atomica.optimization.Measurable

class atomica.optimization.Measurable(measurable_name, t, pop_names=None, weight=1.0)

Bases: object

Optimization objective

A Measurable is a class that returns an objective value based on a simulated Model object. It takes in a Model and returns a scalar value. Often, an optimization may contain multiple Measurable objects, and the objective value returned by each of them is summed together.

Parameters:

• measurable_name – The base measurable class accepts the name of a program (for spending) or a quantity supported by Population.get_variable()

• t – Single year, or a list of two start/stop years. If specifying a single year, that year must appear in the simulation output. The quantity will be summed over all simulation time points

• pop_names – The base Measurable class takes in the names of the populations to use. If multiple populations are provided, the objective will be added across the named populations

• weight – The weight factor multiplies the quantity

Methods

eval
get_baseline	Return cached baseline values
get_objective_val	Return objective value

get_baseline(model)

Return cached baseline values

Similar to get_hard_constraint, sometimes a relative Measurable might be desired e.g. ‘Reduce deaths by at least 50%’. In that case, we need to perform a procedure similar to getting a hard constraint, where the Measurable receives an initial Model object and extracts baseline data for subsequent use in get_objective_val.

Thus, the output of this function is paired to its usage in get_objective_val.

Parameters:

model

Returns:

The value to pass back to the Measurable during optimization

get_objective_val(model, baseline)

Return objective value

This method should return the _unweighted_ objective value. Note that further transformation may occur

Parameters:

• model (Model) – A Model object after integration

• baseline – The baseline variable returned by this Measurable at the start of optimization

Return type:

float

Returns:

A scalar objective value