Parameter Space

Classes related to parameter spaces. Algorithms to search through parameter space - a ParameterGenerator - are defined here also. ParameterGenerator is a generic parent class, specific implementations are sub-classes of this.

API

Classes:

FullGrid([par_space, parspace_settings])	A full cartesian grid.
GridWalk([par_space, parspace_settings])	Walk after the current best fit
LegacyGridSearch([par_space, parspace_settings])	Search around all reasonable models
Parameter([name, fixed, LaTeX, sformat, ...])	Parameter of a model
ParameterGenerator([par_space, ...])	Abstract class for ParameterGenerator
ParameterSpace(system)	A list of all Parameter objects in the Model
SpecificModels([par_space, parspace_settings])	Create specific models.

class parameter_space.FullGrid(par_space=[], parspace_settings=None)

A full cartesian grid.

A full Cartesian grid in all free parameters, with bounds lo/hi and stepsize step. Warning: If several (>3) parameters are free, this will result in a large number of models. This parameter generator is generally not intended for production use.

Parameters:

par_spacedyn.parameter_space.ParameterSpace object

parspace_settingsdict

Methods:

grid([center, par, eps])	Create the grid
specific_generate_method(**kwargs)	Generates new models

grid(center=None, par=None, eps=1e-06)

Create the grid

Span the whole parameter grid defined by self.par_space.par_generator_settings attributes. IN GENERAL THIS WILL RESULT IN A LARGE NUMBER OF MODELS ADDED TO self.model_list! PRIMARILY THIS IS INTENDED FOR TESTING AND DEBUGGING. Clips parameter values to lo/hi attributes. If clipping violates the minstep attribute, the resulting model(s) will not be created. If the minstep attribute is missing, the step attribute will be used instead. Explicitly set minstep=0 to allow arbitrarily small steps down to eps (not recommended).

Parameters:

centerList of center coordinates. Must be in the same sequence as

the parameters in self.par_space. Mandatory argument.

parInternal use only. Gives the parameter to start with. Set

automatically in the recursive process. The default is None.

epsUsed for numerical comparison (relative tolerance), default 1e-6

Returns:

None. Sets self.model_list to the resulting models.

Raises:

ValueError if center is not specified or fixed parameters != center.

specific_generate_method(**kwargs)

Generates new models

Span the whole parameter grid. The center of the grid walk is the parameter set with the smallest chi2 value, depending on the parameter space setting ‘which_chi2’.

Parameters:

None.

Returns:

None. self.model_list is the list of new models.

Raises:

None.

class parameter_space.GridWalk(par_space=[], parspace_settings=None)

Walk after the current best fit

See docstrings for specific_generate_method and check_specific_stopping_critera for full description.

Parameters:

par_spacedyn.parameter_space.ParameterSpace object

parspace_settingsdict

Methods:

grid_walk([center, par, eps])	Walks the grid
specific_generate_method(**kwargs)	Generates new models

grid_walk(center=None, par=None, eps=1e-06)

Walks the grid

Walks the grid defined by self.par_space.par_generator_settings attributes. Clips parameter values to lo/hi attributes. If clipping violates the minstep attribute, the resulting model(s) will not be created. If the minstep attribute is missing, the step attribute will be used instead. Explicitly set minstep=0 to allow arbitrarily small steps (not recommended).

Parameters:

centerList of center coordinates. Must be in the same sequence as

the parameters in self.par_space. Mandatory argument.

parInternal use only. Gives the parameter to start with. Set

automatically in the recursive process. The default is None.

epsUsed for numerical comparison (relative tolerance), default 1e-6

Returns:

None. Sets self.model_list to the resulting models.

Raises:

ValueError if center is not specified or fixed parameters != center.

specific_generate_method(**kwargs)

Generates new models

The center of the grid walk is the parameter set with the smallest chi2 value, depending on the parameter space setting ‘which_chi2’.

Parameters:

None.

Returns:

None.

sets the list self.model_list of new models.

Raises:

None.

class parameter_space.LegacyGridSearch(par_space=[], parspace_settings=None)

Search around all reasonable models

This is the method used by previous code versions AKA schwpy. See docstrings for specific_generate_method and check_specific_stopping_critera for full description.

Parameters:

par_spacedyn.parameter_space.ParameterSpace object

parspace_settingsdict

Methods:

specific_generate_method(**kwargs)

Generates new models

specific_generate_method(**kwargs)

Generates new models

Starts at the initial point. Start the iteration: (i) find all models with \(|\chi^2 - \chi_\mathrm{min}^2|\) within the specified threshold, (ii) for each model within the threshold, seed new models by independently take a step \(\pm 1\) of size step. If no new models are seeded at the end of an iteration, then divide all parameter stepsizes by two till their specified minstep are reached.

