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Generic model

GenericModel

Creates a compartmental model from a dictionary and setting an evolve method.

Source code in compartmental/generic_model.py 
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class GenericModel:
    """Creates a compartmental model from a dictionary and setting an `evolve` method.
    """

    def get_all_params_names(self):
        """Returns a set of all parameters names, fixed or not.

        Returns:
            (set[str]): Set of all parameters names.
        """
        return set(list(self.param_to_index.keys()) + list(self.fixed_param_to_index.keys()))


    def __init__(self, configuration: dict[str, Any]):
        """Creates a model from the configuration given.

        Args:
            configuration (dict[str, Any]): Model configuration.
        """
        self.configuration: dict[str, Any] = copy.deepcopy(configuration)

        self.param_to_index: dict[str, int] = { k:i for i,k in enumerate(self.configuration["params"].keys()) }
        self.fixed_param_to_index: dict[str, int] = { k:i for i,k in enumerate(self.configuration["fixed_params"].keys()) }
        self.compartment_name_to_index: dict[str, int] = { k:i for i,k in enumerate(self.configuration["compartments"].keys()) }


    def populate_model_parameters(self, **kargs):
        """Populates params array. Assigns shortcuts to call them by their name as an attribute.
        """
        parameter_manager = ParametersManager(self.configuration, self)

        REFERENCE_OFFSET = self.configuration["reference"].get("offset", 0)
        # Set offset value if it is a str reference
        if isinstance(REFERENCE_OFFSET, str):
            self.configuration["params"][REFERENCE_OFFSET].update({"type":"int"})

        parameter_manager.populate_params(**kargs)
        parameter_manager.populate_fixed_params()

        for param in self.configuration["params"].keys():
            setattr(self, param, self.params[param])

        for fparam in self.configuration["fixed_params"].keys():
            setattr(self, fparam, self.fixed_params[self.fixed_param_to_index[fparam]])

        if isinstance(REFERENCE_OFFSET, str):
            self.reference_offset = self.params[REFERENCE_OFFSET]
        else:
            self.reference_offset = 0




    def populate_model_compartments(self, **kargs):
        """Populates compartments array. Assigns shortcuts to call them by their name as an attribute.
        """
        N_SIMULATIONS = self.configuration["simulation"]["n_simulations"]
        self.state = CNP.zeros(
            (len(self.configuration["compartments"]), N_SIMULATIONS), dtype=CNP.float64
        )
        self.log_diff = CNP.zeros((N_SIMULATIONS, 1), dtype=CNP.float64)

        for c,i in self.compartment_name_to_index.items():
            C = self.configuration["compartments"][c]
            initial_value = C["initial_value"]
            if isinstance(initial_value, str):
                if initial_value in self.param_to_index.keys():
                    self.state[i,:] = self.params[initial_value]
                continue
            self.state[i,:] = initial_value

        for c,i in self.compartment_name_to_index.items():
            C = self.configuration["compartments"][c]
            minus = C.get("minus_compartments", False)
            if not minus:
                continue
            if not isinstance(minus, list):
                minus = [minus]
            for m in minus:
                self.state[i,:] -= self.state[self.compartment_name_to_index[m],:]

        for comp in self.configuration["compartments"].keys():
            setattr(self, comp, self.state[self.compartment_name_to_index[comp]])


    def evolve(self, step, *args, **kargs):
        """This method must be overwritten to complete the model initialization.

        Args:
            step (int): Step of simulation. Simulation ends when `step = simulation.n_steps`
        """
        ...


    def get_diff(self, step, reference, reference_mask):
        """Returns a value that represents the distance from the simulation to the reference.
        This function can be overwritten if other deffinition os distance is needed.

        Args:
            step (int): Step of simulation. Simulation ends when `step = simulation.n_steps`
            reference (list[list[float]]): Reference(s) values.
            reference_mask (list[int]): Mask to obtain simulation values to compare with the reference(s). 

        Returns:
            (list[float]): Distance from simulations to reference(s).
        """
        index = step + self.reference_offset
        # To only take the diff on the same range for all simulations
        diff = CNP.absolute(CNP.take(self.state, reference_mask, 0)[0].T-reference[CNP.clip(index, 0, self.N_STEPS-1)]) * \
               ((self.reference_offset.max()<=index) * (index<=self.N_STEPS))

        return CNP.log(diff + 1)


    def _internal_run_(self, inner, inner_args: list, outer, outer_args:list,  reference, save_file:str, *args, exclude_pupulate:bool=False, **kargs):
        """Internal function that executes the model.

