶

class ionworkspipeline.data_fits.DataFit(objectives, source='', parameters=None, cost=None, optimizer=None, options=None, cost_logger=None, multistarts=None, initial_guess_sampler=None, num_workers=None)¶

A pipeline element that fits a model to data.

Parameters¶

objectivesionworkspipeline.objectives.Objective or dict

The objective(s) to use for the fit. This can be a single objective, or a dictionary of objectives. Each objective can be any class that implements the method build, which creates a function Objective.run() that takes a dictionary of parameters and returns a scalar or vector cost. In general, we subclass ionworkspipeline.objectives.Objective to implement a particular objective.

sourcestr

A string describing the source of the data.

parametersdict, optional

A dictionary of parameters to fit. The values can be:

an iwp.Parameter object, e.g. iwp.Parameter(“x”)
a pybamm expression, in which case the other parameters should also be explicitly provided as iwp.Parameter objects, e.g.

{

“param”: 2 * pybamm.Parameter(“half-param”),

“half-param”: iwp.Parameter(“half-param”)

}

works, but

{“param”: 2 * iwp.Parameter(“half-param”)} would not work.
a function, containing other parameters, in which case the other parameters should again also be explicitly provided as iwp.Parameter objects, e.g.

{

“main parameter”: lambda x: pybamm.Parameter(“other parameter”) * x**2,

“other parameter”: iwp.Parameter(“other parameter”)

}

The name of the input parameter does not need to match the name of the parameter. In all cases, the DataFit class will automatically process this input to fit for “x”.

costionworkspipeline.costs.Cost, optional

The cost function to use when constructing the objective. If None, uses the optimizer’s default cost function.

optimizerionworkspipeline.optimizers.Optimizer, optional

The optimizer to use for the fit. Default is set by the DataFit subclasses.

optionsdict, optional

A dictionary of options to pass to the data fit.

cost_loggeroptional

A cost logger to use for logging the cost and parameters during the fit. Default is iwp.data_fits.CostLogger with default options.

multistartsint, optional

Number of times to run the optimization from different initial guesses. If None, only runs once from the provided initial guess.

initial_guess_samplerionworkspipeline.samplers.Sampler, optional

Sampler to use for generating initial guesses when using multistarts. Default is iwp.samplers.LatinHypercube.

num_workersint, optional

Number of worker processes to use for parallel optimization. Not supported on Windows. If num_workers = 1, then multiprocessing is disabled. If num_workers = None, then the number of workers is set to the number of CPU cores.

Extends: ionworkspipeline.pipeline.PipelineElement

property all_fit_parameters¶

Get the list of all parameters being fit.

Returns¶

list: List of Parameter objects.

property bounds¶

Get the parameter bounds dictionary.

Returns¶

dict: Dictionary of parameter bounds.

property bounds_arrays¶

Get the upper and lower bounds for the parameters being fit.

Returns¶

tuple: Tuple containing (lower_bounds, upper_bounds) arrays.

check_multistart_initial_guesses()¶

Check that the initial parameter guesses are different for multistarts.

Raises¶

ValueError: If initial guesses are not unique or number of guesses does not match jobs.

property cost¶

Get the cost function.

Returns¶

ionworkspipeline.costs.Cost: Cost function object.

get_fit_results()¶

Get the results of the fit.

Returns¶

dict: Dictionary containing fit results for each objective.

get_fitted_parameters(x_fit, parameter_values)¶

Get the dictionary of fitted parameters from the vector of fitted input parameters. For example, if the parameters are {“x”: 2 * iwp.Parameter(“x_scaled”)} and the fitted input parameters are [0.25], then the fitted parameters will be {“x”: 0.5}.

Parameters¶

x_fitarray_like: The fitted input parameters vector
parameter_valuespybamm.ParameterValues: Other parameter values, which in some cases are required to calculate new parameter values that depend on existing parameter values

Returns¶

dict: A dictionary of fitted parameters.

get_optimizer(objective, eq_constraints=None)¶

Return the optimizer with the objective and bounds set.

Parameters¶

objectivecallable: The objective function to be minimized.
eq_constraintscallable, optional: Equality constraint functions.