Parameters:

None.

Returns:

None.

sets self.model_list is the list of new models.

class parameter_space.Parameter(name=None, fixed=False, LaTeX=None, sformat=None, value=None, par_generator_settings=None, logarithmic=False)

Parameter of a model

Parameters:

namestring

the parameter name (specific components have specific parameter names)

fixedBool

whether or not to fix this parameter during parameter searches

LaTeXstring

a `LaTeX format string to use in plots

sformatstring

a format string for printing parameter values

valuefloat

the value of this parameter in a model; the config file contains an initial value, this is updated during the parameter search; this value can be in log or linear units, depending on the config file

par_generator_settingsdict

settings for the parameter generator

logarithmicBool

whether or not this parameter is specified in log units

Methods:

get_par_value_from_raw_value(raw_value)	Get parameter value from the raw value
get_raw_value_from_par_value(par_value)	Get raw parameter value from parameter
update(**kwargs)	update the parameter
validate()	validate the parameter

Attributes:

par_value

getter method for par_value to be used like an attribute

get_par_value_from_raw_value(raw_value)

Get parameter value from the raw value

In raw values, linearly-sized steps are taken during parameter searches. Currently there is only one possible raw transformation, going to log units. Future ones may include, e.g. isotropic transformations of viewing angles.

Parameters:

raw_valuefloat

the raw parameter value

Returns:

float

the parameter value

get_raw_value_from_par_value(par_value)

Get raw parameter value from parameter

In raw values, linearly-sized steps are taken during parameter searches. Currently there is only one possible raw transformation, going to log units. Future ones may include, e.g. isotropic transformations of viewing angles.

Parameters:

par_valuefloat

the parameter value

Returns:

float

the raw parameter value

property par_value

getter method for par_value to be used like an attribute

update(**kwargs)

update the parameter

validate()

validate the parameter

class parameter_space.ParameterGenerator(par_space=[], parspace_settings=None, name=None)

Abstract class for ParameterGenerator

ParameterGenerator have methods to generate new sets of parameters to evaluate models based on existing models. This is an abstrct class, specific implementations should be implemented as child-classes (e.g. LegacyGridSearch). Every implementation may have a method check_specific_stopping_critera and must have a method specific_generate_method which define the stopping criteria and parameter generation algorithm for that implementation. These are exectuted in addition to generic methods, which are defined in this parent ParameterGenerator class.

Parameters:

par_spacedyn.parameter_space.ParameterSpace object

parspace_settingsdict

parameter space settings

namestring

the name of the particular ParameterGenerator sub-class

Methods:

add_model([model, n_iter])	Add a model
check_generic_stopping_critera()	check generic stopping critera
check_specific_stopping_critera()	checks specific stopping critera
check_stopping_critera()	Check stopping criteria
clip(value, mini, maxi)	clip to lo/hi
generate([current_models, ...])	Generate new parameter sets.
specific_generate_method()	Placeholder.

add_model(model=None, n_iter=0)

Add a model

Adds the model (a list of Parameter objects) to the table self.current_models. The datetime64 column is populated with the current timestamp numpy.datetime64(‘now’, ‘ms’). The ‘which_iter’ column is populated with the argument value n_iter.

Note - here, model refers to a list of Parameter objects, not a Model object. TODO: clarify the naming confusion.

Parameters:

modelList of Parameter objects

n_iterinteger

value to write in ‘which_iter’ column, optional. The default is 0.

Returns:

None.

Raises:

ValueError

If no or empty model is given.

check_generic_stopping_critera()

check generic stopping critera

check_specific_stopping_critera()

checks specific stopping critera

If the last iteration did not improve the chi2 by at least min_delta_chi2, then stop. May be overwritten or extended in each ParameterGenerator class.

Returns:

None

sets Bool to self.status['min_delta_chi2_reached']

check_stopping_critera()

Check stopping criteria

This is a wrapper which checks both the generic stopping criteria and also the specific_stopping_critera revelant for any particular ParameterGenerator used.

clip(value, mini, maxi)

clip to lo/hi

Clips value to the interval [mini, maxi]. Similar to the numpy.clip() method. If mini==maxi, that value is returned.

Parameters:

valuenumeric value

mininumeric value

maxinumeric value

Returns:

min(max(mini, value), maxi)

Raises:

ValueError if mini > maxi

generate(current_models=None, kw_specific_generate_method={})

Generate new parameter sets.

