pycotools3.tasks.ParameterEstimation¶

class pycotools3.tasks.ParameterEstimation(config)[source]¶

Interface to COPASI’s parameter estimation task

Examples

Assuming a ParameterEstimation.Config object has been configured and is called config >>> pe = ParameterEstimation(config)

__init__(config)[source]¶

Configure a the parameter estimation task in copasi

Pycotools supports all the features of parameter estimation configuration as copasi, plus a few additional ones (such as the affected models setting).

Parameters:	config (ParameterEstimation.Config) – An appropriately configured `ParameterEstimation.Config` class

Examples

See ParameterEstimation.Config or ParameterEstimation.Context for detailed information on how to produce a ParameterEstimation.Config object. Note that the ParameterEstimation.Context class is higher level and should be the preferred way of constructing a ParameterEstimation.Config object while the ParameterEstimation.Config class gives you the same level of control as copasi but is bulkier to write.

Assuming the ParameterEstimation.Config class has already been created >>> pe = ParameterEstimation(config)

Methods

__init__(config) Configure a the parameter estimation task in copasi

check_integrity(allowed, given) Method to raise an error when a wrong kwarg is passed to a subclass

convert_bool_to_numeric(dct) CopasiML uses 1’s and 0’s for True or False in some but not all places.

convert_bool_to_numeric2() CopasiML uses 1’s and 0’s for True or False in some but not all places.

do_checks() validate integrity of user input

get_component(model, component, string) Get component called string from model

get_model_objects_from_strings() Get model objects from the strings provided by the user in the Config class :return: list of model.Model objects

get_variable_from_string(m, v[, glob]) Use model entity name to get the pycotools3 variable

read_model(m)

param m:

run(models) Run a parameter estimation using command line copasi.

update_properties(kwargs) method for updating properties from kwargs

Attributes

`fit_dir`	Property holding the directory where the parameter estimation fitting occurs.
`global_quantities`	list of strings of global quantities present in the models
`local_parameters`	list of strings of local parameters in the model
`metabolites`	list of strings of metabolites in the model
`models`	Get models
`models_dir`	A directory containing models
`problem_dir`	Property holding the directory where the parameter estimation problem is stored :returns: str.
`results_directory`	A directory containing results, parameter estimation report files from copasi
`schema`
`valid_methods`

class Config(models, datasets, items, settings={}, defaults=None)[source]¶

A class for holding a parameter estimation configuration

Stores all the settings needed for configuration of a parameter estimation using COPASI.

Examples

>>> ## create a model
>>> antimony_string = '''
...             model TestModel1()
...                 R1: A => B; k1*A;
...                 R2: B => A; k2*B
...                 A = 1
...                 B = 0
...                 k1 = 4;
...                 k2 = 9;
...             end
...             '''
>>> copasi_filename = os.path.join(os.path.dirname(__file__), 'example_model.cps')
>>> with model.BuildAntimony(copasi_filename) as loader:
...     mod = loader.load(antimony_string)
>>> ## Simulate some data from the model and write to file
>>> fname = os.path.join(os.path.dirname(__file__), 'timeseries.txt')
>>> data = self.model.simulate(0, 10, 11)
>>> data.to_csv(fname)
>>> ## create nested dict containing all the relevant arguments for your configuration
>>> config_dict = dict(
...        models=dict(
...             ## model name is the users choice here
...            example1=dict(
...                copasi_file=copasi_filename
...            )
...        ),
...        datasets=dict(
...            experiments=dict(
...                 ## experiment names are the users choice
...                report1=dict(
...                    filename=self.TC1.report_name,
...                ),
...            ),
...            ## our validations entry is empty here
...            ## but if you have validation data this should
...            ## be the same as the experiments section
...            validations=dict(),
...        ),
...        items=dict(
...            fit_items=dict(
...                A=dict(
...                    affected_experiments='report1'
...                ),
...                B=dict(
...                    affected_validation_experiments=['report2']
...                ),
...            k1={},
...            k2={},
...            ),
...            constraint_items=dict(
...                k1=dict(
...                    lower_bound=1e-2,
...                    upper_bound=10
...                )
...            )
...        ),
...        settings=dict(
...            method='genetic_algorithm_sr',
...            population_size=2,
...            number_of_generations=2,
...            working_directory=os.path.dirname(__file__),
...            copy_number=4,
...            pe_number=2,
...            weight_method='value_scaling',
...            validation_weight=2.5,
...            validation_threshold=9,
...            randomize_start_values=True,
...            calculate_statistics=False,
...            create_parameter_sets=False
...        )
...    )
>>> config = ParameterEstimation.Config(**config_dict)

configure()[source]¶

Configure the class for production of parameter estimation config

Like a main method for this class. Uses the other methods in the class to configure a ParameterEstimation.Config object