Returns¶

ionworkspipeline.optimizers.Optimizer: Configured optimizer object.

property initial_guess_sampler¶

Get the initial guess sampler.

Returns¶

ionworkspipeline.samplers.Sampler: Sampler object for generating initial guesses.

property initial_values_dict¶

Get the initial values for the parameters to be used for the fit.

Returns¶

dict: A dictionary of initial values for the parameters being fit.

property initial_values_list¶

Like initial_values_dict(), but returns a list of initial values instead of a dictionary, in the same order as the parameters vector, for use by the optimizer.

Returns¶

list: List of initial parameter values.

property job_id¶

Get the job ID.

Raises¶

AttributeError: Parent tasks do not have job IDs.

property jobs¶

Get the list of optimization jobs.

Returns¶

list: List of DataFitJob objects.

Raises¶

AttributeError: If jobs have not been created yet.

property multiprocessing¶

Whether multiprocessing is used. This property is set by the parent DataFit object.

Returns¶

bool: True if multiprocessing is enabled, False otherwise

property num_jobs¶

Get the number of optimization jobs.

Returns¶

int: Number of jobs.

property num_workers¶

Get the number of worker processes.

Returns¶

int: Number of workers.

property objectives¶

Get the objectives dictionary.

Returns¶

dict: Dictionary of optimization objectives.

property optimizer¶

Get the optimizer object.

Returns¶

ionworkspipeline.optimizers.Optimizer: Optimizer object.

property optimizer_result¶

Get the optimizer result.

Returns¶

iwp.Result: Optimization result object.

Raises¶

AttributeError: If optimization has not been run yet.

property options¶

Get the options dictionary.

Returns¶

dict: Dictionary of options.

property parameters¶

Get the parameters dictionary.

Returns¶

dict: Dictionary of parameters.

plot_fit_results()¶

Plot the results of the fit by calling the plot_fit_results method of each internal callback in each objective. Any user-defined callbacks should be called manually instead of using this method.

Returns¶

dict: Dictionary containing figure and axes objects for each objective.

plot_trace()¶

Plot the cost and each parameter as a function of iteration number.

Returns¶

tuple: Tuple containing figure and axes objects.

process_parameters()¶: Process the parameters to be used for the fit. This function extracts all the ionworkspipeline.Parameter objects from the parameters dictionary, and saves their names in a list. This is used to convert the parameters vector to a dictionary of parameters (in x_to_inputs()).

property results¶

Get the optimization results.

Returns¶

iwp.Result: Results object.

run(parameter_values)¶

Run the optimization to fit the model to data.

Parameters¶

parameter_valuesdict: Dictionary of parameter values to use for the fit.

Returns¶

iwp.Result: Results object containing fitted parameters and optimization results.

sample_initial_guesses(parameter_values, multistarts)¶

Generate multiple initial parameter guesses for multistart optimization.

Parameters¶

parameter_valuesdict: Dictionary of parameter values.
multistartsint: Number of initial guesses to generate.

Returns¶

tuple: Tuple containing (parameters_array, parameter_values_array) with initial guesses.

set_up_depickling()¶: Restore objects after unpickling when using multiprocessing.

set_up_pickling(parameter_values)¶

Prepare objects for pickling when using multiprocessing.

Parameters¶

parameter_valuesdict: Dictionary of parameter values.

spawn_jobs(parameters_array, parameter_values_array)¶

Create job objects for each optimization run.

Parameters¶

parameters_arraylist: List of parameter dictionaries for each job.
parameter_values_arraylist: List of parameter value dictionaries for each job.

Returns¶

list: List of DataFitJob objects.

timeseries_preprocessing()¶: Set up time stepping for the fit.

x_to_inputs(x)¶

Convert the parameters vector to a dictionary of parameters that can be passed as inputs to a PyBaMM solver. This function is used by the fit to convert the parameters vector to a dictionary of parameters that can be passed to the model.

Parameters¶

xarray_like: The parameters vector.

