utilities.quality_indicator
Module Contents
Functions

Computes the additive epsilonindicator between two solutions. 

Computes the additive epsilonindicator between reference point and current onedimensional vector of front. 

Computes the preferencebased quality indicator. 

Computes the hypervolumeindicator between reference front and current approximating point. 
Attributes
 utilities.quality_indicator.epsilon_indicator(s1: numpy.ndarray, s2: numpy.ndarray) float [source]
Computes the additive epsilonindicator between two solutions.
 Parameters:
s1 (np.ndarray) – Solution 1. Should be an onedimensional array.
s2 (np.ndarray) – Solution 2. Should be an onedimensional array.
 Returns:
The maximum distance between the values in s1 and s2.
 Return type:
float
 utilities.quality_indicator.epsilon_indicator_ndims(front: numpy.ndarray, reference_point: numpy.ndarray) list [source]
Computes the additive epsilonindicator between reference point and current onedimensional vector of front.
 Parameters:
front (np.ndarray) – The front that the current reference point is being compared to. Should be set of arrays, where the rows are the solutions and the columns are the objective dimensions.
reference_point (np.ndarray) – The reference point that is compared. Should be onedimensional array.
 Returns:
The list of indicator values.
 Return type:
list
 utilities.quality_indicator.preference_indicator(s1: numpy.ndarray, s2: numpy.ndarray, min_asf_value: float, ref_point: numpy.ndarray, delta: float) float [source]
Computes the preferencebased quality indicator.
 Parameters:
s1 (np.ndarray) – Solution 1. Should be an onedimensional array.
s2 (np.ndarray) – Solution 2. Should be an onedimensional array.
ref_point (np.ndarray) – The reference point should be same shape as front.
min_asf_value (float) – Minimum value of achievement scalarization of the reference_front. Used in normalization.
delta (float) – The spesifity delta allows to set the amplification of the indicator to be closer or farther from the reference point. Smaller delta means that all solutions are in smaller range around the reference point.
 Returns:
 The maximum distance between the values in s1 and s2 taking into account
the reference point and spesifity.
 Return type:
float
 utilities.quality_indicator.hypervolume_indicator(front: numpy.ndarray, reference_point: numpy.ndarray) float [source]
Computes the hypervolumeindicator between reference front and current approximating point.
 Parameters:
front (np.ndarray) – The front that is compared. Should be set of arrays, where the rows are the solutions and the columns are the objective dimensions.
reference_point (np.ndarray) – The reference point that the current front is being compared to. Should be 1D array.
 Returns:
Measures the volume of the objective space dominated by an approximation set.
 Return type:
float