This is a wrapper method around the specific_generate_method of child generator classes. This wrapper does the following:

1. evaluates stopping criteria, and stops if necessary

2. runs the specific_generate_method of the child class, which updates self.model_list with a list of proposal models

3. removes previously run and invalid models from self.model_list

4. converts parameters from raw_values to par_values

5. adds new, valid models to current_models.table

6. update and return the status dictionary

Parameters:

current_modelsdynamite.AllModels

kw_specific_generate_methoddict

keyword arguments passed to the specific_generate_method of the child class

Returns:

dict

a status dictionary, with entries:

• stop: bool, whether or not any stopping criteria are met

• n_new_models: int

and additional Bool entries for the indivdidual stopping criteria:

• last_iter_added_no_new_models

• n_max_mods_reached

• n_max_iter_reached

• plus any criteria specific to the child class

specific_generate_method()

Placeholder.

This is a placeholder. Specific ParameterGenerator sub-classes should have their own specific_generate_method methods.

Parameters:

None.

Returns:

None.

set self.model_list to be the list of newly generated models

class parameter_space.ParameterSpace(system)

A list of all Parameter objects in the Model

Parameters:

systema dyn.physical_system.System object

Methods:

get_param_value_from_raw_value(raw_value)	Get parameter values from raw parameters
get_parameter_from_name(name)	Get a Parameter from the name
get_parset()	Get parset as row of an Astropy Table
get_raw_value_from_param_value(par_val)	Get raw parameter values from parameters
validate_parset(parset)	Validates a parameter set
validate_parspace()	Validates a parameter set

get_param_value_from_raw_value(raw_value)

Get parameter values from raw parameters

In raw values, linearly-sized steps are taken during parameter searches. Currently there is only one possible raw transformation, going to log units. Future ones may include, e.g. isotropic transformations of viewing angles.

Parameters:

raw_valuelist of floats

list of raw parameter values for all models

Returns:

list of floats

list of parameter value

get_parameter_from_name(name)

Get a Parameter from the name

Parameters:

namestring

Returns:

dyn.parameter_space.Parameter

get_parset()

Get parset as row of an Astropy Table

Returns:

parsetrow of an Astropy Table

Contains the values of the individual parameters.

get_raw_value_from_param_value(par_val)

Get raw parameter values from parameters

In raw values, linearly-sized steps are taken during parameter searches. Currently there is only one possible raw transformation, going to log units. Future ones may include, e.g. isotropic transformations of viewing angles.

Parameters:

par_vallist of floats

list of parameter values for all models

Returns:

list of floats

list of raw parameter value

validate_parset(parset)

Validates a parameter set

Validate the values of each component’s parameters by calling the individual components’ validate_parameter methods. Does the same for system parameters. Used by the parameter generators.

Parameters:

parsetlist of Parameter objects

Returns:

Bool

True if validation was successful, False otherwise

validate_parspace()

Validates a parameter set

Validate the values of each component’s parameters by calling the individual components’ validate_parameter methods and check whether parameters are within the specified lo/hi bounds. Does the same for system parameters.

Returns:

None.

Raises:

ValueError

If checks fail due to various reasons.

class parameter_space.SpecificModels(par_space=[], parspace_settings=None)

Create specific models.

Creates models with parameter values according to the entries in the lists fixed_values in a single iteration. If any parameter’s fixed_values entry is missing, its value entry will be used. parspace_settings['generator_settings']['SpecificModels_mode'] determines how models are constructed: list: selects parameter values element-wise. All parameters’ fixed_values lists must be of equal length (or zero length if their respective value entry is to be used). cartesian: Cartesian product of fixed parameter values. The parameters’ fixed_values lists don’t need to be of equal length. May result in a large number of models.

Note that this parameter generator ignores step, minstep, lo, and high. Also, fixed will be ignored if fixed_values is specified.

Further, all models are run in a single iteration and the optimality tolerances in the stopping_criteria section in the configuration file’s parameter_space_settings will be ignored.

Parameters:

par_spacedyn.parameter_space.ParameterSpace object

parspace_settingsdict

Methods:

check_specific_stopping_critera()	The specific stopping critera
specific_generate_method(**kwargs)	Generates the specific models

check_specific_stopping_critera()

The specific stopping critera

Will always stop after creating all specific models.

Returns:

None

sets self.status['min_delta_chi2_reached'] to True

specific_generate_method(**kwargs)

Generates the specific models

Parameters:

None.

Returns:

None.

sets self.model_list, the list of new models.

Inheritance Diagram