Returns¶

dict: A dictionary of parameters.

class ionworkspipeline.data_fits.ArrayDataFit(objectives, **kwargs)¶

A pipeline element that fits a model to data for multiple independent variable values. The data for each independent variable value is fitted separately. The independent variable values should be given as the keys of the objectives dictionary. The value of each key should be a ionworkspipeline.objectives.Objective object. This objective will be used to fit the data for the corresponding independent variable value.

The user-supplied objectives should assign the independent variable value to the custom_parameters attribute of the objective as appropriate. This class simply calls a separate ionworkspipeline.DataFit for each provided objective. It does not pass the independent variable value to the objective, so the user must ensure that the objective is set up to use the independent variable value correctly.

For example, this can be used to fit a model to data at multiple temperatures, or fit each pulse of a GITT experiment separately, with post-processing to extract functional relationships between parameters and the independent variable.

The rest of the parameters are the same as for ionworkspipeline.DataFit.

Extends: ionworkspipeline.data_fits.data_fit.DataFit

check_multistart_initial_guesses()¶: Check that initial guesses can be sampled for all data fits.

property data_fits¶

Get the dictionary of DataFit objects.

Returns¶

dict: Dictionary mapping independent variable values to DataFit objects

run(parameter_values)¶

Run the optimization for each independent variable value.

Parameters¶

parameter_valuesdict: Dictionary of parameter values to use for the fit

Returns¶

iwp.Result: Results object containing fitted parameters and optimization results

class ionworkspipeline.data_fits.CostLogger(plot_every=None, print_every=None)¶

A class to log the cost and parameters during a fit, and plot the results.

Parameters¶

plot_everyfloat, optional: The number of seconds between each plot of the cost and parameters. If None, no plots are generated during the fit, but the final plot can be generated using the plot() method.
print_everyfloat, optional: The number of seconds between each print of the cost and parameters. If None, no prints are generated during the fit, but the final print can be generated using the print() method.

argmin_costs()¶

Get the index of the job with minimum cost.

Returns¶

int: Index of minimum cost

argsort_costs()¶

Sort the jobs in ascending order of cost.

Returns¶

numpy.ndarray: Indices that would sort the costs

property children¶: List of child CostLoggerJob objects.

clear_axes()¶: Clear the plot axes.

property cost¶: List of logged costs.

property fig_axes¶

Get or create figure and axes objects.

Returns¶

tuple: Figure and axes objects, and whether they existed

finish()¶: Finish logging.

property finished¶: Whether logging is finished.

get_log()¶

Get the logged data.

Returns¶

dict: Dictionary of logged values

property is_parent¶: Whether this is a parent logger.

property iteration¶: List of iteration numbers.

log(logs=None, job_id=None)¶

Log cost and parameter values.

Parameters¶

logsdict, optional: Dictionary of values to log
job_idint, optional: ID of the job being logged

property multiprocessing¶: Whether multiprocessing is enabled.

property num_jobs¶: Number of jobs being logged.

property parent¶: Parent CostLogger object.

plot()¶

Plot the cost and parameters.

Returns¶

tuple: Figure and axes objects

property plot_every¶: Seconds between plot updates.

property plot_flag¶: Plot update flag.

plot_refresh(force_plot=False)¶

Update the plot of cost and parameters.

Parameters¶

force_plotbool, optional: If True, force a plot update regardless of timing

Returns¶

tuple: Figure and axes objects

property plot_variables¶: List of variables to plot.

property print_every¶: Seconds between print updates.

reset()¶: Reset all logging data and counters.

set_multiprocessing(multiprocessing)¶

Set whether multiprocessing is being used.

Parameters¶

multiprocessingbool: Whether multiprocessing is enabled

set_parameters(parameters)¶

Set the parameters to be logged.

Parameters¶

parameterslist: List of Parameter objects

property show_plot_iterative¶: Whether to show plots during optimization.

property show_print_iterative¶: Whether to print updates during optimization.

spawn_children(num_jobs)¶

Create child CostLoggerJob objects.

Parameters¶

num_jobsint: Number of child loggers to create

Returns¶

list: List of CostLoggerJob objects

start()¶: Start logging.

property timer¶: Timer object.