Returns:	Operates inplace and returns None

constraint_items¶

The constraint items as nested dict

Type:	Returns

experiment_filenames¶

A list of experiment filesnames

Type:	Returns

experiment_names¶

A list of experiment names

Type:	Returns

experiments¶: The experiments property :returns: datasets.experiments as dict

fit_items¶

The fit items as nested dict

Type:	Returns

from_json(string)[source]¶

Create config object from json format :param string: a valid json string :type string: Str

Returns:	ParameterEstimation.Config

from_yaml(yml)[source]¶

Read config object from yaml file :param yml: full path to text file containing configuration arguments in yaml format :type yml: str

Returns:	ParameterEstimation.Config

model_objects¶

A list of model objects for mapping

Type:	Returns

models_affected_experiments¶

Get which experiment datasets affect which models

Returns:	dict. Keys are model names, value are list of affected models

models_affected_validation_experiments¶

Get which experiment datasets affect which models

Returns:	dict. Keys are model names, value are list of affected models

set_default_fit_items_dct()[source]¶

Configure missing entries for items.fit_items when they are in nested dict format

Returns:	None. Method operates inplace on class attributes

set_default_fit_items_str()[source]¶

Configure missing entries for items.fit_items when they are strings pointing towards model variables

Returns:	None. Method operates inplace on class attributes

to_json()[source]¶

Output arguments as json

Returns: str: All arguments in json format

to_yaml(filename=None)[source]¶

Output arguments as yaml

Parameters:	filename (str, None) – If not None (default), path to write yaml configuration to
Returns:	Config object as string in yaml format

validation_filenames¶

a list of validation filenames

Type:	Returns

validation_names¶

A list of validation names

Type:	Returns

validations¶: The validations property :returns: datasets.validations as dict

class Context(models, experiments, working_directory=None, context='s', parameters='mg', filename=None, validation_experiments={}, settings={})[source]¶

A high level interface to create a ParameterEstimation.Config object.

Enables the construction of a ParameterEstimation.Config object assuming one of several common patterns of usage.

Examples

Assuming that we have two copasi models (mod1 and mod2) and two experimental data files (fname1, fname2), correctly formatted according to the copasi specification. We can generate a config object that specifies the fitting of both experiments to both models and to fit all global and local parameters parameters=’gl’ in each.

with ParameterEstimation.Context(
    [mod1, mod2], [fname1, fname2],
    context='s', parameters='gl') as context:
    context.set('method', 'genetic_algorithm_sr')
    context.set('number_of_generations', 25)
    context.set('population_size', 10)
    config = context.get_config()

pe = ParameterEstimation(config)

Or for profile likelihoods on the first model mod1

get_config_cv()[source]¶: configure for cross validation Returns:

get_config_pl()[source]¶: configure for profile likelihoods Returns:

set(parameter, value)[source]¶

Set the value of parameter to value.

Looks for the first instance of parameter and sets its value to value. To set all values of a parameter, see ParameterEstimation.Config.set_all()

Parameters:	parameter – A key somewhere in the nested structure of the config object value – A value to replace the current value with
Returns:	None

do_checks()[source]¶: validate integrity of user input

fit_dir¶: Property holding the directory where the parameter estimation fitting occurs. This can be enumerated under a single problem directory to group similar parameter estimations :returns: str. A directory.

get_model_objects_from_strings()[source]¶

Get model objects from the strings provided by the user in the Config class :return: list of model.Model objects

Returns:	list of model objects

global_quantities¶

list of strings of global quantities present in the models

Type:	Returns

local_parameters¶

list of strings of local parameters in the model

Type:	Returns

metabolites¶

list of strings of metabolites in the model

Type:	Returns

models¶

Get models

Returns:	the models entry of the `ParameterEstimation.Config` object

models_dir¶

A directory containing models

Each model will be configured in a different directory when multiple models are being configured simultaneously :returns: dct. Location of models directories

problem_dir¶: Property holding the directory where the parameter estimation problem is stored :returns: str. A directory.

results_directory¶

A directory containing results, parameter estimation report files from copasi

Each model configured will have their own results directory :returns: dict[model] = results_directory

run(models)[source]¶

Run a parameter estimation using command line copasi.

Parameters:	models – dict of models. Output from _setup()
Returns:	dict of models. Output from _setup()
Return type:	param models