atomica.optimization.MaximizeMeasurable¶

class atomica.optimization.MaximizeMeasurable(measurable_name, t, pop_names=None)[source]¶

Bases: Measurable

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