desdeo_emo.utilities

This module provides miscellaneous tools

Package Contents

Functions

IsNotebook() → bool

Checks if the current environment is a Jupyter Notebook or a console.

animate_init_(data: Union[numpy.ndarray, pandas.DataFrame, list], filename: str) → dict

Plot the first (or zeroth) iteration of a population.

animate_next_(data: Union[numpy.ndarray, pandas.DataFrame, list], figure: dict, filename: str, generation: int = None) → dict

Plot the next set of individuals in an animation.
desdeo_emo.utilities.IsNotebook() → bool[source]

Checks if the current environment is a Jupyter Notebook or a console.

Returns

True if notebook. False if console

Return type

bool

desdeo_emo.utilities.animate_init_(data: Union[numpy.ndarray, pandas.DataFrame, list], filename: str) → dict[source]

Plot the first (or zeroth) iteration of a population.

Intended as a frames object. Plots Scatter for 2D and 3D data. Plots parallel coordinate plot for higher dimensional data.

Parameters

  • data (Union[np.ndarray, pd.DataFrame, list]) – Contains the data to be plotted. Each row is an individual’s objective values.

  • filename (str) – Contains the name of the file to which the plot is saved.

Returns

Plotly figure object

Return type

dict

desdeo_emo.utilities.animate_next_(data: Union[numpy.ndarray, pandas.DataFrame, list], figure: dict, filename: str, generation: int = None) → dict[source]

Plot the next set of individuals in an animation.

Plots scatter for 2D and 3D data, parallel coordinate plot for 4D and up.

Parameters

  • data (Union[np.ndarray, pd.DataFrame, list]) – The objective values to be plotted

  • figure (dict) – Plotly figure object compatible dict

  • filename (str) – Name of the file to which the plot is saved

  • generation (int) – Iteration Number

Returns

Plotly Figure Object

Return type

dict

class desdeo_emo.utilities.ReferenceVectors(lattice_resolution: int = None, number_of_objectives: int = None, creation_type: str = 'Uniform', vector_type: str = 'Spherical', ref_point: list = None)

Class object for reference vectors.

_create(self, creation_type: str = 'Uniform')

Create the reference vectors.

Parameters

creation_type (str, optional) – ‘Uniform’ creates the reference vectors uniformly using simplex lattice design. ‘Focused’ creates reference vectors symmetrically around a central reference vector. By default ‘Uniform’.

normalize(self)

Normalize the reference vectors to a unit hypersphere.

neighbouring_angles(self) → numpy.ndarray

Calculate neighbouring angles for normalization.

adapt(self, fitness: numpy.ndarray)

Adapt reference vectors. Then normalize.

Parameters

fitness (np.ndarray) –

interactive_adapt_1(self, z: numpy.ndarray, n_solutions: int, translation_param: float = 0.2) → None

Adapt reference vectors using the information about prefererred solution(s) selected by the Decision maker.

Parameters

  • z (np.ndarray) – Preferred solution(s).

  • n_solutions (int) – Number of solutions in total.

  • translation_param (float) – Parameter determining how close the reference vectors are to the central vector

  • defined by using the selected solution (**v**) –

Returns:

interactive_adapt_2(self, z: numpy.ndarray, n_solutions: int, predefined_distance: float = 0.2) → None

Adapt reference vectors by using the information about non-preferred solution(s) selected by the Decision maker. After the Decision maker has specified non-preferred solution(s), Euclidian distance between normalized solution vector(s) and each of the reference vectors are calculated. Those reference vectors that are closer than a predefined distance are either removed or re-positioned somewhere else.

Note

At the moment, only the removal of reference vectors is supported. Repositioning of the reference vectors is not supported.

Note

In case the Decision maker specifies multiple non-preferred solutions, the reference vector(s) for which the distance to any of the non-preferred solutions is less than predefined distance are removed.

Note

Future developer should implement a way for a user to say: “Remove some percentage of objecive space/reference vectors” rather than giving a predefined distance value.

Parameters

  • z (np.ndarray) – Non-preferred solution(s).

  • n_solutions (int) – Number of solutions in total.

  • predefined_distance (float) – The reference vectors that are closer than this distance are either removed or

  • somewhere else. (re-positioned) –

  • value (Default) – 0.2

Returns:

iteractive_adapt_3(self, ref_point, translation_param=0.2)

Adapt reference vectors linearly towards a reference point. Then normalize.

The details can be found in the following paper: Hakanen, Jussi & Chugh, Tinkle & Sindhya, Karthik & Jin, Yaochu & Miettinen, Kaisa. (2016). Connections of Reference Vectors and Different Types of Preference Information in Interactive Multiobjective Evolutionary Algorithms.

Parameters

  • ref_point –

  • translation_param – (Default value = 0.2)

interactive_adapt_4(self, preferred_ranges: numpy.ndarray) → None

Adapt reference vectors by using the information about the Decision maker’s preferred range for each of the objective. Using these ranges, Latin hypercube sampling is applied to generate m number of samples between within these ranges, where m is the number of reference vectors. Normalized vectors constructed of these samples are then set as new reference vectors.

Parameters

preferred_ranges (np.ndarray) – Preferred lower and upper bound for each of the objective function values.

Returns:

slow_interactive_adapt(self, ref_point)

Basically a wrapper around rotate_toward. Slowly rotate ref vectors toward ref_point. Return a boolean value to tell if the ref_point has been reached.

Parameters

ref_point (list or np.ndarray) – The reference vectors will slowly move towards the ref_point.

Returns

True if ref_point has been reached. False otherwise.

Return type

boolean

add_edge_vectors(self)

Add edge vectors to the list of reference vectors.

Used to cover the entire orthant when preference information is provided.