        Args:
            inner (function): Function to call in the main loop.
            inner_args (list): Args given to `inner`.
            outer (function): Function to call after the main loop.
            outer_args (list): Args given to `outer`.
            reference (list[list[float]]): Reference values used to compare with the simulation.
            save_file (str): Filename of path to file.
            exclude_populate (bool, optional): If `False` params and compartments are populated with random values. Defaults to False.
        """
        N_EXECUTIONS = self.configuration["simulation"]["n_executions"]
        self.N_STEPS = self.configuration["simulation"]["n_steps"]

        for execution in range(N_EXECUTIONS):
            progress_bar(f"Model running: ", execution, N_EXECUTIONS, len=min(20, max(N_EXECUTIONS,5)))

            if not exclude_pupulate:
                self.populate_model_parameters(**kargs)
                self.populate_model_compartments(**kargs)

            self._min_offset_: int = self.reference_offset.min()
            self.log_diff[:] = 0

            # for step in range(self.N_STEPS):
            step = CNP.int64(0)
            while (self.reference_offset + step < self.N_STEPS).any():
                inner(self, step, reference, *inner_args, **kargs)
                step += 1
            outer(self, *outer_args, execution_number=execution, **kargs)

        progress_bar(f"Model running: ", N_EXECUTIONS, N_EXECUTIONS, len=min(20, max(N_EXECUTIONS,5)), end='\n')


    def run_no_diff(self, save_file: str, *args, **kargs):
        """Runs the model without computing the diference any reference(s).

        Args:
            save_file (str): Filename of path to file.
        """
        self._internal_run_(
            self.evolve, args, 
            save_parameters_no_diff, (save_file, self.param_to_index.keys(),  self.params), 
            None, save_file,
            *args, **kargs
        )


    def run(self, reference, save_file: str, *args, **kargs):
        """Runs the model computing the diference from the reference(s).

        Args:
            reference (list[list[float]]): Reference(s) values.
            save_file (str): Filename of path to file.
        """

        reference_mask = CNP.array([self.compartment_name_to_index[c] for c in self.configuration["reference"]["compartments"]])

        def inner(model, step, reference, reference_mask, *args, **kargs):
            model.evolve(model, step, *args, **kargs)
            self.log_diff[:,0] += model.get_diff(step, reference, reference_mask)

        def outer(model, save_file, *args, execution_number, **kargs):
            best_params, best_log_diff = get_best_parameters(model.params, model.log_diff, model.configuration["results"]["save_percentage"])
            save_parameters(save_file, model.param_to_index.keys(), best_params, best_log_diff, execution_number=execution_number)

        self._internal_run_(
            inner, (reference_mask, *args), 
            outer, (save_file,), 
            reference, save_file, 
            *args, **kargs
        )

__init__(configuration)

Creates a model from the configuration given.

Parameters:

Name Type Description Default
configuration dict[str, Any]

Model configuration.

 required
Source code in compartmental/generic_model.py 
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def __init__(self, configuration: dict[str, Any]):
    """Creates a model from the configuration given.

    Args:
        configuration (dict[str, Any]): Model configuration.
    """
    self.configuration: dict[str, Any] = copy.deepcopy(configuration)

    self.param_to_index: dict[str, int] = { k:i for i,k in enumerate(self.configuration["params"].keys()) }
    self.fixed_param_to_index: dict[str, int] = { k:i for i,k in enumerate(self.configuration["fixed_params"].keys()) }
    self.compartment_name_to_index: dict[str, int] = { k:i for i,k in enumerate(self.configuration["compartments"].keys()) }

evolve(step, *args, **kargs)

This method must be overwritten to complete the model initialization.

Parameters:

Name Type Description Default
step int

Step of simulation. Simulation ends when step = simulation.n_steps

 required
Source code in compartmental/generic_model.py 
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def evolve(self, step, *args, **kargs):
    """This method must be overwritten to complete the model initialization.

    Args:
        step (int): Step of simulation. Simulation ends when `step = simulation.n_steps`
    """
    ...

get_all_params_names()

Returns a set of all parameters names, fixed or not.

Returns:

Type Description
set[str]

Set of all parameters names.
Source code in compartmental/generic_model.py 
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def get_all_params_names(self):
    """Returns a set of all parameters names, fixed or not.

    Returns:
        (set[str]): Set of all parameters names.
    """
    return set(list(self.param_to_index.keys()) + list(self.fixed_param_to_index.keys()))

get_diff(step, reference, reference_mask)

Returns a value that represents the distance from the simulation to the reference. This function can be overwritten if other deffinition os distance is needed.

Parameters:

Name Type Description Default
step int

Step of simulation. Simulation ends when step = simulation.n_steps

 required
reference list[list[float]]

Reference(s) values.

 required
reference_mask list[int]

Mask to obtain simulation values to compare with the reference(s).

required

Returns:

Type Description
list[float]

Distance from simulations to reference(s).
Source code in compartmental/generic_model.py 
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def get_diff(self, step, reference, reference_mask):
    """Returns a value that represents the distance from the simulation to the reference.
    This function can be overwritten if other deffinition os distance is needed.

