atomica.optimization.DecreaseByMeasurable¶
- class atomica.optimization.DecreaseByMeasurable(measurable_name, t, decrease, pop_names=None, target_type='frac')[source]¶
Bases:
MeasurableDecrease quantity by percentage
This Measurable stores the value of a quantity using the original instructions. It then requires that there is a minimum increase in the value of the quantity during optimization. For example
>>> DecreaseByMeasurable('deaths',2030,0.05)
This Measurable would correspond to an decrease of 5% in the number of deaths in 2030.
The measurable returns
np.infif the condition is violated, and0.0otherwise.- 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
decrease – The amount by which to decrease the measurable (e.g. 0.05 for a 5% decrease). Use
target_type='abs'to specify an absolute decreasepop_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
target_type – Specify fractional ‘frac’ or absolute ‘abs’ decrease (default is fractional)
Methods
evalReturn cached baseline values
Return objective value
- get_baseline(model)[source]¶
Return cached baseline values
Similar to
get_hard_constraint, sometimes a relativeMeasurablemight 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 theMeasurablereceives an initialModelobject and extracts baseline data for subsequent use inget_objective_val.Thus, the output of this function is paired to its usage in
get_objective_val.- Parameters:
model –
- Return type:
- Returns:
The value to pass back to the
Measurableduring optimization