atomica.optimization.MaximizeCascadeConversionRate

class atomica.optimization.MaximizeCascadeConversionRate(cascade_name, t, pop_names='all', weight=1.0)

Bases: Measurable

Maximize overall conversion rate

Maximize conversion summed over all cascade stages

Parameters:

• cascade_name – The name of one of the cascades in the Framework

• t (float) – A single time value e.g. 2020

• pop_names – A single pop name (including ‘all’), a list of populations, or a dict/list of dicts, each with a single aggregation e.g. {'foo':['0-4','5-14']}

• weight – Weighting factor for this Measurable in the overall objective function

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 – A Model object after integration

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

Returns:

A scalar objective value