    Args:
        step (int): Step of simulation. Simulation ends when `step = simulation.n_steps`
        reference (list[list[float]]): Reference(s) values.
        reference_mask (list[int]): Mask to obtain simulation values to compare with the reference(s). 

    Returns:
        (list[float]): Distance from simulations to reference(s).
    """
    index = step + self.reference_offset
    # To only take the diff on the same range for all simulations
    diff = CNP.absolute(CNP.take(self.state, reference_mask, 0)[0].T-reference[CNP.clip(index, 0, self.N_STEPS-1)]) * \
           ((self.reference_offset.max()<=index) * (index<=self.N_STEPS))

    return CNP.log(diff + 1)

populate_model_compartments(**kargs)

Populates compartments array. Assigns shortcuts to call them by their name as an attribute.

Source code in compartmental/generic_model.py 
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def populate_model_compartments(self, **kargs):
    """Populates compartments array. Assigns shortcuts to call them by their name as an attribute.
    """
    N_SIMULATIONS = self.configuration["simulation"]["n_simulations"]
    self.state = CNP.zeros(
        (len(self.configuration["compartments"]), N_SIMULATIONS), dtype=CNP.float64
    )
    self.log_diff = CNP.zeros((N_SIMULATIONS, 1), dtype=CNP.float64)

    for c,i in self.compartment_name_to_index.items():
        C = self.configuration["compartments"][c]
        initial_value = C["initial_value"]
        if isinstance(initial_value, str):
            if initial_value in self.param_to_index.keys():
                self.state[i,:] = self.params[initial_value]
            continue
        self.state[i,:] = initial_value

    for c,i in self.compartment_name_to_index.items():
        C = self.configuration["compartments"][c]
        minus = C.get("minus_compartments", False)
        if not minus:
            continue
        if not isinstance(minus, list):
            minus = [minus]
        for m in minus:
            self.state[i,:] -= self.state[self.compartment_name_to_index[m],:]

    for comp in self.configuration["compartments"].keys():
        setattr(self, comp, self.state[self.compartment_name_to_index[comp]])

populate_model_parameters(**kargs)

Populates params array. Assigns shortcuts to call them by their name as an attribute.

Source code in compartmental/generic_model.py 
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def populate_model_parameters(self, **kargs):
    """Populates params array. Assigns shortcuts to call them by their name as an attribute.
    """
    parameter_manager = ParametersManager(self.configuration, self)

    REFERENCE_OFFSET = self.configuration["reference"].get("offset", 0)
    # Set offset value if it is a str reference
    if isinstance(REFERENCE_OFFSET, str):
        self.configuration["params"][REFERENCE_OFFSET].update({"type":"int"})

    parameter_manager.populate_params(**kargs)
    parameter_manager.populate_fixed_params()

    for param in self.configuration["params"].keys():
        setattr(self, param, self.params[param])

    for fparam in self.configuration["fixed_params"].keys():
        setattr(self, fparam, self.fixed_params[self.fixed_param_to_index[fparam]])

    if isinstance(REFERENCE_OFFSET, str):
        self.reference_offset = self.params[REFERENCE_OFFSET]
    else:
        self.reference_offset = 0

run(reference, save_file, *args, **kargs)

Runs the model computing the diference from the reference(s).

Parameters:

Name Type Description Default
reference list[list[float]]

Reference(s) values.

 required
save_file str

Filename of path to file.

 required
Source code in compartmental/generic_model.py 
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def run(self, reference, save_file: str, *args, **kargs):
    """Runs the model computing the diference from the reference(s).

    Args:
        reference (list[list[float]]): Reference(s) values.
        save_file (str): Filename of path to file.
    """

    reference_mask = CNP.array([self.compartment_name_to_index[c] for c in self.configuration["reference"]["compartments"]])

    def inner(model, step, reference, reference_mask, *args, **kargs):
        model.evolve(model, step, *args, **kargs)
        self.log_diff[:,0] += model.get_diff(step, reference, reference_mask)

    def outer(model, save_file, *args, execution_number, **kargs):
        best_params, best_log_diff = get_best_parameters(model.params, model.log_diff, model.configuration["results"]["save_percentage"])
        save_parameters(save_file, model.param_to_index.keys(), best_params, best_log_diff, execution_number=execution_number)

    self._internal_run_(
        inner, (reference_mask, *args), 
        outer, (save_file,), 
        reference, save_file, 
        *args, **kargs
    )

run_no_diff(save_file, *args, **kargs)

Runs the model without computing the diference any reference(s).

Parameters:

Name Type Description Default
save_file str

Filename of path to file.

 required
Source code in compartmental/generic_model.py 
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def run_no_diff(self, save_file: str, *args, **kargs):
    """Runs the model without computing the diference any reference(s).

    Args:
        save_file (str): Filename of path to file.
    """
    self._internal_run_(
        self.evolve, args, 
        save_parameters_no_diff, (save_file, self.param_to_index.keys(),  self.params), 
        None, save_file,
        *args, **kargs
    )